code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class lowerCAmelCase_ ( __snake_case , unittest.TestCase ):
_UpperCamelCase : str = ShapEPipeline
_UpperCamelCase : Any = ["prompt"]
_UpperCamelCase : int = ["prompt"]
_UpperCamelCase : Union[str, Any] = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
_UpperCamelCase : Optional[Any] = False
@property
def __a ( self ):
return 3_2
@property
def __a ( self ):
return 3_2
@property
def __a ( self ):
return self.time_input_dim * 4
@property
def __a ( self ):
return 8
@property
def __a ( self ):
_lowercase : Any = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def __a ( self ):
torch.manual_seed(0 )
_lowercase : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=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 , )
return CLIPTextModelWithProjection(_lowerCAmelCase )
@property
def __a ( self ):
torch.manual_seed(0 )
_lowercase : Optional[int] = {
'num_attention_heads': 2,
'attention_head_dim': 1_6,
'embedding_dim': self.time_input_dim,
'num_embeddings': 3_2,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
_lowercase : Optional[Any] = PriorTransformer(**_lowerCAmelCase )
return model
@property
def __a ( self ):
torch.manual_seed(0 )
_lowercase : Optional[int] = {
'param_shapes': (
(self.renderer_dim, 9_3),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 1_2,
'background': (
0.1,
0.1,
0.1,
),
}
_lowercase : List[Any] = ShapERenderer(**_lowerCAmelCase )
return model
def __a ( self ):
_lowercase : Optional[Any] = self.dummy_prior
_lowercase : Dict = self.dummy_text_encoder
_lowercase : List[str] = self.dummy_tokenizer
_lowercase : Union[str, Any] = self.dummy_renderer
_lowercase : List[str] = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1_0_2_4 , prediction_type='sample' , use_karras_sigmas=_lowerCAmelCase , clip_sample=_lowerCAmelCase , clip_sample_range=1.0 , )
_lowercase : List[str] = {
'prior': prior,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def __a ( self , _lowerCAmelCase , _lowerCAmelCase=0 ):
if str(_lowerCAmelCase ).startswith('mps' ):
_lowercase : Optional[Any] = torch.manual_seed(_lowerCAmelCase )
else:
_lowercase : Optional[int] = torch.Generator(device=_lowerCAmelCase ).manual_seed(_lowerCAmelCase )
_lowercase : List[Any] = {
'prompt': 'horse',
'generator': generator,
'num_inference_steps': 1,
'frame_size': 3_2,
'output_type': 'np',
}
return inputs
def __a ( self ):
_lowercase : Optional[int] = 'cpu'
_lowercase : List[Any] = self.get_dummy_components()
_lowercase : Tuple = self.pipeline_class(**_lowerCAmelCase )
_lowercase : Union[str, Any] = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
_lowercase : Union[str, Any] = pipe(**self.get_dummy_inputs(_lowerCAmelCase ) )
_lowercase : str = output.images[0]
_lowercase : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (2_0, 3_2, 3_2, 3)
_lowercase : str = np.array(
[
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def __a ( self ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __a ( self ):
_lowercase : List[Any] = torch_device == 'cpu'
_lowercase : Any = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_lowerCAmelCase , relax_max_difference=_lowerCAmelCase , )
def __a ( self ):
_lowercase : Union[str, Any] = self.get_dummy_components()
_lowercase : Optional[int] = self.pipeline_class(**_lowerCAmelCase )
_lowercase : Any = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
_lowercase : str = 1
_lowercase : Optional[int] = 2
_lowercase : List[str] = self.get_dummy_inputs(_lowerCAmelCase )
for key in inputs.keys():
if key in self.batch_params:
_lowercase : int = batch_size * [inputs[key]]
_lowercase : Optional[int] = pipe(**_lowerCAmelCase , num_images_per_prompt=_lowerCAmelCase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class lowerCAmelCase_ ( unittest.TestCase ):
def __a ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self ):
_lowercase : List[str] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_np_out.npy' )
_lowercase : Any = ShapEPipeline.from_pretrained('openai/shap-e' )
_lowercase : List[str] = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
_lowercase : Tuple = torch.Generator(device=_lowerCAmelCase ).manual_seed(0 )
_lowercase : int = pipe(
'a shark' , generator=_lowerCAmelCase , guidance_scale=15.0 , num_inference_steps=6_4 , frame_size=6_4 , output_type='np' , ).images[0]
assert images.shape == (2_0, 6_4, 6_4, 3)
assert_mean_pixel_difference(_lowerCAmelCase , _lowerCAmelCase )
| 66 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def UpperCamelCase__ ( lowerCAmelCase__ ):
lowercase = [False] * len(lowerCAmelCase__ )
lowercase = [-1] * len(lowerCAmelCase__ )
def dfs(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowercase = True
lowercase = c
for u in graph[v]:
if not visited[u]:
dfs(lowerCAmelCase__ ,1 - c )
for i in range(len(lowerCAmelCase__ ) ):
if not visited[i]:
dfs(lowerCAmelCase__ ,0 )
for i in range(len(lowerCAmelCase__ ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
__SCREAMING_SNAKE_CASE : List[str] ={0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 428 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
__SCREAMING_SNAKE_CASE ={
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE =[
"""ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ErnieForCausalLM""",
"""ErnieForMaskedLM""",
"""ErnieForMultipleChoice""",
"""ErnieForNextSentencePrediction""",
"""ErnieForPreTraining""",
"""ErnieForQuestionAnswering""",
"""ErnieForSequenceClassification""",
"""ErnieForTokenClassification""",
"""ErnieModel""",
"""ErniePreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 89 |
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
__SCREAMING_SNAKE_CASE =os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
from run_translation import main # noqa
set_seed(42)
__SCREAMING_SNAKE_CASE ="""sshleifer/student_marian_en_ro_6_1"""
__SCREAMING_SNAKE_CASE ="""sshleifer/tiny-mbart"""
@require_torch
class __magic_name__ ( __UpperCAmelCase):
'''simple docstring'''
def _A ( self: List[Any] , _lowerCamelCase: List[Any]=False , _lowerCamelCase: List[Any]=None , _lowerCamelCase: int=True , _lowerCamelCase: Optional[int]=True , _lowerCamelCase: Any=True , _lowerCamelCase: Any=True , ):
SCREAMING_SNAKE_CASE_ = self.run_trainer(
eval_steps=1 , max_len=12 , model_name=_lowerCamelCase , num_train_epochs=1 , distributed=_lowerCamelCase , extra_args_str=_lowerCamelCase , predict_with_generate=_lowerCamelCase , do_train=_lowerCamelCase , do_eval=_lowerCamelCase , do_predict=_lowerCamelCase , )
SCREAMING_SNAKE_CASE_ = TrainerState.load_from_json(os.path.join(_lowerCamelCase , '''trainer_state.json''' ) ).log_history
if not do_eval:
return
SCREAMING_SNAKE_CASE_ = [log for log in logs if '''eval_loss''' in log.keys()]
SCREAMING_SNAKE_CASE_ = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
SCREAMING_SNAKE_CASE_ = eval_metrics[-1]
assert isinstance(last_step_stats['''eval_bleu'''] , _lowerCamelCase )
assert not math.isnan(float(last_step_stats['''eval_loss'''] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def _A ( self: Optional[int] ):
self.run_seqaseq_quick()
@require_torch_multi_gpu
def _A ( self: Optional[Any] ):
self.run_seqaseq_quick(distributed=_lowerCamelCase )
@require_torch_multi_gpu
def _A ( self: List[Any] ):
self.run_seqaseq_quick(distributed=_lowerCamelCase )
@unittest.skip('''Requires an update of the env running those tests''' )
@require_torch_multi_gpu
@require_fairscale
def _A ( self: Optional[int] ):
self.run_seqaseq_quick(distributed=_lowerCamelCase , extra_args_str='''--sharded_ddp simple''' )
@unittest.skip('''Requires an update of the env running those tests''' )
@require_torch_multi_gpu
@require_fairscale
def _A ( self: Optional[Any] ):
self.run_seqaseq_quick(distributed=_lowerCamelCase , 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 _A ( self: Union[str, Any] ):
self.run_seqaseq_quick(distributed=_lowerCamelCase , extra_args_str='''--sharded_ddp zero_dp_2''' , predict_with_generate=_lowerCamelCase )
@unittest.skip('''Requires an update of the env running those tests''' )
@require_torch_multi_gpu
@require_fairscale
def _A ( self: Optional[Any] ):
self.run_seqaseq_quick(
distributed=_lowerCamelCase , extra_args_str='''--sharded_ddp zero_dp_2 --fp16''' , predict_with_generate=_lowerCamelCase )
@require_apex
@require_torch_gpu
def _A ( self: List[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=_lowerCamelCase , 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=_lowerCamelCase , extra_args_str='''--fp16 --fp16_backend=apex''' )
@parameterized.expand(['''base''', '''low''', '''high''', '''mixed'''] )
@require_torch_multi_gpu
def _A ( self: Union[str, Any] , _lowerCamelCase: Dict ):
# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
SCREAMING_SNAKE_CASE_ = {
# 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},
}
SCREAMING_SNAKE_CASE_ = experiments[experiment_id]
SCREAMING_SNAKE_CASE_ = {'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
SCREAMING_SNAKE_CASE_ = '''Running training'''
with CaptureStderr() as cl:
self.run_seqaseq_quick(**_lowerCamelCase , extra_args_str=data['''extra_args_str'''] )
SCREAMING_SNAKE_CASE_ = len(re.findall(_lowerCamelCase , cl.err ) )
self.assertEqual(_lowerCamelCase , data['''n_matches'''] )
@slow
def _A ( self: Any ):
SCREAMING_SNAKE_CASE_ = self.run_trainer(
eval_steps=2 , max_len=1_28 , model_name=_lowerCamelCase , learning_rate=3E-4 , num_train_epochs=10 , distributed=_lowerCamelCase , )
# Check metrics
SCREAMING_SNAKE_CASE_ = TrainerState.load_from_json(os.path.join(_lowerCamelCase , '''trainer_state.json''' ) ).log_history
SCREAMING_SNAKE_CASE_ = [log for log in logs if '''eval_loss''' in log.keys()]
SCREAMING_SNAKE_CASE_ = eval_metrics[0]
SCREAMING_SNAKE_CASE_ = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats['''eval_bleu'''] , _lowerCamelCase )
# test if do_predict saves generations and metrics
SCREAMING_SNAKE_CASE_ = os.listdir(_lowerCamelCase )
SCREAMING_SNAKE_CASE_ = {os.path.basename(_lowerCamelCase ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def _A ( self: Optional[int] ):
from transformers.training_args import OptimizerNames
def train_and_return_metrics(_lowerCamelCase: str ) -> Tuple[int, float]:
SCREAMING_SNAKE_CASE_ = '''--skip_memory_metrics 0'''
SCREAMING_SNAKE_CASE_ = self.run_trainer(
max_len=1_28 , model_name=_lowerCamelCase , learning_rate=3E-4 , num_train_epochs=1 , optim=_lowerCamelCase , distributed=_lowerCamelCase , extra_args_str=_lowerCamelCase , do_eval=_lowerCamelCase , do_predict=_lowerCamelCase , n_gpus_to_use=1 , )
# Check metrics
SCREAMING_SNAKE_CASE_ = TrainerState.load_from_json(Path(_lowerCamelCase , '''trainer_state.json''' ) ).log_history
SCREAMING_SNAKE_CASE_ = int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20 )
SCREAMING_SNAKE_CASE_ = int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20 )
SCREAMING_SNAKE_CASE_ = logs[0]['''train_loss''']
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
SCREAMING_SNAKE_CASE_ = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
SCREAMING_SNAKE_CASE_ = gpu_peak_mem_orig + gpu_alloc_mem_orig
SCREAMING_SNAKE_CASE_ = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
SCREAMING_SNAKE_CASE_ = 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
SCREAMING_SNAKE_CASE_ = 1_20
# 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(
_lowerCamelCase , _lowerCamelCase , '''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(
_lowerCamelCase , _lowerCamelCase , '''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(
_lowerCamelCase , _lowerCamelCase , f"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}" )
def _A ( self: Union[str, Any] , _lowerCamelCase: int , _lowerCamelCase: str , _lowerCamelCase: int , _lowerCamelCase: float = 3E-3 , _lowerCamelCase: str = "adafactor" , _lowerCamelCase: bool = False , _lowerCamelCase: str = None , _lowerCamelCase: int = 0 , _lowerCamelCase: bool = True , _lowerCamelCase: bool = True , _lowerCamelCase: bool = True , _lowerCamelCase: bool = True , _lowerCamelCase: int = None , ):
SCREAMING_SNAKE_CASE_ = self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
SCREAMING_SNAKE_CASE_ = self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE_ = 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(_lowerCamelCase )}\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(_lowerCamelCase )}\n --group_by_length\n --label_smoothing_factor 0.1\n --target_lang ro_RO\n --source_lang en_XX\n ".split()
SCREAMING_SNAKE_CASE_ = 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(_lowerCamelCase )}\n ".split()
SCREAMING_SNAKE_CASE_ = '''
--do_predict
'''.split()
SCREAMING_SNAKE_CASE_ = []
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:
SCREAMING_SNAKE_CASE_ = get_gpu_count()
SCREAMING_SNAKE_CASE_ = get_torch_dist_unique_port()
SCREAMING_SNAKE_CASE_ = 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()
SCREAMING_SNAKE_CASE_ = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(_lowerCamelCase , env=self.get_env() )
else:
SCREAMING_SNAKE_CASE_ = ['''run_translation.py'''] + args
with patch.object(_lowerCamelCase , '''argv''' , _lowerCamelCase ):
main()
return output_dir
| 89 | 1 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowerCAmelCase__ : str ={
"""cola""": 2,
"""mnli""": 3,
"""mrpc""": 2,
"""sst-2""": 2,
"""sts-b""": 1,
"""qqp""": 2,
"""qnli""": 2,
"""rte""": 2,
"""wnli""": 2,
}
logging.set_verbosity_info()
def a__ ( A__, A__, A__, A__=None ):
SCREAMING_SNAKE_CASE_ : Optional[int] = XLNetConfig.from_json_file(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_ : Dict = finetuning_task.lower() if finetuning_task is not None else ""
if finetuning_task in GLUE_TASKS_NUM_LABELS:
print(F'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}''' )
SCREAMING_SNAKE_CASE_ : int = finetuning_task
SCREAMING_SNAKE_CASE_ : int = GLUE_TASKS_NUM_LABELS[finetuning_task]
SCREAMING_SNAKE_CASE_ : Dict = XLNetForSequenceClassification(_UpperCAmelCase )
elif "squad" in finetuning_task:
SCREAMING_SNAKE_CASE_ : Tuple = finetuning_task
SCREAMING_SNAKE_CASE_ : Any = XLNetForQuestionAnswering(_UpperCAmelCase )
else:
SCREAMING_SNAKE_CASE_ : Tuple = XLNetLMHeadModel(_UpperCAmelCase )
# Load weights from tf checkpoint
load_tf_weights_in_xlnet(_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase )
# Save pytorch-model
SCREAMING_SNAKE_CASE_ : List[str] = os.path.join(_UpperCAmelCase, _UpperCAmelCase )
SCREAMING_SNAKE_CASE_ : Optional[int] = os.path.join(_UpperCAmelCase, _UpperCAmelCase )
print(F'''Save PyTorch model to {os.path.abspath(_UpperCAmelCase )}''' )
torch.save(model.state_dict(), _UpperCAmelCase )
print(F'''Save configuration file to {os.path.abspath(_UpperCAmelCase )}''' )
with open(_UpperCAmelCase, 'w', encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowerCAmelCase__ : int =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--xlnet_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained XLNet model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
required=True,
help='Path to the folder to store the PyTorch model or dataset/vocab.',
)
parser.add_argument(
'--finetuning_task',
default=None,
type=str,
help='Name of a task on which the XLNet TensorFlow model was fine-tuned',
)
lowerCAmelCase__ : Any =parser.parse_args()
print(args)
convert_xlnet_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
)
| 101 |
def UpperCamelCase ( _UpperCAmelCase : list[int] , _UpperCAmelCase : list[int] ) -> tuple[float, float]:
'''simple docstring'''
if not len(_UpperCAmelCase ) == len(_UpperCAmelCase ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError("Both a & b of two equations can't be zero." )
# Extract the coefficients
_lowercase , _lowercase , _lowercase : List[str] = equationa
_lowercase , _lowercase , _lowercase : List[str] = equationa
# Calculate the determinants of the matrices
_lowercase : List[Any] = aa * ba - aa * ba
_lowercase : Tuple = ca * ba - ca * ba
_lowercase : Union[str, Any] = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("Infinite solutions. (Consistent system)" )
else:
raise ValueError("No solution. (Inconsistent system)" )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
_lowercase : str = determinant_x / determinant
_lowercase : Union[str, Any] = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 461 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__lowerCAmelCase :Any = (7_20, 12_80) # Height, Width
__lowerCAmelCase :Optional[int] = (0.4, 0.6) # if height or width lower than this scale, drop it.
__lowerCAmelCase :int = 1 / 1_00
__lowerCAmelCase :int = ''''''
__lowerCAmelCase :str = ''''''
__lowerCAmelCase :int = ''''''
__lowerCAmelCase :List[str] = 2_50
def A ( ):
_snake_case , _snake_case : Tuple = get_dataset(_UpperCAmelCase , _UpperCAmelCase )
for index in range(_UpperCAmelCase ):
_snake_case : Optional[int] = random.sample(range(len(_UpperCAmelCase ) ) , 4 )
_snake_case , _snake_case , _snake_case : int = update_image_and_anno(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , filter_scale=_UpperCAmelCase , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
_snake_case : Tuple = random_chars(32 )
_snake_case : Any = path.split(os.sep )[-1].rsplit("." , 1 )[0]
_snake_case : int = 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 : Optional[Any] = []
for anno in new_annos:
_snake_case : Any = anno[3] - anno[1]
_snake_case : int = anno[4] - anno[2]
_snake_case : str = anno[1] + width / 2
_snake_case : Dict = anno[2] + height / 2
_snake_case : Dict = 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 A ( UpperCAmelCase , UpperCAmelCase ):
_snake_case : int = []
_snake_case : Tuple = []
for label_file in glob.glob(os.path.join(_UpperCAmelCase , "*.txt" ) ):
_snake_case : List[str] = label_file.split(os.sep )[-1].rsplit("." , 1 )[0]
with open(_UpperCAmelCase ) as in_file:
_snake_case : Union[str, Any] = in_file.readlines()
_snake_case : int = os.path.join(_UpperCAmelCase , F"""{label_name}.jpg""" )
_snake_case : str = []
for obj_list in obj_lists:
_snake_case : List[str] = obj_list.rstrip("\n" ).split(" " )
_snake_case : List[Any] = float(obj[1] ) - float(obj[3] ) / 2
_snake_case : List[Any] = float(obj[2] ) - float(obj[4] ) / 2
_snake_case : Union[str, Any] = float(obj[1] ) + float(obj[3] ) / 2
_snake_case : Optional[Any] = 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 A ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 0.0 , ):
_snake_case : Optional[int] = np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
_snake_case : Dict = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
_snake_case : str = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
_snake_case : List[Any] = int(scale_x * output_size[1] )
_snake_case : Union[str, Any] = int(scale_y * output_size[0] )
_snake_case : List[str] = []
_snake_case : int = []
for i, index in enumerate(_UpperCAmelCase ):
_snake_case : Optional[int] = all_img_list[index]
path_list.append(_UpperCAmelCase )
_snake_case : Optional[int] = all_annos[index]
_snake_case : List[str] = cva.imread(_UpperCAmelCase )
if i == 0: # top-left
_snake_case : List[Any] = cva.resize(_UpperCAmelCase , (divid_point_x, divid_point_y) )
_snake_case : List[Any] = img
for bbox in img_annos:
_snake_case : Any = bbox[1] * scale_x
_snake_case : Optional[Any] = bbox[2] * scale_y
_snake_case : Tuple = bbox[3] * scale_x
_snake_case : int = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
_snake_case : Tuple = cva.resize(_UpperCAmelCase , (output_size[1] - divid_point_x, divid_point_y) )
_snake_case : Union[str, Any] = img
for bbox in img_annos:
_snake_case : List[Any] = scale_x + bbox[1] * (1 - scale_x)
_snake_case : Any = bbox[2] * scale_y
_snake_case : List[str] = scale_x + bbox[3] * (1 - scale_x)
_snake_case : List[str] = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
_snake_case : int = cva.resize(_UpperCAmelCase , (divid_point_x, output_size[0] - divid_point_y) )
_snake_case : str = img
for bbox in img_annos:
_snake_case : str = bbox[1] * scale_x
_snake_case : Optional[int] = scale_y + bbox[2] * (1 - scale_y)
_snake_case : Optional[int] = bbox[3] * scale_x
_snake_case : Optional[Any] = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
_snake_case : Any = cva.resize(
_UpperCAmelCase , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
_snake_case : List[Any] = img
for bbox in img_annos:
_snake_case : int = scale_x + bbox[1] * (1 - scale_x)
_snake_case : List[str] = scale_y + bbox[2] * (1 - scale_y)
_snake_case : Union[str, Any] = scale_x + bbox[3] * (1 - scale_x)
_snake_case : Optional[Any] = 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 : str = [
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 A ( UpperCAmelCase ):
assert number_char > 1, "The number of character should greater than 1"
_snake_case : List[Any] = ascii_lowercase + digits
return "".join(random.choice(_UpperCAmelCase ) for _ in range(_UpperCAmelCase ) )
if __name__ == "__main__":
main()
print('DONE ✅') | 716 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def A ( UpperCAmelCase ):
if hor == 128:
_snake_case : int = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
_snake_case : Tuple = (32, 128, 256)
_snake_case : str = ("UpResnetBlock1D", "UpResnetBlock1D")
elif hor == 32:
_snake_case : List[Any] = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
_snake_case : Optional[int] = (32, 64, 128, 256)
_snake_case : int = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
_snake_case : Dict = torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch""" )
_snake_case : Dict = model.state_dict()
_snake_case : Tuple = {
"down_block_types": down_block_types,
"block_out_channels": block_out_channels,
"up_block_types": up_block_types,
"layers_per_block": 1,
"use_timestep_embedding": True,
"out_block_type": "OutConv1DBlock",
"norm_num_groups": 8,
"downsample_each_block": False,
"in_channels": 14,
"out_channels": 14,
"extra_in_channels": 0,
"time_embedding_type": "positional",
"flip_sin_to_cos": False,
"freq_shift": 1,
"sample_size": 65_536,
"mid_block_type": "MidResTemporalBlock1D",
"act_fn": "mish",
}
_snake_case : Any = UNetaDModel(**UpperCAmelCase )
print(F"""length of state dict: {len(state_dict.keys() )}""" )
print(F"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
_snake_case : List[Any] = dict(zip(model.state_dict().keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
_snake_case : Optional[int] = state_dict.pop(UpperCAmelCase )
hf_value_function.load_state_dict(UpperCAmelCase )
torch.save(hf_value_function.state_dict() , F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin""" )
with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json""" , "w" ) as f:
json.dump(UpperCAmelCase , UpperCAmelCase )
def A ( ):
_snake_case : Any = {
"in_channels": 14,
"down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
"up_block_types": (),
"out_block_type": "ValueFunction",
"mid_block_type": "ValueFunctionMidBlock1D",
"block_out_channels": (32, 64, 128, 256),
"layers_per_block": 1,
"downsample_each_block": True,
"sample_size": 65_536,
"out_channels": 14,
"extra_in_channels": 0,
"time_embedding_type": "positional",
"use_timestep_embedding": True,
"flip_sin_to_cos": False,
"freq_shift": 1,
"norm_num_groups": 8,
"act_fn": "mish",
}
_snake_case : Dict = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch" )
_snake_case : Optional[int] = model
_snake_case : List[str] = UNetaDModel(**UpperCAmelCase )
print(F"""length of state dict: {len(state_dict.keys() )}""" )
print(F"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
_snake_case : Optional[int] = dict(zip(state_dict.keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
_snake_case : Any = state_dict.pop(UpperCAmelCase )
hf_value_function.load_state_dict(UpperCAmelCase )
torch.save(hf_value_function.state_dict() , "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin" )
with open("hub/hopper-medium-v2/value_function/config.json" , "w" ) as f:
json.dump(UpperCAmelCase , UpperCAmelCase )
if __name__ == "__main__":
unet(32)
# unet(128)
value_function() | 278 | 0 |
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 lowerCAmelCase_ :
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=1_3 , _lowerCAmelCase=2 , _lowerCAmelCase=2_4 , _lowerCAmelCase=1_6 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=3_2 , _lowerCAmelCase=5 , _lowerCAmelCase=4 , _lowerCAmelCase=3_7 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=1_0 , _lowerCAmelCase=0.02 , _lowerCAmelCase=None , _lowerCAmelCase=2 , _lowerCAmelCase=2 , ):
_lowercase : List[Any] = parent
_lowercase : Dict = batch_size
_lowercase : int = patch_size
_lowercase : int = max_length
_lowercase : Optional[Any] = num_mel_bins
_lowercase : Any = is_training
_lowercase : int = use_labels
_lowercase : Dict = hidden_size
_lowercase : int = num_hidden_layers
_lowercase : Any = num_attention_heads
_lowercase : str = intermediate_size
_lowercase : Tuple = hidden_act
_lowercase : Optional[int] = hidden_dropout_prob
_lowercase : List[Any] = attention_probs_dropout_prob
_lowercase : Dict = type_sequence_label_size
_lowercase : List[str] = initializer_range
_lowercase : List[Any] = scope
_lowercase : str = frequency_stride
_lowercase : Optional[Any] = time_stride
# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
_lowercase : Optional[Any] = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
_lowercase : int = (self.max_length - self.patch_size) // self.time_stride + 1
_lowercase : List[Any] = frequency_out_dimension * time_out_dimension
_lowercase : Tuple = num_patches + 2
def __a ( self ):
_lowercase : List[str] = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] )
_lowercase : Dict = None
if self.use_labels:
_lowercase : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowercase : Union[str, Any] = self.get_config()
return config, input_values, labels
def __a ( self ):
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=_lowerCAmelCase , initializer_range=self.initializer_range , frequency_stride=self.frequency_stride , time_stride=self.time_stride , )
def __a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
_lowercase : Dict = ASTModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
_lowercase : List[str] = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __a ( self ):
_lowercase : Optional[int] = self.prepare_config_and_inputs()
(
(
_lowercase
) , (
_lowercase
) , (
_lowercase
) ,
) : Union[str, Any] = config_and_inputs
_lowercase : Optional[int] = {'input_values': input_values}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ ( __snake_case , __snake_case , unittest.TestCase ):
_UpperCamelCase : List[Any] = (
(
ASTModel,
ASTForAudioClassification,
)
if is_torch_available()
else ()
)
_UpperCamelCase : Tuple = (
{"audio-classification": ASTForAudioClassification, "feature-extraction": ASTModel}
if is_torch_available()
else {}
)
_UpperCamelCase : int = False
_UpperCamelCase : Optional[Any] = False
_UpperCamelCase : Any = False
_UpperCamelCase : str = False
def __a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
if pipeline_test_casse_name == "AudioClassificationPipelineTests":
return True
return False
def __a ( self ):
_lowercase : Optional[Any] = ASTModelTester(self )
_lowercase : List[Any] = ConfigTester(self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=3_7 )
def __a ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason='AST does not use inputs_embeds' )
def __a ( self ):
pass
def __a ( self ):
_lowercase , _lowercase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase : List[Any] = model_class(_lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_lowercase : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCAmelCase , nn.Linear ) )
def __a ( self ):
_lowercase , _lowercase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase : List[Any] = model_class(_lowerCAmelCase )
_lowercase : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowercase : List[str] = [*signature.parameters.keys()]
_lowercase : Tuple = ['input_values']
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def __a ( self ):
_lowercase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
@slow
def __a ( self ):
for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowercase : Any = ASTModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def __magic_name__ ( ) -> Any:
_lowercase : List[Any] = hf_hub_download(
repo_id='nielsr/audio-spectogram-transformer-checkpoint' , filename='sample_audio.flac' , repo_type='dataset' )
_lowercase , _lowercase : Tuple = torchaudio.load(SCREAMING_SNAKE_CASE )
return audio, sampling_rate
@require_torch
@require_torchaudio
class lowerCAmelCase_ ( unittest.TestCase ):
@cached_property
def __a ( self ):
return (
ASTFeatureExtractor.from_pretrained('MIT/ast-finetuned-audioset-10-10-0.4593' )
if is_torchaudio_available()
else None
)
@slow
def __a ( self ):
_lowercase : List[Any] = self.default_feature_extractor
_lowercase : List[Any] = ASTForAudioClassification.from_pretrained('MIT/ast-finetuned-audioset-10-10-0.4593' ).to(_lowerCAmelCase )
_lowercase : List[Any] = self.default_feature_extractor
_lowercase , _lowercase : Any = prepare_audio()
_lowercase : int = audio.squeeze().numpy()
_lowercase : List[str] = feature_extractor(_lowerCAmelCase , sampling_rate=_lowerCAmelCase , return_tensors='pt' ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
_lowercase : List[str] = model(**_lowerCAmelCase )
# verify the logits
_lowercase : Any = torch.Size((1, 5_2_7) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
_lowercase : Dict = torch.tensor([-0.87_60, -7.00_42, -8.66_02] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1E-4 ) )
| 66 |
from PIL import Image
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Image:
def brightness(SCREAMING_SNAKE_CASE ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError('level must be between -255.0 (black) and 255.0 (white)' )
return img.point(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
# Load image
with Image.open("image_data/lena.jpg") as img:
# Change brightness to 100
UpperCamelCase = change_brightness(img, 100)
brigt_img.save("image_data/lena_brightness.png", format="png")
| 66 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
_A = "ctrl"
_A = ["past_key_values"]
_A = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self , lowercase__=24_6534 , lowercase__=256 , lowercase__=1280 , lowercase__=8192 , lowercase__=48 , lowercase__=16 , lowercase__=0.1 , lowercase__=0.1 , lowercase__=1e-6 , lowercase__=0.02 , lowercase__=True , **lowercase__ , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = vocab_size
SCREAMING_SNAKE_CASE_ : Any = n_positions
SCREAMING_SNAKE_CASE_ : Any = n_embd
SCREAMING_SNAKE_CASE_ : Any = n_layer
SCREAMING_SNAKE_CASE_ : Optional[int] = n_head
SCREAMING_SNAKE_CASE_ : List[Any] = dff
SCREAMING_SNAKE_CASE_ : List[str] = resid_pdrop
SCREAMING_SNAKE_CASE_ : List[Any] = embd_pdrop
SCREAMING_SNAKE_CASE_ : str = layer_norm_epsilon
SCREAMING_SNAKE_CASE_ : str = initializer_range
SCREAMING_SNAKE_CASE_ : Optional[Any] = use_cache
super().__init__(**lowercase__ )
| 714 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : list[int] ) -> list[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = len(SCREAMING_SNAKE_CASE_ )
for i in range(SCREAMING_SNAKE_CASE_ ):
for j in range(i + 1 , SCREAMING_SNAKE_CASE_ ):
if numbers[j] < numbers[i]:
SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ : Tuple = numbers[j], numbers[i]
return numbers
if __name__ == "__main__":
snake_case_ = input('Enter numbers separated by a comma:\n').strip()
snake_case_ = [int(item) for item in user_input.split(',')]
print(exchange_sort(unsorted))
| 68 | 0 |
'''simple docstring'''
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowercase = abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def __A ( _SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(snake_case_ )
def __A ( _SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
from diffusers.utils.testing_utils import pytest_terminal_summary_main
__SCREAMING_SNAKE_CASE : int = terminalreporter.config.getoption("--make-reports" )
if make_reports:
pytest_terminal_summary_main(snake_case_ , id=snake_case_ )
| 211 | '''simple docstring'''
import random
def UpperCamelCase_ ( snake_case_ : int ) -> bool:
'''simple docstring'''
__lowerCAmelCase = num - 1
__lowerCAmelCase = 0
while s % 2 == 0:
__lowerCAmelCase = s // 2
t += 1
for _ in range(5 ):
__lowerCAmelCase = random.randrange(2 , num - 1 )
__lowerCAmelCase = pow(snake_case_ , snake_case_ , snake_case_ )
if v != 1:
__lowerCAmelCase = 0
while v != (num - 1):
if i == t - 1:
return False
else:
__lowerCAmelCase = i + 1
__lowerCAmelCase = (v**2) % num
return True
def UpperCamelCase_ ( snake_case_ : int ) -> bool:
'''simple docstring'''
if num < 2:
return False
__lowerCAmelCase = [
2,
3,
5,
7,
11,
13,
17,
19,
23,
29,
31,
37,
41,
43,
47,
53,
59,
61,
67,
71,
73,
79,
83,
89,
97,
1_01,
1_03,
1_07,
1_09,
1_13,
1_27,
1_31,
1_37,
1_39,
1_49,
1_51,
1_57,
1_63,
1_67,
1_73,
1_79,
1_81,
1_91,
1_93,
1_97,
1_99,
2_11,
2_23,
2_27,
2_29,
2_33,
2_39,
2_41,
2_51,
2_57,
2_63,
2_69,
2_71,
2_77,
2_81,
2_83,
2_93,
3_07,
3_11,
3_13,
3_17,
3_31,
3_37,
3_47,
3_49,
3_53,
3_59,
3_67,
3_73,
3_79,
3_83,
3_89,
3_97,
4_01,
4_09,
4_19,
4_21,
4_31,
4_33,
4_39,
4_43,
4_49,
4_57,
4_61,
4_63,
4_67,
4_79,
4_87,
4_91,
4_99,
5_03,
5_09,
5_21,
5_23,
5_41,
5_47,
5_57,
5_63,
5_69,
5_71,
5_77,
5_87,
5_93,
5_99,
6_01,
6_07,
6_13,
6_17,
6_19,
6_31,
6_41,
6_43,
6_47,
6_53,
6_59,
6_61,
6_73,
6_77,
6_83,
6_91,
7_01,
7_09,
7_19,
7_27,
7_33,
7_39,
7_43,
7_51,
7_57,
7_61,
7_69,
7_73,
7_87,
7_97,
8_09,
8_11,
8_21,
8_23,
8_27,
8_29,
8_39,
8_53,
8_57,
8_59,
8_63,
8_77,
8_81,
8_83,
8_87,
9_07,
9_11,
9_19,
9_29,
9_37,
9_41,
9_47,
9_53,
9_67,
9_71,
9_77,
9_83,
9_91,
9_97,
]
if num in low_primes:
return True
for prime in low_primes:
if (num % prime) == 0:
return False
return rabin_miller(snake_case_ )
def UpperCamelCase_ ( snake_case_ : int = 10_24 ) -> int:
'''simple docstring'''
while True:
__lowerCAmelCase = random.randrange(2 ** (keysize - 1) , 2 ** (keysize) )
if is_prime_low_num(snake_case_ ):
return num
if __name__ == "__main__":
_A : List[Any] = generate_large_prime()
print(('''Prime number:''', num))
print(('''is_prime_low_num:''', is_prime_low_num(num)))
| 427 | 0 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from transformers.utils import is_sentencepiece_available
if is_sentencepiece_available():
from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
if is_sentencepiece_available():
SCREAMING_SNAKE_CASE_ = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
SCREAMING_SNAKE_CASE_ = 12_80_22
SCREAMING_SNAKE_CASE_ = 12_80_28
@require_sentencepiece
class snake_case_ ( a_ ,unittest.TestCase ):
__lowerCAmelCase = MaMaaaTokenizer
__lowerCAmelCase = False
__lowerCAmelCase = False
__lowerCAmelCase = True
def snake_case_ ( self ):
super().setUp()
a_ : str = ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
a_ : List[str] = dict(zip(a_ , range(len(a_ ) ) ) )
a_ : List[str] = Path(self.tmpdirname )
save_json(a_ , save_dir / VOCAB_FILES_NAMES["vocab_file"] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(a_ , save_dir / VOCAB_FILES_NAMES["spm_file"] )
a_ : Optional[int] = MaMaaaTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def snake_case_ ( self , **a_ ):
return MaMaaaTokenizer.from_pretrained(self.tmpdirname , **a_ )
def snake_case_ ( self , a_ ):
return (
"This is a test",
"This is a test",
)
def snake_case_ ( self ):
a_ : str = "</s>"
a_ : List[str] = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_ ) , a_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_ ) , a_ )
def snake_case_ ( self ):
a_ : Optional[int] = self.get_tokenizer()
a_ : List[Any] = list(tokenizer.get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "</s>" )
self.assertEqual(vocab_keys[1] , "<unk>" )
self.assertEqual(vocab_keys[-1] , "<s>" )
self.assertEqual(len(a_ ) , tokenizer.vocab_size + len(tokenizer.get_added_vocab() ) )
@unittest.skip("Skip this test while all models are still to be uploaded." )
def snake_case_ ( self ):
pass
def snake_case_ ( self ):
a_ : List[str] = self.get_tokenizer()
a_ : Tuple = tokenizer.tokenize("This is a test" )
self.assertListEqual(a_ , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(a_ ) , [2, 3, 4, 5, 6] , )
a_ : Tuple = tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6] )
self.assertListEqual(a_ , ["▁This", "▁is", "▁a", "▁t", "est"] )
a_ : Dict = tokenizer.convert_tokens_to_string(a_ )
self.assertEqual(a_ , "This is a test" )
@slow
def snake_case_ ( self ):
# fmt: off
a_ : List[Any] = {"input_ids": [[1_2_8_0_2_2, 1_1_0_1_0_8, 3_9_7, 1_1, 3_8_2_7_2, 2_2_4_7, 1_2_4_8_1_1, 2_8_5, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 3_9_5_3_4, 4_4_2_8, 3_9_7, 1_0_1_9, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 4_1_3_3_7, 1_6_7_8_6, 2_4_1, 7, 2_0_2_1_4, 1_7, 1_2_5_6_9_0, 1_0_3_9_8, 7, 4_4_3_7_8, 5_8_0_6_9, 6_8_3_4_2, 7_7_9_8, 7_3_4_3, 1_1, 2_9_9, 3_3_3_1_0, 4, 1_5_8, 3_7_3_5_0, 9_4_0_7_7, 4_5_6_9, 2_9_9, 3_3_3_1_0, 9_0, 4, 5_2_8_4_0, 2_9_0, 4, 3_1_2_7_0, 1_1_2, 2_9_9, 6_8_2, 4, 5_2_8_4_0, 3_9_9_5_3, 1_4_0_7_9, 1_9_3, 5_2_5_1_9, 9_0_8_9_4, 1_7_8_9_4, 1_2_0_6_9_7, 1_1, 4_0_4_4_5, 5_5_1, 1_7, 1_0_1_9, 5_2_5_1_9, 9_0_8_9_4, 1_7_7_5_6, 9_6_3, 1_1, 4_0_4_4_5, 4_8_0, 1_7, 9_7_9_2, 1_1_2_0, 5_1_7_3, 1_3_9_3, 6_2_4_0, 1_6_7_8_6, 2_4_1, 1_2_0_9_9_6, 2_8, 1_2_4_5, 1_3_9_3, 1_1_8_2_4_0, 1_1_1_2_3, 1_0_1_9, 9_3_6_1_2, 2_6_9_1, 1_0_6_1_8, 9_8_0_5_8, 1_2_0_4_0_9, 1_9_2_8, 2_7_9, 4, 4_0_6_8_3, 3_6_7, 1_7_8, 2_0_7, 1_0_1_9, 1_0_3, 1_0_3_1_2_1, 5_0_6, 6_5_2_9_6, 5, 2], [1_2_8_0_2_2, 2_1_2_1_7, 3_6_7, 1_1_7, 1_2_5_4_5_0, 1_2_8, 7_1_9, 7, 7_3_0_8, 4_0, 9_3_6_1_2, 1_2_6_6_9, 1_1_1_6, 1_6_7_0_4, 7_1, 1_7_7_8_5, 3_6_9_9, 1_5_5_9_2, 3_5, 1_4_4, 9_5_8_4, 2_4_1, 1_1_9_4_3, 7_1_3, 9_5_0, 7_9_9, 2_2_4_7, 8_8_4_2_7, 1_5_0, 1_4_9, 1_1_8_8_1_3, 1_2_0_7_0_6, 1_0_1_9, 1_0_6_9_0_6, 8_1_5_1_8, 2_8, 1_2_2_4, 2_2_7_9_9, 3_9_7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1_2_8_0_2_2, 1_6_5_8, 1_2_3_3_1_1, 5_1_5_5, 5_5_7_8, 4_7_2_2, 2_7_9, 1_4_9_4_7, 2_3_6_6, 1_1_2_0, 1_1_9_7, 1_4, 1_3_4_8, 9_2_3_2, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=a_ , model_name="facebook/m2m100_418M" , revision="c168bae485c864188cf9aa0e4108b0b6934dc91e" , )
@require_torch
@require_sentencepiece
@require_tokenizers
class snake_case_ ( unittest.TestCase ):
__lowerCAmelCase = "facebook/m2m100_418M"
__lowerCAmelCase = [
"In my opinion, there are two levels of response from the French government.",
"NSA Affair Emphasizes Complete Lack of Debate on Intelligence",
]
__lowerCAmelCase = [
"Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
"L'affaire NSA souligne l'absence totale de débat sur le renseignement",
]
# fmt: off
__lowerCAmelCase = [EN_CODE, 5_9_3, 1_9_4_9, 1_1_5_7_8_1, 4, 7_1_5_8_6, 4_2_3_4, 6_0_6_3_3, 1_2_6_2_3_3, 4_3_2, 1_2_3_8_0_8, 1_5_5_9_2, 1_1_9_7, 1_1_7_1_3_2, 1_2_0_6_1_8, 5, 2]
@classmethod
def snake_case_ ( cls ):
a_ : MaMaaaTokenizer = MaMaaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="en" , tgt_lang="fr" )
a_ : Optional[Any] = 1
return cls
def snake_case_ ( self ):
self.assertEqual(self.tokenizer.get_lang_id("ar" ) , 1_2_8_0_0_6 )
self.assertEqual(self.tokenizer.get_lang_id("en" ) , 1_2_8_0_2_2 )
self.assertEqual(self.tokenizer.get_lang_id("ro" ) , 1_2_8_0_7_6 )
self.assertEqual(self.tokenizer.get_lang_id("mr" ) , 1_2_8_0_6_3 )
def snake_case_ ( self ):
a_ : Tuple = self.tokenizer.get_vocab()
self.assertEqual(len(a_ ) , self.tokenizer.vocab_size )
self.assertEqual(vocab["<unk>"] , 3 )
self.assertIn(self.tokenizer.get_lang_token("en" ) , a_ )
def snake_case_ ( self ):
a_ : Optional[int] = "en"
a_ : Optional[int] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , a_ )
def snake_case_ ( self ):
self.assertIn(a_ , self.tokenizer.all_special_ids )
# fmt: off
a_ : List[Any] = [FR_CODE, 5_3_6_4, 8_2, 8_6_4_2, 4, 2_9_4, 4_7, 8, 1_4_0_2_8, 1_3_6, 3_2_8_6, 9_7_0_6, 6, 9_0_7_9_7, 6, 1_4_4_0_1_2, 1_6_2, 8_8_1_2_8, 3_0_0_6_1, 5, 2]
# fmt: on
a_ : List[Any] = self.tokenizer.decode(a_ , skip_special_tokens=a_ )
a_ : Dict = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=a_ )
self.assertEqual(a_ , a_ )
self.assertNotIn(self.tokenizer.eos_token , a_ )
def snake_case_ ( self ):
a_ : Any = tempfile.mkdtemp()
a_ : List[str] = self.tokenizer.lang_token_to_id
self.tokenizer.save_pretrained(a_ )
a_ : Tuple = MaMaaaTokenizer.from_pretrained(a_ )
self.assertDictEqual(new_tok.lang_token_to_id , a_ )
@require_torch
def snake_case_ ( self ):
a_ : Optional[Any] = "en"
a_ : Dict = "fr"
a_ : Optional[int] = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=a_ , return_tensors="pt" )
a_ : List[str] = shift_tokens_right(
batch["labels"] , self.tokenizer.pad_token_id , self.tokenizer.eos_token_id )
for k in batch:
a_ : Dict = batch[k].tolist()
# batch = {k: v.tolist() for k,v in batch.items()}
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
# batch.decoder_inputs_ids[0][0] ==
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == FR_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
@require_torch
def snake_case_ ( self ):
a_ : Dict = "mr"
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("mr" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
a_ : List[str] = "zh"
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("zh" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
@require_torch
def snake_case_ ( self ):
a_ : List[str] = "mr"
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("mr" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
a_ : Union[str, Any] = "zh"
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("zh" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
@require_torch
def snake_case_ ( self ):
a_ : Optional[int] = self.tokenizer._build_translation_inputs("A test" , return_tensors="pt" , src_lang="en" , tgt_lang="ar" )
self.assertEqual(
nested_simplify(a_ ) , {
# en_XX, A, test, EOS
"input_ids": [[1_2_8_0_2_2, 5_8, 4_1_8_3, 2]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 1_2_8_0_0_6,
} , ) | 370 |
"""simple docstring"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_317_044_064_679_887_385_961_981 and not allow_probable:
raise ValueError(
"Warning: upper bound of deterministic test is exceeded. "
"Pass allow_probable=True to allow probabilistic test. "
"A return value of True indicates a probable prime." )
# array bounds provided by analysis
a_ : Optional[Any] = [
2_047,
1_373_653,
25_326_001,
3_215_031_751,
2_152_302_898_747,
3_474_749_660_383,
341_550_071_728_321,
1,
3_825_123_056_546_413_051,
1,
1,
318_665_857_834_031_151_167_461,
3_317_044_064_679_887_385_961_981,
]
a_ : Optional[int] = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
for idx, _p in enumerate(SCREAMING_SNAKE_CASE__, 1 ):
if n < _p:
# then we have our last prime to check
a_ : List[Any] = primes[:idx]
break
a_ , a_ : Tuple = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
a_ : str = False
for r in range(SCREAMING_SNAKE_CASE__ ):
a_ : List[str] = pow(SCREAMING_SNAKE_CASE__, d * 2**r, SCREAMING_SNAKE_CASE__ )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
a_ : List[Any] = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def lowerCAmelCase_ ( ) -> None:
assert not miller_rabin(561 )
assert miller_rabin(563 )
# 2047
assert not miller_rabin(838_201 )
assert miller_rabin(838_207 )
# 1_373_653
assert not miller_rabin(17_316_001 )
assert miller_rabin(17_316_017 )
# 25_326_001
assert not miller_rabin(3_078_386_641 )
assert miller_rabin(3_078_386_653 )
# 3_215_031_751
assert not miller_rabin(1_713_045_574_801 )
assert miller_rabin(1_713_045_574_819 )
# 2_152_302_898_747
assert not miller_rabin(2_779_799_728_307 )
assert miller_rabin(2_779_799_728_327 )
# 3_474_749_660_383
assert not miller_rabin(113_850_023_909_441 )
assert miller_rabin(113_850_023_909_527 )
# 341_550_071_728_321
assert not miller_rabin(1_275_041_018_848_804_351 )
assert miller_rabin(1_275_041_018_848_804_391 )
# 3_825_123_056_546_413_051
assert not miller_rabin(79_666_464_458_507_787_791_867 )
assert miller_rabin(79_666_464_458_507_787_791_951 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(552_840_677_446_647_897_660_333 )
assert miller_rabin(552_840_677_446_647_897_660_359 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin() | 370 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class _lowerCAmelCase :
__UpperCAmelCase : str = field(
metadata={'''help''': '''The output directory where the model will be written.'''} , )
__UpperCAmelCase : str = field(
metadata={
'''help''': (
'''The encoder model checkpoint for weights initialization.'''
'''Don\'t set if you want to train an encoder model from scratch.'''
)
} , )
__UpperCAmelCase : str = field(
metadata={
'''help''': (
'''The decoder model checkpoint for weights initialization.'''
'''Don\'t set if you want to train a decoder model from scratch.'''
)
} , )
__UpperCAmelCase : Optional[str] = field(
default=A_ , metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''} )
__UpperCAmelCase : Optional[str] = field(
default=A_ , metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''} )
def __lowerCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
snake_case : Union[str, Any] = HfArgumentParser((ModelArguments,) )
(snake_case) : str = parser.parse_args_into_dataclasses()
# Load pretrained model and tokenizer
# Use explicit specified encoder config
if model_args.encoder_config_name:
snake_case : Union[str, Any] = AutoConfig.from_pretrained(model_args.encoder_config_name )
# Use pretrained encoder model's config
else:
snake_case : Dict = AutoConfig.from_pretrained(model_args.encoder_model_name_or_path )
# Use explicit specified decoder config
if model_args.decoder_config_name:
snake_case : List[str] = AutoConfig.from_pretrained(model_args.decoder_config_name )
# Use pretrained decoder model's config
else:
snake_case : Dict = AutoConfig.from_pretrained(model_args.decoder_model_name_or_path )
# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
snake_case : List[str] = True
snake_case : List[str] = True
snake_case : Optional[int] = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path , decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path , encoder_config=lowercase , decoder_config=lowercase , )
# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
snake_case : Tuple = decoder_config.decoder_start_token_id
snake_case : Union[str, Any] = decoder_config.pad_token_id
if decoder_start_token_id is None:
snake_case : List[str] = decoder_config.bos_token_id
if pad_token_id is None:
snake_case : Dict = decoder_config.eos_token_id
# This is necessary to make Flax's generate() work
snake_case : Union[str, Any] = decoder_config.eos_token_id
snake_case : List[Any] = decoder_start_token_id
snake_case : Union[str, Any] = pad_token_id
snake_case : Union[str, Any] = AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )
snake_case : Optional[Any] = AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
snake_case : Union[str, Any] = tokenizer.convert_ids_to_tokens(model.config.pad_token_id )
model.save_pretrained(model_args.output_dir )
image_processor.save_pretrained(model_args.output_dir )
tokenizer.save_pretrained(model_args.output_dir )
if __name__ == "__main__":
main()
| 178 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class __A ( A_ ):
UpperCamelCase :List[str] = '''gpt_neo'''
UpperCamelCase :Tuple = ['''past_key_values''']
UpperCamelCase :Optional[int] = {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__(self , __magic_name__=50257 , __magic_name__=2048 , __magic_name__=2048 , __magic_name__=24 , __magic_name__=[[["global", "local"], 12]] , __magic_name__=16 , __magic_name__=None , __magic_name__=256 , __magic_name__="gelu_new" , __magic_name__=0.0 , __magic_name__=0.0 , __magic_name__=0.0 , __magic_name__=0.1 , __magic_name__=1E-5 , __magic_name__=0.02 , __magic_name__=True , __magic_name__=50256 , __magic_name__=50256 , **__magic_name__ , ):
lowerCamelCase__ : Dict = vocab_size
lowerCamelCase__ : Tuple = max_position_embeddings
lowerCamelCase__ : str = hidden_size
lowerCamelCase__ : List[Any] = num_layers
lowerCamelCase__ : List[Any] = num_heads
lowerCamelCase__ : str = intermediate_size
lowerCamelCase__ : str = window_size
lowerCamelCase__ : List[Any] = activation_function
lowerCamelCase__ : Any = resid_dropout
lowerCamelCase__ : Dict = embed_dropout
lowerCamelCase__ : str = attention_dropout
lowerCamelCase__ : str = classifier_dropout
lowerCamelCase__ : str = layer_norm_epsilon
lowerCamelCase__ : Optional[int] = initializer_range
lowerCamelCase__ : int = use_cache
lowerCamelCase__ : List[Any] = bos_token_id
lowerCamelCase__ : int = eos_token_id
lowerCamelCase__ : str = attention_types
lowerCamelCase__ : List[str] = self.expand_attention_types_params(__magic_name__ )
if len(self.attention_layers ) != self.num_layers:
raise ValueError(
"""Configuration for convolutional module is incorrect. """
"""It is required that `len(config.attention_layers)` == `config.num_layers` """
f"but is `len(config.attention_layers) = {len(self.attention_layers )}`, "
f"`config.num_layers = {self.num_layers}`. "
"""`config.attention_layers` is prepared using `config.attention_types`. """
"""Please verify the value of `config.attention_types` argument.""" )
super().__init__(bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
@staticmethod
def _snake_case (__magic_name__ ):
lowerCamelCase__ : Optional[Any] = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def _A (UpperCamelCase : Dict , UpperCamelCase : Dict , UpperCamelCase : Optional[int] , UpperCamelCase : Tuple ) ->int:
'''simple docstring'''
import torch
lowerCamelCase__ : Any = input.size()
lowerCamelCase__ : Tuple = len(UpperCamelCase )
lowerCamelCase__ : str = shape[dimension]
lowerCamelCase__ : Optional[int] = torch.arange(0 , UpperCamelCase , UpperCamelCase )
lowerCamelCase__ : Optional[Any] = torch.div(sizedim - size , UpperCamelCase , rounding_mode="""floor""" ) + 1
lowerCamelCase__ : Tuple = torch.arange(UpperCamelCase ) + low_indices[:min_length][:, None]
lowerCamelCase__ : Dict = [slice(UpperCamelCase )] * rank
lowerCamelCase__ : Union[str, Any] = indices
lowerCamelCase__ : Optional[int] = input[s]
lowerCamelCase__ : int = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(UpperCamelCase )
def _A (UpperCamelCase : List[Any] , UpperCamelCase : Optional[int] ) ->Tuple:
'''simple docstring'''
import torch
lowerCamelCase__ : List[Any] = torch.arange(1 , UpperCamelCase )
lowerCamelCase__ : Any = torch.remainder(UpperCamelCase , UpperCamelCase )
lowerCamelCase__ : Optional[int] = remainders == 0
lowerCamelCase__ : List[str] = candidates[divisor_indices]
lowerCamelCase__ : List[Any] = torch.max(UpperCamelCase )
return largest_divisor, torch.div(UpperCamelCase , UpperCamelCase , rounding_mode="""floor""" )
class __A ( A_ ):
@property
def _snake_case (self ):
lowerCamelCase__ : str = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(__magic_name__ , direction="""inputs""" )
lowerCamelCase__ : Any = {0: """batch""", 1: """past_sequence + sequence"""}
else:
lowerCamelCase__ : int = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def _snake_case (self ):
return self._config.num_heads
def _snake_case (self , __magic_name__ , __magic_name__ = -1 , __magic_name__ = -1 , __magic_name__ = False , __magic_name__ = None , ):
lowerCamelCase__ : Union[str, Any] = super(__magic_name__ , self ).generate_dummy_inputs(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
# We need to order the input in the way they appears in the forward()
lowerCamelCase__ : List[Any] = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
lowerCamelCase__ ,lowerCamelCase__ : str = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
lowerCamelCase__ : Any = seqlen + 2
lowerCamelCase__ : List[Any] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
lowerCamelCase__ : Dict = [
(torch.zeros(__magic_name__ ), torch.zeros(__magic_name__ )) for _ in range(self.num_layers )
]
lowerCamelCase__ : Dict = common_inputs["""attention_mask"""]
if self.use_past:
lowerCamelCase__ : int = ordered_inputs["""attention_mask"""].dtype
lowerCamelCase__ : List[Any] = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(__magic_name__ , __magic_name__ , dtype=__magic_name__ )] , dim=1 )
return ordered_inputs
@property
def _snake_case (self ):
return 13
| 157 | 0 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class _lowercase ( unittest.TestCase ):
def A ( self : Tuple ) -> int:
"""simple docstring"""
a = get_activation("swish" )
self.assertIsInstance(__lowerCAmelCase , nn.SiLU )
self.assertEqual(act(torch.tensor(-100 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def A ( self : Optional[Any] ) -> int:
"""simple docstring"""
a = get_activation("silu" )
self.assertIsInstance(__lowerCAmelCase , nn.SiLU )
self.assertEqual(act(torch.tensor(-100 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def A ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
a = get_activation("mish" )
self.assertIsInstance(__lowerCAmelCase , nn.Mish )
self.assertEqual(act(torch.tensor(-200 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def A ( self : int ) -> List[Any]:
"""simple docstring"""
a = get_activation("gelu" )
self.assertIsInstance(__lowerCAmelCase , nn.GELU )
self.assertEqual(act(torch.tensor(-100 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
| 32 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _lowercase :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
a = ""
a = ""
a = []
a = 0
a = 256
a = 0
a = 0
a = 0
a = 0
def A ( self : Optional[Any] , __lowerCAmelCase : Any ) -> int:
"""simple docstring"""
a = cva.imread(__lowerCAmelCase , 0 )
a = copy.deepcopy(self.img )
a , a , a = plt.hist(self.img.ravel() , 256 , [0, 256] , label="x" )
a = np.sum(__lowerCAmelCase )
for i in range(len(__lowerCAmelCase ) ):
a = x[i] / self.k
self.sk += prk
a = (self.L - 1) * self.sk
if self.rem != 0:
a = int(last % last )
a = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(__lowerCAmelCase )
a = int(np.ma.count(self.img ) / self.img[1].size )
a = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
a = self.img[j][i]
if num != self.last_list[num]:
a = self.last_list[num]
cva.imwrite("output_data/output.jpg" , self.img )
def A ( self : Any ) -> int:
"""simple docstring"""
plt.hist(self.img.ravel() , 256 , [0, 256] )
def A ( self : Any ) -> int:
"""simple docstring"""
cva.imshow("Output-Image" , self.img )
cva.imshow("Input-Image" , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
A_ : List[Any] = os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
A_ : int = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 32 | 1 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = math.inf , SCREAMING_SNAKE_CASE__ = -math.inf , SCREAMING_SNAKE_CASE__ = math.inf , SCREAMING_SNAKE_CASE__ = -math.inf , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = 1_00 , SCREAMING_SNAKE_CASE__ = 0.01 , SCREAMING_SNAKE_CASE__ = 1 , ):
'''simple docstring'''
_snake_case = False
_snake_case = search_prob
_snake_case = start_temperate
_snake_case = []
_snake_case = 0
_snake_case = None
while not search_end:
_snake_case = current_state.score()
if best_state is None or current_score > best_state.score():
_snake_case = current_state
scores.append(SCREAMING_SNAKE_CASE__ )
iterations += 1
_snake_case = None
_snake_case = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
_snake_case = random.randint(0 , len(SCREAMING_SNAKE_CASE__ ) - 1 ) # picking a random neighbor
_snake_case = neighbors.pop(SCREAMING_SNAKE_CASE__ )
_snake_case = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
_snake_case = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
_snake_case = picked_neighbor
else:
_snake_case = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
_snake_case = picked_neighbor
_snake_case = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
_snake_case = True
else:
_snake_case = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
plt.xlabel("Iterations" )
plt.ylabel("Function values" )
plt.show()
return best_state
if __name__ == "__main__":
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
__magic_name__ : Dict = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
__magic_name__ : List[str] = simulated_annealing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"""The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
F'and 50 > y > - 5 found via hill climbing: {local_min.score()}'
)
# starting the problem with initial coordinates (12, 47)
__magic_name__ : Tuple = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
__magic_name__ : str = simulated_annealing(
prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"""The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
F'and 50 > y > - 5 found via hill climbing: {local_min.score()}'
)
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return (3 * x**2) - (6 * y)
__magic_name__ : Tuple = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
__magic_name__ : List[Any] = simulated_annealing(prob, find_max=False, visualization=True)
print(
"""The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
F'{local_min.score()}'
)
__magic_name__ : List[str] = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
__magic_name__ : Any = simulated_annealing(prob, find_max=True, visualization=True)
print(
"""The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
F'{local_min.score()}'
)
| 672 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__magic_name__ : Dict = logging.get_logger(__name__)
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE__ , np.ndarray ):
return list(tensor.shape )
_snake_case = tf.shape(SCREAMING_SNAKE_CASE__ )
if tensor.shape == tf.TensorShape(SCREAMING_SNAKE_CASE__ ):
return dynamic
_snake_case = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(SCREAMING_SNAKE_CASE__ )]
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
return tf.nn.softmax(logits=logits + 1E-9 , axis=SCREAMING_SNAKE_CASE__ , name=SCREAMING_SNAKE_CASE__ )
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=1E-5 , SCREAMING_SNAKE_CASE__=-1 ):
'''simple docstring'''
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis." )
# Get mean and variance on the axis to be normalized
_snake_case , _snake_case = tf.nn.moments(SCREAMING_SNAKE_CASE__ , axes=[axis] , keepdims=SCREAMING_SNAKE_CASE__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
_snake_case = [1] * inputs.shape.rank
_snake_case = shape_list(SCREAMING_SNAKE_CASE__ )[axis]
_snake_case = tf.reshape(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
_snake_case = tf.reshape(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Compute layer normalization using the batch_normalization
# function.
_snake_case = tf.nn.batch_normalization(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , offset=SCREAMING_SNAKE_CASE__ , scale=SCREAMING_SNAKE_CASE__ , variance_epsilon=SCREAMING_SNAKE_CASE__ , )
return outputs
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__=-1 ):
'''simple docstring'''
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
_snake_case = tf.shape(SCREAMING_SNAKE_CASE__ )
_snake_case = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
_snake_case = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE__ , tf.Tensor ):
_snake_case = tf.convert_to_tensor(SCREAMING_SNAKE_CASE__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
_snake_case = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
_snake_case = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
_snake_case = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = "input_ids" ):
'''simple docstring'''
tf.debugging.assert_less(
SCREAMING_SNAKE_CASE__ , tf.cast(SCREAMING_SNAKE_CASE__ , dtype=tensor.dtype ) , message=(
f'''The maximum value of {tensor_name} ({tf.math.reduce_max(SCREAMING_SNAKE_CASE__ )}) must be smaller than the embedding '''
f'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = 6_45_12
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
_snake_case = [x for x in data if len(SCREAMING_SNAKE_CASE__ ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"The following attributes cannot be saved to HDF5 file because "
f'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
f'''bytes: {bad_attributes}''' )
_snake_case = np.asarray(SCREAMING_SNAKE_CASE__ )
_snake_case = 1
_snake_case = np.array_split(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
_snake_case = np.array_split(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(SCREAMING_SNAKE_CASE__ ):
_snake_case = chunk_data
else:
_snake_case = data
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if name in group.attrs:
_snake_case = [n.decode("utf8" ) if hasattr(SCREAMING_SNAKE_CASE__ , "decode" ) else n for n in group.attrs[name]]
else:
_snake_case = []
_snake_case = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("utf8" ) if hasattr(SCREAMING_SNAKE_CASE__ , "decode" ) else n for n in group.attrs["%s%d" % (name, chunk_id)]] )
chunk_id += 1
return data
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def _expand_single_ad_tensor(SCREAMING_SNAKE_CASE__ ):
if isinstance(SCREAMING_SNAKE_CASE__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(SCREAMING_SNAKE_CASE__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , SCREAMING_SNAKE_CASE__ )
| 672 | 1 |
import numpy
# List of input, output pairs
A = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
A = (((515, 22, 13), 555), ((61, 35, 49), 150))
A = [2, 4, 1, 5]
A = len(train_data)
A = 0.009
def _lowerCamelCase( lowerCAmelCase__ : Any , lowerCAmelCase__ : Any="train" ):
'''simple docstring'''
return calculate_hypothesis_value(_lowerCamelCase , _lowerCamelCase ) - output(
_lowerCamelCase , _lowerCamelCase )
def _lowerCamelCase( lowerCAmelCase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Dict = 0
for i in range(len(_lowerCamelCase ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def _lowerCamelCase( lowerCAmelCase__ : Any , lowerCAmelCase__ : List[Any] ):
'''simple docstring'''
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def _lowerCamelCase( lowerCAmelCase__ : Dict , lowerCAmelCase__ : Tuple ):
'''simple docstring'''
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def _lowerCamelCase( lowerCAmelCase__ : Dict , lowerCAmelCase__ : Optional[Any]=m ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : int = 0
for i in range(_lowerCamelCase ):
if index == -1:
summation_value += _error(_lowerCamelCase )
else:
summation_value += _error(_lowerCamelCase ) * train_data[i][0][index]
return summation_value
def _lowerCamelCase( lowerCAmelCase__ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Tuple = summation_of_cost_derivative(_lowerCamelCase , _lowerCamelCase ) / m
return cost_derivative_value
def _lowerCamelCase( ):
'''simple docstring'''
global parameter_vector
# Tune these values to set a tolerance value for predicted output
SCREAMING_SNAKE_CASE_ : List[str] = 0.000_002
SCREAMING_SNAKE_CASE_ : List[str] = 0
SCREAMING_SNAKE_CASE_ : Any = 0
while True:
j += 1
SCREAMING_SNAKE_CASE_ : int = [0, 0, 0, 0]
for i in range(0 , len(_lowerCamelCase ) ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = get_cost_derivative(i - 1 )
SCREAMING_SNAKE_CASE_ : Any = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
_lowerCamelCase , _lowerCamelCase , atol=_lowerCamelCase , rtol=_lowerCamelCase , ):
break
SCREAMING_SNAKE_CASE_ : Any = temp_parameter_vector
print(('Number of iterations:', j) )
def _lowerCamelCase( ):
'''simple docstring'''
for i in range(len(_lowerCamelCase ) ):
print(('Actual output value:', output(_lowerCamelCase , 'test' )) )
print(('Hypothesis output:', calculate_hypothesis_value(_lowerCamelCase , 'test' )) )
if __name__ == "__main__":
run_gradient_descent()
print('\nTesting gradient descent for a linear hypothesis function.\n')
test_gradient_descent() | 706 |
def _lowerCamelCase( ):
'''simple docstring'''
return 1
def _lowerCamelCase( lowerCAmelCase__ : int ):
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def _lowerCamelCase( lowerCAmelCase__ : int ):
'''simple docstring'''
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(lowerCAmelCase__ )
def _lowerCamelCase( lowerCAmelCase__ : int ):
'''simple docstring'''
return 0 if x < 0 else ten_pence(x - 10 ) + five_pence(lowerCAmelCase__ )
def _lowerCamelCase( lowerCAmelCase__ : int ):
'''simple docstring'''
return 0 if x < 0 else twenty_pence(x - 20 ) + ten_pence(lowerCAmelCase__ )
def _lowerCamelCase( lowerCAmelCase__ : int ):
'''simple docstring'''
return 0 if x < 0 else fifty_pence(x - 50 ) + twenty_pence(lowerCAmelCase__ )
def _lowerCamelCase( lowerCAmelCase__ : int ):
'''simple docstring'''
return 0 if x < 0 else one_pound(x - 100 ) + fifty_pence(lowerCAmelCase__ )
def _lowerCamelCase( lowerCAmelCase__ : int ):
'''simple docstring'''
return 0 if x < 0 else two_pound(x - 200 ) + one_pound(lowerCAmelCase__ )
def _lowerCamelCase( lowerCAmelCase__ : int = 200 ):
'''simple docstring'''
return two_pound(lowerCAmelCase__ )
if __name__ == "__main__":
print(solution(int(input().strip()))) | 97 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE = {'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTConfig', 'ViTOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE = ['ViTFeatureExtractor']
SCREAMING_SNAKE_CASE = ['ViTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE = [
'VIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'ViTForImageClassification',
'ViTForMaskedImageModeling',
'ViTModel',
'ViTPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE = [
'TFViTForImageClassification',
'TFViTModel',
'TFViTPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE = [
'FlaxViTForImageClassification',
'FlaxViTModel',
'FlaxViTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 485 |
from sklearn.metrics import matthews_corrcoef
import datasets
SCREAMING_SNAKE_CASE = '\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n'
SCREAMING_SNAKE_CASE = '\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric("matthews_correlation")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results[\'matthews_correlation\'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric("matthews_correlation")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results[\'matthews_correlation\'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric("matthews_correlation")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results[\'matthews_correlation\'], 2))\n -0.25\n'
SCREAMING_SNAKE_CASE = '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A_ ( datasets.Metric ):
'''simple docstring'''
def snake_case__ ( self) -> Any:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''int32'''),
'''references''': datasets.Value('''int32'''),
}) , reference_urls=[
'''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'''
] , )
def snake_case__ ( self , _A , _A , _A=None) -> Any:
"""simple docstring"""
return {
"matthews_correlation": float(matthews_corrcoef(_A , _A , sample_weight=_A)),
}
| 485 | 1 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class __snake_case ( UpperCamelCase__ ):
'''simple docstring'''
lowerCAmelCase__ = 42
class __snake_case ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
@register_to_config
def __init__( self : Optional[Any] , A : int = 16 , A : int = 88 , A : Optional[int] = None , A : Optional[int] = None , A : int = 1 , A : float = 0.0 , A : int = 32 , A : Optional[int] = None , A : bool = False , A : Optional[int] = None , A : str = "geglu" , A : bool = True , A : bool = True , ):
super().__init__()
__snake_case: List[Any] = num_attention_heads
__snake_case: List[str] = attention_head_dim
__snake_case: Dict = num_attention_heads * attention_head_dim
__snake_case: Union[str, Any] = in_channels
__snake_case: Dict = torch.nn.GroupNorm(num_groups=__A , num_channels=__A , eps=1E-6 , affine=__A )
__snake_case: str = nn.Linear(__A , __A )
# 3. Define transformers blocks
__snake_case: List[Any] = nn.ModuleList(
[
BasicTransformerBlock(
__A , __A , __A , dropout=__A , cross_attention_dim=__A , activation_fn=__A , attention_bias=__A , double_self_attention=__A , norm_elementwise_affine=__A , )
for d in range(__A )
] )
__snake_case: List[Any] = nn.Linear(__A , __A )
def UpperCAmelCase__ ( self : Any , A : int , A : int=None , A : Tuple=None , A : str=None , A : str=1 , A : str=None , A : bool = True , ):
__snake_case , __snake_case , __snake_case , __snake_case: List[str] = hidden_states.shape
__snake_case: str = batch_frames // num_frames
__snake_case: Optional[int] = hidden_states
__snake_case: str = hidden_states[None, :].reshape(__A , __A , __A , __A , __A )
__snake_case: int = hidden_states.permute(0 , 2 , 1 , 3 , 4 )
__snake_case: Dict = self.norm(__A )
__snake_case: int = hidden_states.permute(0 , 3 , 4 , 2 , 1 ).reshape(batch_size * height * width , __A , __A )
__snake_case: Any = self.proj_in(__A )
# 2. Blocks
for block in self.transformer_blocks:
__snake_case: Any = block(
__A , encoder_hidden_states=__A , timestep=__A , cross_attention_kwargs=__A , class_labels=__A , )
# 3. Output
__snake_case: List[str] = self.proj_out(__A )
__snake_case: Optional[int] = (
hidden_states[None, None, :]
.reshape(__A , __A , __A , __A , __A )
.permute(0 , 3 , 4 , 1 , 2 )
.contiguous()
)
__snake_case: Dict = hidden_states.reshape(__A , __A , __A , __A )
__snake_case: int = hidden_states + residual
if not return_dict:
return (output,)
return TransformerTemporalModelOutput(sample=__A )
| 715 |
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
__UpperCAmelCase : Any = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'{bindir}/../../examples/pytorch/translation'):
from run_translation import main # noqa
set_seed(42)
__UpperCAmelCase : List[Any] = "sshleifer/student_marian_en_ro_6_1"
__UpperCAmelCase : Optional[Any] = "sshleifer/tiny-mbart"
@require_torch
class __snake_case ( __lowerCamelCase ):
'''simple docstring'''
def UpperCAmelCase__ ( self : int , A : Tuple=False , A : Dict=None , A : List[Any]=True , A : Any=True , A : Optional[Any]=True , A : int=True , ):
__snake_case: Dict = 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 , )
__snake_case: Any = TrainerState.load_from_json(os.path.join(A , """trainer_state.json""" ) ).log_history
if not do_eval:
return
__snake_case: List[Any] = [log for log in logs if """eval_loss""" in log.keys()]
__snake_case: int = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
__snake_case: int = 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 UpperCAmelCase__ ( self : Union[str, Any] ):
self.run_seqaseq_quick()
@require_torch_multi_gpu
def UpperCAmelCase__ ( self : Optional[int] ):
self.run_seqaseq_quick(distributed=A )
@require_torch_multi_gpu
def UpperCAmelCase__ ( self : 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 UpperCAmelCase__ ( self : Any ):
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 UpperCAmelCase__ ( self : Optional[Any] ):
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 UpperCAmelCase__ ( self : Union[str, 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 UpperCAmelCase__ ( self : 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 UpperCAmelCase__ ( self : 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 UpperCAmelCase__ ( self : Tuple , A : List[Any] ):
# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
__snake_case: Tuple = {
# 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},
}
__snake_case: int = experiments[experiment_id]
__snake_case: Dict = {"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
__snake_case: str = """Running training"""
with CaptureStderr() as cl:
self.run_seqaseq_quick(**A , extra_args_str=data["""extra_args_str"""] )
__snake_case: List[str] = len(re.findall(A , cl.err ) )
self.assertEqual(A , data["""n_matches"""] )
@slow
def UpperCAmelCase__ ( self : Dict ):
__snake_case: Optional[int] = self.run_trainer(
eval_steps=2 , max_len=128 , model_name=A , learning_rate=3E-4 , num_train_epochs=10 , distributed=A , )
# Check metrics
__snake_case: Optional[int] = TrainerState.load_from_json(os.path.join(A , """trainer_state.json""" ) ).log_history
__snake_case: Any = [log for log in logs if """eval_loss""" in log.keys()]
__snake_case: Tuple = eval_metrics[0]
__snake_case: Optional[int] = 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
__snake_case: List[str] = os.listdir(A )
__snake_case: List[str] = {os.path.basename(A ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def UpperCAmelCase__ ( self : Any ):
from transformers.training_args import OptimizerNames
def train_and_return_metrics(A : str ) -> Tuple[int, float]:
__snake_case: List[Any] = """--skip_memory_metrics 0"""
__snake_case: Tuple = 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
__snake_case: Any = TrainerState.load_from_json(Path(A , """trainer_state.json""" ) ).log_history
__snake_case: Tuple = int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20 )
__snake_case: Union[str, Any] = int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20 )
__snake_case: int = logs[0]["""train_loss"""]
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
__snake_case , __snake_case , __snake_case: Tuple = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
__snake_case , __snake_case , __snake_case: List[str] = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
__snake_case: Dict = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
__snake_case: Optional[int] = gpu_peak_mem_orig + gpu_alloc_mem_orig
__snake_case: str = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
__snake_case: Tuple = 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
__snake_case: Any = 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 UpperCAmelCase__ ( self : 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 , ):
__snake_case: str = self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
__snake_case: str = self.get_auto_remove_tmp_dir()
__snake_case: List[str] = f'''
--model_name_or_path {model_name}
--train_file {data_dir}/train.json
--validation_file {data_dir}/val.json
--test_file {data_dir}/test.json
--output_dir {output_dir}
--overwrite_output_dir
--max_train_samples 8
--max_source_length {max_len}
--max_target_length {max_len}
--do_train
--num_train_epochs {str(A )}
--per_device_train_batch_size 4
--learning_rate {learning_rate}
--warmup_steps 8
--logging_steps 0
--logging_strategy no
--save_steps {str(A )}
--group_by_length
--label_smoothing_factor 0.1
--target_lang ro_RO
--source_lang en_XX
'''.split()
__snake_case: str = f'''
--do_eval
--per_device_eval_batch_size 4
--max_eval_samples 8
--val_max_target_length {max_len}
--evaluation_strategy steps
--eval_steps {str(A )}
'''.split()
__snake_case: Dict = """
--do_predict
""".split()
__snake_case: Tuple = []
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:
__snake_case: Optional[int] = get_gpu_count()
__snake_case: List[Any] = get_torch_dist_unique_port()
__snake_case: Union[str, Any] = f'''
-m torch.distributed.run
--nproc_per_node={n_gpus_to_use}
--master_port={master_port}
{self.examples_dir_str}/pytorch/translation/run_translation.py
'''.split()
__snake_case: Optional[int] = [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:
__snake_case: int = ["""run_translation.py"""] + args
with patch.object(A , """argv""" , A ):
main()
return output_dir
| 155 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
if isinstance(__snake_case , np.ndarray ):
return list(tensor.shape )
lowerCAmelCase__ : Optional[int] = tf.shape(__snake_case )
if tensor.shape == tf.TensorShape(__snake_case ):
return dynamic
lowerCAmelCase__ : int = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(__snake_case )]
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase = None , UpperCamelCase = None ):
"""simple docstring"""
return tf.nn.softmax(logits=logits + 1e-9 , axis=__snake_case , name=__snake_case )
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase=1e-5 , UpperCamelCase=-1 ):
"""simple docstring"""
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__snake_case , __snake_case ):
raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis.""" )
# Get mean and variance on the axis to be normalized
lowerCAmelCase__ , lowerCAmelCase__ : Any = tf.nn.moments(__snake_case , axes=[axis] , keepdims=__snake_case )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowerCAmelCase__ : Union[str, Any] = [1] * inputs.shape.rank
lowerCAmelCase__ : Tuple = shape_list(__snake_case )[axis]
lowerCAmelCase__ : Optional[int] = tf.reshape(__snake_case , __snake_case )
lowerCAmelCase__ : Any = tf.reshape(__snake_case , __snake_case )
# Compute layer normalization using the batch_normalization
# function.
lowerCAmelCase__ : int = tf.nn.batch_normalization(
__snake_case , __snake_case , __snake_case , offset=__snake_case , scale=__snake_case , variance_epsilon=__snake_case , )
return outputs
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase=0 , UpperCamelCase=-1 ):
"""simple docstring"""
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
lowerCAmelCase__ : Any = tf.shape(__snake_case )
lowerCAmelCase__ : int = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
lowerCAmelCase__ : Any = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(__snake_case , __snake_case )
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
if not isinstance(__snake_case , tf.Tensor ):
lowerCAmelCase__ : List[str] = tf.convert_to_tensor(__snake_case ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowerCAmelCase__ : str = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowerCAmelCase__ : Dict = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
lowerCAmelCase__ : Optional[int] = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase = "input_ids" ):
"""simple docstring"""
tf.debugging.assert_less(
__snake_case , tf.cast(__snake_case , dtype=tensor.dtype ) , message=(
f"""The maximum value of {tensor_name} ({tf.math.reduce_max(__snake_case )}) must be smaller than the embedding """
f"""layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
) , )
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = 64512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
lowerCAmelCase__ : Tuple = [x for x in data if len(__snake_case ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"""The following attributes cannot be saved to HDF5 file because """
f"""they are larger than {HDF5_OBJECT_HEADER_LIMIT} """
f"""bytes: {bad_attributes}""" )
lowerCAmelCase__ : Tuple = np.asarray(__snake_case )
lowerCAmelCase__ : Optional[int] = 1
lowerCAmelCase__ : Dict = np.array_split(__snake_case , __snake_case )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
lowerCAmelCase__ : Dict = np.array_split(__snake_case , __snake_case )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(__snake_case ):
lowerCAmelCase__ : Optional[Any] = chunk_data
else:
lowerCAmelCase__ : Optional[int] = data
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
if name in group.attrs:
lowerCAmelCase__ : List[Any] = [n.decode("""utf8""" ) if hasattr(__snake_case , """decode""" ) else n for n in group.attrs[name]]
else:
lowerCAmelCase__ : Dict = []
lowerCAmelCase__ : Any = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""" ) if hasattr(__snake_case , """decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
chunk_id += 1
return data
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
def _expand_single_ad_tensor(UpperCamelCase ):
if isinstance(__snake_case , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(__snake_case , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , __snake_case )
| 565 |
'''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
a_ : Any = {"""configuration_timm_backbone""": ["""TimmBackboneConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Optional[Any] = ["""TimmBackbone"""]
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
a_ : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 676 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"microsoft/swin-tiny-patch4-window7-224": (
"https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class __SCREAMING_SNAKE_CASE ( _a , _a ):
UpperCAmelCase = '''swin'''
UpperCAmelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , __UpperCamelCase=224 , __UpperCamelCase=4 , __UpperCamelCase=3 , __UpperCamelCase=96 , __UpperCamelCase=[2, 2, 6, 2] , __UpperCamelCase=[3, 6, 12, 24] , __UpperCamelCase=7 , __UpperCamelCase=4.0 , __UpperCamelCase=True , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.1 , __UpperCamelCase="gelu" , __UpperCamelCase=False , __UpperCamelCase=0.02 , __UpperCamelCase=1e-5 , __UpperCamelCase=32 , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase , ) -> Optional[int]:
super().__init__(**_A )
_a = image_size
_a = patch_size
_a = num_channels
_a = embed_dim
_a = depths
_a = len(_A )
_a = num_heads
_a = window_size
_a = mlp_ratio
_a = qkv_bias
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = drop_path_rate
_a = hidden_act
_a = use_absolute_embeddings
_a = layer_norm_eps
_a = initializer_range
_a = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_a = int(embed_dim * 2 ** (len(_A ) - 1) )
_a = ["stem"] + [f"stage{idx}" for idx in range(1 , len(_A ) + 1 )]
_a , _a = get_aligned_output_features_output_indices(
out_features=_A , out_indices=_A , stage_names=self.stage_names )
class __SCREAMING_SNAKE_CASE ( _a ):
UpperCAmelCase = version.parse('''1.11''' )
@property
def a_ ( self ) -> Any:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def a_ ( self ) -> Tuple:
return 1e-4
| 714 |
'''simple docstring'''
from manim import *
class __SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
def a_ ( self ) -> str:
_a = Rectangle(height=0.5 , width=0.5 )
_a = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_a = [mem.copy() for i in range(6 )]
_a = [mem.copy() for i in range(6 )]
_a = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
_a = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
_a = VGroup(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
_a = Text("CPU" , font_size=24 )
_a = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0.5 , aligned_edge=__UpperCamelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__UpperCamelCase )
_a = [mem.copy() for i in range(1 )]
_a = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
_a = Text("GPU" , font_size=24 )
_a = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0.5 , aligned_edge=__UpperCamelCase )
gpu.align_to(__UpperCamelCase , __UpperCamelCase )
gpu.set_x(gpu.get_x() - 1 )
self.add(__UpperCamelCase )
_a = [mem.copy() for i in range(6 )]
_a = VGroup(*__UpperCamelCase ).arrange(__UpperCamelCase , buff=0 )
_a = Text("Model" , font_size=24 )
_a = Group(__UpperCamelCase , __UpperCamelCase ).arrange(__UpperCamelCase , buff=0.5 , aligned_edge=__UpperCamelCase )
model.move_to([3, -1.0, 0] )
self.play(
Create(__UpperCamelCase , run_time=1 ) , Create(__UpperCamelCase , run_time=1 ) , Create(__UpperCamelCase , run_time=1 ) , )
_a = MarkupText(
f"First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM." , font_size=24 , )
_a = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_a = MarkupText(
f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(__UpperCamelCase , run_time=2.5 ) , Write(__UpperCamelCase ) , Write(__UpperCamelCase ) )
self.add(__UpperCamelCase )
_a = []
_a = []
_a = []
for i, rect in enumerate(__UpperCamelCase ):
_a = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(__UpperCamelCase , opacity=0.7 )
cpu_target.move_to(__UpperCamelCase )
cpu_target.generate_target()
_a = 0.46 / 4
_a = 0.46 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__UpperCamelCase )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=__UpperCamelCase , buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=__UpperCamelCase , buff=0.0 )
cpu_targs.append(__UpperCamelCase )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(__UpperCamelCase ) )
second_animations.append(MoveToTarget(__UpperCamelCase , run_time=1.5 ) )
self.play(*__UpperCamelCase )
self.play(*__UpperCamelCase )
self.wait()
| 276 | 0 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
a_ = logging.get_logger(__name__)
# General docstring
a_ = '''RegNetConfig'''
# Base docstring
a_ = '''facebook/regnet-y-040'''
a_ = [1, 1088, 7, 7]
# Image classification docstring
a_ = '''facebook/regnet-y-040'''
a_ = '''tabby, tabby cat'''
a_ = [
'''facebook/regnet-y-040''',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class lowercase__ ( nn.Module ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 3 , __UpperCAmelCase = 1 , __UpperCAmelCase = 1 , __UpperCAmelCase = "relu" , )-> int:
'''simple docstring'''
super().__init__()
lowerCAmelCase__ = nn.Convad(
__UpperCAmelCase , __UpperCAmelCase , kernel_size=__UpperCAmelCase , stride=__UpperCAmelCase , padding=kernel_size // 2 , groups=__UpperCAmelCase , bias=__UpperCAmelCase , )
lowerCAmelCase__ = nn.BatchNormad(__UpperCAmelCase )
lowerCAmelCase__ = ACTaFN[activation] if activation is not None else nn.Identity()
def UpperCAmelCase ( self , __UpperCAmelCase )-> str:
'''simple docstring'''
lowerCAmelCase__ = self.convolution(__UpperCAmelCase )
lowerCAmelCase__ = self.normalization(__UpperCAmelCase )
lowerCAmelCase__ = self.activation(__UpperCAmelCase )
return hidden_state
class lowercase__ ( nn.Module ):
def __init__( self , __UpperCAmelCase )-> Dict:
'''simple docstring'''
super().__init__()
lowerCAmelCase__ = RegNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act )
lowerCAmelCase__ = config.num_channels
def UpperCAmelCase ( self , __UpperCAmelCase )-> Tuple:
'''simple docstring'''
lowerCAmelCase__ = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
lowerCAmelCase__ = self.embedder(__UpperCAmelCase )
return hidden_state
class lowercase__ ( nn.Module ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 2 )-> Union[str, Any]:
'''simple docstring'''
super().__init__()
lowerCAmelCase__ = nn.Convad(__UpperCAmelCase , __UpperCAmelCase , kernel_size=1 , stride=__UpperCAmelCase , bias=__UpperCAmelCase )
lowerCAmelCase__ = nn.BatchNormad(__UpperCAmelCase )
def UpperCAmelCase ( self , __UpperCAmelCase )-> Tensor:
'''simple docstring'''
lowerCAmelCase__ = self.convolution(__UpperCAmelCase )
lowerCAmelCase__ = self.normalization(__UpperCAmelCase )
return hidden_state
class lowercase__ ( nn.Module ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase )-> int:
'''simple docstring'''
super().__init__()
lowerCAmelCase__ = nn.AdaptiveAvgPoolad((1, 1) )
lowerCAmelCase__ = nn.Sequential(
nn.Convad(__UpperCAmelCase , __UpperCAmelCase , kernel_size=1 ) , nn.ReLU() , nn.Convad(__UpperCAmelCase , __UpperCAmelCase , kernel_size=1 ) , nn.Sigmoid() , )
def UpperCAmelCase ( self , __UpperCAmelCase )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = self.pooler(__UpperCAmelCase )
lowerCAmelCase__ = self.attention(__UpperCAmelCase )
lowerCAmelCase__ = hidden_state * attention
return hidden_state
class lowercase__ ( nn.Module ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 1 )-> Tuple:
'''simple docstring'''
super().__init__()
lowerCAmelCase__ = in_channels != out_channels or stride != 1
lowerCAmelCase__ = max(1 , out_channels // config.groups_width )
lowerCAmelCase__ = (
RegNetShortCut(__UpperCAmelCase , __UpperCAmelCase , stride=__UpperCAmelCase ) if should_apply_shortcut else nn.Identity()
)
lowerCAmelCase__ = nn.Sequential(
RegNetConvLayer(__UpperCAmelCase , __UpperCAmelCase , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(__UpperCAmelCase , __UpperCAmelCase , stride=__UpperCAmelCase , groups=__UpperCAmelCase , activation=config.hidden_act ) , RegNetConvLayer(__UpperCAmelCase , __UpperCAmelCase , kernel_size=1 , activation=__UpperCAmelCase ) , )
lowerCAmelCase__ = ACTaFN[config.hidden_act]
def UpperCAmelCase ( self , __UpperCAmelCase )-> Optional[Any]:
'''simple docstring'''
lowerCAmelCase__ = hidden_state
lowerCAmelCase__ = self.layer(__UpperCAmelCase )
lowerCAmelCase__ = self.shortcut(__UpperCAmelCase )
hidden_state += residual
lowerCAmelCase__ = self.activation(__UpperCAmelCase )
return hidden_state
class lowercase__ ( nn.Module ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 1 )-> Any:
'''simple docstring'''
super().__init__()
lowerCAmelCase__ = in_channels != out_channels or stride != 1
lowerCAmelCase__ = max(1 , out_channels // config.groups_width )
lowerCAmelCase__ = (
RegNetShortCut(__UpperCAmelCase , __UpperCAmelCase , stride=__UpperCAmelCase ) if should_apply_shortcut else nn.Identity()
)
lowerCAmelCase__ = nn.Sequential(
RegNetConvLayer(__UpperCAmelCase , __UpperCAmelCase , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(__UpperCAmelCase , __UpperCAmelCase , stride=__UpperCAmelCase , groups=__UpperCAmelCase , activation=config.hidden_act ) , RegNetSELayer(__UpperCAmelCase , reduced_channels=int(round(in_channels / 4 ) ) ) , RegNetConvLayer(__UpperCAmelCase , __UpperCAmelCase , kernel_size=1 , activation=__UpperCAmelCase ) , )
lowerCAmelCase__ = ACTaFN[config.hidden_act]
def UpperCAmelCase ( self , __UpperCAmelCase )-> Any:
'''simple docstring'''
lowerCAmelCase__ = hidden_state
lowerCAmelCase__ = self.layer(__UpperCAmelCase )
lowerCAmelCase__ = self.shortcut(__UpperCAmelCase )
hidden_state += residual
lowerCAmelCase__ = self.activation(__UpperCAmelCase )
return hidden_state
class lowercase__ ( nn.Module ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 2 , __UpperCAmelCase = 2 , )-> str:
'''simple docstring'''
super().__init__()
lowerCAmelCase__ = RegNetXLayer if config.layer_type == "x" else RegNetYLayer
lowerCAmelCase__ = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , stride=__UpperCAmelCase , ) , *[layer(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) for _ in range(depth - 1 )] , )
def UpperCAmelCase ( self , __UpperCAmelCase )-> str:
'''simple docstring'''
lowerCAmelCase__ = self.layers(__UpperCAmelCase )
return hidden_state
class lowercase__ ( nn.Module ):
def __init__( self , __UpperCAmelCase )-> Optional[int]:
'''simple docstring'''
super().__init__()
lowerCAmelCase__ = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
__UpperCAmelCase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
lowerCAmelCase__ = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(__UpperCAmelCase , config.depths[1:] ):
self.stages.append(RegNetStage(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , depth=__UpperCAmelCase ) )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = False , __UpperCAmelCase = True )-> BaseModelOutputWithNoAttention:
'''simple docstring'''
lowerCAmelCase__ = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowerCAmelCase__ = hidden_states + (hidden_state,)
lowerCAmelCase__ = stage_module(__UpperCAmelCase )
if output_hidden_states:
lowerCAmelCase__ = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=__UpperCAmelCase , hidden_states=__UpperCAmelCase )
class lowercase__ ( _UpperCAmelCase ):
a_ =RegNetConfig
a_ ="""regnet"""
a_ ="""pixel_values"""
a_ =True
def UpperCAmelCase ( self , __UpperCAmelCase )-> Union[str, Any]:
'''simple docstring'''
if isinstance(__UpperCAmelCase , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode="fan_out" , nonlinearity="relu" )
elif isinstance(__UpperCAmelCase , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase=False )-> List[Any]:
'''simple docstring'''
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ = value
a_ = r'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
a_ = r'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"""The bare RegNet model outputting raw features without any specific head on top.""", _UpperCAmelCase, )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class lowercase__ ( _UpperCAmelCase ):
def __init__( self , __UpperCAmelCase )-> int:
'''simple docstring'''
super().__init__(__UpperCAmelCase )
lowerCAmelCase__ = config
lowerCAmelCase__ = RegNetEmbeddings(__UpperCAmelCase )
lowerCAmelCase__ = RegNetEncoder(__UpperCAmelCase )
lowerCAmelCase__ = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__UpperCAmelCase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__UpperCAmelCase , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None )-> BaseModelOutputWithPoolingAndNoAttention:
'''simple docstring'''
lowerCAmelCase__ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowerCAmelCase__ = return_dict if return_dict is not None else self.config.use_return_dict
lowerCAmelCase__ = self.embedder(__UpperCAmelCase )
lowerCAmelCase__ = self.encoder(
__UpperCAmelCase , output_hidden_states=__UpperCAmelCase , return_dict=__UpperCAmelCase )
lowerCAmelCase__ = encoder_outputs[0]
lowerCAmelCase__ = self.pooler(__UpperCAmelCase )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=__UpperCAmelCase , pooler_output=__UpperCAmelCase , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
"""
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""", _UpperCAmelCase, )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class lowercase__ ( _UpperCAmelCase ):
def __init__( self , __UpperCAmelCase )-> Any:
'''simple docstring'''
super().__init__(__UpperCAmelCase )
lowerCAmelCase__ = config.num_labels
lowerCAmelCase__ = RegNetModel(__UpperCAmelCase )
# classification head
lowerCAmelCase__ = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__UpperCAmelCase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__UpperCAmelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def UpperCAmelCase ( self , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , )-> ImageClassifierOutputWithNoAttention:
'''simple docstring'''
lowerCAmelCase__ = return_dict if return_dict is not None else self.config.use_return_dict
lowerCAmelCase__ = self.regnet(__UpperCAmelCase , output_hidden_states=__UpperCAmelCase , return_dict=__UpperCAmelCase )
lowerCAmelCase__ = outputs.pooler_output if return_dict else outputs[1]
lowerCAmelCase__ = self.classifier(__UpperCAmelCase )
lowerCAmelCase__ = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowerCAmelCase__ = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowerCAmelCase__ = "single_label_classification"
else:
lowerCAmelCase__ = "multi_label_classification"
if self.config.problem_type == "regression":
lowerCAmelCase__ = MSELoss()
if self.num_labels == 1:
lowerCAmelCase__ = loss_fct(logits.squeeze() , labels.squeeze() )
else:
lowerCAmelCase__ = loss_fct(__UpperCAmelCase , __UpperCAmelCase )
elif self.config.problem_type == "single_label_classification":
lowerCAmelCase__ = CrossEntropyLoss()
lowerCAmelCase__ = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowerCAmelCase__ = BCEWithLogitsLoss()
lowerCAmelCase__ = loss_fct(__UpperCAmelCase , __UpperCAmelCase )
if not return_dict:
lowerCAmelCase__ = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=__UpperCAmelCase , logits=__UpperCAmelCase , hidden_states=outputs.hidden_states )
| 339 |
import functools
def _a ( UpperCamelCase_ : list[int] , UpperCamelCase_ : list[int] ) -> int:
"""simple docstring"""
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ) or not all(isinstance(UpperCamelCase_ , UpperCamelCase_ ) for day in days ):
raise ValueError("The parameter days should be a list of integers" )
if len(UpperCamelCase_ ) != 3 or not all(isinstance(UpperCamelCase_ , UpperCamelCase_ ) for cost in costs ):
raise ValueError("The parameter costs should be a list of three integers" )
if len(UpperCamelCase_ ) == 0:
return 0
if min(UpperCamelCase_ ) <= 0:
raise ValueError("All days elements should be greater than 0" )
if max(UpperCamelCase_ ) >= 366:
raise ValueError("All days elements should be less than 366" )
lowerCAmelCase__ = set(UpperCamelCase_ )
@functools.cache
def dynamic_programming(UpperCamelCase_ : int ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 | 1 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
lowerCamelCase : Dict = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
' Distillation'
)
)
parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
parser.add_argument('--model_name', default='roberta-large', type=str)
parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
parser.add_argument('--vocab_transform', action='store_true')
lowerCamelCase : Union[str, Any] = parser.parse_args()
if args.model_type == "roberta":
lowerCamelCase : int = RobertaForMaskedLM.from_pretrained(args.model_name)
lowerCamelCase : List[Any] = 'roberta'
elif args.model_type == "gpt2":
lowerCamelCase : str = GPTaLMHeadModel.from_pretrained(args.model_name)
lowerCamelCase : Dict = 'transformer'
lowerCamelCase : List[Any] = model.state_dict()
lowerCamelCase : List[Any] = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
lowerCamelCase : Optional[int] = state_dict[f"""{prefix}.{param_name}"""]
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
lowerCamelCase : List[Any] = f"""{prefix}.embeddings.{w}.weight"""
lowerCamelCase : List[Any] = state_dict[param_name]
for w in ["weight", "bias"]:
lowerCamelCase : List[Any] = f"""{prefix}.embeddings.LayerNorm.{w}"""
lowerCamelCase : Tuple = state_dict[param_name]
# Transformer Blocks #
lowerCamelCase : Optional[int] = 0
for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
lowerCamelCase : Any = state_dict[
f"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
]
lowerCamelCase : Union[str, Any] = state_dict[f"""{prefix}.h.{teacher_idx}.attn.bias"""]
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
lowerCamelCase : Tuple = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
lowerCamelCase : Union[str, Any] = state_dict[f"""{layer}"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
lowerCamelCase : Union[str, Any] = state_dict[f"""lm_head.dense.{w}"""]
lowerCamelCase : Union[str, Any] = state_dict[f"""lm_head.layer_norm.{w}"""]
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
lowerCamelCase : Optional[int] = state_dict[f"""{prefix}.ln_f.{w}"""]
lowerCamelCase : Optional[int] = state_dict['lm_head.weight']
print(f"""N layers selected for distillation: {std_idx}""")
print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 702 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase : Tuple = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : int = [
'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
'ViTMAEForPreTraining',
'ViTMAELayer',
'ViTMAEModel',
'ViTMAEPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Dict = [
'TFViTMAEForPreTraining',
'TFViTMAEModel',
'TFViTMAEPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
lowerCamelCase : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 684 | 0 |
"""simple docstring"""
import os
def snake_case ( ) -> List[str]:
with open(os.path.dirname(lowerCAmelCase_ ) + '''/grid.txt''' ) as f:
_snake_case = [] # noqa: E741
for _ in range(20 ):
l.append([int(lowerCAmelCase_ ) for x in f.readline().split()] )
_snake_case = 0
# right
for i in range(20 ):
for j in range(17 ):
_snake_case = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
if temp > maximum:
_snake_case = temp
# down
for i in range(17 ):
for j in range(20 ):
_snake_case = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
if temp > maximum:
_snake_case = temp
# diagonal 1
for i in range(17 ):
for j in range(17 ):
_snake_case = l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
if temp > maximum:
_snake_case = temp
# diagonal 2
for i in range(17 ):
for j in range(3 , 20 ):
_snake_case = l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
if temp > maximum:
_snake_case = temp
return maximum
if __name__ == "__main__":
print(solution())
| 103 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {"configuration_plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLBartConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = ["PLBartTokenizer"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"PLBART_PRETRAINED_MODEL_ARCHIVE_LIST",
"PLBartForCausalLM",
"PLBartForConditionalGeneration",
"PLBartForSequenceClassification",
"PLBartModel",
"PLBartPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 143 | 0 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> Optional[int]:
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
snake_case__ = len(__lowerCAmelCase )
snake_case__ = max(__lowerCAmelCase )
snake_case__ = min(__lowerCAmelCase )
# create the counting array
snake_case__ = coll_max + 1 - coll_min
snake_case__ = [0] * counting_arr_length
# count how much a number appears in the collection
for number in collection:
counting_arr[number - coll_min] += 1
# sum each position with it's predecessors. now, counting_arr[i] tells
# us how many elements <= i has in the collection
for i in range(1 , __lowerCAmelCase ):
snake_case__ = counting_arr[i] + counting_arr[i - 1]
# create the output collection
snake_case__ = [0] * coll_len
# place the elements in the output, respecting the original order (stable
# sort) from end to begin, updating counting_arr
for i in reversed(range(0 , __lowerCAmelCase ) ):
snake_case__ = collection[i]
counting_arr[collection[i] - coll_min] -= 1
return ordered
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> Optional[int]:
return "".join([chr(__lowerCAmelCase ) for i in counting_sort([ord(__lowerCAmelCase ) for c in string] )] )
if __name__ == "__main__":
# Test string sort
assert counting_sort_string("""thisisthestring""") == "eghhiiinrsssttt"
lowerCamelCase__ : Optional[Any] = input("""Enter numbers separated by a comma:\n""").strip()
lowerCamelCase__ : List[str] = [int(item) for item in user_input.split(""",""")]
print(counting_sort(unsorted))
| 208 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase__ : Optional[int] = logging.get_logger()
@dataclass
class __magic_name__ :
'''simple docstring'''
__lowercase : nn.Module
__lowercase : List[nn.Module] = field(default_factory=snake_case_ )
__lowercase : list = field(default_factory=snake_case_ )
def SCREAMING_SNAKE_CASE__ ( self:Optional[int] , _a:int , _a:Tensor , _a:Tensor ):
snake_case__ = len(list(m.modules() ) ) == 1 or isinstance(_a , nn.Convad ) or isinstance(_a , nn.BatchNormad )
if has_not_submodules:
self.traced.append(_a )
def __call__( self:int , _a:Tensor ):
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(_a )
[x.remove() for x in self.handles]
return self
@property
def SCREAMING_SNAKE_CASE__ ( self:List[Any] ):
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda _a : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class __magic_name__ :
'''simple docstring'''
__lowercase : nn.Module
__lowercase : nn.Module
__lowercase : int = 0
__lowercase : List = field(default_factory=snake_case_ )
__lowercase : List = field(default_factory=snake_case_ )
def __call__( self:List[Any] , _a:Tensor ):
snake_case__ = Tracker(self.dest )(_a ).parametrized
snake_case__ = Tracker(self.src )(_a ).parametrized
snake_case__ = list(filter(lambda _a : type(_a ) not in self.src_skip , _a ) )
snake_case__ = list(filter(lambda _a : type(_a ) not in self.dest_skip , _a ) )
if len(_a ) != len(_a ):
raise Exception(
F"""Numbers of operations are different. Source module has {len(_a )} operations while"""
F""" destination module has {len(_a )}.""" )
for dest_m, src_m in zip(_a , _a ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F"""Transfered from={src_m} to={dest_m}""" )
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = True ) -> List[str]:
print(F"""Converting {name}...""" )
with torch.no_grad():
snake_case__ = timm.create_model(__lowerCAmelCase , pretrained=__lowerCAmelCase ).eval()
snake_case__ = ResNetForImageClassification(__lowerCAmelCase ).eval()
snake_case__ = ModuleTransfer(src=__lowerCAmelCase , dest=__lowerCAmelCase )
snake_case__ = torch.randn((1, 3, 224, 224) )
module_transfer(__lowerCAmelCase )
assert torch.allclose(from_model(__lowerCAmelCase ) , our_model(__lowerCAmelCase ).logits ), "The model logits don't match the original one."
snake_case__ = F"""resnet{'-'.join(name.split('resnet' ) )}"""
print(__lowerCAmelCase )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message='''Add model''' , use_temp_dir=__lowerCAmelCase , )
# we can use the convnext one
snake_case__ = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message='''Add image processor''' , use_temp_dir=__lowerCAmelCase , )
print(F"""Pushed {checkpoint_name}""" )
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = True ) -> List[str]:
snake_case__ = '''imagenet-1k-id2label.json'''
snake_case__ = 1000
snake_case__ = (1, num_labels)
snake_case__ = '''huggingface/label-files'''
snake_case__ = num_labels
snake_case__ = json.load(open(hf_hub_download(__lowerCAmelCase , __lowerCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
snake_case__ = {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
snake_case__ = idalabel
snake_case__ = {v: k for k, v in idalabel.items()}
snake_case__ = partial(__lowerCAmelCase , num_labels=__lowerCAmelCase , idalabel=__lowerCAmelCase , labelaid=__lowerCAmelCase )
snake_case__ = {
'''resnet18''': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[64, 128, 256, 512] , layer_type='''basic''' ),
'''resnet26''': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='''bottleneck''' ),
'''resnet34''': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[64, 128, 256, 512] , layer_type='''basic''' ),
'''resnet50''': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='''bottleneck''' ),
'''resnet101''': ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='''bottleneck''' ),
'''resnet152''': ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='''bottleneck''' ),
}
if model_name:
convert_weight_and_push(__lowerCAmelCase , names_to_config[model_name] , __lowerCAmelCase , __lowerCAmelCase )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return config, expected_shape
if __name__ == "__main__":
lowerCamelCase__ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default=None,
type=str,
help=(
"""The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
""" currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=Path,
required=True,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
default=True,
type=bool,
required=False,
help="""If True, push model and image processor to the hub.""",
)
lowerCamelCase__ : str = parser.parse_args()
lowerCamelCase__ : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 208 | 1 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class A :
@staticmethod
def lowerCAmelCase__ ( *_lowerCAmelCase: Tuple , **_lowerCAmelCase: Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
pass
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =hashlib.mda(image.tobytes() )
return m.hexdigest()[:1_0]
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =np.array(lowercase__ )
UpperCAmelCase_ =npimg.shape
return {"hash": hashimage(lowercase__ ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class A ( unittest.TestCase ):
_snake_case =dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
_snake_case =dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def lowerCAmelCase__ ( self: str , _lowerCAmelCase: Optional[int] , _lowerCAmelCase: int , _lowerCAmelCase: str ) -> Any:
'''simple docstring'''
UpperCAmelCase_ =MaskGenerationPipeline(model=_lowerCAmelCase , image_processor=_lowerCAmelCase )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def lowerCAmelCase__ ( self: Optional[Any] , _lowerCAmelCase: int , _lowerCAmelCase: Optional[Any] ) -> Dict:
'''simple docstring'''
pass
@require_tf
@unittest.skip("Image segmentation not implemented in TF" )
def lowerCAmelCase__ ( self: str ) -> int:
'''simple docstring'''
pass
@slow
@require_torch
def lowerCAmelCase__ ( self: Optional[int] ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase_ =pipeline("mask-generation" , model="facebook/sam-vit-huge" )
UpperCAmelCase_ =image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg" , points_per_batch=256 )
# Shortening by hashing
UpperCAmelCase_ =[]
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(_lowerCAmelCase ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [
{"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
{"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
{"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
{"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
{"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
{"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
{"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
{"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
{"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
{"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
{"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
{"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
{"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
{"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
{"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
{"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
{"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
{"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
{"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
{"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
{"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
{"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
{"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
{"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
{"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
{"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
{"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
{"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
{"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
{"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
] , )
# fmt: on
@require_torch
@slow
def lowerCAmelCase__ ( self: str ) -> Any:
'''simple docstring'''
UpperCAmelCase_ ="facebook/sam-vit-huge"
UpperCAmelCase_ =pipeline("mask-generation" , model=_lowerCAmelCase )
UpperCAmelCase_ =image_segmenter(
"http://images.cocodataset.org/val2017/000000039769.jpg" , pred_iou_thresh=1 , points_per_batch=256 )
# Shortening by hashing
UpperCAmelCase_ =[]
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(_lowerCAmelCase ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [
{"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
{"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
{"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
{"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
{"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
] , )
| 54 |
'''simple docstring'''
__UpperCAmelCase = """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
| 379 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ = {
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InformerConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"InformerForPrediction",
"InformerModel",
"InformerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 715 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = "https://openaipublic.azureedge.net/jukebox/models/"
UpperCAmelCase__ = {
"jukebox-1b-lyrics": [
"5b/vqvae.pth.tar",
"5b/prior_level_0.pth.tar",
"5b/prior_level_1.pth.tar",
"1b_lyrics/prior_level_2.pth.tar",
],
"jukebox-5b-lyrics": [
"5b/vqvae.pth.tar",
"5b/prior_level_0.pth.tar",
"5b/prior_level_1.pth.tar",
"5b_lyrics/prior_level_2.pth.tar",
],
}
def A ( _UpperCAmelCase : List[str] ) -> Tuple:
'''simple docstring'''
if key.endswith('.model.1.bias' ) and len(key.split('.' ) ) > 10:
_UpperCAmelCase = key.replace('.model.1.bias' , '.conv1d_1.bias' )
elif key.endswith('.model.1.weight' ) and len(key.split('.' ) ) > 10:
_UpperCAmelCase = key.replace('.model.1.weight' , '.conv1d_1.weight' )
elif key.endswith('.model.3.bias' ) and len(key.split('.' ) ) > 10:
_UpperCAmelCase = key.replace('.model.3.bias' , '.conv1d_2.bias' )
elif key.endswith('.model.3.weight' ) and len(key.split('.' ) ) > 10:
_UpperCAmelCase = key.replace('.model.3.weight' , '.conv1d_2.weight' )
if "conditioner_blocks.0." in key:
_UpperCAmelCase = key.replace('conditioner_blocks.0' , 'conditioner_blocks' )
if "prime_prior" in key:
_UpperCAmelCase = key.replace('prime_prior' , 'encoder' )
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
_UpperCAmelCase = key.replace('.emb.' , '.' )
if key.endswith('k' ): # replace vqvae.X.k with vqvae.X.codebook
return key.replace('.k' , '.codebook' )
if "y_emb." in key:
return key.replace('y_emb.' , 'metadata_embedding.' )
if "x_emb.emb." in key:
_UpperCAmelCase = key.replace('0.x_emb.emb' , 'embed_tokens' )
if "prime_state_ln" in key:
return key.replace('prime_state_ln' , 'encoder.final_layer_norm' )
if ".ln" in key:
return key.replace('.ln' , '.layer_norm' )
if "_ln" in key:
return key.replace('_ln' , '_layer_norm' )
if "prime_state_proj" in key:
return key.replace('prime_state_proj' , 'encoder.proj_in' )
if "prime_x_out" in key:
return key.replace('prime_x_out' , 'encoder.lm_head' )
if "prior.x_out" in key:
return key.replace('x_out' , 'fc_proj_out' )
if "x_emb" in key:
return key.replace('x_emb' , 'embed_tokens' )
return key
def A ( _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = {}
import re
_UpperCAmelCase = re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)' )
_UpperCAmelCase = re.compile(
R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)' )
_UpperCAmelCase = re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)' )
_UpperCAmelCase = re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)' )
_UpperCAmelCase = re.compile(
R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)' )
_UpperCAmelCase = re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)' )
_UpperCAmelCase = re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)' )
_UpperCAmelCase = re.compile(
R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)' )
_UpperCAmelCase = re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)' )
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(_UpperCAmelCase ):
_UpperCAmelCase = re_encoder_block_conv_in.match(_UpperCAmelCase )
_UpperCAmelCase = regex_match.groups()
_UpperCAmelCase = int(groups[2] ) * 2 + int(groups[3] )
_UpperCAmelCase = F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
_UpperCAmelCase = re_encoder_block_conv_in.sub(_UpperCAmelCase , _UpperCAmelCase )
elif re_encoder_block_resnet.fullmatch(_UpperCAmelCase ):
_UpperCAmelCase = re_encoder_block_resnet.match(_UpperCAmelCase )
_UpperCAmelCase = regex_match.groups()
_UpperCAmelCase = int(groups[2] ) * 2 + int(groups[3] )
_UpperCAmelCase = {'1': 1, '3': 2}[groups[-2]]
_UpperCAmelCase = F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
_UpperCAmelCase = F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
_UpperCAmelCase = prefix + resnet_block
_UpperCAmelCase = re_encoder_block_resnet.sub(_UpperCAmelCase , _UpperCAmelCase )
elif re_encoder_block_proj_out.fullmatch(_UpperCAmelCase ):
_UpperCAmelCase = re_encoder_block_proj_out.match(_UpperCAmelCase )
_UpperCAmelCase = regex_match.groups()
_UpperCAmelCase = F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
_UpperCAmelCase = re_encoder_block_proj_out.sub(_UpperCAmelCase , _UpperCAmelCase )
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(_UpperCAmelCase ):
_UpperCAmelCase = re_decoder_block_conv_out.match(_UpperCAmelCase )
_UpperCAmelCase = regex_match.groups()
_UpperCAmelCase = int(groups[2] ) * 2 + int(groups[3] ) - 2
_UpperCAmelCase = F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
_UpperCAmelCase = re_decoder_block_conv_out.sub(_UpperCAmelCase , _UpperCAmelCase )
elif re_decoder_block_resnet.fullmatch(_UpperCAmelCase ):
_UpperCAmelCase = re_decoder_block_resnet.match(_UpperCAmelCase )
_UpperCAmelCase = regex_match.groups()
_UpperCAmelCase = int(groups[2] ) * 2 + int(groups[3] ) - 2
_UpperCAmelCase = {'1': 1, '3': 2}[groups[-2]]
_UpperCAmelCase = F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
_UpperCAmelCase = F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
_UpperCAmelCase = prefix + resnet_block
_UpperCAmelCase = re_decoder_block_resnet.sub(_UpperCAmelCase , _UpperCAmelCase )
elif re_decoder_block_proj_in.fullmatch(_UpperCAmelCase ):
_UpperCAmelCase = re_decoder_block_proj_in.match(_UpperCAmelCase )
_UpperCAmelCase = regex_match.groups()
_UpperCAmelCase = F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
_UpperCAmelCase = re_decoder_block_proj_in.sub(_UpperCAmelCase , _UpperCAmelCase )
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(_UpperCAmelCase ):
_UpperCAmelCase = re_prior_cond_conv_out.match(_UpperCAmelCase )
_UpperCAmelCase = regex_match.groups()
_UpperCAmelCase = int(groups[1] ) * 2 + int(groups[2] ) - 2
_UpperCAmelCase = F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
_UpperCAmelCase = re_prior_cond_conv_out.sub(_UpperCAmelCase , _UpperCAmelCase )
elif re_prior_cond_resnet.fullmatch(_UpperCAmelCase ):
_UpperCAmelCase = re_prior_cond_resnet.match(_UpperCAmelCase )
_UpperCAmelCase = regex_match.groups()
_UpperCAmelCase = int(groups[1] ) * 2 + int(groups[2] ) - 2
_UpperCAmelCase = {'1': 1, '3': 2}[groups[-2]]
_UpperCAmelCase = F"conditioner_blocks.upsampler.upsample_block.{block_index}."
_UpperCAmelCase = F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
_UpperCAmelCase = prefix + resnet_block
_UpperCAmelCase = re_prior_cond_resnet.sub(_UpperCAmelCase , _UpperCAmelCase )
elif re_prior_cond_proj_in.fullmatch(_UpperCAmelCase ):
_UpperCAmelCase = re_prior_cond_proj_in.match(_UpperCAmelCase )
_UpperCAmelCase = regex_match.groups()
_UpperCAmelCase = F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
_UpperCAmelCase = re_prior_cond_proj_in.sub(_UpperCAmelCase , _UpperCAmelCase )
# keep original key
else:
_UpperCAmelCase = original_key
_UpperCAmelCase = replace_key(_UpperCAmelCase )
if F"{key_prefix}.{key}" not in model_state_dict or key is None:
print(F"failed converting {original_key} to {key}, does not match" )
# handle missmatched shape
elif value.shape != model_state_dict[F"{key_prefix}.{key}"].shape:
_UpperCAmelCase = model_state_dict[F"{key_prefix}.{key}"]
print(F"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match" )
_UpperCAmelCase = original_key
_UpperCAmelCase = original_key
_UpperCAmelCase = value
return new_dict
@torch.no_grad()
def A ( _UpperCAmelCase : List[str]=None , _UpperCAmelCase : Dict=None ) -> Dict:
'''simple docstring'''
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/' )[-1]}" ):
_UpperCAmelCase = requests.get(F"{PREFIX}{file}" , allow_redirects=_UpperCAmelCase )
os.makedirs(F"{pytorch_dump_folder_path}/" , exist_ok=_UpperCAmelCase )
open(F"{pytorch_dump_folder_path}/{file.split('/' )[-1]}" , 'wb' ).write(r.content )
_UpperCAmelCase = MODEL_MAPPING[model_name.split('/' )[-1]]
_UpperCAmelCase = JukeboxConfig.from_pretrained(_UpperCAmelCase )
_UpperCAmelCase = JukeboxModel(_UpperCAmelCase )
_UpperCAmelCase = []
_UpperCAmelCase = {}
for i, dict_name in enumerate(_UpperCAmelCase ):
_UpperCAmelCase = torch.load(F"{pytorch_dump_folder_path}/{dict_name.split('/' )[-1]}" )['model']
_UpperCAmelCase = {}
for k in old_dic.keys():
if k.endswith('.b' ):
_UpperCAmelCase = old_dic[k]
elif k.endswith('.w' ):
_UpperCAmelCase = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
_UpperCAmelCase = old_dic[k]
else:
_UpperCAmelCase = old_dic[k]
_UpperCAmelCase = 'vqvae' if i == 0 else F"priors.{3 - i}"
_UpperCAmelCase = fix_jukebox_keys(_UpperCAmelCase , model.state_dict() , _UpperCAmelCase , _UpperCAmelCase )
weight_dict.append(_UpperCAmelCase )
_UpperCAmelCase = weight_dict.pop(0 )
model.vqvae.load_state_dict(_UpperCAmelCase )
for i in range(len(_UpperCAmelCase ) ):
model.priors[i].load_state_dict(weight_dict[2 - i] )
Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
with open(F"{pytorch_dump_folder_path}/mapping.json" , 'w' ) as txtfile:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_UpperCAmelCase )
return weight_dict
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="jukebox-5b-lyrics",
type=str,
help="Name of the model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="jukebox-5b-lyrics-converted",
type=str,
help="Path to the output PyTorch model directory.",
)
UpperCAmelCase__ = parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 639 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
lowercase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
lowercase_ = '\n Examples:\n ```py\n >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n >>> pipe_prior.to("cuda")\n >>> prompt = "red cat, 4k photo"\n >>> out = pipe_prior(prompt)\n >>> image_emb = out.image_embeds\n >>> zero_image_emb = out.negative_image_embeds\n >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n >>> pipe.to("cuda")\n >>> image = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=50,\n ... ).images\n >>> image[0].save("cat.png")\n ```\n'
def a ( A__ : Any , A__ : Optional[Any] , A__ : Optional[Any]=8 ) -> int:
"""simple docstring"""
_lowercase =height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
_lowercase =width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class __lowerCAmelCase ( SCREAMING_SNAKE_CASE ):
def __init__( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , ) -> Any:
'''simple docstring'''
super().__init__()
self.register_modules(
unet=lowerCAmelCase , scheduler=lowerCAmelCase , movq=lowerCAmelCase , )
_lowercase =2 ** (len(self.movq.config.block_out_channels ) - 1)
def A__ ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> Tuple:
'''simple docstring'''
if latents is None:
_lowercase =randn_tensor(lowerCAmelCase , generator=lowerCAmelCase , device=lowerCAmelCase , dtype=lowerCAmelCase )
else:
if latents.shape != shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
_lowercase =latents.to(lowerCAmelCase )
_lowercase =latents * scheduler.init_noise_sigma
return latents
def A__ ( self , lowerCAmelCase=0 ) -> Optional[int]:
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
_lowercase =torch.device(F'''cuda:{gpu_id}''' )
_lowercase =[
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowerCAmelCase , lowerCAmelCase )
def A__ ( self , lowerCAmelCase=0 ) -> str:
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version('>=' , '0.17.0.dev0' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' )
_lowercase =torch.device(F'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to('cpu' , silence_dtype_warnings=lowerCAmelCase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
_lowercase =None
for cpu_offloaded_model in [self.unet, self.movq]:
_lowercase , _lowercase =cpu_offload_with_hook(lowerCAmelCase , lowerCAmelCase , prev_module_hook=lowerCAmelCase )
# We'll offload the last model manually.
_lowercase =hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def A__ ( self ) -> Tuple:
'''simple docstring'''
if not hasattr(self.unet , '_hf_hook' ):
return self.device
for module in self.unet.modules():
if (
hasattr(lowerCAmelCase , '_hf_hook' )
and hasattr(module._hf_hook , 'execution_device' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(lowerCAmelCase )
def __call__( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = 512 , lowerCAmelCase = 512 , lowerCAmelCase = 100 , lowerCAmelCase = 4.0 , lowerCAmelCase = 1 , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = "pil" , lowerCAmelCase = True , ) -> List[str]:
'''simple docstring'''
_lowercase =self._execution_device
_lowercase =guidance_scale > 1.0
if isinstance(lowerCAmelCase , lowerCAmelCase ):
_lowercase =torch.cat(lowerCAmelCase , dim=0 )
_lowercase =image_embeds.shape[0] * num_images_per_prompt
if isinstance(lowerCAmelCase , lowerCAmelCase ):
_lowercase =torch.cat(lowerCAmelCase , dim=0 )
if do_classifier_free_guidance:
_lowercase =image_embeds.repeat_interleave(lowerCAmelCase , dim=0 )
_lowercase =negative_image_embeds.repeat_interleave(lowerCAmelCase , dim=0 )
_lowercase =torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=lowerCAmelCase )
self.scheduler.set_timesteps(lowerCAmelCase , device=lowerCAmelCase )
_lowercase =self.scheduler.timesteps
_lowercase =self.unet.config.in_channels
_lowercase , _lowercase =downscale_height_and_width(lowerCAmelCase , lowerCAmelCase , self.movq_scale_factor )
# create initial latent
_lowercase =self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , self.scheduler , )
for i, t in enumerate(self.progress_bar(lowerCAmelCase ) ):
# expand the latents if we are doing classifier free guidance
_lowercase =torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_lowercase ={'image_embeds': image_embeds}
_lowercase =self.unet(
sample=lowerCAmelCase , timestep=lowerCAmelCase , encoder_hidden_states=lowerCAmelCase , added_cond_kwargs=lowerCAmelCase , return_dict=lowerCAmelCase , )[0]
if do_classifier_free_guidance:
_lowercase , _lowercase =noise_pred.split(latents.shape[1] , dim=1 )
_lowercase , _lowercase =noise_pred.chunk(2 )
_lowercase , _lowercase =variance_pred.chunk(2 )
_lowercase =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
_lowercase =torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , 'variance_type' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
_lowercase , _lowercase =noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
_lowercase =self.scheduler.step(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , generator=lowerCAmelCase , )[0]
# post-processing
_lowercase =self.movq.decode(lowerCAmelCase , force_not_quantize=lowerCAmelCase )['sample']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
_lowercase =image * 0.5 + 0.5
_lowercase =image.clamp(0 , 1 )
_lowercase =image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
_lowercase =self.numpy_to_pil(lowerCAmelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCAmelCase )
| 291 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowercase_ = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
_a = None
_a = None
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilder ):
_a = datasets.Audio()
_a = """audio"""
_a = AudioFolderConfig
_a = 42 # definition at the bottom of the script
_a = AudioClassification(audio_column="""audio""" , label_column="""label""" )
lowercase_ = [
'.aiff',
'.au',
'.avr',
'.caf',
'.flac',
'.htk',
'.svx',
'.mat4',
'.mat5',
'.mpc2k',
'.ogg',
'.paf',
'.pvf',
'.raw',
'.rf64',
'.sd2',
'.sds',
'.ircam',
'.voc',
'.w64',
'.wav',
'.nist',
'.wavex',
'.wve',
'.xi',
'.mp3',
'.opus',
]
lowercase_ = AUDIO_EXTENSIONS
| 291 | 1 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
while second != 0:
UpperCamelCase_: Optional[Any] = first & second
first ^= second
UpperCamelCase_: Any = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCamelCase_ : List[Any] = int(input("""Enter the first number: """).strip())
lowerCamelCase_ : Tuple = int(input("""Enter the second number: """).strip())
print(F"""{add(first, second) = }""")
| 670 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : int , snake_case_ : Optional[Any]=None , snake_case_ : List[str]=None ):
UpperCamelCase_: List[Any] = data
UpperCamelCase_: List[Any] = previous
UpperCamelCase_: Tuple = next_node
def __str__( self : Dict ):
return f'''{self.data}'''
def lowerCAmelCase__ ( self : List[str] ):
return self.data
def lowerCAmelCase__ ( self : Any ):
return self.next
def lowerCAmelCase__ ( self : List[str] ):
return self.previous
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , snake_case_ : int ):
UpperCamelCase_: Union[str, Any] = head
def __iter__( self : Union[str, Any] ):
return self
def lowerCAmelCase__ ( self : Union[str, Any] ):
if not self.current:
raise StopIteration
else:
UpperCamelCase_: Dict = self.current.get_data()
UpperCamelCase_: Tuple = self.current.get_next()
return value
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : int ):
UpperCamelCase_: Optional[int] = None # First node in list
UpperCamelCase_: Dict = None # Last node in list
def __str__( self : Tuple ):
UpperCamelCase_: int = self.head
UpperCamelCase_: Tuple = []
while current is not None:
nodes.append(current.get_data() )
UpperCamelCase_: List[str] = current.get_next()
return " ".join(str(snake_case_ ) for node in nodes )
def __contains__( self : int , snake_case_ : int ):
UpperCamelCase_: Optional[Any] = self.head
while current:
if current.get_data() == value:
return True
UpperCamelCase_: Any = current.get_next()
return False
def __iter__( self : Any ):
return LinkedListIterator(self.head )
def lowerCAmelCase__ ( self : Tuple ):
if self.head:
return self.head.get_data()
return None
def lowerCAmelCase__ ( self : Optional[Any] ):
if self.tail:
return self.tail.get_data()
return None
def lowerCAmelCase__ ( self : Optional[int] , snake_case_ : Node ):
if self.head is None:
UpperCamelCase_: Tuple = node
UpperCamelCase_: Optional[int] = node
else:
self.insert_before_node(self.head , snake_case_ )
def lowerCAmelCase__ ( self : Optional[Any] , snake_case_ : Node ):
if self.head is None:
self.set_head(snake_case_ )
else:
self.insert_after_node(self.tail , snake_case_ )
def lowerCAmelCase__ ( self : List[Any] , snake_case_ : int ):
UpperCamelCase_: Any = Node(snake_case_ )
if self.head is None:
self.set_head(snake_case_ )
else:
self.set_tail(snake_case_ )
def lowerCAmelCase__ ( self : Optional[Any] , snake_case_ : Node , snake_case_ : Node ):
UpperCamelCase_: str = node
UpperCamelCase_: int = node.previous
if node.get_previous() is None:
UpperCamelCase_: int = node_to_insert
else:
UpperCamelCase_: Dict = node_to_insert
UpperCamelCase_: int = node_to_insert
def lowerCAmelCase__ ( self : Dict , snake_case_ : Node , snake_case_ : Node ):
UpperCamelCase_: Tuple = node
UpperCamelCase_: Dict = node.next
if node.get_next() is None:
UpperCamelCase_: Union[str, Any] = node_to_insert
else:
UpperCamelCase_: str = node_to_insert
UpperCamelCase_: int = node_to_insert
def lowerCAmelCase__ ( self : Tuple , snake_case_ : int , snake_case_ : int ):
UpperCamelCase_: Union[str, Any] = 1
UpperCamelCase_: List[str] = Node(snake_case_ )
UpperCamelCase_: Optional[Any] = self.head
while node:
if current_position == position:
self.insert_before_node(snake_case_ , snake_case_ )
return
current_position += 1
UpperCamelCase_: Dict = node.next
self.insert_after_node(self.tail , snake_case_ )
def lowerCAmelCase__ ( self : int , snake_case_ : int ):
UpperCamelCase_: Union[str, Any] = self.head
while node:
if node.get_data() == item:
return node
UpperCamelCase_: List[Any] = node.get_next()
raise Exception("""Node not found""" )
def lowerCAmelCase__ ( self : List[Any] , snake_case_ : List[str] ):
if (node := self.get_node(snake_case_ )) is not None:
if node == self.head:
UpperCamelCase_: Optional[int] = self.head.get_next()
if node == self.tail:
UpperCamelCase_: Union[str, Any] = self.tail.get_previous()
self.remove_node_pointers(snake_case_ )
@staticmethod
def lowerCAmelCase__ ( snake_case_ : Node ):
if node.get_next():
UpperCamelCase_: str = node.previous
if node.get_previous():
UpperCamelCase_: int = node.next
UpperCamelCase_: List[str] = None
UpperCamelCase_: int = None
def lowerCAmelCase__ ( self : str ):
return self.head is None
def A__ ( ) -> None:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 670 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
lowerCAmelCase : Union[str, Any] = list[tuple[int, int]]
lowerCAmelCase : Optional[Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
lowerCAmelCase : Optional[int] = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
_lowerCAmelCase : Any = pos_x
_lowerCAmelCase : List[Any] = pos_y
_lowerCAmelCase : Union[str, Any] = (pos_y, pos_x)
_lowerCAmelCase : int = goal_x
_lowerCAmelCase : Any = goal_y
_lowerCAmelCase : int = parent
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self , snake_case__ , snake_case__ ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = Node(start[1] , start[0] , goal[1] , goal[0] , snake_case__ )
_lowerCAmelCase : str = Node(goal[1] , goal[0] , goal[1] , goal[0] , snake_case__ )
_lowerCAmelCase : Dict = [self.start]
_lowerCAmelCase : str = False
def a ( self ):
'''simple docstring'''
while self.node_queue:
_lowerCAmelCase : Any = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
_lowerCAmelCase : Optional[int] = True
return self.retrace_path(snake_case__ )
_lowerCAmelCase : Union[str, Any] = self.get_successors(snake_case__ )
for node in successors:
self.node_queue.append(snake_case__ )
if not self.reached:
return [self.start.pos]
return None
def a ( self , snake_case__ ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = []
for action in delta:
_lowerCAmelCase : int = parent.pos_x + action[1]
_lowerCAmelCase : Any = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(snake_case__ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(snake_case__ , snake_case__ , self.target.pos_y , self.target.pos_x , snake_case__ ) )
return successors
def a ( self , snake_case__ ):
'''simple docstring'''
_lowerCAmelCase : int = node
_lowerCAmelCase : int = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
_lowerCAmelCase : Any = current_node.parent
path.reverse()
return path
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self , snake_case__ , snake_case__ ):
'''simple docstring'''
_lowerCAmelCase : Dict = BreadthFirstSearch(snake_case__ , snake_case__ )
_lowerCAmelCase : Optional[int] = BreadthFirstSearch(snake_case__ , snake_case__ )
_lowerCAmelCase : Dict = False
def a ( self ):
'''simple docstring'''
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
_lowerCAmelCase : Union[str, Any] = self.fwd_bfs.node_queue.pop(0 )
_lowerCAmelCase : Optional[Any] = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
_lowerCAmelCase : List[Any] = True
return self.retrace_bidirectional_path(
snake_case__ , snake_case__ )
_lowerCAmelCase : Union[str, Any] = current_bwd_node
_lowerCAmelCase : List[Any] = current_fwd_node
_lowerCAmelCase : Optional[Any] = {
self.fwd_bfs: self.fwd_bfs.get_successors(snake_case__ ),
self.bwd_bfs: self.bwd_bfs.get_successors(snake_case__ ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(snake_case__ )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def a ( self , snake_case__ , snake_case__ ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.fwd_bfs.retrace_path(snake_case__ )
_lowerCAmelCase : List[Any] = self.bwd_bfs.retrace_path(snake_case__ )
bwd_path.pop()
bwd_path.reverse()
_lowerCAmelCase : Union[str, Any] = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
lowerCAmelCase : Union[str, Any] = (0, 0)
lowerCAmelCase : int = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
lowerCAmelCase : List[str] = time.time()
lowerCAmelCase : Any = BreadthFirstSearch(init, goal)
lowerCAmelCase : List[str] = bfs.search()
lowerCAmelCase : List[Any] = time.time() - start_bfs_time
print("""Unidirectional BFS computation time : """, bfs_time)
lowerCAmelCase : Dict = time.time()
lowerCAmelCase : Union[str, Any] = BidirectionalBreadthFirstSearch(init, goal)
lowerCAmelCase : Any = bd_bfs.search()
lowerCAmelCase : List[Any] = time.time() - start_bd_bfs_time
print("""Bidirectional BFS computation time : """, bd_bfs_time)
| 444 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
lowerCAmelCase : Optional[int] = datasets.logging.get_logger(__name__)
lowerCAmelCase : List[str] = """\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for Text Generation},
author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
booktitle={ACL},
year={2020},
url={https://arxiv.org/abs/2004.04696}
}
"""
lowerCAmelCase : List[Any] = """\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).
See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""
lowerCAmelCase : str = """
BLEURT score.
Args:
`predictions` (list of str): prediction/candidate sentences
`references` (list of str): reference sentences
`checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.
Returns:
'scores': List of scores.
Examples:
>>> predictions = [\"hello there\", \"general kenobi\"]
>>> references = [\"hello there\", \"general kenobi\"]
>>> bleurt = datasets.load_metric(\"bleurt\")
>>> results = bleurt.compute(predictions=predictions, references=references)
>>> print([round(v, 2) for v in results[\"scores\"]])
[1.03, 1.04]
"""
lowerCAmelCase : Optional[Any] = {
"""bleurt-tiny-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip""",
"""bleurt-tiny-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip""",
"""bleurt-base-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip""",
"""bleurt-base-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip""",
"""bleurt-large-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip""",
"""bleurt-large-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip""",
"""BLEURT-20-D3""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip""",
"""BLEURT-20-D6""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip""",
"""BLEURT-20-D12""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip""",
"""BLEURT-20""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip""",
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCamelCase__ ( datasets.Metric ):
"""simple docstring"""
def a ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='https://github.com/google-research/bleurt' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/google-research/bleurt'] , reference_urls=['https://github.com/google-research/bleurt', 'https://arxiv.org/abs/2004.04696'] , )
def a ( self , snake_case__ ):
'''simple docstring'''
if self.config_name == "default":
logger.warning(
'Using default BLEURT-Base checkpoint for sequence maximum length 128. '
'You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').' )
_lowerCAmelCase : Tuple = 'bleurt-base-128'
if self.config_name.lower() in CHECKPOINT_URLS:
_lowerCAmelCase : int = self.config_name.lower()
elif self.config_name.upper() in CHECKPOINT_URLS:
_lowerCAmelCase : str = self.config_name.upper()
else:
raise KeyError(
F'{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}' )
# download the model checkpoint specified by self.config_name and set up the scorer
_lowerCAmelCase : Optional[Any] = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
_lowerCAmelCase : str = score.BleurtScorer(os.path.join(snake_case__ , snake_case__ ) )
def a ( self , snake_case__ , snake_case__ ):
'''simple docstring'''
_lowerCAmelCase : Tuple = self.scorer.score(references=snake_case__ , candidates=snake_case__ )
return {"scores": scores}
| 444 | 1 |
import socket
def lowerCamelCase__ ( ) -> int:
'''simple docstring'''
_snake_case = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
_snake_case = socket.gethostname()
_snake_case = 12_312
sock.connect((host, port) )
sock.send(b'Hello server!' )
with open('Received_file' , 'wb' ) as out_file:
print('File opened' )
print('Receiving data...' )
while True:
_snake_case = sock.recv(1_024 )
if not data:
break
out_file.write(__A )
print('Successfully received the file' )
sock.close()
print('Connection closed' )
if __name__ == "__main__":
main()
| 715 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
UpperCAmelCase_ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class UpperCamelCase_ ( nn.Module ):
def __init__( self , lowerCAmelCase_ ) -> int:
super().__init__()
_snake_case = torchvision.models.resnetaaa(pretrained=lowerCAmelCase_ )
_snake_case = list(model.children() )[:-2]
_snake_case = nn.Sequential(*lowerCAmelCase_ )
_snake_case = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def lowerCAmelCase ( self , lowerCAmelCase_ ) -> str:
# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
_snake_case = self.pool(self.model(lowerCAmelCase_ ) )
_snake_case = torch.flatten(lowerCAmelCase_ , start_dim=2 )
_snake_case = out.transpose(1 , 2 ).contiguous()
return out # BxNx2048
class UpperCamelCase_ ( _lowerCamelCase ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> Optional[Any]:
_snake_case = [json.loads(lowerCAmelCase_ ) for l in open(lowerCAmelCase_ )]
_snake_case = os.path.dirname(lowerCAmelCase_ )
_snake_case = tokenizer
_snake_case = labels
_snake_case = len(lowerCAmelCase_ )
_snake_case = max_seq_length
_snake_case = transforms
def __len__( self ) -> Any:
return len(self.data )
def __getitem__( self , lowerCAmelCase_ ) -> Optional[int]:
_snake_case = torch.LongTensor(self.tokenizer.encode(self.data[index]['text'] , add_special_tokens=lowerCAmelCase_ ) )
_snake_case , _snake_case , _snake_case = sentence[0], sentence[1:-1], sentence[-1]
_snake_case = sentence[: self.max_seq_length]
_snake_case = torch.zeros(self.n_classes )
_snake_case = 1
_snake_case = Image.open(os.path.join(self.data_dir , self.data[index]['img'] ) ).convert('RGB' )
_snake_case = self.transforms(lowerCAmelCase_ )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def lowerCAmelCase ( self ) -> Tuple:
_snake_case = Counter()
for row in self.data:
label_freqs.update(row['label'] )
return label_freqs
def lowerCamelCase__ ( UpperCamelCase__ : str ) -> Dict:
'''simple docstring'''
_snake_case = [len(row['sentence'] ) for row in batch]
_snake_case , _snake_case = len(UpperCamelCase__ ), max(UpperCamelCase__ )
_snake_case = torch.zeros(UpperCamelCase__ , UpperCamelCase__ , dtype=torch.long )
_snake_case = torch.zeros(UpperCamelCase__ , UpperCamelCase__ , dtype=torch.long )
for i_batch, (input_row, length) in enumerate(zip(UpperCamelCase__ , UpperCamelCase__ ) ):
_snake_case = input_row['sentence']
_snake_case = 1
_snake_case = torch.stack([row['image'] for row in batch] )
_snake_case = torch.stack([row['label'] for row in batch] )
_snake_case = torch.stack([row['image_start_token'] for row in batch] )
_snake_case = torch.stack([row['image_end_token'] for row in batch] )
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def lowerCamelCase__ ( ) -> str:
'''simple docstring'''
return [
"Crime",
"Drama",
"Thriller",
"Action",
"Comedy",
"Romance",
"Documentary",
"Short",
"Mystery",
"History",
"Family",
"Adventure",
"Fantasy",
"Sci-Fi",
"Western",
"Horror",
"Sport",
"War",
"Music",
"Musical",
"Animation",
"Biography",
"Film-Noir",
]
def lowerCamelCase__ ( ) -> Tuple:
'''simple docstring'''
return transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.4677_7044, 0.4453_1429, 0.4066_1017] , std=[0.1222_1994, 0.1214_5835, 0.1438_0469] , ),
] )
| 541 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {
'''configuration_blip_2''': [
'''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip2Config''',
'''Blip2QFormerConfig''',
'''Blip2VisionConfig''',
],
'''processing_blip_2''': ['''Blip2Processor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
'''BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Blip2Model''',
'''Blip2QFormerModel''',
'''Blip2PreTrainedModel''',
'''Blip2ForConditionalGeneration''',
'''Blip2VisionModel''',
]
if TYPE_CHECKING:
from .configuration_blip_a import (
BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlipaConfig,
BlipaQFormerConfig,
BlipaVisionConfig,
)
from .processing_blip_a import BlipaProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip_a import (
BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipaForConditionalGeneration,
BlipaModel,
BlipaPreTrainedModel,
BlipaQFormerModel,
BlipaVisionModel,
)
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 547 | 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 _lowerCAmelCase ( unittest.TestCase ):
def __init__( self : Tuple , __snake_case : int , __snake_case : List[str]=13 , __snake_case : int=7 , __snake_case : int=True , __snake_case : int=True , __snake_case : Dict=True , __snake_case : str=True , __snake_case : Dict=99 , __snake_case : Optional[int]=32 , __snake_case : Optional[Any]=5 , __snake_case : Union[str, Any]=4 , __snake_case : Union[str, Any]=37 , __snake_case : int="gelu" , __snake_case : Union[str, Any]=0.1 , __snake_case : Union[str, Any]=0.1 , __snake_case : Any=512 , __snake_case : Dict=16 , __snake_case : Optional[int]=2 , __snake_case : str=0.0_2 , __snake_case : int=4 , ):
lowerCamelCase :Union[str, Any] = parent
lowerCamelCase :str = batch_size
lowerCamelCase :Dict = seq_length
lowerCamelCase :int = is_training
lowerCamelCase :int = use_attention_mask
lowerCamelCase :Optional[Any] = use_token_type_ids
lowerCamelCase :int = use_labels
lowerCamelCase :List[Any] = vocab_size
lowerCamelCase :str = hidden_size
lowerCamelCase :Optional[int] = num_hidden_layers
lowerCamelCase :Tuple = num_attention_heads
lowerCamelCase :Tuple = intermediate_size
lowerCamelCase :Tuple = hidden_act
lowerCamelCase :Any = hidden_dropout_prob
lowerCamelCase :List[str] = attention_probs_dropout_prob
lowerCamelCase :Any = max_position_embeddings
lowerCamelCase :Dict = type_vocab_size
lowerCamelCase :int = type_sequence_label_size
lowerCamelCase :str = initializer_range
lowerCamelCase :Any = num_choices
def snake_case ( self : Any ):
lowerCamelCase :List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase :Any = None
if self.use_attention_mask:
lowerCamelCase :int = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase :Dict = None
if self.use_token_type_ids:
lowerCamelCase :Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCamelCase :List[str] = 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=__snake_case , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def snake_case ( self : Any ):
lowerCamelCase :int = self.prepare_config_and_inputs()
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase :Any = config_and_inputs
lowerCamelCase :str = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def snake_case ( self : str ):
lowerCamelCase :Optional[int] = self.prepare_config_and_inputs()
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase :Optional[Any] = config_and_inputs
lowerCamelCase :List[Any] = True
lowerCamelCase :Union[str, Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowerCamelCase :str = 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 _lowerCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
_UpperCAmelCase = True
_UpperCAmelCase = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def snake_case ( self : Optional[int] ):
lowerCamelCase :str = FlaxRobertaPreLayerNormModelTester(self )
@slow
def snake_case ( self : Any ):
for model_class_name in self.all_model_classes:
lowerCamelCase :Dict = model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' , from_pt=__snake_case )
lowerCamelCase :Optional[Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(__snake_case )
@require_flax
class _lowerCAmelCase ( unittest.TestCase ):
@slow
def snake_case ( self : Tuple ):
lowerCamelCase :List[str] = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' , from_pt=__snake_case )
lowerCamelCase :int = np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] , dtype=jnp.intaa )
lowerCamelCase :List[str] = model(__snake_case )[0]
lowerCamelCase :Tuple = [1, 11, 50265]
self.assertEqual(list(output.shape ) , __snake_case )
# compare the actual values for a slice.
lowerCamelCase :Optional[Any] = 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] , __snake_case , atol=1e-4 ) )
@slow
def snake_case ( self : Any ):
lowerCamelCase :Optional[int] = FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' , from_pt=__snake_case )
lowerCamelCase :Union[str, Any] = np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] , dtype=jnp.intaa )
lowerCamelCase :int = model(__snake_case )[0]
# compare the actual values for a slice.
lowerCamelCase :List[Any] = 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] , __snake_case , atol=1e-4 ) )
| 166 | 0 |
"""simple docstring"""
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowercase = '''<<<<<<< This should probably be modified because it mentions: '''
lowercase = '''=======
>>>>>>>
'''
lowercase = [
'''TextEncoderConfig''',
'''ByteTextEncoder''',
'''SubwordTextEncoder''',
'''encoder_config''',
'''maybe_build_from_corpus''',
'''manual_dir''',
]
lowercase = [
# (pattern, replacement)
# Order is important here for some replacements
(r'''tfds\.core''', r'''datasets'''),
(r'''tf\.io\.gfile\.GFile''', r'''open'''),
(r'''tf\.([\w\d]+)''', r'''datasets.Value(\'\1\')'''),
(r'''tfds\.features\.Text\(\)''', r'''datasets.Value(\'string\')'''),
(r'''tfds\.features\.Text\(''', r'''datasets.Value(\'string\'),'''),
(r'''features\s*=\s*tfds.features.FeaturesDict\(''', r'''features=datasets.Features('''),
(r'''tfds\.features\.FeaturesDict\(''', r'''dict('''),
(r'''The TensorFlow Datasets Authors''', r'''The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'''),
(r'''tfds\.''', r'''datasets.'''),
(r'''dl_manager\.manual_dir''', r'''self.config.data_dir'''),
(r'''self\.builder_config''', r'''self.config'''),
]
def UpperCAmelCase ( A : Namespace ):
'''simple docstring'''
return ConvertCommand(args.tfds_path , args.datasets_directory )
class lowercase__ ( A ):
'''simple docstring'''
@staticmethod
def lowerCamelCase_ ( snake_case ) -> str:
_UpperCAmelCase = parser.add_parser(
'convert' , help='Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.' , )
train_parser.add_argument(
'--tfds_path' , type=snake_case , required=snake_case , help='Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.' , )
train_parser.add_argument(
'--datasets_directory' , type=snake_case , required=snake_case , help='Path to the HuggingFace Datasets folder.' )
train_parser.set_defaults(func=snake_case )
def __init__( self , snake_case , snake_case , *snake_case ) -> Any:
_UpperCAmelCase = get_logger('datasets-cli/converting' )
_UpperCAmelCase = tfds_path
_UpperCAmelCase = datasets_directory
def lowerCamelCase_ ( self ) -> Optional[Any]:
if os.path.isdir(self._tfds_path ):
_UpperCAmelCase = os.path.abspath(self._tfds_path )
elif os.path.isfile(self._tfds_path ):
_UpperCAmelCase = os.path.dirname(self._tfds_path )
else:
raise ValueError('--tfds_path is neither a directory nor a file. Please check path.' )
_UpperCAmelCase = os.path.abspath(self._datasets_directory )
self._logger.info(f'Converting datasets from {abs_tfds_path} to {abs_datasets_path}' )
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = {}
if os.path.isdir(self._tfds_path ):
_UpperCAmelCase = os.listdir(snake_case )
else:
_UpperCAmelCase = [os.path.basename(self._tfds_path )]
for f_name in file_names:
self._logger.info(f'Looking at file {f_name}' )
_UpperCAmelCase = os.path.join(snake_case , snake_case )
_UpperCAmelCase = os.path.join(snake_case , snake_case )
if not os.path.isfile(snake_case ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
self._logger.info('Skipping file' )
continue
with open(snake_case , encoding='utf-8' ) as f:
_UpperCAmelCase = f.readlines()
_UpperCAmelCase = []
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = []
for line in lines:
_UpperCAmelCase = line
# Convert imports
if "import tensorflow.compat.v2 as tf" in out_line:
continue
elif "@tfds.core" in out_line:
continue
elif "builder=self" in out_line:
continue
elif "import tensorflow_datasets.public_api as tfds" in out_line:
_UpperCAmelCase = 'import datasets\n'
elif "import tensorflow" in out_line:
# order is important here
_UpperCAmelCase = ''
continue
elif "from absl import logging" in out_line:
_UpperCAmelCase = 'from datasets import logging\n'
elif "getLogger" in out_line:
_UpperCAmelCase = out_line.replace('getLogger' , 'get_logger' )
elif any(expression in out_line for expression in TO_HIGHLIGHT ):
_UpperCAmelCase = True
_UpperCAmelCase = list(filter(lambda snake_case : e in out_line , snake_case ) )
out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(snake_case ) + '\n' )
out_lines.append(snake_case )
out_lines.append(snake_case )
continue
else:
for pattern, replacement in TO_CONVERT:
_UpperCAmelCase = re.sub(snake_case , snake_case , snake_case )
# Take care of saving utilities (to later move them together with main script)
if "tensorflow_datasets" in out_line:
_UpperCAmelCase = re.match(r'from\stensorflow_datasets.*import\s([^\.\r\n]+)' , snake_case )
tfds_imports.extend(imp.strip() for imp in match.group(1 ).split(',' ) )
_UpperCAmelCase = 'from . import ' + match.group(1 )
# Check we have not forget anything
if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
raise ValueError(f'Error converting {out_line.strip()}' )
if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
_UpperCAmelCase = True
out_lines.append(snake_case )
if is_builder or "wmt" in f_name:
# We create a new directory for each dataset
_UpperCAmelCase = f_name.replace('.py' , '' )
_UpperCAmelCase = os.path.join(snake_case , snake_case )
_UpperCAmelCase = os.path.join(snake_case , snake_case )
os.makedirs(snake_case , exist_ok=snake_case )
self._logger.info(f'Adding directory {output_dir}' )
imports_to_builder_map.update({imp: output_dir for imp in tfds_imports} )
else:
# Utilities will be moved at the end
utils_files.append(snake_case )
if needs_manual_update:
with_manual_update.append(snake_case )
with open(snake_case , 'w' , encoding='utf-8' ) as f:
f.writelines(snake_case )
self._logger.info(f'Converted in {output_file}' )
for utils_file in utils_files:
try:
_UpperCAmelCase = os.path.basename(snake_case )
_UpperCAmelCase = imports_to_builder_map[f_name.replace('.py' , '' )]
self._logger.info(f'Moving {dest_folder} to {utils_file}' )
shutil.copy(snake_case , snake_case )
except KeyError:
self._logger.error(f'Cannot find destination folder for {utils_file}. Please copy manually.' )
if with_manual_update:
for file_path in with_manual_update:
self._logger.warning(
f'You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.' )
| 24 |
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def UpperCAmelCase ( A : int , A : int , A : int ):
'''simple docstring'''
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
_UpperCAmelCase = b * b - 4 * a * c
_UpperCAmelCase = (-b + sqrt(A )) / (2 * a)
_UpperCAmelCase = (-b - sqrt(A )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = quadratic_roots(a=5 , b=6 , c=1 )
print(f'The solutions are: {solutiona} and {solutiona}' )
if __name__ == "__main__":
main()
| 24 | 1 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session')
def lowerCamelCase__ ():
SCREAMING_SNAKE_CASE = 10
SCREAMING_SNAKE_CASE = datasets.Features(
{
'tokens': datasets.Sequence(datasets.Value('string')),
'labels': datasets.Sequence(datasets.ClassLabel(names=['negative', 'positive'])),
'answers': datasets.Sequence(
{
'text': datasets.Value('string'),
'answer_start': datasets.Value('int32'),
}),
'id': datasets.Value('int64'),
})
SCREAMING_SNAKE_CASE = datasets.Dataset.from_dict(
{
'tokens': [['foo'] * 5] * n,
'labels': [[1] * 5] * n,
'answers': [{'answer_start': [97], 'text': ['1976']}] * 10,
'id': list(range(_UpperCAmelCase)),
} , features=_UpperCAmelCase , )
return dataset
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp('data') / 'file.arrow')
dataset.map(cache_file_name=_UpperCAmelCase)
return filename
# FILE_CONTENT + files
a_ : Tuple = '\\n Text data.\n Second line of data.'
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'file.txt'
SCREAMING_SNAKE_CASE = FILE_CONTENT
with open(_UpperCAmelCase , 'w') as f:
f.write(_UpperCAmelCase)
return filename
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
import bza
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'file.txt.bz2'
SCREAMING_SNAKE_CASE = bytes(_UpperCAmelCase , 'utf-8')
with bza.open(_UpperCAmelCase , 'wb') as f:
f.write(_UpperCAmelCase)
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
import gzip
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp('data') / 'file.txt.gz')
SCREAMING_SNAKE_CASE = bytes(_UpperCAmelCase , 'utf-8')
with gzip.open(_UpperCAmelCase , 'wb') as f:
f.write(_UpperCAmelCase)
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
if datasets.config.LZ4_AVAILABLE:
import lza.frame
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'file.txt.lz4'
SCREAMING_SNAKE_CASE = bytes(_UpperCAmelCase , 'utf-8')
with lza.frame.open(_UpperCAmelCase , 'wb') as f:
f.write(_UpperCAmelCase)
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'file.txt.7z'
with pyazr.SevenZipFile(_UpperCAmelCase , 'w') as archive:
archive.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase))
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
import tarfile
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'file.txt.tar'
with tarfile.TarFile(_UpperCAmelCase , 'w') as f:
f.add(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase))
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
import lzma
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'file.txt.xz'
SCREAMING_SNAKE_CASE = bytes(_UpperCAmelCase , 'utf-8')
with lzma.open(_UpperCAmelCase , 'wb') as f:
f.write(_UpperCAmelCase)
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
import zipfile
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'file.txt.zip'
with zipfile.ZipFile(_UpperCAmelCase , 'w') as f:
f.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase))
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'file.txt.zst'
SCREAMING_SNAKE_CASE = bytes(_UpperCAmelCase , 'utf-8')
with zstd.open(_UpperCAmelCase , 'wb') as f:
f.write(_UpperCAmelCase)
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'file.xml'
SCREAMING_SNAKE_CASE = textwrap.dedent(
'\\n <?xml version="1.0" encoding="UTF-8" ?>\n <tmx version="1.4">\n <header segtype="sentence" srclang="ca" />\n <body>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 1</seg></tuv>\n <tuv xml:lang="en"><seg>Content 1</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 2</seg></tuv>\n <tuv xml:lang="en"><seg>Content 2</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 3</seg></tuv>\n <tuv xml:lang="en"><seg>Content 3</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 4</seg></tuv>\n <tuv xml:lang="en"><seg>Content 4</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 5</seg></tuv>\n <tuv xml:lang="en"><seg>Content 5</seg></tuv>\n </tu>\n </body>\n </tmx>')
with open(_UpperCAmelCase , 'w') as f:
f.write(_UpperCAmelCase)
return filename
a_ : Tuple = [
{'col_1': '0', 'col_2': 0, 'col_3': 0.0},
{'col_1': '1', 'col_2': 1, 'col_3': 1.0},
{'col_1': '2', 'col_2': 2, 'col_3': 2.0},
{'col_1': '3', 'col_2': 3, 'col_3': 3.0},
]
a_ : Any = [
{'col_1': '4', 'col_2': 4, 'col_3': 4.0},
{'col_1': '5', 'col_2': 5, 'col_3': 5.0},
]
a_ : List[Any] = {
'col_1': ['0', '1', '2', '3'],
'col_2': [0, 1, 2, 3],
'col_3': [0.0, 1.0, 2.0, 3.0],
}
a_ : Dict = [
{'col_3': 0.0, 'col_1': '0', 'col_2': 0},
{'col_3': 1.0, 'col_1': '1', 'col_2': 1},
]
a_ : List[Any] = [
{'col_1': 's0', 'col_2': 0, 'col_3': 0.0},
{'col_1': 's1', 'col_2': 1, 'col_3': 1.0},
{'col_1': 's2', 'col_2': 2, 'col_3': 2.0},
{'col_1': 's3', 'col_2': 3, 'col_3': 3.0},
]
@pytest.fixture(scope='session')
def lowerCamelCase__ ():
return DATA_DICT_OF_LISTS
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = datasets.Dataset.from_dict(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp('data') / 'dataset.arrow')
dataset.map(cache_file_name=_UpperCAmelCase)
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp('data') / 'dataset.sqlite')
with contextlib.closing(sqlitea.connect(_UpperCAmelCase)) as con:
SCREAMING_SNAKE_CASE = con.cursor()
cur.execute('CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)')
for item in DATA:
cur.execute('INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)' , tuple(item.values()))
con.commit()
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp('data') / 'dataset.csv')
with open(_UpperCAmelCase , 'w' , newline='') as f:
SCREAMING_SNAKE_CASE = csv.DictWriter(_UpperCAmelCase , fieldnames=['col_1', 'col_2', 'col_3'])
writer.writeheader()
for item in DATA:
writer.writerow(_UpperCAmelCase)
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp('data') / 'dataset2.csv')
with open(_UpperCAmelCase , 'w' , newline='') as f:
SCREAMING_SNAKE_CASE = csv.DictWriter(_UpperCAmelCase , fieldnames=['col_1', 'col_2', 'col_3'])
writer.writeheader()
for item in DATA:
writer.writerow(_UpperCAmelCase)
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
import bza
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'dataset.csv.bz2'
with open(_UpperCAmelCase , 'rb') as f:
SCREAMING_SNAKE_CASE = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(_UpperCAmelCase , 'wb') as f:
f.write(_UpperCAmelCase)
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'dataset.csv.zip'
with zipfile.ZipFile(_UpperCAmelCase , 'w') as f:
f.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase))
f.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase))
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'dataset.csv.zip'
with zipfile.ZipFile(_UpperCAmelCase , 'w') as f:
f.write(_UpperCAmelCase , arcname=os.path.basename(csv_path.replace('.csv' , '.CSV')))
f.write(_UpperCAmelCase , arcname=os.path.basename(csva_path.replace('.csv' , '.CSV')))
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'dataset_with_dir.csv.zip'
with zipfile.ZipFile(_UpperCAmelCase , 'w') as f:
f.write(_UpperCAmelCase , arcname=os.path.join('main_dir' , os.path.basename(_UpperCAmelCase)))
f.write(_UpperCAmelCase , arcname=os.path.join('main_dir' , os.path.basename(_UpperCAmelCase)))
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp('data') / 'dataset.parquet')
SCREAMING_SNAKE_CASE = pa.schema(
{
'col_1': pa.string(),
'col_2': pa.intaa(),
'col_3': pa.floataa(),
})
with open(_UpperCAmelCase , 'wb') as f:
SCREAMING_SNAKE_CASE = pq.ParquetWriter(_UpperCAmelCase , schema=_UpperCAmelCase)
SCREAMING_SNAKE_CASE = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(_UpperCAmelCase))] for k in DATA[0]} , schema=_UpperCAmelCase)
writer.write_table(_UpperCAmelCase)
writer.close()
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp('data') / 'dataset.json')
SCREAMING_SNAKE_CASE = {'data': DATA}
with open(_UpperCAmelCase , 'w') as f:
json.dump(_UpperCAmelCase , _UpperCAmelCase)
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp('data') / 'dataset.json')
SCREAMING_SNAKE_CASE = {'data': DATA_DICT_OF_LISTS}
with open(_UpperCAmelCase , 'w') as f:
json.dump(_UpperCAmelCase , _UpperCAmelCase)
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp('data') / 'dataset.jsonl')
with open(_UpperCAmelCase , 'w') as f:
for item in DATA:
f.write(json.dumps(_UpperCAmelCase) + '\n')
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp('data') / 'dataset2.jsonl')
with open(_UpperCAmelCase , 'w') as f:
for item in DATA:
f.write(json.dumps(_UpperCAmelCase) + '\n')
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp('data') / 'dataset_312.jsonl')
with open(_UpperCAmelCase , 'w') as f:
for item in DATA_312:
f.write(json.dumps(_UpperCAmelCase) + '\n')
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp('data') / 'dataset-str.jsonl')
with open(_UpperCAmelCase , 'w') as f:
for item in DATA_STR:
f.write(json.dumps(_UpperCAmelCase) + '\n')
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
import gzip
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp('data') / 'dataset.txt.gz')
with open(_UpperCAmelCase , 'rb') as orig_file:
with gzip.open(_UpperCAmelCase , 'wb') as zipped_file:
zipped_file.writelines(_UpperCAmelCase)
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
import gzip
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp('data') / 'dataset.jsonl.gz')
with open(_UpperCAmelCase , 'rb') as orig_file:
with gzip.open(_UpperCAmelCase , 'wb') as zipped_file:
zipped_file.writelines(_UpperCAmelCase)
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'dataset.jsonl.zip'
with zipfile.ZipFile(_UpperCAmelCase , 'w') as f:
f.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase))
f.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase))
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'dataset_nested.jsonl.zip'
with zipfile.ZipFile(_UpperCAmelCase , 'w') as f:
f.write(_UpperCAmelCase , arcname=os.path.join('nested' , os.path.basename(_UpperCAmelCase)))
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'dataset_with_dir.jsonl.zip'
with zipfile.ZipFile(_UpperCAmelCase , 'w') as f:
f.write(_UpperCAmelCase , arcname=os.path.join('main_dir' , os.path.basename(_UpperCAmelCase)))
f.write(_UpperCAmelCase , arcname=os.path.join('main_dir' , os.path.basename(_UpperCAmelCase)))
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'dataset.jsonl.tar'
with tarfile.TarFile(_UpperCAmelCase , 'w') as f:
f.add(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase))
f.add(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase))
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'dataset_nested.jsonl.tar'
with tarfile.TarFile(_UpperCAmelCase , 'w') as f:
f.add(_UpperCAmelCase , arcname=os.path.join('nested' , os.path.basename(_UpperCAmelCase)))
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = ['0', '1', '2', '3']
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp('data') / 'dataset.txt')
with open(_UpperCAmelCase , 'w') as f:
for item in data:
f.write(item + '\n')
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = ['0', '1', '2', '3']
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp('data') / 'dataset2.txt')
with open(_UpperCAmelCase , 'w') as f:
for item in data:
f.write(item + '\n')
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = ['0', '1', '2', '3']
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'dataset.abc'
with open(_UpperCAmelCase , 'w') as f:
for item in data:
f.write(item + '\n')
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'dataset.text.zip'
with zipfile.ZipFile(_UpperCAmelCase , 'w') as f:
f.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase))
f.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase))
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'dataset_with_dir.text.zip'
with zipfile.ZipFile(_UpperCAmelCase , 'w') as f:
f.write(_UpperCAmelCase , arcname=os.path.join('main_dir' , os.path.basename(_UpperCAmelCase)))
f.write(_UpperCAmelCase , arcname=os.path.join('main_dir' , os.path.basename(_UpperCAmelCase)))
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'dataset.ext.zip'
with zipfile.ZipFile(_UpperCAmelCase , 'w') as f:
f.write(_UpperCAmelCase , arcname=os.path.basename('unsupported.ext'))
f.write(_UpperCAmelCase , arcname=os.path.basename('unsupported_2.ext'))
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = '\n'.join(['First', 'Second\u2029with Unicode new line', 'Third'])
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp('data') / 'dataset_with_unicode_new_lines.txt')
with open(_UpperCAmelCase , 'w' , encoding='utf-8') as f:
f.write(_UpperCAmelCase)
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ ():
return os.path.join('tests' , 'features' , 'data' , 'test_image_rgb.jpg')
@pytest.fixture(scope='session')
def lowerCamelCase__ ():
return os.path.join('tests' , 'features' , 'data' , 'test_audio_44100.wav')
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data') / 'dataset.img.zip'
with zipfile.ZipFile(_UpperCAmelCase , 'w') as f:
f.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase))
f.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase).replace('.jpg' , '2.jpg'))
return path
@pytest.fixture(scope='session')
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp('data_dir')
(data_dir / "subdir").mkdir()
with open(data_dir / 'subdir' / 'train.txt' , 'w') as f:
f.write('foo\n' * 10)
with open(data_dir / 'subdir' / 'test.txt' , 'w') as f:
f.write('bar\n' * 10)
# hidden file
with open(data_dir / 'subdir' / '.test.txt' , 'w') as f:
f.write('bar\n' * 10)
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / '.subdir' / 'train.txt' , 'w') as f:
f.write('foo\n' * 10)
with open(data_dir / '.subdir' / 'test.txt' , 'w') as f:
f.write('bar\n' * 10)
return data_dir
| 73 |
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_ : Any = 'true'
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase=82 , _UpperCAmelCase=16):
set_seed(42)
SCREAMING_SNAKE_CASE = RegressionModel()
SCREAMING_SNAKE_CASE = deepcopy(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = RegressionDataset(length=_UpperCAmelCase)
SCREAMING_SNAKE_CASE = DataLoader(_UpperCAmelCase , batch_size=_UpperCAmelCase)
model.to(accelerator.device)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = accelerator.prepare(_UpperCAmelCase , _UpperCAmelCase)
return model, ddp_model, dataloader
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase=False):
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased')
SCREAMING_SNAKE_CASE = load_dataset('glue' , 'mrpc' , split='validation')
def tokenize_function(_UpperCAmelCase):
SCREAMING_SNAKE_CASE = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=_UpperCAmelCase , max_length=_UpperCAmelCase)
return outputs
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE = dataset.map(
_UpperCAmelCase , batched=_UpperCAmelCase , remove_columns=['idx', 'sentence1', 'sentence2'] , )
SCREAMING_SNAKE_CASE = tokenized_datasets.rename_column('label' , 'labels')
def collate_fn(_UpperCAmelCase):
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 lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = Accelerator(dispatch_batches=_UpperCAmelCase , split_batches=_UpperCAmelCase)
SCREAMING_SNAKE_CASE = get_dataloader(_UpperCAmelCase , not dispatch_batches)
SCREAMING_SNAKE_CASE = AutoModelForSequenceClassification.from_pretrained(
'hf-internal-testing/mrpc-bert-base-cased' , return_dict=_UpperCAmelCase)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = accelerator.prepare(_UpperCAmelCase , _UpperCAmelCase)
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = []
for batch in dataloader:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = batch.values()
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(_UpperCAmelCase)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = accelerator.gather_for_metrics((logit, target))
logits_and_targets.append((logit, target))
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = [], []
for logit, targ in logits_and_targets:
logits.append(_UpperCAmelCase)
targs.append(_UpperCAmelCase)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = torch.cat(_UpperCAmelCase), torch.cat(_UpperCAmelCase)
return logits, targs
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase=82 , _UpperCAmelCase=False , _UpperCAmelCase=False , _UpperCAmelCase=16):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = get_basic_setup(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = generate_predictions(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase)
assert (
len(_UpperCAmelCase) == num_samples
), F'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(_UpperCAmelCase)}'''
def lowerCamelCase__ (_UpperCAmelCase = False , _UpperCAmelCase = False):
SCREAMING_SNAKE_CASE = evaluate.load('glue' , 'mrpc')
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = get_mrpc_setup(_UpperCAmelCase , _UpperCAmelCase)
# First do baseline
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = setup['no']
model.to(_UpperCAmelCase)
model.eval()
for batch in dataloader:
batch.to(_UpperCAmelCase)
with torch.inference_mode():
SCREAMING_SNAKE_CASE = model(**_UpperCAmelCase)
SCREAMING_SNAKE_CASE = outputs.logits.argmax(dim=-1)
metric.add_batch(predictions=_UpperCAmelCase , references=batch['labels'])
SCREAMING_SNAKE_CASE = metric.compute()
# Then do distributed
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = setup['ddp']
model.eval()
for batch in dataloader:
with torch.inference_mode():
SCREAMING_SNAKE_CASE = model(**_UpperCAmelCase)
SCREAMING_SNAKE_CASE = outputs.logits.argmax(dim=-1)
SCREAMING_SNAKE_CASE = batch['labels']
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = accelerator.gather_for_metrics((preds, references))
metric.add_batch(predictions=_UpperCAmelCase , references=_UpperCAmelCase)
SCREAMING_SNAKE_CASE = 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 lowerCamelCase__ ():
SCREAMING_SNAKE_CASE = 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]:
SCREAMING_SNAKE_CASE = 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**')
SCREAMING_SNAKE_CASE = Accelerator()
test_torch_metrics(_UpperCAmelCase , 512)
accelerator.state._reset_state()
def lowerCamelCase__ (_UpperCAmelCase):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 73 | 1 |
"""simple docstring"""
import pytest
_lowerCAmelCase = """__dummy_dataset1__"""
_lowerCAmelCase = """
import json
import os
import datasets
REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}
class __DummyDataset1__(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features(
{
\"tokens\": datasets.Sequence(datasets.Value(\"string\")),
\"ner_tags\": datasets.Sequence(
datasets.features.ClassLabel(
names=[
\"O\",
\"B-PER\",
\"I-PER\",
\"B-ORG\",
\"I-ORG\",
\"B-LOC\",
\"I-LOC\",
]
)
),
\"langs\": datasets.Sequence(datasets.Value(\"string\")),
\"spans\": datasets.Sequence(datasets.Value(\"string\")),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),
datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),
]
def _generate_examples(self, filepath):
with open(filepath, \"r\", encoding=\"utf-8\") as f:
for i, line in enumerate(f):
yield i, json.loads(line)
"""
@pytest.fixture
def lowerCamelCase__ ( ):
'''simple docstring'''
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def lowerCamelCase__ ( ):
'''simple docstring'''
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def lowerCamelCase__ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = dataset_loading_script_name
_lowerCAmelCase : Union[str, Any] = tmp_path / 'datasets' / script_name
script_dir.mkdir(parents=_lowerCamelCase )
_lowerCAmelCase : Any = script_dir / f"""{script_name}.py"""
with open(_lowerCamelCase , 'w' ) as f:
f.write(_lowerCamelCase )
return str(_lowerCamelCase )
| 16 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( a__ , unittest.TestCase ):
_UpperCAmelCase = DanceDiffusionPipeline
_UpperCAmelCase = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {
"callback",
"latents",
"callback_steps",
"output_type",
"num_images_per_prompt",
}
_UpperCAmelCase = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
_UpperCAmelCase = False
_UpperCAmelCase = False
def __lowerCamelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
_lowerCAmelCase : List[Any] = UNetaDModel(
block_out_channels=(32, 32, 64) ,extra_in_channels=16 ,sample_size=512 ,sample_rate=1_6000 ,in_channels=2 ,out_channels=2 ,flip_sin_to_cos=_A ,use_timestep_embedding=_A ,time_embedding_type='fourier' ,mid_block_type='UNetMidBlock1D' ,down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D') ,up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip') ,)
_lowerCAmelCase : int = IPNDMScheduler()
_lowerCAmelCase : Union[str, Any] = {
'unet': unet,
'scheduler': scheduler,
}
return components
def __lowerCamelCase ( self ,_A ,_A=0 ):
'''simple docstring'''
if str(_A ).startswith('mps' ):
_lowerCAmelCase : str = torch.manual_seed(_A )
else:
_lowerCAmelCase : Optional[Any] = torch.Generator(device=_A ).manual_seed(_A )
_lowerCAmelCase : int = {
'batch_size': 1,
'generator': generator,
'num_inference_steps': 4,
}
return inputs
def __lowerCamelCase ( self ):
'''simple docstring'''
_lowerCAmelCase : List[str] = 'cpu' # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase : int = self.get_dummy_components()
_lowerCAmelCase : Optional[Any] = DanceDiffusionPipeline(**_A )
_lowerCAmelCase : int = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
_lowerCAmelCase : Union[str, Any] = self.get_dummy_inputs(_A )
_lowerCAmelCase : List[str] = pipe(**_A )
_lowerCAmelCase : List[Any] = output.audios
_lowerCAmelCase : List[str] = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
_lowerCAmelCase : Optional[Any] = np.array([-0.7_2_6_5, 1.0_0_0_0, -0.8_3_8_8, 0.1_1_7_5, 0.9_4_9_8, -1.0_0_0_0] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def __lowerCamelCase ( self ):
'''simple docstring'''
return super().test_save_load_local()
@skip_mps
def __lowerCamelCase ( self ):
'''simple docstring'''
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
@skip_mps
def __lowerCamelCase ( self ):
'''simple docstring'''
return super().test_save_load_optional_components()
@skip_mps
def __lowerCamelCase ( self ):
'''simple docstring'''
return super().test_attention_slicing_forward_pass()
def __lowerCamelCase ( self ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def __lowerCamelCase ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCamelCase ( self ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = torch_device
_lowerCAmelCase : int = DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k' )
_lowerCAmelCase : int = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
_lowerCAmelCase : Optional[int] = torch.manual_seed(0 )
_lowerCAmelCase : str = pipe(generator=_A ,num_inference_steps=100 ,audio_length_in_s=4.0_9_6 )
_lowerCAmelCase : str = output.audios
_lowerCAmelCase : List[str] = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
_lowerCAmelCase : Union[str, Any] = np.array([-0.0_1_9_2, -0.0_2_3_1, -0.0_3_1_8, -0.0_0_5_9, 0.0_0_0_2, -0.0_0_2_0] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
def __lowerCamelCase ( self ):
'''simple docstring'''
_lowerCAmelCase : str = torch_device
_lowerCAmelCase : Tuple = DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k' ,torch_dtype=torch.floataa )
_lowerCAmelCase : Optional[int] = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
_lowerCAmelCase : Union[str, Any] = torch.manual_seed(0 )
_lowerCAmelCase : Optional[int] = pipe(generator=_A ,num_inference_steps=100 ,audio_length_in_s=4.0_9_6 )
_lowerCAmelCase : Union[str, Any] = output.audios
_lowerCAmelCase : int = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
_lowerCAmelCase : List[str] = np.array([-0.0_3_6_7, -0.0_4_8_8, -0.0_7_7_1, -0.0_5_2_5, -0.0_4_4_4, -0.0_3_4_1] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
| 16 | 1 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[str] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Union[str, Any]=13 , UpperCamelCase__ : Tuple=7 , UpperCamelCase__ : Dict=6 , UpperCamelCase__ : Optional[int]=17 , UpperCamelCase__ : Optional[int]=23 , UpperCamelCase__ : List[Any]=11 , UpperCamelCase__ : Optional[Any]=True , ):
A = parent
A = batch_size
A = seq_length
A = act_dim
A = state_dim
A = hidden_size
A = max_length
A = is_training
def UpperCamelCase ( self : str ):
A = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
A = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
A = floats_tensor((self.batch_size, self.seq_length, 1) )
A = floats_tensor((self.batch_size, self.seq_length, 1) )
A = ids_tensor((self.batch_size, self.seq_length) , vocab_size=1000 )
A = random_attention_mask((self.batch_size, self.seq_length) )
A = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def UpperCamelCase ( self : Any ):
return DecisionTransformerConfig(
batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , )
def UpperCamelCase ( self : Dict , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : int , ):
A = DecisionTransformerModel(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
A = model(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
self.parent.assertEqual(result.state_preds.shape , states.shape )
self.parent.assertEqual(result.action_preds.shape , actions.shape )
self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def UpperCamelCase ( self : str ):
A = self.prepare_config_and_inputs()
(
(
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) ,
) = config_and_inputs
A = {
'states': states,
'actions': actions,
'rewards': rewards,
'returns_to_go': returns_to_go,
'timesteps': timesteps,
'attention_mask': attention_mask,
}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( __lowercase , __lowercase , __lowercase , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = (DecisionTransformerModel,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE : int = ()
SCREAMING_SNAKE_CASE : Union[str, Any] = {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
SCREAMING_SNAKE_CASE : str = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
SCREAMING_SNAKE_CASE : List[str] = False
SCREAMING_SNAKE_CASE : Tuple = False
SCREAMING_SNAKE_CASE : str = False
SCREAMING_SNAKE_CASE : List[Any] = False
SCREAMING_SNAKE_CASE : int = False
SCREAMING_SNAKE_CASE : Dict = False
SCREAMING_SNAKE_CASE : Tuple = False
SCREAMING_SNAKE_CASE : Any = False
SCREAMING_SNAKE_CASE : str = False
def UpperCamelCase ( self : Union[str, Any] ):
A = DecisionTransformerModelTester(self )
A = ConfigTester(self , config_class=UpperCamelCase__ , hidden_size=37 )
def UpperCamelCase ( self : Dict ):
self.config_tester.run_common_tests()
def UpperCamelCase ( self : Dict ):
A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
@slow
def UpperCamelCase ( self : List[Any] ):
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A = DecisionTransformerModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
def UpperCamelCase ( self : Dict ):
A , A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A = model_class(UpperCamelCase__ )
A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A = [*signature.parameters.keys()]
A = [
'states',
'actions',
'rewards',
'returns_to_go',
'timesteps',
'attention_mask',
]
self.assertListEqual(arg_names[: len(UpperCamelCase__ )] , UpperCamelCase__ )
@require_torch
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase ( self : List[str] ):
A = 2 # number of steps of autoregressive prediction we will perform
A = 10 # defined by the RL environment, may be normalized
A = DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-expert' )
A = model.to(UpperCamelCase__ )
A = model.config
torch.manual_seed(0 )
A = torch.randn(1 , 1 , config.state_dim ).to(device=UpperCamelCase__ , dtype=torch.floataa ) # env.reset()
A = torch.tensor(
[[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]] , device=UpperCamelCase__ )
A = torch.tensor(UpperCamelCase__ , device=UpperCamelCase__ , dtype=torch.floataa ).reshape(1 , 1 , 1 )
A = state
A = torch.zeros(1 , 0 , config.act_dim , device=UpperCamelCase__ , dtype=torch.floataa )
A = torch.zeros(1 , 0 , device=UpperCamelCase__ , dtype=torch.floataa )
A = torch.tensor(0 , device=UpperCamelCase__ , dtype=torch.long ).reshape(1 , 1 )
for step in range(UpperCamelCase__ ):
A = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=UpperCamelCase__ )] , dim=1 )
A = torch.cat([rewards, torch.zeros(1 , 1 , device=UpperCamelCase__ )] , dim=1 )
A = torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
A , A , A = model(
states=UpperCamelCase__ , actions=UpperCamelCase__ , rewards=UpperCamelCase__ , returns_to_go=UpperCamelCase__ , timesteps=UpperCamelCase__ , attention_mask=UpperCamelCase__ , return_dict=UpperCamelCase__ , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1e-4 ) )
A , A , A , A = ( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=UpperCamelCase__ , dtype=torch.floataa ),
1.0,
False,
{},
)
A = action_pred[0, -1]
A = torch.cat([states, state] , dim=1 )
A = returns_to_go[0, -1] - reward
A = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
A = torch.cat(
[timesteps, torch.ones((1, 1) , device=UpperCamelCase__ , dtype=torch.long ) * (step + 1)] , dim=1 )
| 699 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[Any] , UpperCamelCase__ : Collection[float] | None = None ):
if components is None:
A = []
A = list(UpperCamelCase__ )
def __len__( self : List[Any] ):
return len(self.__components )
def __str__( self : str ):
return "(" + ",".join(map(UpperCamelCase__ , self.__components ) ) + ")"
def __add__( self : str , UpperCamelCase__ : Vector ):
A = len(self )
if size == len(UpperCamelCase__ ):
A = [self.__components[i] + other.component(UpperCamelCase__ ) for i in range(UpperCamelCase__ )]
return Vector(UpperCamelCase__ )
else:
raise Exception('must have the same size' )
def __sub__( self : Dict , UpperCamelCase__ : Vector ):
A = len(self )
if size == len(UpperCamelCase__ ):
A = [self.__components[i] - other.component(UpperCamelCase__ ) for i in range(UpperCamelCase__ )]
return Vector(UpperCamelCase__ )
else: # error case
raise Exception('must have the same size' )
@overload
def __mul__( self : Tuple , UpperCamelCase__ : float ):
...
@overload
def __mul__( self : Dict , UpperCamelCase__ : Vector ):
...
def __mul__( self : Union[str, Any] , UpperCamelCase__ : float | Vector ):
if isinstance(UpperCamelCase__ , (float, int) ):
A = [c * other for c in self.__components]
return Vector(UpperCamelCase__ )
elif isinstance(UpperCamelCase__ , UpperCamelCase__ ) and len(self ) == len(UpperCamelCase__ ):
A = len(self )
A = [self.__components[i] * other.component(UpperCamelCase__ ) for i in range(UpperCamelCase__ )]
return sum(UpperCamelCase__ )
else: # error case
raise Exception('invalid operand!' )
def UpperCamelCase ( self : Union[str, Any] ):
return Vector(self.__components )
def UpperCamelCase ( self : Optional[Any] , UpperCamelCase__ : int ):
if isinstance(UpperCamelCase__ , UpperCamelCase__ ) and -len(self.__components ) <= i < len(self.__components ):
return self.__components[i]
else:
raise Exception('index out of range' )
def UpperCamelCase ( self : Any , UpperCamelCase__ : int , UpperCamelCase__ : float ):
assert -len(self.__components ) <= pos < len(self.__components )
A = value
def UpperCamelCase ( self : str ):
if len(self.__components ) == 0:
raise Exception('Vector is empty' )
A = [c**2 for c in self.__components]
return math.sqrt(sum(UpperCamelCase__ ) )
def UpperCamelCase ( self : Any , UpperCamelCase__ : Vector , UpperCamelCase__ : bool = False ):
A = self * other
A = self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den ) )
else:
return math.acos(num / den )
def __UpperCamelCase (lowerCAmelCase : int ) -> Vector:
assert isinstance(lowerCAmelCase, lowerCAmelCase )
return Vector([0] * dimension )
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> Vector:
assert isinstance(lowerCAmelCase, lowerCAmelCase ) and (isinstance(lowerCAmelCase, lowerCAmelCase ))
A = [0] * dimension
A = 1
return Vector(lowerCAmelCase )
def __UpperCamelCase (lowerCAmelCase : float, lowerCAmelCase : Vector, lowerCAmelCase : Vector ) -> Vector:
assert (
isinstance(lowerCAmelCase, lowerCAmelCase )
and isinstance(lowerCAmelCase, lowerCAmelCase )
and (isinstance(lowerCAmelCase, (int, float) ))
)
return x * scalar + y
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int, lowerCAmelCase : int ) -> Vector:
random.seed(lowerCAmelCase )
A = [random.randint(lowerCAmelCase, lowerCAmelCase ) for _ in range(lowerCAmelCase )]
return Vector(lowerCAmelCase )
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[str] , UpperCamelCase__ : list[list[float]] , UpperCamelCase__ : int , UpperCamelCase__ : int ):
A = matrix
A = w
A = h
def __str__( self : int ):
A = ''
for i in range(self.__height ):
ans += "|"
for j in range(self.__width ):
if j < self.__width - 1:
ans += str(self.__matrix[i][j] ) + ","
else:
ans += str(self.__matrix[i][j] ) + "|\n"
return ans
def __add__( self : Optional[Any] , UpperCamelCase__ : Matrix ):
if self.__width == other.width() and self.__height == other.height():
A = []
for i in range(self.__height ):
A = [
self.__matrix[i][j] + other.component(UpperCamelCase__ , UpperCamelCase__ )
for j in range(self.__width )
]
matrix.append(UpperCamelCase__ )
return Matrix(UpperCamelCase__ , self.__width , self.__height )
else:
raise Exception('matrix must have the same dimension!' )
def __sub__( self : Dict , UpperCamelCase__ : Matrix ):
if self.__width == other.width() and self.__height == other.height():
A = []
for i in range(self.__height ):
A = [
self.__matrix[i][j] - other.component(UpperCamelCase__ , UpperCamelCase__ )
for j in range(self.__width )
]
matrix.append(UpperCamelCase__ )
return Matrix(UpperCamelCase__ , self.__width , self.__height )
else:
raise Exception('matrices must have the same dimension!' )
@overload
def __mul__( self : int , UpperCamelCase__ : float ):
...
@overload
def __mul__( self : Union[str, Any] , UpperCamelCase__ : Vector ):
...
def __mul__( self : Tuple , UpperCamelCase__ : float | Vector ):
if isinstance(UpperCamelCase__ , UpperCamelCase__ ): # matrix-vector
if len(UpperCamelCase__ ) == self.__width:
A = zero_vector(self.__height )
for i in range(self.__height ):
A = [
self.__matrix[i][j] * other.component(UpperCamelCase__ )
for j in range(self.__width )
]
ans.change_component(UpperCamelCase__ , sum(UpperCamelCase__ ) )
return ans
else:
raise Exception(
'vector must have the same size as the '
'number of columns of the matrix!' )
elif isinstance(UpperCamelCase__ , (int, float) ): # matrix-scalar
A = [
[self.__matrix[i][j] * other for j in range(self.__width )]
for i in range(self.__height )
]
return Matrix(UpperCamelCase__ , self.__width , self.__height )
return None
def UpperCamelCase ( self : Optional[int] ):
return self.__height
def UpperCamelCase ( self : List[Any] ):
return self.__width
def UpperCamelCase ( self : List[str] , UpperCamelCase__ : int , UpperCamelCase__ : int ):
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception('change_component: indices out of bounds' )
def UpperCamelCase ( self : str , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : float ):
if 0 <= x < self.__height and 0 <= y < self.__width:
A = value
else:
raise Exception('change_component: indices out of bounds' )
def UpperCamelCase ( self : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : int ):
if self.__height != self.__width:
raise Exception('Matrix is not square' )
A = self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(UpperCamelCase__ ) ):
A = minor[i][:y] + minor[i][y + 1 :]
return Matrix(UpperCamelCase__ , self.__width - 1 , self.__height - 1 ).determinant()
def UpperCamelCase ( self : str , UpperCamelCase__ : int , UpperCamelCase__ : int ):
if self.__height != self.__width:
raise Exception('Matrix is not square' )
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(UpperCamelCase__ , UpperCamelCase__ )
else:
raise Exception('Indices out of bounds' )
def UpperCamelCase ( self : Tuple ):
if self.__height != self.__width:
raise Exception('Matrix is not square' )
if self.__height < 1:
raise Exception('Matrix has no element' )
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
A = [
self.__matrix[0][y] * self.cofactor(0 , UpperCamelCase__ ) for y in range(self.__width )
]
return sum(UpperCamelCase__ )
def __UpperCamelCase (lowerCAmelCase : int ) -> Matrix:
A = [[0] * n for _ in range(lowerCAmelCase )]
return Matrix(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int, lowerCAmelCase : int, lowerCAmelCase : int ) -> Matrix:
random.seed(lowerCAmelCase )
A = [
[random.randint(lowerCAmelCase, lowerCAmelCase ) for _ in range(lowerCAmelCase )] for _ in range(lowerCAmelCase )
]
return Matrix(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
| 699 | 1 |
'''simple docstring'''
import pytest
__lowercase = '''__dummy_dataset1__'''
__lowercase = '''
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}
class __DummyDataset1__(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
]
)
),
"langs": datasets.Sequence(datasets.Value("string")),
"spans": datasets.Sequence(datasets.Value("string")),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
]
def _generate_examples(self, filepath):
with open(filepath, "r", encoding="utf-8") as f:
for i, line in enumerate(f):
yield i, json.loads(line)
'''
@pytest.fixture
def SCREAMING_SNAKE_CASE__ ( ):
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def SCREAMING_SNAKE_CASE__ ( ):
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def SCREAMING_SNAKE_CASE__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
lowerCAmelCase_ : str =dataset_loading_script_name
lowerCAmelCase_ : List[str] =tmp_path / '''datasets''' / script_name
script_dir.mkdir(parents=_SCREAMING_SNAKE_CASE )
lowerCAmelCase_ : Optional[int] =script_dir / f'{script_name}.py'
with open(_SCREAMING_SNAKE_CASE , '''w''' ) as f:
f.write(_SCREAMING_SNAKE_CASE )
return str(_SCREAMING_SNAKE_CASE )
| 305 |
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
__lowercase = {
'''return_dict''': False,
'''output_hidden_states''': True,
'''output_attentions''': True,
'''torchscript''': True,
'''torch_dtype''': '''float16''',
'''use_bfloat16''': True,
'''tf_legacy_loss''': True,
'''pruned_heads''': {'''a''': 1},
'''tie_word_embeddings''': False,
'''is_decoder''': True,
'''cross_attention_hidden_size''': 1_28,
'''add_cross_attention''': True,
'''tie_encoder_decoder''': True,
'''max_length''': 50,
'''min_length''': 3,
'''do_sample''': True,
'''early_stopping''': True,
'''num_beams''': 3,
'''num_beam_groups''': 3,
'''diversity_penalty''': 0.5,
'''temperature''': 2.0,
'''top_k''': 10,
'''top_p''': 0.7,
'''typical_p''': 0.2,
'''repetition_penalty''': 0.8,
'''length_penalty''': 0.8,
'''no_repeat_ngram_size''': 5,
'''encoder_no_repeat_ngram_size''': 5,
'''bad_words_ids''': [1, 2, 3],
'''num_return_sequences''': 3,
'''chunk_size_feed_forward''': 5,
'''output_scores''': True,
'''return_dict_in_generate''': True,
'''forced_bos_token_id''': 2,
'''forced_eos_token_id''': 3,
'''remove_invalid_values''': True,
'''architectures''': ['''BertModel'''],
'''finetuning_task''': '''translation''',
'''id2label''': {0: '''label'''},
'''label2id''': {'''label''': '''0'''},
'''tokenizer_class''': '''BertTokenizerFast''',
'''prefix''': '''prefix''',
'''bos_token_id''': 6,
'''pad_token_id''': 7,
'''eos_token_id''': 8,
'''sep_token_id''': 9,
'''decoder_start_token_id''': 10,
'''exponential_decay_length_penalty''': (5, 1.01),
'''suppress_tokens''': [0, 1],
'''begin_suppress_tokens''': 2,
'''task_specific_params''': {'''translation''': '''some_params'''},
'''problem_type''': '''regression''',
}
@is_staging_test
class _snake_case ( unittest.TestCase ):
"""simple docstring"""
@classmethod
def __A ( cls : List[str] ):
lowerCAmelCase_ : str =TOKEN
HfFolder.save_token(UpperCamelCase_ )
@classmethod
def __A ( cls : Union[str, Any] ):
try:
delete_repo(token=cls._token , repo_id='''test-config''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-config-org''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''test-dynamic-config''' )
except HTTPError:
pass
def __A ( self : Dict ):
lowerCAmelCase_ : List[Any] =BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('''test-config''' , use_auth_token=self._token )
lowerCAmelCase_ : List[str] =BertConfig.from_pretrained(F'{USER}/test-config' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCamelCase_ , getattr(UpperCamelCase_ , UpperCamelCase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-config''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(UpperCamelCase_ , repo_id='''test-config''' , push_to_hub=UpperCamelCase_ , use_auth_token=self._token )
lowerCAmelCase_ : List[Any] =BertConfig.from_pretrained(F'{USER}/test-config' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCamelCase_ , getattr(UpperCamelCase_ , UpperCamelCase_ ) )
def __A ( self : Tuple ):
lowerCAmelCase_ : Optional[int] =BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('''valid_org/test-config-org''' , use_auth_token=self._token )
lowerCAmelCase_ : Union[str, Any] =BertConfig.from_pretrained('''valid_org/test-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCamelCase_ , getattr(UpperCamelCase_ , UpperCamelCase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-config-org''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
UpperCamelCase_ , repo_id='''valid_org/test-config-org''' , push_to_hub=UpperCamelCase_ , use_auth_token=self._token )
lowerCAmelCase_ : int =BertConfig.from_pretrained('''valid_org/test-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCamelCase_ , getattr(UpperCamelCase_ , UpperCamelCase_ ) )
def __A ( self : str ):
CustomConfig.register_for_auto_class()
lowerCAmelCase_ : List[str] =CustomConfig(attribute=42 )
config.push_to_hub('''test-dynamic-config''' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {'''AutoConfig''': '''custom_configuration.CustomConfig'''} )
lowerCAmelCase_ : Optional[int] =AutoConfig.from_pretrained(F'{USER}/test-dynamic-config' , trust_remote_code=UpperCamelCase_ )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , '''CustomConfig''' )
self.assertEqual(new_config.attribute , 42 )
class _snake_case ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : Dict ):
lowerCAmelCase_ : int =GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
lowerCAmelCase_ : str =c.n_embd + 1 # int
lowerCAmelCase_ : Tuple =c.resid_pdrop + 1.0 # float
lowerCAmelCase_ : Optional[Any] =not c.scale_attn_weights # bool
lowerCAmelCase_ : Union[str, Any] =c.summary_type + '''foo''' # str
c.update_from_string(
F'n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}' )
self.assertEqual(UpperCamelCase_ , c.n_embd , '''mismatch for key: n_embd''' )
self.assertEqual(UpperCamelCase_ , c.resid_pdrop , '''mismatch for key: resid_pdrop''' )
self.assertEqual(UpperCamelCase_ , c.scale_attn_weights , '''mismatch for key: scale_attn_weights''' )
self.assertEqual(UpperCamelCase_ , c.summary_type , '''mismatch for key: summary_type''' )
def __A ( self : Union[str, Any] ):
lowerCAmelCase_ : Union[str, Any] =PretrainedConfig()
lowerCAmelCase_ : Dict =[key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
UpperCamelCase_ , ['''is_encoder_decoder''', '''_name_or_path''', '''_commit_hash''', '''transformers_version'''] )
lowerCAmelCase_ : Tuple =[key for key, value in config_common_kwargs.items() if value == getattr(UpperCamelCase_ , UpperCamelCase_ )]
if len(UpperCamelCase_ ) > 0:
raise ValueError(
'''The following keys are set with the default values in'''
''' `test_configuration_common.config_common_kwargs` pick another value for them:'''
F' {", ".join(UpperCamelCase_ )}.' )
def __A ( self : Optional[int] ):
with self.assertRaises(UpperCamelCase_ ):
# config is in subfolder, the following should not work without specifying the subfolder
lowerCAmelCase_ : str =BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert-subfolder''' )
lowerCAmelCase_ : int =BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert-subfolder''' , subfolder='''bert''' )
self.assertIsNotNone(UpperCamelCase_ )
def __A ( self : List[Any] ):
# A mock response for an HTTP head request to emulate server down
lowerCAmelCase_ : List[str] =mock.Mock()
lowerCAmelCase_ : List[str] =500
lowerCAmelCase_ : Tuple ={}
lowerCAmelCase_ : Union[str, Any] =HTTPError
lowerCAmelCase_ : Dict ={}
# Download this model to make sure it's in the cache.
lowerCAmelCase_ : Dict =BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('''requests.Session.request''' , return_value=UpperCamelCase_ ) as mock_head:
lowerCAmelCase_ : List[str] =BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
# This check we did call the fake head request
mock_head.assert_called()
def __A ( self : Dict ):
# This test is for deprecated behavior and can be removed in v5
lowerCAmelCase_ : Union[str, Any] =BertConfig.from_pretrained(
'''https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json''' )
def __A ( self : int ):
lowerCAmelCase_ : Union[str, Any] =AutoConfig.from_pretrained('''bert-base-cased''' )
lowerCAmelCase_ : Tuple =['''config.4.0.0.json''']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(UpperCamelCase_ )
lowerCAmelCase_ : str =2
json.dump(configuration.to_dict() , open(os.path.join(UpperCamelCase_ , '''config.4.0.0.json''' ) , '''w''' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
lowerCAmelCase_ : Optional[Any] =AutoConfig.from_pretrained(UpperCamelCase_ )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
lowerCAmelCase_ : List[Any] =['''config.42.0.0.json''']
lowerCAmelCase_ : Optional[int] =768
configuration.save_pretrained(UpperCamelCase_ )
shutil.move(os.path.join(UpperCamelCase_ , '''config.4.0.0.json''' ) , os.path.join(UpperCamelCase_ , '''config.42.0.0.json''' ) )
lowerCAmelCase_ : int =AutoConfig.from_pretrained(UpperCamelCase_ )
self.assertEqual(new_configuration.hidden_size , 768 )
def __A ( self : int ):
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
lowerCAmelCase_ : Dict ='''hf-internal-testing/test-two-configs'''
import transformers as new_transformers
lowerCAmelCase_ : Tuple ='''v4.0.0'''
lowerCAmelCase_ , lowerCAmelCase_ : int =new_transformers.models.auto.AutoConfig.from_pretrained(
UpperCamelCase_ , return_unused_kwargs=UpperCamelCase_ )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(UpperCamelCase_ , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
lowerCAmelCase_ : Optional[Any] ='''v3.0.0'''
lowerCAmelCase_ : List[Any] =old_transformers.models.auto.AutoConfig.from_pretrained(UpperCamelCase_ )
self.assertEqual(old_configuration.hidden_size , 768 )
| 305 | 1 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __lowerCamelCase ( _UpperCAmelCase ):
"""simple docstring"""
def a ( self : str , SCREAMING_SNAKE_CASE__ : int ) -> Any:
with open(_UpperCAmelCase , encoding="utf-8" ) as input_file:
lowerCAmelCase__ = re.compile(r"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)" )
lowerCAmelCase__ = input_file.read()
lowerCAmelCase__ = regexp.search(_UpperCAmelCase )
return match
def a ( self : str , SCREAMING_SNAKE_CASE__ : str ) -> Optional[int]:
with open(_UpperCAmelCase , encoding="utf-8" ) as input_file:
lowerCAmelCase__ = re.compile(r"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()" , re.DOTALL )
lowerCAmelCase__ = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
lowerCAmelCase__ = regexp.finditer(_UpperCAmelCase )
lowerCAmelCase__ = [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 a ( self : Any ) -> Tuple:
lowerCAmelCase__ = Path("./datasets" )
lowerCAmelCase__ = 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 a ( self : Dict ) -> int:
lowerCAmelCase__ = Path("./datasets" )
lowerCAmelCase__ = 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.' )
| 61 | """simple docstring"""
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase = 1_0 , __UpperCAmelCase = 2_2 ) -> int:
lowercase__: Optional[Any] = range(1 , __UpperCAmelCase )
lowercase__: Any = range(1 , __UpperCAmelCase )
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) = }''')
| 586 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__: Optional[int] = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
'tokenization_deberta': ['DebertaTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__: Any = ['DebertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__: Optional[int] = [
'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'DebertaForMaskedLM',
'DebertaForQuestionAnswering',
'DebertaForSequenceClassification',
'DebertaForTokenClassification',
'DebertaModel',
'DebertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__: Union[str, Any] = [
'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDebertaForMaskedLM',
'TFDebertaForQuestionAnswering',
'TFDebertaForSequenceClassification',
'TFDebertaForTokenClassification',
'TFDebertaModel',
'TFDebertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
lowerCAmelCase__: Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 720 |
import math
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE = 0.1 ) -> int:
SCREAMING_SNAKE_CASE_ : str = 3
SCREAMING_SNAKE_CASE_ : Dict = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(SCREAMING_SNAKE_CASE )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 311 | 0 |
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 : Dict , UpperCamelCase__ : Dict , UpperCamelCase__ : str=13 , UpperCamelCase__ : Any=7 , UpperCamelCase__ : List[str]=True , UpperCamelCase__ : str=True , UpperCamelCase__ : List[Any]=True , UpperCamelCase__ : Tuple=True , UpperCamelCase__ : Optional[int]=99 , UpperCamelCase__ : str=32 , UpperCamelCase__ : str=5 , UpperCamelCase__ : Tuple=4 , UpperCamelCase__ : Any=37 , UpperCamelCase__ : str="gelu" , UpperCamelCase__ : Optional[int]=0.1 , UpperCamelCase__ : Optional[Any]=0.1 , UpperCamelCase__ : List[str]=512 , UpperCamelCase__ : List[str]=16 , UpperCamelCase__ : Dict=2 , UpperCamelCase__ : int=0.02 , UpperCamelCase__ : Union[str, Any]=4 , ):
A = parent
A = batch_size
A = seq_length
A = is_training
A = use_attention_mask
A = use_token_type_ids
A = use_labels
A = vocab_size
A = hidden_size
A = num_hidden_layers
A = num_attention_heads
A = intermediate_size
A = hidden_act
A = hidden_dropout_prob
A = attention_probs_dropout_prob
A = max_position_embeddings
A = type_vocab_size
A = type_sequence_label_size
A = initializer_range
A = num_choices
def UpperCamelCase ( self : List[str] ):
A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A = None
if self.use_attention_mask:
A = random_attention_mask([self.batch_size, self.seq_length] )
A = None
if self.use_token_type_ids:
A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
A = 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=UpperCamelCase__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCamelCase ( self : List[Any] ):
A = self.prepare_config_and_inputs()
A , A , A , A = config_and_inputs
A = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class _UpperCAmelCase ( __lowercase , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = True
SCREAMING_SNAKE_CASE : int = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCamelCase ( self : Any ):
A = FlaxRoFormerModelTester(self )
@slow
def UpperCamelCase ( self : Dict ):
for model_class_name in self.all_model_classes:
A = model_class_name.from_pretrained('junnyu/roformer_chinese_small' , from_pt=UpperCamelCase__ )
A = model(np.ones((1, 1) ) )
self.assertIsNotNone(UpperCamelCase__ )
@require_flax
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase ( self : List[str] ):
A = FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base' )
A = jnp.array([[0, 1, 2, 3, 4, 5]] )
A = model(UpperCamelCase__ )[0]
A = 50000
A = (1, 6, vocab_size)
self.assertEqual(output.shape , UpperCamelCase__ )
A = jnp.array(
[[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , UpperCamelCase__ , atol=1e-4 ) )
| 699 |
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,
)
_UpperCAmelCase = {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = ["XGLMTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = ["XGLMTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
"XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"XGLMForCausalLM",
"XGLMModel",
"XGLMPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
"FlaxXGLMForCausalLM",
"FlaxXGLMModel",
"FlaxXGLMPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
"TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXGLMForCausalLM",
"TFXGLMModel",
"TFXGLMPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm import XGLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm_fast import XGLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
TFXGLMPreTrainedModel,
)
else:
import sys
_UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 699 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class SCREAMING_SNAKE_CASE ( _UpperCAmelCase ):
"""simple docstring"""
A_ = "openai-gpt"
A_ = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self: Optional[int] , __A: str=4_04_78 , __A: Optional[Any]=5_12 , __A: str=7_68 , __A: List[str]=12 , __A: Tuple=12 , __A: str="gelu" , __A: Union[str, Any]=0.1 , __A: str=0.1 , __A: Dict=0.1 , __A: int=1e-5 , __A: List[str]=0.02 , __A: Dict="cls_index" , __A: List[Any]=True , __A: int=None , __A: List[str]=True , __A: List[Any]=0.1 , **__A: List[Any] , ) -> str:
_A = vocab_size
_A = n_positions
_A = n_embd
_A = n_layer
_A = n_head
_A = afn
_A = resid_pdrop
_A = embd_pdrop
_A = attn_pdrop
_A = layer_norm_epsilon
_A = initializer_range
_A = summary_type
_A = summary_use_proj
_A = summary_activation
_A = summary_first_dropout
_A = summary_proj_to_labels
super().__init__(**lowercase_ )
| 708 |
import itertools
import string
from collections.abc import Generator, Iterable
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = iter(_lowercase )
while True:
_A = tuple(itertools.islice(_lowercase , _lowercase ) )
if not chunk:
return
yield chunk
def __A ( _lowercase ):
'''simple docstring'''
_A = ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
_A = ''''''
if len(_lowercase ) < 2:
return dirty
for i in range(len(_lowercase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_lowercase ) & 1:
clean += "X"
return clean
def __A ( _lowercase ):
'''simple docstring'''
_A = '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
_A = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_lowercase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_lowercase )
return table
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = prepare_input(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = generate_table(_lowercase )
_A = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
_A ,_A = divmod(table.index(_lowercase ) , 5 )
_A ,_A = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 62 | 0 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
__UpperCamelCase : List[Any] = {
"""/attention/""": """/0/SelfAttention/""",
"""/self_attention/""": """/0/SelfAttention/""",
"""/encoder_decoder_attention/""": """/1/EncDecAttention/""",
"""value""": """v""",
"""query""": """q""",
"""key""": """k""",
"""out""": """o""",
"""pre_self_attention_layer_norm""": """0/layer_norm""",
"""pre_cross_attention_layer_norm""": """1/layer_norm""",
"""pre_attention_layer_norm""": """0/layer_norm""", # previously 1, but seems wrong
"""token_embedder""": """shared""",
"""encoder_norm""": """final_layer_norm""",
"""decoder_norm""": """final_layer_norm""",
"""relpos_bias/rel_embedding""": """block/0/layer/0/SelfAttention/relative_attention_bias/weight""",
"""router/router_weights/w/""": """router/classifier/""",
"""roer/roer_weights/w/""": """router/classifier/""",
"""logits_dense""": """lm_head""",
}
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Optional[int] ):
lowerCAmelCase = list(s_dict.keys() )
for key in keys:
lowerCAmelCase = R".*/layers_(\d+)"
lowerCAmelCase = key
if re.match(_UpperCAmelCase , _UpperCAmelCase ):
lowerCAmelCase = re.sub(R'layers_(\d+)' , R'block/\1/layer' , _UpperCAmelCase )
lowerCAmelCase = R"(encoder|decoder)\/"
if re.match(_UpperCAmelCase , _UpperCAmelCase ):
lowerCAmelCase = re.match(_UpperCAmelCase , _UpperCAmelCase ).groups()
if groups[0] == "encoder":
lowerCAmelCase = re.sub(R'/mlp/' , R'/1/mlp/' , _UpperCAmelCase )
lowerCAmelCase = re.sub(R'/pre_mlp_layer_norm/' , R'/1/layer_norm/' , _UpperCAmelCase )
elif groups[0] == "decoder":
lowerCAmelCase = re.sub(R'/mlp/' , R'/2/mlp/' , _UpperCAmelCase )
lowerCAmelCase = re.sub(R'/pre_mlp_layer_norm/' , R'/2/layer_norm/' , _UpperCAmelCase )
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
lowerCAmelCase = new_key.replace(_UpperCAmelCase , _UpperCAmelCase )
print(F'{key} -> {new_key}' )
lowerCAmelCase = s_dict.pop(_UpperCAmelCase )
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
lowerCAmelCase = s_dict[
"encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
lowerCAmelCase = s_dict[
"decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys() ):
if "expert" in key:
lowerCAmelCase = s_dict[key].shape[0]
lowerCAmelCase = s_dict[key]
for idx in range(_UpperCAmelCase ):
lowerCAmelCase = expert_weihts[idx]
print(F'{key} -> {key.replace("expert/" , "nested fstring" )}' )
s_dict.pop(_UpperCAmelCase )
return s_dict
__UpperCamelCase : Dict = {
"""NUM_ENCODER_LAYERS""": """num_layers""",
"""NUM_DECODER_LAYERS""": """num_decoder_layers""",
"""NUM_HEADS""": """num_heads""",
"""HEAD_DIM""": """d_kv""",
"""EMBED_DIM""": """d_model""",
"""MLP_DIM""": """d_ff""",
"""NUM_SELECTED_EXPERTS""": """num_selected_experts""",
"""NUM_ENCODER_SPARSE_LAYERS""": """num_sparse_encoder_layers""",
"""NUM_DECODER_SPARSE_LAYERS""": """num_sparse_decoder_layers""",
"""dense.MlpBlock.activations""": """feed_forward_proj""",
}
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[Any] ):
import regex as re
with open(_UpperCAmelCase , 'r' ) as f:
lowerCAmelCase = f.read()
lowerCAmelCase = re.findall(R'(.*) = ([0-9.]*)' , _UpperCAmelCase )
lowerCAmelCase = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
lowerCAmelCase = float(_UpperCAmelCase ) if "." in value else int(_UpperCAmelCase )
lowerCAmelCase = re.findall(R'(.*activations) = \(\'(.*)\',\)' , _UpperCAmelCase )[0]
lowerCAmelCase = str(activation[1] )
lowerCAmelCase = num_experts
lowerCAmelCase = SwitchTransformersConfig(**_UpperCAmelCase )
return config
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Optional[int] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Dict=None , _UpperCAmelCase : List[Any]="./" , _UpperCAmelCase : Optional[int]=8 ):
print(F'Loading flax weights from : {flax_checkpoint_path}' )
lowerCAmelCase = checkpoints.load_tax_checkpoint(_UpperCAmelCase )
if gin_file is not None:
lowerCAmelCase = convert_gin_to_config(_UpperCAmelCase , _UpperCAmelCase )
else:
lowerCAmelCase = SwitchTransformersConfig.from_pretrained(_UpperCAmelCase )
lowerCAmelCase = SwitchTransformersForConditionalGeneration(_UpperCAmelCase )
lowerCAmelCase = flax_params["target"]
lowerCAmelCase = flatten_dict(_UpperCAmelCase , sep='/' )
lowerCAmelCase = rename_keys(_UpperCAmelCase )
lowerCAmelCase = unflatten_dict(_UpperCAmelCase , sep='/' )
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(_UpperCAmelCase , _UpperCAmelCase )
print(F'Save PyTorch model to {pytorch_dump_path}' )
pt_model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
__UpperCamelCase : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--switch_t5x_checkpoint_path''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
''' model architecture. If not provided, a `gin_file` has to be provided.'''
),
)
parser.add_argument(
'''--gin_file''',
default=None,
type=str,
required=False,
help='''Path to the gin config file. If not provided, a `config_file` has to be passed ''',
)
parser.add_argument(
'''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
)
parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
__UpperCamelCase : Union[str, Any] = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 4 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[Any] = {
"""configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LxmertConfig"""],
"""tokenization_lxmert""": ["""LxmertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = ["""LxmertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Tuple = [
"""LxmertEncoder""",
"""LxmertForPreTraining""",
"""LxmertForQuestionAnswering""",
"""LxmertModel""",
"""LxmertPreTrainedModel""",
"""LxmertVisualFeatureEncoder""",
"""LxmertXLayer""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Dict = [
"""TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFLxmertForPreTraining""",
"""TFLxmertMainLayer""",
"""TFLxmertModel""",
"""TFLxmertPreTrainedModel""",
"""TFLxmertVisualFeatureEncoder""",
]
if TYPE_CHECKING:
from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
from .tokenization_lxmert import LxmertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_lxmert_fast import LxmertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lxmert import (
LxmertEncoder,
LxmertForPreTraining,
LxmertForQuestionAnswering,
LxmertModel,
LxmertPreTrainedModel,
LxmertVisualFeatureEncoder,
LxmertXLayer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_lxmert import (
TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLxmertForPreTraining,
TFLxmertMainLayer,
TFLxmertModel,
TFLxmertPreTrainedModel,
TFLxmertVisualFeatureEncoder,
)
else:
import sys
a : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 218 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
lowerCamelCase_ = {
"vocab_file": {
"allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
},
"merges_file": {
"allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
},
}
lowerCamelCase_ = {"allegro/herbert-base-cased": 5_14}
lowerCamelCase_ = {}
class __A( _lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = HerbertTokenizer
def __init__(self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_="</s>" , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
A__ , A__ , tokenizer_file=A__ , cls_token=A__ , unk_token=A__ , pad_token=A__ , mask_token=A__ , sep_token=A__ , **A__ , )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase__ = [self.cls_token_id]
UpperCamelCase__ = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A__ , token_ids_a=A__ , already_has_special_tokens=A__ )
if token_ids_a is None:
return [1] + ([0] * len(A__ )) + [1]
return [1] + ([0] * len(A__ )) + [1] + ([0] * len(A__ )) + [1]
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase__ = [self.sep_token_id]
UpperCamelCase__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase__ = self._tokenizer.model.save(A__ , name=A__ )
return tuple(A__ )
| 700 |
def __magic_name__ ( __a : str , __a : str ):
'''simple docstring'''
UpperCamelCase__ = len(__a )
UpperCamelCase__ = len(__a )
UpperCamelCase__ = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
UpperCamelCase__ = True
for i in range(__a ):
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()
| 86 | 0 |
def lowercase ( a , a = 0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :List[str] = length or len(a )
SCREAMING_SNAKE_CASE_ :List[str] = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
SCREAMING_SNAKE_CASE_ :int = list_data[i + 1], list_data[i]
SCREAMING_SNAKE_CASE_ :List[Any] = True
return list_data if not swapped else bubble_sort(a , length - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 631 |
"""simple docstring"""
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
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_features", "is_longer"]
def __init__( self : List[str] , UpperCamelCase_ : Dict=64 , UpperCamelCase_ : Tuple=48000 , UpperCamelCase_ : List[Any]=480 , UpperCamelCase_ : List[str]=10 , UpperCamelCase_ : str=1024 , UpperCamelCase_ : List[str]=0.0 , UpperCamelCase_ : Optional[int]=False , UpperCamelCase_ : float = 0 , UpperCamelCase_ : float = 14000 , UpperCamelCase_ : int = None , UpperCamelCase_ : str = "fusion" , UpperCamelCase_ : str = "repeatpad" , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
super().__init__(
feature_size=UpperCamelCase_ , sampling_rate=UpperCamelCase_ , padding_value=UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : Union[str, Any] = top_db
__UpperCAmelCase : Optional[Any] = truncation
__UpperCAmelCase : str = padding
__UpperCAmelCase : int = fft_window_size
__UpperCAmelCase : str = (fft_window_size >> 1) + 1
__UpperCAmelCase : List[Any] = hop_length
__UpperCAmelCase : Optional[Any] = max_length_s
__UpperCAmelCase : Tuple = max_length_s * sampling_rate
__UpperCAmelCase : str = sampling_rate
__UpperCAmelCase : int = frequency_min
__UpperCAmelCase : Optional[Any] = frequency_max
__UpperCAmelCase : Any = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=UpperCamelCase_ , min_frequency=UpperCamelCase_ , max_frequency=UpperCamelCase_ , sampling_rate=UpperCamelCase_ , norm=UpperCamelCase_ , mel_scale="htk" , )
__UpperCAmelCase : Any = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=UpperCamelCase_ , min_frequency=UpperCamelCase_ , max_frequency=UpperCamelCase_ , sampling_rate=UpperCamelCase_ , norm="slaney" , mel_scale="slaney" , )
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Dict = copy.deepcopy(self.__dict__)
__UpperCAmelCase : str = 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 a_ ( self : int , UpperCamelCase_ : np.array , UpperCamelCase_ : Optional[np.array] = None):
"""simple docstring"""
__UpperCAmelCase : List[Any] = spectrogram(
UpperCamelCase_ , window_function(self.fft_window_size , "hann") , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=UpperCamelCase_ , log_mel="dB" , )
return log_mel_spectrogram.T
def a_ ( self : List[Any] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : Optional[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 : str = [0]
if len(ranges[2]) == 0:
# if the audio is too short, we just use the first chunk
__UpperCAmelCase : Dict = [0]
# randomly choose index for each part
__UpperCAmelCase : Dict = np.random.choice(ranges[0])
__UpperCAmelCase : List[str] = np.random.choice(ranges[1])
__UpperCAmelCase : List[Any] = np.random.choice(ranges[2])
__UpperCAmelCase : List[Any] = mel[idx_front : idx_front + chunk_frames, :]
__UpperCAmelCase : List[str] = mel[idx_middle : idx_middle + chunk_frames, :]
__UpperCAmelCase : List[str] = mel[idx_back : idx_back + chunk_frames, :]
__UpperCAmelCase : Tuple = torch.tensor(mel[None, None, :])
__UpperCAmelCase : Union[str, Any] = torch.nn.functional.interpolate(
UpperCamelCase_ , size=[chunk_frames, 64] , mode="bilinear" , align_corners=UpperCamelCase_)
__UpperCAmelCase : Union[str, 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 a_ ( self : Optional[Any] , UpperCamelCase_ : np.array , UpperCamelCase_ : Any , UpperCamelCase_ : Tuple , UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
__UpperCAmelCase : List[str] = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
__UpperCAmelCase : List[Any] = len(UpperCamelCase_) - max_length
__UpperCAmelCase : int = np.random.randint(0 , overflow + 1)
__UpperCAmelCase : Union[str, Any] = waveform[idx : idx + max_length]
__UpperCAmelCase : Union[str, Any] = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters_slaney)[None, :]
elif truncation == "fusion":
__UpperCAmelCase : Any = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters)
__UpperCAmelCase : Dict = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
__UpperCAmelCase : Tuple = 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 : List[str] = np.stack([mel, mel, mel, mel] , axis=0)
__UpperCAmelCase : Any = False
else:
__UpperCAmelCase : List[str] = self._random_mel_fusion(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = True
else:
raise NotImplementedError(F"data_truncating {truncation} not implemented")
else:
__UpperCAmelCase : Optional[Any] = 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 : Tuple = int(max_length / len(UpperCamelCase_))
__UpperCAmelCase : List[str] = np.stack(np.tile(UpperCamelCase_ , n_repeat + 1))[:max_length]
if padding == "repeatpad":
__UpperCAmelCase : Union[str, Any] = int(max_length / len(UpperCamelCase_))
__UpperCAmelCase : Optional[Any] = np.stack(np.tile(UpperCamelCase_ , UpperCamelCase_))
__UpperCAmelCase : int = np.pad(UpperCamelCase_ , (0, max_length - waveform.shape[0]) , mode="constant" , constant_values=0)
if truncation == "fusion":
__UpperCAmelCase : Any = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters)
__UpperCAmelCase : List[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0)
else:
__UpperCAmelCase : Optional[int] = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters_slaney)[None, :]
return input_mel, longer
def __call__( self : Dict , UpperCamelCase_ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , UpperCamelCase_ : str = None , UpperCamelCase_ : Optional[str] = None , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[Union[str, TensorType]] = None , **UpperCamelCase_ : Any , ):
"""simple docstring"""
__UpperCAmelCase : int = truncation if truncation is not None else self.truncation
__UpperCAmelCase : Optional[Any] = 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 : List[str] = isinstance(UpperCamelCase_ , 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 : str = is_batched_numpy or (
isinstance(UpperCamelCase_ , (list, tuple)) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list)))
)
if is_batched:
__UpperCAmelCase : Dict = [np.asarray(UpperCamelCase_ , dtype=np.floataa) for speech in raw_speech]
elif not is_batched and not isinstance(UpperCamelCase_ , np.ndarray):
__UpperCAmelCase : Tuple = np.asarray(UpperCamelCase_ , dtype=np.floataa)
elif isinstance(UpperCamelCase_ , np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
__UpperCAmelCase : Optional[int] = raw_speech.astype(np.floataa)
# always return batch
if not is_batched:
__UpperCAmelCase : int = [np.asarray(UpperCamelCase_)]
# convert to mel spectrogram, truncate and pad if needed.
__UpperCAmelCase : Optional[int] = [
self._get_input_mel(UpperCamelCase_ , max_length if max_length else self.nb_max_samples , UpperCamelCase_ , UpperCamelCase_)
for waveform in raw_speech
]
__UpperCAmelCase : Tuple = []
__UpperCAmelCase : List[Any] = []
for mel, longer in padded_inputs:
input_mel.append(UpperCamelCase_)
is_longer.append(UpperCamelCase_)
if truncation == "fusion" and sum(UpperCamelCase_) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
__UpperCAmelCase : Any = np.random.randint(0 , len(UpperCamelCase_))
__UpperCAmelCase : Optional[int] = True
if isinstance(input_mel[0] , UpperCamelCase_):
__UpperCAmelCase : Tuple = [np.asarray(UpperCamelCase_ , dtype=np.floataa) for feature in input_mel]
# is_longer is a list of bool
__UpperCAmelCase : List[str] = [[longer] for longer in is_longer]
__UpperCAmelCase : Optional[int] = {"input_features": input_mel, "is_longer": is_longer}
__UpperCAmelCase : Optional[int] = BatchFeature(UpperCamelCase_)
if return_tensors is not None:
__UpperCAmelCase : Any = input_features.convert_to_tensors(UpperCamelCase_)
return input_features
| 77 | 0 |
import math
def _snake_case ( SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
_lowerCAmelCase : str = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(SCREAMING_SNAKE_CASE )
def _snake_case ( SCREAMING_SNAKE_CASE = 1 / 12345 ) -> int:
"""simple docstring"""
_lowerCAmelCase : Optional[int] = 0
_lowerCAmelCase : Union[str, Any] = 0
_lowerCAmelCase : str = 3
while True:
_lowerCAmelCase : Dict = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(SCREAMING_SNAKE_CASE ):
_lowerCAmelCase : List[str] = int(SCREAMING_SNAKE_CASE )
total_partitions += 1
if check_partition_perfect(SCREAMING_SNAKE_CASE ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(SCREAMING_SNAKE_CASE )
integer += 1
if __name__ == "__main__":
print(f'''{solution() = }''')
| 710 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging
if is_vision_available():
import PIL
if is_torch_available():
import torch
__UpperCAmelCase = logging.get_logger(__name__)
class A__ ( A ):
"""simple docstring"""
_lowercase : List[Any] = ['''pixel_values''']
def __init__( self : Tuple , A_ : bool = True , A_ : Dict[str, int] = None , A_ : PILImageResampling = PILImageResampling.BILINEAR , A_ : bool = True , A_ : Union[int, float] = 1 / 2_5_5 , A_ : bool = True , A_ : Dict[str, int] = None , A_ : bool = True , **A_ : List[Any] , ):
'''simple docstring'''
super().__init__(**A_ )
_lowerCAmelCase : Optional[int] = size if size is not None else {"shortest_edge": 2_2_4}
_lowerCAmelCase : Optional[int] = get_size_dict(A_ , default_to_square=A_ )
_lowerCAmelCase : Any = crop_size if crop_size is not None else {"height": 2_5_6, "width": 2_5_6}
_lowerCAmelCase : str = get_size_dict(A_ , param_name="crop_size" )
_lowerCAmelCase : Any = do_resize
_lowerCAmelCase : Optional[Any] = size
_lowerCAmelCase : str = resample
_lowerCAmelCase : Optional[Any] = do_rescale
_lowerCAmelCase : Dict = rescale_factor
_lowerCAmelCase : Any = do_center_crop
_lowerCAmelCase : List[Any] = crop_size
_lowerCAmelCase : List[Any] = do_flip_channel_order
def __magic_name__ ( self : Tuple , A_ : np.ndarray , A_ : Dict[str, int] , A_ : PILImageResampling = PIL.Image.BILINEAR , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : Dict , ):
'''simple docstring'''
_lowerCAmelCase : Any = get_size_dict(A_ , default_to_square=A_ )
if "shortest_edge" not in size:
raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}''' )
_lowerCAmelCase : Union[str, Any] = get_resize_output_image_size(A_ , size=size["shortest_edge"] , default_to_square=A_ )
return resize(A_ , size=A_ , resample=A_ , data_format=A_ , **A_ )
def __magic_name__ ( self : Union[str, Any] , A_ : np.ndarray , A_ : Dict[str, int] , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : List[str] , ):
'''simple docstring'''
_lowerCAmelCase : str = 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()}''' )
return center_crop(A_ , size=(size["height"], size["width"]) , data_format=A_ , **A_ )
def __magic_name__ ( self : Tuple , A_ : np.ndarray , A_ : Union[int, float] , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : Any , ):
'''simple docstring'''
return rescale(A_ , scale=A_ , data_format=A_ , **A_ )
def __magic_name__ ( self : Optional[Any] , A_ : np.ndarray , A_ : Optional[Union[str, ChannelDimension]] = None ):
'''simple docstring'''
return flip_channel_order(A_ , data_format=A_ )
def __magic_name__ ( self : List[Any] , A_ : ImageInput , A_ : bool = None , A_ : Dict[str, int] = None , A_ : PILImageResampling = None , A_ : bool = None , A_ : float = None , A_ : bool = None , A_ : Dict[str, int] = None , A_ : bool = None , A_ : Optional[Union[str, TensorType]] = None , A_ : ChannelDimension = ChannelDimension.FIRST , **A_ : Tuple , ):
'''simple docstring'''
_lowerCAmelCase : str = do_resize if do_resize is not None else self.do_resize
_lowerCAmelCase : Dict = resample if resample is not None else self.resample
_lowerCAmelCase : str = do_rescale if do_rescale is not None else self.do_rescale
_lowerCAmelCase : List[Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCAmelCase : Tuple = do_center_crop if do_center_crop is not None else self.do_center_crop
_lowerCAmelCase : str = (
do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
)
_lowerCAmelCase : str = size if size is not None else self.size
_lowerCAmelCase : str = get_size_dict(A_ , default_to_square=A_ )
_lowerCAmelCase : Optional[Any] = crop_size if crop_size is not None else self.crop_size
_lowerCAmelCase : List[str] = get_size_dict(A_ , param_name="crop_size" )
_lowerCAmelCase : Dict = make_list_of_images(A_ )
if not valid_images(A_ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
# All transformations expect numpy arrays.
_lowerCAmelCase : Optional[int] = [to_numpy_array(A_ ) for image in images]
if do_resize:
_lowerCAmelCase : Any = [self.resize(image=A_ , size=A_ , resample=A_ ) for image in images]
if do_center_crop:
_lowerCAmelCase : Tuple = [self.center_crop(image=A_ , size=A_ ) for image in images]
if do_rescale:
_lowerCAmelCase : Any = [self.rescale(image=A_ , scale=A_ ) for image in images]
# the pretrained checkpoints assume images are BGR, not RGB
if do_flip_channel_order:
_lowerCAmelCase : Dict = [self.flip_channel_order(image=A_ ) for image in images]
_lowerCAmelCase : Optional[Any] = [to_channel_dimension_format(A_ , A_ ) for image in images]
_lowerCAmelCase : Tuple = {"pixel_values": images}
return BatchFeature(data=A_ , tensor_type=A_ )
def __magic_name__ ( self : List[Any] , A_ : Union[str, Any] , A_ : List[Tuple] = None ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = 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_ ):
_lowerCAmelCase : Dict = target_sizes.numpy()
_lowerCAmelCase : List[Any] = []
for idx in range(len(A_ ) ):
_lowerCAmelCase : Tuple = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="bilinear" , align_corners=A_ )
_lowerCAmelCase : Any = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(A_ )
else:
_lowerCAmelCase : Tuple = logits.argmax(dim=1 )
_lowerCAmelCase : Tuple = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 503 | 0 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
def UpperCamelCase( self ):
_UpperCAmelCase = '''ylacombe/bark-small'''
_UpperCAmelCase = tempfile.mkdtemp()
_UpperCAmelCase = '''en_speaker_1'''
_UpperCAmelCase = '''This is a test string'''
_UpperCAmelCase = '''speaker_embeddings_path.json'''
_UpperCAmelCase = '''speaker_embeddings'''
def UpperCamelCase( self , **_UpperCamelCase ):
return AutoTokenizer.from_pretrained(self.checkpoint , **_UpperCamelCase )
def UpperCamelCase( self ):
shutil.rmtree(self.tmpdirname )
def UpperCamelCase( self ):
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BarkProcessor(tokenizer=_UpperCamelCase )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def UpperCamelCase( self ):
_UpperCAmelCase = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
_UpperCAmelCase = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
_UpperCAmelCase = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token='''(BOS)''' , eos_token='''(EOS)''' , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def UpperCamelCase( self ):
_UpperCAmelCase = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
_UpperCAmelCase = 35
_UpperCAmelCase = 2
_UpperCAmelCase = 8
_UpperCAmelCase = {
'''semantic_prompt''': np.ones(_UpperCamelCase ),
'''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len) ),
'''fine_prompt''': np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
_UpperCAmelCase = processor(text=self.input_string , voice_preset=_UpperCamelCase )
_UpperCAmelCase = inputs['''history_prompt''']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(_UpperCamelCase , np.array([] ) ).tolist() )
# test loading voice preset from npz file
_UpperCAmelCase = os.path.join(self.tmpdirname , '''file.npz''' )
np.savez(_UpperCamelCase , **_UpperCamelCase )
_UpperCAmelCase = processor(text=self.input_string , voice_preset=_UpperCamelCase )
_UpperCAmelCase = inputs['''history_prompt''']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(_UpperCamelCase , np.array([] ) ).tolist() )
# test loading voice preset from the hub
_UpperCAmelCase = processor(text=self.input_string , voice_preset=self.voice_preset )
def UpperCamelCase( self ):
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BarkProcessor(tokenizer=_UpperCamelCase )
_UpperCAmelCase = processor(text=self.input_string )
_UpperCAmelCase = tokenizer(
self.input_string , padding='''max_length''' , max_length=256 , add_special_tokens=_UpperCamelCase , return_attention_mask=_UpperCamelCase , return_token_type_ids=_UpperCamelCase , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() ) | 32 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = OrderedDict(
[
# Base model mapping
("albert", "FlaxAlbertModel"),
("bart", "FlaxBartModel"),
("beit", "FlaxBeitModel"),
("bert", "FlaxBertModel"),
("big_bird", "FlaxBigBirdModel"),
("blenderbot", "FlaxBlenderbotModel"),
("blenderbot-small", "FlaxBlenderbotSmallModel"),
("clip", "FlaxCLIPModel"),
("distilbert", "FlaxDistilBertModel"),
("electra", "FlaxElectraModel"),
("gpt-sw3", "FlaxGPT2Model"),
("gpt2", "FlaxGPT2Model"),
("gpt_neo", "FlaxGPTNeoModel"),
("gptj", "FlaxGPTJModel"),
("longt5", "FlaxLongT5Model"),
("marian", "FlaxMarianModel"),
("mbart", "FlaxMBartModel"),
("mt5", "FlaxMT5Model"),
("opt", "FlaxOPTModel"),
("pegasus", "FlaxPegasusModel"),
("regnet", "FlaxRegNetModel"),
("resnet", "FlaxResNetModel"),
("roberta", "FlaxRobertaModel"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
("roformer", "FlaxRoFormerModel"),
("t5", "FlaxT5Model"),
("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
("vit", "FlaxViTModel"),
("wav2vec2", "FlaxWav2Vec2Model"),
("whisper", "FlaxWhisperModel"),
("xglm", "FlaxXGLMModel"),
("xlm-roberta", "FlaxXLMRobertaModel"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for pre-training mapping
("albert", "FlaxAlbertForPreTraining"),
("bart", "FlaxBartForConditionalGeneration"),
("bert", "FlaxBertForPreTraining"),
("big_bird", "FlaxBigBirdForPreTraining"),
("electra", "FlaxElectraForPreTraining"),
("longt5", "FlaxLongT5ForConditionalGeneration"),
("mbart", "FlaxMBartForConditionalGeneration"),
("mt5", "FlaxMT5ForConditionalGeneration"),
("roberta", "FlaxRobertaForMaskedLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
("roformer", "FlaxRoFormerForMaskedLM"),
("t5", "FlaxT5ForConditionalGeneration"),
("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
("whisper", "FlaxWhisperForConditionalGeneration"),
("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Masked LM mapping
("albert", "FlaxAlbertForMaskedLM"),
("bart", "FlaxBartForConditionalGeneration"),
("bert", "FlaxBertForMaskedLM"),
("big_bird", "FlaxBigBirdForMaskedLM"),
("distilbert", "FlaxDistilBertForMaskedLM"),
("electra", "FlaxElectraForMaskedLM"),
("mbart", "FlaxMBartForConditionalGeneration"),
("roberta", "FlaxRobertaForMaskedLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
("roformer", "FlaxRoFormerForMaskedLM"),
("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("bart", "FlaxBartForConditionalGeneration"),
("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
("encoder-decoder", "FlaxEncoderDecoderModel"),
("longt5", "FlaxLongT5ForConditionalGeneration"),
("marian", "FlaxMarianMTModel"),
("mbart", "FlaxMBartForConditionalGeneration"),
("mt5", "FlaxMT5ForConditionalGeneration"),
("pegasus", "FlaxPegasusForConditionalGeneration"),
("t5", "FlaxT5ForConditionalGeneration"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Image-classsification
("beit", "FlaxBeitForImageClassification"),
("regnet", "FlaxRegNetForImageClassification"),
("resnet", "FlaxResNetForImageClassification"),
("vit", "FlaxViTForImageClassification"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Causal LM mapping
("bart", "FlaxBartForCausalLM"),
("bert", "FlaxBertForCausalLM"),
("big_bird", "FlaxBigBirdForCausalLM"),
("electra", "FlaxElectraForCausalLM"),
("gpt-sw3", "FlaxGPT2LMHeadModel"),
("gpt2", "FlaxGPT2LMHeadModel"),
("gpt_neo", "FlaxGPTNeoForCausalLM"),
("gptj", "FlaxGPTJForCausalLM"),
("opt", "FlaxOPTForCausalLM"),
("roberta", "FlaxRobertaForCausalLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
("xglm", "FlaxXGLMForCausalLM"),
("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Sequence Classification mapping
("albert", "FlaxAlbertForSequenceClassification"),
("bart", "FlaxBartForSequenceClassification"),
("bert", "FlaxBertForSequenceClassification"),
("big_bird", "FlaxBigBirdForSequenceClassification"),
("distilbert", "FlaxDistilBertForSequenceClassification"),
("electra", "FlaxElectraForSequenceClassification"),
("mbart", "FlaxMBartForSequenceClassification"),
("roberta", "FlaxRobertaForSequenceClassification"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
("roformer", "FlaxRoFormerForSequenceClassification"),
("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Question Answering mapping
("albert", "FlaxAlbertForQuestionAnswering"),
("bart", "FlaxBartForQuestionAnswering"),
("bert", "FlaxBertForQuestionAnswering"),
("big_bird", "FlaxBigBirdForQuestionAnswering"),
("distilbert", "FlaxDistilBertForQuestionAnswering"),
("electra", "FlaxElectraForQuestionAnswering"),
("mbart", "FlaxMBartForQuestionAnswering"),
("roberta", "FlaxRobertaForQuestionAnswering"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
("roformer", "FlaxRoFormerForQuestionAnswering"),
("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Token Classification mapping
("albert", "FlaxAlbertForTokenClassification"),
("bert", "FlaxBertForTokenClassification"),
("big_bird", "FlaxBigBirdForTokenClassification"),
("distilbert", "FlaxDistilBertForTokenClassification"),
("electra", "FlaxElectraForTokenClassification"),
("roberta", "FlaxRobertaForTokenClassification"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
("roformer", "FlaxRoFormerForTokenClassification"),
("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
]
)
UpperCAmelCase_ = OrderedDict(
[
# Model for Multiple Choice mapping
("albert", "FlaxAlbertForMultipleChoice"),
("bert", "FlaxBertForMultipleChoice"),
("big_bird", "FlaxBigBirdForMultipleChoice"),
("distilbert", "FlaxDistilBertForMultipleChoice"),
("electra", "FlaxElectraForMultipleChoice"),
("roberta", "FlaxRobertaForMultipleChoice"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
("roformer", "FlaxRoFormerForMultipleChoice"),
("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("bert", "FlaxBertForNextSentencePrediction"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
("whisper", "FlaxWhisperForConditionalGeneration"),
]
)
UpperCAmelCase_ = OrderedDict(
[
("whisper", "FlaxWhisperForAudioClassification"),
]
)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
UpperCAmelCase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[str] = FLAX_MODEL_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModel)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Optional[Any] = FLAX_MODEL_FOR_PRETRAINING_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[Any] = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[str] = FLAX_MODEL_FOR_MASKED_LM_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Dict = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : List[str] = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Dict = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Union[str, Any] = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="token classification"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Union[str, Any] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Any = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Dict = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="image classification"
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : Optional[int] = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
UpperCAmelCase_ = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")
class __UpperCamelCase ( _BaseAutoModelClass ):
__A : str = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
UpperCAmelCase_ = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
) | 32 | 1 |
"""simple docstring"""
a_ = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def UpperCAmelCase_ ( __a : Any ):
'''simple docstring'''
_lowerCamelCase : List[Any] = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared += DIGITS_SQUARED[number % 10_00_00]
number //= 10_00_00
return sum_of_digits_squared
# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.
# The other one ends with 1 and has only one element 1.
# So 58 and 1 are chosen to be declared at the starting.
# Changed dictionary to an array to quicken the solution
a_ = [None] * 10_00_00_00
a_ = True
a_ = False
def UpperCAmelCase_ ( __a : Any ):
'''simple docstring'''
if CHAINS[number - 1] is not None:
return CHAINS[number - 1] # type: ignore
_lowerCamelCase : Optional[int] = chain(next_number(__A ) )
_lowerCamelCase : int = number_chain
while number < 10_00_00_00:
_lowerCamelCase : Dict = number_chain
number *= 10
return number_chain
def UpperCAmelCase_ ( __a : Dict = 10_00_00_00 ):
'''simple docstring'''
for i in range(1 , __A ):
if CHAINS[i] is None:
chain(i + 1 )
return CHAINS[:number].count(__A )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F"{solution() = }")
| 715 |
"""simple docstring"""
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCAmelCase_ ( __a : Any ):
'''simple docstring'''
_lowerCamelCase : List[str] = tmp_path / 'file.csv'
_lowerCamelCase : List[str] = textwrap.dedent(
'\\n header1,header2\n 1,2\n 10,20\n ' )
with open(__a , 'w' ) as f:
f.write(__a )
return str(__a )
@pytest.fixture
def UpperCAmelCase_ ( __a : List[str] ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] = tmp_path / 'malformed_file.csv'
_lowerCamelCase : int = textwrap.dedent(
'\\n header1,header2\n 1,2\n 10,20,\n ' )
with open(__a , 'w' ) as f:
f.write(__a )
return str(__a )
@pytest.fixture
def UpperCAmelCase_ ( __a : Union[str, Any] , __a : Optional[Any] ):
'''simple docstring'''
_lowerCamelCase : Tuple = tmp_path / 'csv_with_image.csv'
_lowerCamelCase : Tuple = textwrap.dedent(
f"\\n image\n {image_file}\n " )
with open(__a , 'w' ) as f:
f.write(__a )
return str(__a )
@pytest.fixture
def UpperCAmelCase_ ( __a : Any ):
'''simple docstring'''
_lowerCamelCase : List[Any] = tmp_path / 'csv_with_label.csv'
_lowerCamelCase : Union[str, Any] = textwrap.dedent(
'\\n label\n good\n bad\n good\n ' )
with open(__a , 'w' ) as f:
f.write(__a )
return str(__a )
@pytest.fixture
def UpperCAmelCase_ ( __a : Tuple ):
'''simple docstring'''
_lowerCamelCase : str = tmp_path / 'csv_with_int_list.csv'
_lowerCamelCase : int = textwrap.dedent(
'\\n int_list\n 1 2 3\n 4 5 6\n 7 8 9\n ' )
with open(__a , 'w' ) as f:
f.write(__a )
return str(__a )
def UpperCAmelCase_ ( __a : int , __a : Optional[Any] , __a : List[str] ):
'''simple docstring'''
_lowerCamelCase : int = Csv()
_lowerCamelCase : List[Any] = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(__a , match='Error tokenizing data' ):
for _ in generator:
pass
assert any(
record.levelname == 'ERROR'
and 'Failed to read file' in record.message
and os.path.basename(__a ) in record.message
for record in caplog.records )
@require_pil
def UpperCAmelCase_ ( __a : Optional[int] ):
'''simple docstring'''
with open(__a , encoding='utf-8' ) as f:
_lowerCamelCase : Any = f.read().splitlines()[1]
_lowerCamelCase : Tuple = Csv(encoding='utf-8' , features=Features({'image': Image()} ) )
_lowerCamelCase : int = csv._generate_tables([[csv_file_with_image]] )
_lowerCamelCase : Tuple = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('image' ).type == Image()()
_lowerCamelCase : Union[str, Any] = pa_table.to_pydict()['image']
assert generated_content == [{"path": image_file, "bytes": None}]
def UpperCAmelCase_ ( __a : Union[str, Any] ):
'''simple docstring'''
with open(__a , encoding='utf-8' ) as f:
_lowerCamelCase : List[Any] = f.read().splitlines()[1:]
_lowerCamelCase : Tuple = Csv(encoding='utf-8' , features=Features({'label': ClassLabel(names=['good', 'bad'] )} ) )
_lowerCamelCase : Any = csv._generate_tables([[csv_file_with_label]] )
_lowerCamelCase : Optional[int] = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('label' ).type == ClassLabel(names=['good', 'bad'] )()
_lowerCamelCase : str = pa_table.to_pydict()['label']
assert generated_content == [ClassLabel(names=['good', 'bad'] ).straint(__a ) for label in labels]
def UpperCAmelCase_ ( __a : Optional[int] ):
'''simple docstring'''
_lowerCamelCase : Any = Csv(encoding='utf-8' , sep=',' , converters={'int_list': lambda __a : [int(__a ) for i in x.split()]} )
_lowerCamelCase : Union[str, Any] = csv._generate_tables([[csv_file_with_int_list]] )
_lowerCamelCase : Any = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field('int_list' ).type )
_lowerCamelCase : Optional[Any] = pa_table.to_pydict()['int_list']
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 349 | 0 |
from __future__ import annotations
def A ( lowercase__ : int | float | str , lowercase__ : int | float | str ) -> list[str]:
if nth_term == "":
return [""]
UpperCamelCase__ :Dict = int(lowercase__ )
UpperCamelCase__ :Union[str, Any] = int(lowercase__ )
UpperCamelCase__ :list[str] = []
for temp in range(int(lowercase__ ) ):
series.append(f"""1 / {pow(temp + 1 , int(lowercase__ ) )}""" if series else """1""" )
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase = int(input("Enter the last number (nth term) of the P-Series"))
UpperCamelCase = int(input("Enter the power for P-Series"))
print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
print(p_series(nth_term, power)) | 45 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {"""vocab_file""": """spiece.model"""}
lowerCAmelCase__ = {
"""vocab_file""": {
"""AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
"""AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
"""AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
"""AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
"""AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
}
}
lowerCAmelCase__ = {
"""AI-Sweden/gpt-sw3-126m""": 2_0_4_8,
"""AI-Sweden/gpt-sw3-350m""": 2_0_4_8,
"""AI-Sweden/gpt-sw3-1.6b""": 2_0_4_8,
"""AI-Sweden/gpt-sw3-6.7b""": 2_0_4_8,
"""AI-Sweden/gpt-sw3-20b""": 2_0_4_8,
}
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = VOCAB_FILES_NAMES
__lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase = ['input_ids', 'attention_mask']
def __init__( self , lowercase , lowercase=False , lowercase=False , lowercase=False , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase = None , **lowercase , ) -> None:
'''simple docstring'''
A__ = {} if sp_model_kwargs is None else sp_model_kwargs
A__ = kwargs.get("name_or_path" )
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored" )
A__ = "None"
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
A__ = "<|endoftext|>" if eos_token is None else eos_token
A__ = "<unk>" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
A__ = unk_token if pad_token is None else pad_token
A__ = eos_token if bos_token is None else bos_token
else:
A__ = "<pad>" if pad_token is None else pad_token
A__ = "<s>" if bos_token is None else bos_token
super().__init__(
do_lower_case=lowercase , remove_space=lowercase , keep_accents=lowercase , bos_token=lowercase , eos_token=lowercase , unk_token=lowercase , pad_token=lowercase , sp_model_kwargs=self.sp_model_kwargs , **lowercase , )
A__ = do_lower_case
A__ = remove_space
A__ = keep_accents
A__ = vocab_file
A__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowercase )
# Used for whitespace normalization in input texts
# fmt : off
A__ = {" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "", ""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
A__ = re.compile(
F'[{"".join(map(lowercase , list(range(0 , 9 ) ) + list(range(11 , 32 ) ) + list(range(127 , 160 ) ) + [160, 173, 8203] ) )}]' )
def __getstate__( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = self.__dict__.copy()
A__ = None
return state
def __setstate__( self , lowercase ) -> List[Any]:
'''simple docstring'''
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 )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
return len(self.sp_model )
def UpperCamelCase ( self , lowercase ) -> str:
'''simple docstring'''
A__ = self.non_printing_characters_re.sub("" , lowercase )
# Normalize whitespaces
A__ = "".join([char if char not in self.whitespaces else " " for char in text] )
# NFC Unicode normalization
A__ = unicodedata.normalize("NFC" , lowercase )
return text
def UpperCamelCase ( self , lowercase , **lowercase ) -> List[str]:
'''simple docstring'''
A__ = self.preprocess_text(lowercase )
return self.sp_model.encode(lowercase , out_type=lowercase )
def UpperCamelCase ( self , lowercase ) -> int:
'''simple docstring'''
return self.sp_model.PieceToId(lowercase )
def UpperCamelCase ( self , lowercase ) -> str:
'''simple docstring'''
return self.sp_model.IdToPiece(lowercase )
@staticmethod
def UpperCamelCase ( lowercase ) -> str:
'''simple docstring'''
return out_string
def UpperCamelCase ( self , lowercase ) -> str:
'''simple docstring'''
A__ = []
A__ = ""
A__ = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowercase ) + token
A__ = True
A__ = []
else:
current_sub_tokens.append(lowercase )
A__ = False
out_string += self.sp_model.decode(lowercase )
return out_string
def UpperCamelCase ( self ) -> Dict[str, int]:
'''simple docstring'''
A__ = {self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase ( self , lowercase , lowercase = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowercase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
A__ = os.path.join(
lowercase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowercase )
elif not os.path.isfile(self.vocab_file ):
with open(lowercase , "wb" ) as fi:
A__ = self.sp_model.serialized_model_proto()
fi.write(lowercase )
return (out_vocab_file,)
def UpperCamelCase ( self , lowercase , lowercase = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
'''simple docstring'''
if isinstance(lowercase , lowercase ):
A__ = self.preprocess_text(lowercase )
A__ = self.sp_model.encode(lowercase )
else:
A__ = [self.preprocess_text(lowercase ) for t in text]
A__ = self.sp_model.encode(lowercase )
if return_tensors is True or return_tensors == "pt":
A__ = torch.tensor(lowercase )
return token_ids
def UpperCamelCase ( self , lowercase ) -> str:
'''simple docstring'''
return self.sp_model.decode(lowercase )
def UpperCamelCase ( self , lowercase ) -> List[int]:
'''simple docstring'''
A__ = [F'User: {text}' if is_user else F'Bot: {text}' for is_user, text in conversation.iter_texts()]
A__ = (
F'{self.eos_token}{self.bos_token}' + F'{self.bos_token}'.join(lowercase ) + F'{self.bos_token}Bot:'
)
return self.encode(text=lowercase )
| 514 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : Dict = logging.get_logger(__name__)
lowercase : str = {
"""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 A__ ( __UpperCAmelCase ):
"""simple docstring"""
__A : Union[str, Any] = '''distilbert'''
__A : List[Any] = {
'''hidden_size''': '''dim''',
'''num_attention_heads''': '''n_heads''',
'''num_hidden_layers''': '''n_layers''',
}
def __init__( self , lowercase=3_0522 , lowercase=512 , lowercase=False , lowercase=6 , lowercase=12 , lowercase=768 , lowercase=4 * 768 , lowercase=0.1 , lowercase=0.1 , lowercase="gelu" , lowercase=0.02 , lowercase=0.1 , lowercase=0.2 , lowercase=0 , **lowercase , ) -> List[str]:
'''simple docstring'''
a__ : List[Any] = vocab_size
a__ : Optional[int] = max_position_embeddings
a__ : Any = sinusoidal_pos_embds
a__ : str = n_layers
a__ : int = n_heads
a__ : List[str] = dim
a__ : Optional[Any] = hidden_dim
a__ : List[str] = dropout
a__ : Any = attention_dropout
a__ : List[str] = activation
a__ : str = initializer_range
a__ : List[Any] = qa_dropout
a__ : Any = seq_classif_dropout
super().__init__(**lowercase , pad_token_id=lowercase)
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
@property
def __lowercase ( self) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
a__ : List[Any] = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
a__ : List[Any] = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
])
| 392 |
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def A_ ( A__ , A__ = True , A__ = math.inf , A__ = -math.inf , A__ = math.inf , A__ = -math.inf , A__ = False , A__ = 100 , A__ = 0.01 , A__ = 1 , ) -> Any:
a__ : List[str] = False
a__ : Optional[int] = search_prob
a__ : Any = start_temperate
a__ : Any = []
a__ : int = 0
a__ : Any = None
while not search_end:
a__ : Tuple = current_state.score()
if best_state is None or current_score > best_state.score():
a__ : Optional[Any] = current_state
scores.append(A__ )
iterations += 1
a__ : Union[str, Any] = None
a__ : List[str] = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
a__ : Optional[int] = random.randint(0 , len(A__ ) - 1 ) # picking a random neighbor
a__ : List[str] = neighbors.pop(A__ )
a__ : List[Any] = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
a__ : int = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
a__ : Optional[Any] = picked_neighbor
else:
a__ : List[Any] = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
a__ : Tuple = picked_neighbor
a__ : Union[str, Any] = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
a__ : int = True
else:
a__ : Union[str, Any] = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(A__ ) , A__ )
plt.xlabel('Iterations' )
plt.ylabel('Function values' )
plt.show()
return best_state
if __name__ == "__main__":
def A_ ( A__ , A__ ) -> Optional[Any]:
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
lowercase : List[Any] = SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
lowercase : int = simulated_annealing(
prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
)
print(
"""The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
# starting the problem with initial coordinates (12, 47)
lowercase : List[str] = SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
lowercase : List[Any] = simulated_annealing(
prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
)
print(
"""The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
def A_ ( A__ , A__ ) -> Optional[Any]:
return (3 * x**2) - (6 * y)
lowercase : Tuple = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
lowercase : Optional[Any] = simulated_annealing(prob, find_max=False, visualization=True)
print(
"""The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
F"""{local_min.score()}"""
)
lowercase : str = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
lowercase : List[Any] = simulated_annealing(prob, find_max=True, visualization=True)
print(
"""The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
F"""{local_min.score()}"""
)
| 392 | 1 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.basicConfig(level=logging.DEBUG)
__SCREAMING_SNAKE_CASE : List[str] = logging.getLogger()
__SCREAMING_SNAKE_CASE : Dict = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self , lowercase_ ):
os.makedirs(lowercase_ , exist_ok=lowercase_ )
_snake_case : Tuple = {"source": "What is love ?", "target": "life"}
_snake_case : List[str] = {"train": 12, "val": 2, "test": 2}
for split in ["train", "test", "val"]:
for field in ["source", "target"]:
_snake_case : Dict = "\n".join([contents[field]] * n_lines[split] )
with open(os.path.join(lowercase_ , f"""{split}.{field}""" ) , "w" ) as f:
f.write(lowercase_ )
def UpperCamelCase ( self , lowercase_ , lowercase_ = "pytorch" ):
_snake_case : Any = self.get_auto_remove_tmp_dir()
_snake_case : Tuple = os.path.join(lowercase_ , "output" )
_snake_case : List[str] = os.path.join(lowercase_ , "data" )
self._create_dummy_data(data_dir=lowercase_ )
_snake_case : str = f"""
--data_dir {data_dir} \
--output_dir {output_dir} \
--model_name_or_path facebook/rag-sequence-base \
--model_type rag_sequence \
--do_train \
--do_predict \
--n_val -1 \
--val_check_interval 1.0 \
--train_batch_size 2 \
--eval_batch_size 1 \
--max_source_length 25 \
--max_target_length 25 \
--val_max_target_length 25 \
--test_max_target_length 25 \
--label_smoothing 0.1 \
--dropout 0.1 \
--attention_dropout 0.1 \
--weight_decay 0.001 \
--adam_epsilon 1e-08 \
--max_grad_norm 0.1 \
--lr_scheduler polynomial \
--learning_rate 3e-04 \
--num_train_epochs 1 \
--warmup_steps 4 \
--gradient_accumulation_steps 1 \
--distributed-port 8787 \
--use_dummy_dataset 1 \
--distributed_retriever {distributed_retriever} \
""".split()
if gpus > 0:
testargs.append(f"""--gpus={gpus}""" )
if is_apex_available():
testargs.append("--fp16" )
else:
testargs.append("--gpus=0" )
testargs.append("--distributed_backend=ddp_cpu" )
testargs.append("--num_processes=2" )
_snake_case : Optional[Any] = [sys.executable, str(Path(finetune_rag.__file__ ).resolve() )] + testargs
execute_subprocess_async(lowercase_ , env=self.get_env() )
_snake_case : str = os.path.join(lowercase_ , "metrics.json" )
with open(lowercase_ ) as f:
_snake_case : Optional[int] = json.load(lowercase_ )
return result
@require_torch_gpu
def UpperCamelCase ( self ):
_snake_case : Any = self._run_finetune(gpus=1 )
self.assertGreaterEqual(result["test"][0]["test_avg_em"] , 0.2 )
@require_torch_multi_gpu
def UpperCamelCase ( self ):
_snake_case : Optional[Any] = self._run_finetune(gpus=2 )
self.assertGreaterEqual(result["test"][0]["test_avg_em"] , 0.2 )
@require_torch_gpu
@require_ray
def UpperCamelCase ( self ):
_snake_case : Dict = self._run_finetune(gpus=1 , distributed_retriever="ray" )
self.assertGreaterEqual(result["test"][0]["test_avg_em"] , 0.2 )
@require_torch_multi_gpu
@require_ray
def UpperCamelCase ( self ):
_snake_case : List[str] = self._run_finetune(gpus=1 , distributed_retriever="ray" )
self.assertGreaterEqual(result["test"][0]["test_avg_em"] , 0.2 ) | 670 | from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def snake_case (__lowercase ) -> Any:
'''simple docstring'''
if not path:
return "pipe"
for ext in PipelineDataFormat.SUPPORTED_FORMATS:
if path.endswith(__lowercase ):
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 snake_case (__lowercase ) -> Any:
'''simple docstring'''
_snake_case : int = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
_snake_case : List[Any] = try_infer_format_from_ext(args.input ) if args.format == "infer" else args.format
_snake_case : Optional[int] = PipelineDataFormat.from_str(
format=__lowercase , output_path=args.output , input_path=args.input , column=args.column if args.column else nlp.default_input_names , overwrite=args.overwrite , )
return RunCommand(__lowercase , __lowercase )
class lowercase_ ( __snake_case ):
def __init__( self , lowercase_ , lowercase_ ):
_snake_case : str = nlp
_snake_case : str = reader
@staticmethod
def UpperCamelCase ( lowercase_ ):
_snake_case : Dict = 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=lowercase_ , help="Path to the file to use for inference" )
run_parser.add_argument("--output" , type=lowercase_ , help="Path to the file that will be used post to write results." )
run_parser.add_argument("--model" , type=lowercase_ , help="Name or path to the model to instantiate." )
run_parser.add_argument("--config" , type=lowercase_ , help="Name or path to the model's config to instantiate." )
run_parser.add_argument(
"--tokenizer" , type=lowercase_ , help="Name of the tokenizer to use. (default: same as the model name)" )
run_parser.add_argument(
"--column" , type=lowercase_ , help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)" , )
run_parser.add_argument(
"--format" , type=lowercase_ , default="infer" , choices=PipelineDataFormat.SUPPORTED_FORMATS , help="Input format to read from" , )
run_parser.add_argument(
"--device" , type=lowercase_ , 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=lowercase_ )
def UpperCamelCase ( self ):
_snake_case ,_snake_case : Tuple = self._nlp, []
for entry in self._reader:
_snake_case : Optional[Any] = nlp(**lowercase_ ) if self._reader.is_multi_columns else nlp(lowercase_ )
if isinstance(lowercase_ , lowercase_ ):
outputs.append(lowercase_ )
else:
outputs += output
# Saving data
if self._nlp.binary_output:
_snake_case : str = self._reader.save_binary(lowercase_ )
logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}""" )
else:
self._reader.save(lowercase_ ) | 670 | 1 |
'''simple docstring'''
def A_ ( SCREAMING_SNAKE_CASE_ = 3 , SCREAMING_SNAKE_CASE_ = 7 , SCREAMING_SNAKE_CASE_ = 1_00_00_00 ) ->int:
lowercase_ = 0
lowercase_ = 1
for current_denominator in range(1 , limit + 1 ):
lowercase_ = current_denominator * numerator // denominator
if current_denominator % denominator == 0:
current_numerator -= 1
if current_numerator * max_denominator > current_denominator * max_numerator:
lowercase_ = current_numerator
lowercase_ = current_denominator
return max_numerator
if __name__ == "__main__":
print(solution(numerator=3, denominator=7, limit=100_0000))
| 603 | '''simple docstring'''
# Lint as: python3
import itertools
import os
import re
__snake_case = re.compile(r"""([A-Z]+)([A-Z][a-z])""")
__snake_case = re.compile(r"""([a-z\d])([A-Z])""")
__snake_case = re.compile(r"""(?<!_)_(?!_)""")
__snake_case = re.compile(r"""(_{2,})""")
__snake_case = r"""^\w+(\.\w+)*$"""
__snake_case = r"""<>:/\|?*"""
def A_ ( SCREAMING_SNAKE_CASE_ ) ->Union[str, Any]:
lowercase_ = _uppercase_uppercase_re.sub(r"""\1_\2""" , SCREAMING_SNAKE_CASE_ )
lowercase_ = _lowercase_uppercase_re.sub(r"""\1_\2""" , SCREAMING_SNAKE_CASE_ )
return name.lower()
def A_ ( SCREAMING_SNAKE_CASE_ ) ->List[Any]:
lowercase_ = _single_underscore_re.split(SCREAMING_SNAKE_CASE_ )
lowercase_ = [_multiple_underscores_re.split(SCREAMING_SNAKE_CASE_ ) for n in name]
return "".join(n.capitalize() for n in itertools.chain.from_iterable(SCREAMING_SNAKE_CASE_ ) if n != """""" )
def A_ ( SCREAMING_SNAKE_CASE_ ) ->Any:
if os.path.basename(SCREAMING_SNAKE_CASE_ ) != name:
raise ValueError(f"""Should be a dataset name, not a path: {name}""" )
return camelcase_to_snakecase(SCREAMING_SNAKE_CASE_ )
def A_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ->Any:
if os.path.basename(SCREAMING_SNAKE_CASE_ ) != name:
raise ValueError(f"""Should be a dataset name, not a path: {name}""" )
if not re.match(_split_re , SCREAMING_SNAKE_CASE_ ):
raise ValueError(f"""Split name should match '{_split_re}'' but got '{split}'.""" )
return f"""{filename_prefix_for_name(SCREAMING_SNAKE_CASE_ )}-{split}"""
def A_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) ->Tuple:
lowercase_ = filename_prefix_for_split(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if filetype_suffix:
prefix += f""".{filetype_suffix}"""
lowercase_ = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return f"""{filepath}*"""
def A_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None ) ->Optional[Any]:
lowercase_ = filename_prefix_for_split(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowercase_ = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if shard_lengths:
lowercase_ = len(SCREAMING_SNAKE_CASE_ )
lowercase_ = [f"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(SCREAMING_SNAKE_CASE_ )]
if filetype_suffix:
lowercase_ = [filename + f""".{filetype_suffix}""" for filename in filenames]
return filenames
else:
lowercase_ = prefix
if filetype_suffix:
filename += f""".{filetype_suffix}"""
return [filename]
| 603 | 1 |
"""simple docstring"""
from jiwer import compute_measures
import datasets
_lowerCAmelCase : Optional[Any] = '''\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''
_lowerCAmelCase : Optional[int] = '''\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.
The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.
This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.
Word error rate can then be computed as:
WER = (S + D + I) / N = (S + D + I) / (S + D + C)
where
S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).
This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
'''
_lowerCAmelCase : Tuple = '''
Compute WER score of transcribed segments against references.
Args:
references: List of references for each speech input.
predictions: List of transcriptions to score.
concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.
Returns:
(float): the word error rate
Examples:
>>> predictions = ["this is the prediction", "there is an other sample"]
>>> references = ["this is the reference", "there is another one"]
>>> wer = datasets.load_metric("wer")
>>> wer_score = wer.compute(predictions=predictions, references=references)
>>> print(wer_score)
0.5
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A_ ( datasets.Metric ):
def _lowercase ( self: Union[str, Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
"predictions": datasets.Value("string" ,id="sequence" ),
"references": datasets.Value("string" ,id="sequence" ),
} ) ,codebase_urls=["https://github.com/jitsi/jiwer/"] ,reference_urls=[
"https://en.wikipedia.org/wiki/Word_error_rate",
] ,)
def _lowercase ( self: List[Any] ,__lowerCAmelCase: List[str]=None ,__lowerCAmelCase: Optional[Any]=None ,__lowerCAmelCase: Union[str, Any]=False ):
'''simple docstring'''
if concatenate_texts:
return compute_measures(__lowerCAmelCase ,__lowerCAmelCase )["wer"]
else:
_lowerCamelCase : List[Any] = 0
_lowerCamelCase : Tuple = 0
for prediction, reference in zip(__lowerCAmelCase ,__lowerCAmelCase ):
_lowerCamelCase : str = compute_measures(__lowerCAmelCase ,__lowerCAmelCase )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total | 46 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
),
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct_text_model'
UpperCamelCase__ = ['past_key_values']
UpperCamelCase__ = {
'hidden_size': 'hidden_size',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , snake_case_=5_02_44 , snake_case_=7_68 , snake_case_=64 , snake_case_=20_48 , snake_case_=12 , snake_case_=12 , snake_case_=32 , snake_case_=1_28 , snake_case_=0.1 , snake_case_=1E-6 , snake_case_=1.0 , snake_case_="gelu_new" , snake_case_=0 , snake_case_=False , snake_case_=0 , snake_case_=1 , snake_case_=False , snake_case_=True , **snake_case_ , ):
lowercase =vocab_size
lowercase =hidden_size
lowercase =d_kv
lowercase =d_ff
lowercase =num_layers
lowercase =num_heads
lowercase =relative_attention_num_buckets
lowercase =relative_attention_max_distance
lowercase =dropout_rate
lowercase =layer_norm_epsilon
lowercase =initializer_factor
lowercase =use_cache
lowercase =eos_token_id
lowercase =decoder_start_token_id
# for backwards compatibility
lowercase =dense_act_fn
super().__init__(
pad_token_id=snake_case_ , eos_token_id=snake_case_ , decoder_start_token_id=snake_case_ , tie_word_embeddings=snake_case_ , is_decoder=snake_case_ , **snake_case_ , )
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
cls._set_token_in_kwargs(snake_case_ )
lowercase , lowercase =cls.get_config_dict(snake_case_ , **snake_case_ )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
lowercase =config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case_ , **snake_case_ )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct_vision_model'
def __init__( self , snake_case_=7_68 , snake_case_=7_68 , snake_case_=20_48 , snake_case_=64 , snake_case_=12 , snake_case_=12 , snake_case_="gelu_new" , snake_case_=1E-6 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=1E-10 , snake_case_=1.0 , snake_case_=40_96 , snake_case_=32 , snake_case_=1_28 , **snake_case_ , ):
super().__init__(**snake_case_ )
lowercase =hidden_size
lowercase =patch_embed_hidden_size
lowercase =d_ff
lowercase =dropout_rate
lowercase =num_hidden_layers
lowercase =num_attention_heads
lowercase =initializer_range
lowercase =initializer_factor
lowercase =attention_dropout
lowercase =layer_norm_eps
lowercase =dense_act_fn
lowercase =seq_len
lowercase =relative_attention_num_buckets
lowercase =relative_attention_max_distance
lowercase =d_kv
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
cls._set_token_in_kwargs(snake_case_ )
lowercase , lowercase =cls.get_config_dict(snake_case_ , **snake_case_ )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
lowercase =config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case_ , **snake_case_ )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct'
UpperCamelCase__ = True
def __init__( self , snake_case_=None , snake_case_=None , snake_case_=1.0 , snake_case_=0.02 , snake_case_=False , snake_case_=False , snake_case_=True , **snake_case_ , ):
super().__init__(tie_word_embeddings=snake_case_ , is_encoder_decoder=snake_case_ , **snake_case_ )
if text_config is None:
lowercase ={}
logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.''' )
if vision_config is None:
lowercase ={}
logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.''' )
lowercase =PixaStructTextConfig(**snake_case_ )
lowercase =PixaStructVisionConfig(**snake_case_ )
lowercase =self.text_config.decoder_start_token_id
lowercase =self.text_config.pad_token_id
lowercase =self.text_config.eos_token_id
lowercase =initializer_factor
lowercase =initializer_range
lowercase =self.initializer_range
lowercase =self.initializer_range
lowercase =is_vqa
@classmethod
def _A( cls , snake_case_ , snake_case_ , **snake_case_ ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **snake_case_ )
def _A( self ):
lowercase =copy.deepcopy(self.__dict__ )
lowercase =self.text_config.to_dict()
lowercase =self.vision_config.to_dict()
lowercase =self.__class__.model_type
return output
| 72 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}
class a ( __magic_name__ ):
_snake_case = '''switch_transformers'''
_snake_case = ['''past_key_values''']
_snake_case = {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self : Optional[Any], SCREAMING_SNAKE_CASE_ : Tuple=3_21_28, SCREAMING_SNAKE_CASE_ : List[Any]=7_68, SCREAMING_SNAKE_CASE_ : Dict=64, SCREAMING_SNAKE_CASE_ : int=20_48, SCREAMING_SNAKE_CASE_ : int=64, SCREAMING_SNAKE_CASE_ : str=12, SCREAMING_SNAKE_CASE_ : str=3, SCREAMING_SNAKE_CASE_ : str=12, SCREAMING_SNAKE_CASE_ : List[Any]=3, SCREAMING_SNAKE_CASE_ : str=12, SCREAMING_SNAKE_CASE_ : Dict=8, SCREAMING_SNAKE_CASE_ : Any=False, SCREAMING_SNAKE_CASE_ : str=0.01, SCREAMING_SNAKE_CASE_ : Tuple="float32", SCREAMING_SNAKE_CASE_ : Optional[Any]=False, SCREAMING_SNAKE_CASE_ : str=32, SCREAMING_SNAKE_CASE_ : str=1_28, SCREAMING_SNAKE_CASE_ : int=0.1, SCREAMING_SNAKE_CASE_ : List[str]=1e-6, SCREAMING_SNAKE_CASE_ : Tuple=0.001, SCREAMING_SNAKE_CASE_ : Optional[int]=0.001, SCREAMING_SNAKE_CASE_ : List[Any]=1.0, SCREAMING_SNAKE_CASE_ : Optional[Any]="relu", SCREAMING_SNAKE_CASE_ : List[str]=True, SCREAMING_SNAKE_CASE_ : Tuple=False, SCREAMING_SNAKE_CASE_ : str=True, SCREAMING_SNAKE_CASE_ : Union[str, Any]=0, SCREAMING_SNAKE_CASE_ : int=1, **SCREAMING_SNAKE_CASE_ : Dict, ):
snake_case : int = vocab_size
snake_case : Dict = d_model
snake_case : Tuple = d_kv
snake_case : Optional[int] = d_ff
snake_case : Tuple = num_sparse_encoder_layers
snake_case : List[Any] = num_layers
snake_case : str = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
snake_case : Union[str, Any] = num_sparse_decoder_layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_encoder_layers > 0:
snake_case : List[Any] = self.num_layers // self.num_sparse_encoder_layers
else:
snake_case : List[str] = self.num_layers # HACK: this will create 0 sparse layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_decoder_layers > 0:
snake_case : Optional[Any] = self.num_decoder_layers // self.num_sparse_decoder_layers
else:
snake_case : Tuple = self.num_decoder_layers # HACK: this will create 0 sparse layers
snake_case : Optional[Any] = num_heads
snake_case : int = num_experts
snake_case : Dict = expert_capacity
snake_case : Optional[int] = router_bias
snake_case : Any = router_jitter_noise
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(F"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}""" )
snake_case : int = router_dtype
snake_case : Dict = router_ignore_padding_tokens
snake_case : Optional[Any] = relative_attention_num_buckets
snake_case : Dict = relative_attention_max_distance
snake_case : Tuple = dropout_rate
snake_case : Union[str, Any] = layer_norm_epsilon
snake_case : Optional[int] = initializer_factor
snake_case : Optional[Any] = feed_forward_proj
snake_case : Dict = use_cache
snake_case : List[Any] = add_router_probs
snake_case : List[str] = router_z_loss_coef
snake_case : List[str] = router_aux_loss_coef
snake_case : Dict = self.feed_forward_proj.split('''-''' )
snake_case : int = act_info[-1]
snake_case : Tuple = act_info[0] == '''gated'''
if len(SCREAMING_SNAKE_CASE_ ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE_ ) > 2:
raise ValueError(
F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
'''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
'''\'gated-gelu\' or \'relu\'''' )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
snake_case : List[Any] = '''gelu_new'''
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_, eos_token_id=SCREAMING_SNAKE_CASE_, is_encoder_decoder=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_, )
| 555 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase = logging.getLogger()
def A ( ):
snake_case : Dict = argparse.ArgumentParser()
parser.add_argument('''-f''' )
snake_case : Any = parser.parse_args()
return args.f
def A ( A_ : str ):
snake_case : str = {}
snake_case : Union[str, Any] = os.path.join(A_ , '''all_results.json''' )
if os.path.exists(A_ ):
with open(A_ , '''r''' ) as f:
snake_case : List[str] = json.load(A_ )
else:
raise ValueError(F"""can't find {path}""" )
return results
def A ( ):
snake_case : Any = torch.cuda.is_available() and torch_device == '''cuda'''
return is_using_cuda and is_apex_available()
UpperCAmelCase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class a ( __magic_name__ ):
@classmethod
def __snake_case ( cls : Tuple ):
# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
snake_case : int = tempfile.mkdtemp()
snake_case : Optional[Any] = os.path.join(cls.tmpdir, '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
snake_case : Tuple = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def __snake_case ( cls : Optional[int] ):
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ, {'''WANDB_MODE''': '''offline'''} )
def __snake_case ( self : Any ):
snake_case : Any = self.get_auto_remove_tmp_dir()
snake_case : Optional[Any] = F"""
{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--seed=42
--checkpointing_steps epoch
--with_tracking
""".split()
if is_cuda_and_apex_available():
testargs.append('''--fp16''' )
run_command(self._launch_args + testargs )
snake_case : int = get_results(SCREAMING_SNAKE_CASE_ )
self.assertGreaterEqual(result['''eval_accuracy'''], 0.75 )
self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_, '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_, '''glue_no_trainer''' ) ) )
@mock.patch.dict(os.environ, {'''WANDB_MODE''': '''offline'''} )
def __snake_case ( self : Tuple ):
snake_case : int = self.get_auto_remove_tmp_dir()
snake_case : str = F"""
{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--checkpointing_steps epoch
--with_tracking
""".split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
snake_case : List[str] = get_results(SCREAMING_SNAKE_CASE_ )
self.assertLess(result['''perplexity'''], 1_00 )
self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_, '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_, '''clm_no_trainer''' ) ) )
@mock.patch.dict(os.environ, {'''WANDB_MODE''': '''offline'''} )
def __snake_case ( self : List[Any] ):
snake_case : Union[str, Any] = self.get_auto_remove_tmp_dir()
snake_case : int = F"""
{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--num_train_epochs=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
snake_case : List[Any] = get_results(SCREAMING_SNAKE_CASE_ )
self.assertLess(result['''perplexity'''], 42 )
self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_, '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_, '''mlm_no_trainer''' ) ) )
@mock.patch.dict(os.environ, {'''WANDB_MODE''': '''offline'''} )
def __snake_case ( self : Dict ):
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
snake_case : Any = 7 if get_gpu_count() > 1 else 2
snake_case : Optional[int] = self.get_auto_remove_tmp_dir()
snake_case : str = F"""
{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
snake_case : Optional[int] = get_results(SCREAMING_SNAKE_CASE_ )
self.assertGreaterEqual(result['''eval_accuracy'''], 0.75 )
self.assertLess(result['''train_loss'''], 0.5 )
self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_, '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_, '''ner_no_trainer''' ) ) )
@unittest.skip(reason='''Fix me @muellerzr''' )
@mock.patch.dict(os.environ, {'''WANDB_MODE''': '''offline'''} )
def __snake_case ( self : Tuple ):
snake_case : Tuple = self.get_auto_remove_tmp_dir()
snake_case : Optional[int] = F"""
{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--seed=42
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
snake_case : str = get_results(SCREAMING_SNAKE_CASE_ )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result['''eval_f1'''], 28 )
self.assertGreaterEqual(result['''eval_exact'''], 28 )
self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_, '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_, '''qa_no_trainer''' ) ) )
@mock.patch.dict(os.environ, {'''WANDB_MODE''': '''offline'''} )
def __snake_case ( self : Optional[int] ):
snake_case : Any = self.get_auto_remove_tmp_dir()
snake_case : List[Any] = F"""
{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--max_train_steps=20
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--with_tracking
""".split()
run_command(self._launch_args + testargs )
snake_case : Union[str, Any] = get_results(SCREAMING_SNAKE_CASE_ )
self.assertGreaterEqual(result['''eval_accuracy'''], 0.8 )
self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_, '''swag_no_trainer''' ) ) )
@slow
@mock.patch.dict(os.environ, {'''WANDB_MODE''': '''offline'''} )
def __snake_case ( self : str ):
snake_case : str = self.get_auto_remove_tmp_dir()
snake_case : Any = F"""
{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
snake_case : str = get_results(SCREAMING_SNAKE_CASE_ )
self.assertGreaterEqual(result['''eval_rouge1'''], 10 )
self.assertGreaterEqual(result['''eval_rouge2'''], 2 )
self.assertGreaterEqual(result['''eval_rougeL'''], 7 )
self.assertGreaterEqual(result['''eval_rougeLsum'''], 7 )
self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_, '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_, '''summarization_no_trainer''' ) ) )
@slow
@mock.patch.dict(os.environ, {'''WANDB_MODE''': '''offline'''} )
def __snake_case ( self : int ):
snake_case : Any = self.get_auto_remove_tmp_dir()
snake_case : Optional[int] = F"""
{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--num_beams=6
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--source_lang en_XX
--target_lang ro_RO
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
snake_case : Optional[int] = get_results(SCREAMING_SNAKE_CASE_ )
self.assertGreaterEqual(result['''eval_bleu'''], 30 )
self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_, '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_, '''translation_no_trainer''' ) ) )
@slow
def __snake_case ( self : Union[str, Any] ):
snake_case : Any = logging.StreamHandler(sys.stdout )
logger.addHandler(SCREAMING_SNAKE_CASE_ )
snake_case : Union[str, Any] = self.get_auto_remove_tmp_dir()
snake_case : Dict = F"""
{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
--dataset_name huggingface/semantic-segmentation-test-sample
--output_dir {tmp_dir}
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
""".split()
run_command(self._launch_args + testargs )
snake_case : List[Any] = get_results(SCREAMING_SNAKE_CASE_ )
self.assertGreaterEqual(result['''eval_overall_accuracy'''], 0.10 )
@mock.patch.dict(os.environ, {'''WANDB_MODE''': '''offline'''} )
def __snake_case ( self : Union[str, Any] ):
snake_case : str = self.get_auto_remove_tmp_dir()
snake_case : Optional[Any] = F"""
{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
--model_name_or_path google/vit-base-patch16-224-in21k
--dataset_name hf-internal-testing/cats_vs_dogs_sample
--learning_rate 1e-4
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--max_train_steps 2
--train_val_split 0.1
--seed 42
--output_dir {tmp_dir}
--with_tracking
--checkpointing_steps 1
""".split()
if is_cuda_and_apex_available():
testargs.append('''--fp16''' )
run_command(self._launch_args + testargs )
snake_case : Tuple = get_results(SCREAMING_SNAKE_CASE_ )
# The base model scores a 25%
self.assertGreaterEqual(result['''eval_accuracy'''], 0.6 )
self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_, '''step_1''' ) ) )
self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_, '''image_classification_no_trainer''' ) ) )
| 555 | 1 |
from typing import Dict
from .base import GenericTensor, Pipeline
class UpperCAmelCase__ ( snake_case__ ):
def snake_case_ ( self , A__=None , A__=None , A__=None , **A__ ):
"""simple docstring"""
if tokenize_kwargs is None:
UpperCAmelCase_: Tuple = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
"truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)" )
UpperCAmelCase_: int = truncation
UpperCAmelCase_: Optional[Any] = tokenize_kwargs
UpperCAmelCase_: Optional[int] = {}
if return_tensors is not None:
UpperCAmelCase_: Optional[int] = return_tensors
return preprocess_params, {}, postprocess_params
def snake_case_ ( self , A__ , **A__ ):
"""simple docstring"""
UpperCAmelCase_: Union[str, Any] = self.framework
UpperCAmelCase_: Tuple = self.tokenizer(_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
return model_inputs
def snake_case_ ( self , A__ ):
"""simple docstring"""
UpperCAmelCase_: str = self.model(**_SCREAMING_SNAKE_CASE )
return model_outputs
def snake_case_ ( self , A__ , A__=False ):
"""simple docstring"""
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__( self , *A__ , **A__ ):
"""simple docstring"""
return super().__call__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) | 137 |
'''simple docstring'''
import argparse
import copy
def lowercase__ ( __UpperCamelCase )-> Union[str, Any]:
UpperCamelCase = {}
with open(__UpperCamelCase ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
UpperCamelCase = []
_list.append([line.split()[1], line.split()[2]] )
UpperCamelCase = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
UpperCamelCase = []
_list.append([line.split()[0], line.split()[2]] )
UpperCamelCase = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Union[str, Any]:
with open(__UpperCamelCase ) as f:
UpperCamelCase = f.read(1 )
UpperCamelCase = start_node
UpperCamelCase = []
UpperCamelCase = start_node
UpperCamelCase = 0
while visiting not in first_solution:
UpperCamelCase = 10000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(__UpperCamelCase ) and k[0] not in first_solution:
UpperCamelCase = k[1]
UpperCamelCase = k[0]
first_solution.append(__UpperCamelCase )
UpperCamelCase = distance_of_first_solution + int(__UpperCamelCase )
UpperCamelCase = best_node
first_solution.append(__UpperCamelCase )
UpperCamelCase = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
UpperCamelCase = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10000
)
return first_solution, distance_of_first_solution
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> List[Any]:
UpperCamelCase = []
for n in solution[1:-1]:
UpperCamelCase = solution.index(__UpperCamelCase )
for kn in solution[1:-1]:
UpperCamelCase = solution.index(__UpperCamelCase )
if n == kn:
continue
UpperCamelCase = copy.deepcopy(__UpperCamelCase )
UpperCamelCase = kn
UpperCamelCase = n
UpperCamelCase = 0
for k in _tmp[:-1]:
UpperCamelCase = _tmp[_tmp.index(__UpperCamelCase ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
UpperCamelCase = distance + int(i[1] )
_tmp.append(__UpperCamelCase )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
UpperCamelCase = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda __UpperCamelCase : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Union[str, Any]:
UpperCamelCase = 1
UpperCamelCase = first_solution
UpperCamelCase = []
UpperCamelCase = distance_of_first_solution
UpperCamelCase = solution
while count <= iters:
UpperCamelCase = find_neighborhood(__UpperCamelCase , __UpperCamelCase )
UpperCamelCase = 0
UpperCamelCase = neighborhood[index_of_best_solution]
UpperCamelCase = len(__UpperCamelCase ) - 1
UpperCamelCase = False
while not found:
UpperCamelCase = 0
while i < len(__UpperCamelCase ):
if best_solution[i] != solution[i]:
UpperCamelCase = best_solution[i]
UpperCamelCase = solution[i]
break
UpperCamelCase = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
UpperCamelCase = True
UpperCamelCase = best_solution[:-1]
UpperCamelCase = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
UpperCamelCase = cost
UpperCamelCase = solution
else:
UpperCamelCase = index_of_best_solution + 1
UpperCamelCase = neighborhood[index_of_best_solution]
if len(__UpperCamelCase ) >= size:
tabu_list.pop(0 )
UpperCamelCase = count + 1
return best_solution_ever, best_cost
def lowercase__ ( __UpperCamelCase=None )-> Tuple:
UpperCamelCase = generate_neighbours(args.File )
UpperCamelCase ,UpperCamelCase = generate_first_solution(
args.File , __UpperCamelCase )
UpperCamelCase ,UpperCamelCase = tabu_search(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , args.Iterations , args.Size , )
print(F"Best solution: {best_sol}, with total distance: {best_cost}." )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser(description='Tabu Search')
parser.add_argument(
'-f',
'--File',
type=str,
help='Path to the file containing the data',
required=True,
)
parser.add_argument(
'-i',
'--Iterations',
type=int,
help='How many iterations the algorithm should perform',
required=True,
)
parser.add_argument(
'-s', '--Size', type=int, help='Size of the tabu list', required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 301 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase = {
'''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
'''SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Swinv2ForImageClassification''',
'''Swinv2ForMaskedImageModeling''',
'''Swinv2Model''',
'''Swinv2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
__lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 703 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCamelCase = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AltCLIPConfig''',
'''AltCLIPTextConfig''',
'''AltCLIPVisionConfig''',
],
'''processing_altclip''': ['''AltCLIPProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
'''ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''AltCLIPPreTrainedModel''',
'''AltCLIPModel''',
'''AltCLIPTextModel''',
'''AltCLIPVisionModel''',
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
__lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 478 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
lowerCAmelCase : Tuple = logging.get_logger(__name__)
lowerCAmelCase : int = {
'openai/imagegpt-small': '',
'openai/imagegpt-medium': '',
'openai/imagegpt-large': '',
}
class SCREAMING_SNAKE_CASE__ ( snake_case_):
lowerCAmelCase_ = """imagegpt"""
lowerCAmelCase_ = ["""past_key_values"""]
lowerCAmelCase_ = {
"""hidden_size""": """n_embd""",
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , A_=512 + 1 , A_=32 * 32 , A_=512 , A_=24 , A_=8 , A_=None , A_="quick_gelu" , A_=0.1 , A_=0.1 , A_=0.1 , A_=1e-5 , A_=0.02 , A_=True , A_=True , A_=False , A_=False , A_=False , **A_ , )-> str:
'''simple docstring'''
UpperCamelCase = vocab_size
UpperCamelCase = n_positions
UpperCamelCase = n_embd
UpperCamelCase = n_layer
UpperCamelCase = n_head
UpperCamelCase = n_inner
UpperCamelCase = activation_function
UpperCamelCase = resid_pdrop
UpperCamelCase = embd_pdrop
UpperCamelCase = attn_pdrop
UpperCamelCase = layer_norm_epsilon
UpperCamelCase = initializer_range
UpperCamelCase = scale_attn_weights
UpperCamelCase = use_cache
UpperCamelCase = scale_attn_by_inverse_layer_idx
UpperCamelCase = reorder_and_upcast_attn
UpperCamelCase = tie_word_embeddings
super().__init__(tie_word_embeddings=A_ , **A_ )
class SCREAMING_SNAKE_CASE__ ( snake_case_):
@property
def UpperCAmelCase_ ( self )-> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('input_ids', {0: 'batch', 1: 'sequence'}),
] )
def UpperCAmelCase_ ( self , A_ , A_ = 1 , A_ = -1 , A_ = False , A_ = None , A_ = 3 , A_ = 32 , A_ = 32 , )-> Mapping[str, Any]:
'''simple docstring'''
UpperCamelCase = self._generate_dummy_images(A_ , A_ , A_ , A_ )
UpperCamelCase = dict(preprocessor(images=A_ , return_tensors=A_ ) )
return inputs
| 3 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def UpperCamelCase ( __lowerCamelCase : int = 8 ):
snake_case : int = ascii_letters + digits + punctuation
return "".join(secrets.choice(__lowerCamelCase ) for _ in range(__lowerCamelCase ) )
def UpperCamelCase ( __lowerCamelCase : str , __lowerCamelCase : int ):
# Password Generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i -= len(__lowerCamelCase )
snake_case : Any = i // 3
snake_case : Optional[int] = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
snake_case : Tuple = (
chars_incl
+ random(__lowerCamelCase , quotient + remainder )
+ random(__lowerCamelCase , __lowerCamelCase )
+ random(__lowerCamelCase , __lowerCamelCase )
)
snake_case : Optional[Any] = list(__lowerCamelCase )
shuffle(__lowerCamelCase )
return "".join(__lowerCamelCase )
# random is a generalised function for letters, characters and numbers
def UpperCamelCase ( __lowerCamelCase : str , __lowerCamelCase : int ):
return "".join(secrets.choice(__lowerCamelCase ) for _ in range(__lowerCamelCase ) )
def UpperCamelCase ( __lowerCamelCase : List[str] , __lowerCamelCase : str ):
pass # Put your code here...
def UpperCamelCase ( __lowerCamelCase : Optional[int] , __lowerCamelCase : List[str] ):
pass # Put your code here...
def UpperCamelCase ( __lowerCamelCase : Any , __lowerCamelCase : List[Any] ):
pass # Put your code here...
def UpperCamelCase ( __lowerCamelCase : str , __lowerCamelCase : int = 8 ):
if len(__lowerCamelCase ) < min_length:
# Your Password must be at least 8 characters long
return False
snake_case : Dict = any(char in ascii_uppercase for char in password )
snake_case : Optional[int] = any(char in ascii_lowercase for char in password )
snake_case : str = any(char in digits for char in password )
snake_case : str = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def UpperCamelCase ( ):
snake_case : int = int(input("Please indicate the max length of your password: " ).strip() )
snake_case : Union[str, Any] = input(
"Please indicate the characters that must be in your password: " ).strip()
print("Password generated:" , password_generator(__lowerCamelCase ) )
print(
"Alternative Password generated:" , alternative_password_generator(__lowerCamelCase , __lowerCamelCase ) , )
print("[If you are thinking of using this passsword, You better save it.]" )
if __name__ == "__main__":
main()
| 204 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class __magic_name__ ( unittest.TestCase):
def __init__( self : Any ,__SCREAMING_SNAKE_CASE : str ,__SCREAMING_SNAKE_CASE : str=7 ,__SCREAMING_SNAKE_CASE : Dict=3 ,__SCREAMING_SNAKE_CASE : str=1_8 ,__SCREAMING_SNAKE_CASE : int=3_0 ,__SCREAMING_SNAKE_CASE : List[str]=4_0_0 ,__SCREAMING_SNAKE_CASE : int=True ,__SCREAMING_SNAKE_CASE : int=None ,__SCREAMING_SNAKE_CASE : Union[str, Any]=True ,__SCREAMING_SNAKE_CASE : Dict=None ,__SCREAMING_SNAKE_CASE : str=True ,__SCREAMING_SNAKE_CASE : Optional[int]=[0.4814_5466, 0.457_8275, 0.4082_1073] ,__SCREAMING_SNAKE_CASE : int=[0.2686_2954, 0.2613_0258, 0.2757_7711] ,__SCREAMING_SNAKE_CASE : Union[str, Any]=True ,):
UpperCAmelCase = size if size is not None else {"height": 2_2_4, "width": 2_2_4}
UpperCAmelCase = crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = num_channels
UpperCAmelCase = image_size
UpperCAmelCase = min_resolution
UpperCAmelCase = max_resolution
UpperCAmelCase = do_resize
UpperCAmelCase = size
UpperCAmelCase = do_center_crop
UpperCAmelCase = crop_size
UpperCAmelCase = do_normalize
UpperCAmelCase = image_mean
UpperCAmelCase = image_std
UpperCAmelCase = do_convert_rgb
def _UpperCAmelCase ( self : List[Any] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def _UpperCAmelCase ( self : str ,__SCREAMING_SNAKE_CASE : List[str]=False ,__SCREAMING_SNAKE_CASE : List[str]=False ,__SCREAMING_SNAKE_CASE : Optional[Any]=False ):
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
UpperCAmelCase = []
for i in range(self.batch_size ):
image_inputs.append(
np.random.randint(
2_5_5 ,size=(self.num_channels, self.max_resolution, self.max_resolution) ,dtype=np.uinta ) )
else:
UpperCAmelCase = []
for i in range(self.batch_size ):
UpperCAmelCase , UpperCAmelCase = np.random.choice(np.arange(self.min_resolution ,self.max_resolution ) ,2 )
image_inputs.append(np.random.randint(2_5_5 ,size=(self.num_channels, width, height) ,dtype=np.uinta ) )
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
UpperCAmelCase = [Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE ,0 ,-1 ) ) for x in image_inputs]
if torchify:
UpperCAmelCase = [torch.from_numpy(__SCREAMING_SNAKE_CASE ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class __magic_name__ ( _a , unittest.TestCase):
_UpperCAmelCase : Dict = ChineseCLIPImageProcessor if is_vision_available() else None
def _UpperCAmelCase ( self : Any ):
UpperCAmelCase = ChineseCLIPImageProcessingTester(self ,do_center_crop=__SCREAMING_SNAKE_CASE )
@property
def _UpperCAmelCase ( self : Any ):
return self.image_processor_tester.prepare_image_processor_dict()
def _UpperCAmelCase ( self : Tuple ):
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,"do_resize" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,"size" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,"do_center_crop" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,"center_crop" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,"do_normalize" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,"image_mean" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,"image_std" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,"do_convert_rgb" ) )
def _UpperCAmelCase ( self : str ):
UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{"height": 2_2_4, "width": 2_2_4} )
self.assertEqual(image_processor.crop_size ,{"height": 1_8, "width": 1_8} )
UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict ,size=4_2 ,crop_size=8_4 )
self.assertEqual(image_processor.size ,{"shortest_edge": 4_2} )
self.assertEqual(image_processor.crop_size ,{"height": 8_4, "width": 8_4} )
def _UpperCAmelCase ( self : Union[str, Any] ):
pass
def _UpperCAmelCase ( self : Union[str, Any] ):
# Initialize image_processing
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase = self.image_processor_tester.prepare_inputs(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
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) ,)
# Test batched
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,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) ,)
def _UpperCAmelCase ( self : Optional[Any] ):
# Initialize image_processing
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase = self.image_processor_tester.prepare_inputs(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
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) ,)
# Test batched
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,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) ,)
def _UpperCAmelCase ( self : Any ):
# Initialize image_processing
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase = self.image_processor_tester.prepare_inputs(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
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) ,)
# Test batched
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,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) ,)
@require_torch
@require_vision
class __magic_name__ ( _a , unittest.TestCase):
_UpperCAmelCase : Tuple = ChineseCLIPImageProcessor if is_vision_available() else None
def _UpperCAmelCase ( self : List[Any] ):
UpperCAmelCase = ChineseCLIPImageProcessingTester(self ,num_channels=4 ,do_center_crop=__SCREAMING_SNAKE_CASE )
UpperCAmelCase = 3
@property
def _UpperCAmelCase ( self : List[Any] ):
return self.image_processor_tester.prepare_image_processor_dict()
def _UpperCAmelCase ( self : Any ):
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,"do_resize" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,"size" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,"do_center_crop" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,"center_crop" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,"do_normalize" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,"image_mean" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,"image_std" ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,"do_convert_rgb" ) )
def _UpperCAmelCase ( self : Union[str, Any] ):
pass
def _UpperCAmelCase ( self : Optional[Any] ):
# Initialize image_processing
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase = self.image_processor_tester.prepare_inputs(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
self.assertEqual(
encoded_images.shape ,(
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) ,)
# Test batched
UpperCAmelCase = image_processing(__SCREAMING_SNAKE_CASE ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) ,)
| 405 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __magic_name__ ( _a):
@require_torch
def _UpperCAmelCase ( self : Tuple ):
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
UpperCAmelCase = "\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n "
UpperCAmelCase = "\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n "
UpperCAmelCase = "\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError(\"Offline mode is enabled, we shouldn't access internet\")\nsocket.socket = offline_socket\n "
# Force fetching the files so that we can use the cache
UpperCAmelCase = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(__SCREAMING_SNAKE_CASE )
BertModel.from_pretrained(__SCREAMING_SNAKE_CASE )
BertTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
pipeline(task="fill-mask" ,model=__SCREAMING_SNAKE_CASE )
# baseline - just load from_pretrained with normal network
UpperCAmelCase = [sys.executable, "-c", "\n".join([load, run, mock] )]
# should succeed
UpperCAmelCase = self.get_env()
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
UpperCAmelCase = "1"
UpperCAmelCase = subprocess.run(__SCREAMING_SNAKE_CASE ,env=__SCREAMING_SNAKE_CASE ,check=__SCREAMING_SNAKE_CASE ,capture_output=__SCREAMING_SNAKE_CASE )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn("success" ,result.stdout.decode() )
@require_torch
def _UpperCAmelCase ( self : Optional[int] ):
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
UpperCAmelCase = "\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n "
UpperCAmelCase = "\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n "
UpperCAmelCase = "\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Faking flaky internet\")\nsocket.socket = offline_socket\n "
# Force fetching the files so that we can use the cache
UpperCAmelCase = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(__SCREAMING_SNAKE_CASE )
BertModel.from_pretrained(__SCREAMING_SNAKE_CASE )
BertTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
pipeline(task="fill-mask" ,model=__SCREAMING_SNAKE_CASE )
# baseline - just load from_pretrained with normal network
UpperCAmelCase = [sys.executable, "-c", "\n".join([load, run, mock] )]
# should succeed
UpperCAmelCase = self.get_env()
UpperCAmelCase = subprocess.run(__SCREAMING_SNAKE_CASE ,env=__SCREAMING_SNAKE_CASE ,check=__SCREAMING_SNAKE_CASE ,capture_output=__SCREAMING_SNAKE_CASE )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn("success" ,result.stdout.decode() )
@require_torch
def _UpperCAmelCase ( self : str ):
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
UpperCAmelCase = "\nfrom transformers import BertConfig, BertModel, BertTokenizer\n "
UpperCAmelCase = "\nmname = \"hf-internal-testing/tiny-random-bert-sharded\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint(\"success\")\n "
UpperCAmelCase = "\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n "
# baseline - just load from_pretrained with normal network
UpperCAmelCase = [sys.executable, "-c", "\n".join([load, run] )]
# should succeed
UpperCAmelCase = self.get_env()
UpperCAmelCase = subprocess.run(__SCREAMING_SNAKE_CASE ,env=__SCREAMING_SNAKE_CASE ,check=__SCREAMING_SNAKE_CASE ,capture_output=__SCREAMING_SNAKE_CASE )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn("success" ,result.stdout.decode() )
# next emulate no network
UpperCAmelCase = [sys.executable, "-c", "\n".join([load, mock, run] )]
# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
# env["TRANSFORMERS_OFFLINE"] = "0"
# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
# self.assertEqual(result.returncode, 1, result.stderr)
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
UpperCAmelCase = "1"
UpperCAmelCase = subprocess.run(__SCREAMING_SNAKE_CASE ,env=__SCREAMING_SNAKE_CASE ,check=__SCREAMING_SNAKE_CASE ,capture_output=__SCREAMING_SNAKE_CASE )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn("success" ,result.stdout.decode() )
@require_torch
def _UpperCAmelCase ( self : Dict ):
UpperCAmelCase = "\nfrom transformers import pipeline\n "
UpperCAmelCase = "\nmname = \"hf-internal-testing/tiny-random-bert\"\npipe = pipeline(model=mname)\n "
UpperCAmelCase = "\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n "
UpperCAmelCase = self.get_env()
UpperCAmelCase = "1"
UpperCAmelCase = [sys.executable, "-c", "\n".join([load, mock, run] )]
UpperCAmelCase = subprocess.run(__SCREAMING_SNAKE_CASE ,env=__SCREAMING_SNAKE_CASE ,check=__SCREAMING_SNAKE_CASE ,capture_output=__SCREAMING_SNAKE_CASE )
self.assertEqual(result.returncode ,1 ,result.stderr )
self.assertIn(
"You cannot infer task automatically within `pipeline` when using offline mode" ,result.stderr.decode().replace("\n" ,"" ) ,)
@require_torch
def _UpperCAmelCase ( self : Any ):
UpperCAmelCase = "\nfrom transformers import AutoModel\n "
UpperCAmelCase = "\nmname = \"hf-internal-testing/test_dynamic_model\"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint(\"success\")\n "
# baseline - just load from_pretrained with normal network
UpperCAmelCase = [sys.executable, "-c", "\n".join([load, run] )]
# should succeed
UpperCAmelCase = self.get_env()
UpperCAmelCase = subprocess.run(__SCREAMING_SNAKE_CASE ,env=__SCREAMING_SNAKE_CASE ,check=__SCREAMING_SNAKE_CASE ,capture_output=__SCREAMING_SNAKE_CASE )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn("success" ,result.stdout.decode() )
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
UpperCAmelCase = "1"
UpperCAmelCase = subprocess.run(__SCREAMING_SNAKE_CASE ,env=__SCREAMING_SNAKE_CASE ,check=__SCREAMING_SNAKE_CASE ,capture_output=__SCREAMING_SNAKE_CASE )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn("success" ,result.stdout.decode() )
| 405 | 1 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = len(lowerCAmelCase__ )
for i in range(n - 1 ):
for j in range(i + 1 , lowerCAmelCase__ ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def a__ ( lowerCAmelCase__ ):
if len(lowerCAmelCase__ ) <= 1:
return arr, 0
UpperCAmelCase_ = len(lowerCAmelCase__ ) // 2
UpperCAmelCase_ = arr[0:mid]
UpperCAmelCase_ = arr[mid:]
UpperCAmelCase_ , UpperCAmelCase_ = count_inversions_recursive(lowerCAmelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = count_inversions_recursive(lowerCAmelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = _count_cross_inversions(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase_ = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = []
UpperCAmelCase_ = UpperCAmelCase_ = UpperCAmelCase_ = 0
while i < len(lowerCAmelCase__ ) and j < len(lowerCAmelCase__ ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(lowerCAmelCase__ ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(lowerCAmelCase__ ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def a__ ( ):
UpperCAmelCase_ = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
UpperCAmelCase_ = count_inversions_bf(lowerCAmelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = count_inversions_recursive(lowerCAmelCase__ )
assert num_inversions_bf == num_inversions_recursive == 8
print("number of inversions = " , lowerCAmelCase__ )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
UpperCAmelCase_ = count_inversions_bf(lowerCAmelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = count_inversions_recursive(lowerCAmelCase__ )
assert num_inversions_bf == num_inversions_recursive == 0
print("number of inversions = " , lowerCAmelCase__ )
# an empty list should also have zero inversions
UpperCAmelCase_ = []
UpperCAmelCase_ = count_inversions_bf(lowerCAmelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = count_inversions_recursive(lowerCAmelCase__ )
assert num_inversions_bf == num_inversions_recursive == 0
print("number of inversions = " , lowerCAmelCase__ )
if __name__ == "__main__":
main()
| 82 |
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = hex_num.strip()
if not hex_num:
raise ValueError('''No value was passed to the function''' )
__lowercase = hex_num[0] == '''-'''
if is_negative:
__lowercase = hex_num[1:]
try:
__lowercase = int(_UpperCamelCase , 16 )
except ValueError:
raise ValueError('''Invalid value was passed to the function''' )
__lowercase = ''''''
while int_num > 0:
__lowercase = str(int_num % 2 ) + bin_str
int_num >>= 1
return int(('''-''' + bin_str) if is_negative else bin_str )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 639 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
UpperCamelCase : str = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
warnings.warn(
'The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use SegformerImageProcessor instead.' , __UpperCAmelCase , )
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
| 293 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : int = {
"EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
lowercase = "gptj"
lowercase = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self , __UpperCAmelCase=5_0400 , __UpperCAmelCase=2048 , __UpperCAmelCase=4096 , __UpperCAmelCase=28 , __UpperCAmelCase=16 , __UpperCAmelCase=64 , __UpperCAmelCase=None , __UpperCAmelCase="gelu_new" , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=1E-5 , __UpperCAmelCase=0.0_2 , __UpperCAmelCase=True , __UpperCAmelCase=5_0256 , __UpperCAmelCase=5_0256 , __UpperCAmelCase=False , **__UpperCAmelCase , ):
'''simple docstring'''
__UpperCamelCase = vocab_size
__UpperCamelCase = n_positions
__UpperCamelCase = n_embd
__UpperCamelCase = n_layer
__UpperCamelCase = n_head
__UpperCamelCase = n_inner
__UpperCamelCase = rotary_dim
__UpperCamelCase = activation_function
__UpperCamelCase = resid_pdrop
__UpperCamelCase = embd_pdrop
__UpperCamelCase = attn_pdrop
__UpperCamelCase = layer_norm_epsilon
__UpperCamelCase = initializer_range
__UpperCamelCase = use_cache
__UpperCamelCase = bos_token_id
__UpperCamelCase = eos_token_id
super().__init__(
bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , tie_word_embeddings=__UpperCAmelCase , **__UpperCAmelCase )
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase = "default" , __UpperCAmelCase = None , __UpperCAmelCase = False , ):
'''simple docstring'''
super().__init__(__UpperCAmelCase , task=__UpperCAmelCase , patching_specs=__UpperCAmelCase , use_past=__UpperCAmelCase )
if not getattr(self._config , 'pad_token_id' , __UpperCAmelCase ):
# TODO: how to do that better?
__UpperCamelCase = 0
@property
def UpperCAmelCase ( self ):
'''simple docstring'''
__UpperCamelCase = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
if self.use_past:
self.fill_with_past_key_values_(__UpperCAmelCase , direction='inputs' )
__UpperCamelCase = {0: 'batch', 1: 'past_sequence + sequence'}
else:
__UpperCamelCase = {0: 'batch', 1: 'sequence'}
return common_inputs
@property
def UpperCAmelCase ( self ):
'''simple docstring'''
return self._config.n_layer
@property
def UpperCAmelCase ( self ):
'''simple docstring'''
return self._config.n_head
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = -1 , __UpperCAmelCase = -1 , __UpperCAmelCase = False , __UpperCAmelCase = None , ):
'''simple docstring'''
__UpperCamelCase = super(__UpperCAmelCase , self ).generate_dummy_inputs(
__UpperCAmelCase , batch_size=__UpperCAmelCase , seq_length=__UpperCAmelCase , is_pair=__UpperCAmelCase , framework=__UpperCAmelCase )
# We need to order the input in the way they appears in the forward()
__UpperCamelCase = OrderedDict({'input_ids': common_inputs['input_ids']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
__UpperCamelCase , __UpperCamelCase = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
__UpperCamelCase = seqlen + 2
__UpperCamelCase = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
__UpperCamelCase = [
(torch.zeros(__UpperCAmelCase ), torch.zeros(__UpperCAmelCase )) for _ in range(self.num_layers )
]
__UpperCamelCase = common_inputs['attention_mask']
if self.use_past:
__UpperCamelCase = ordered_inputs['attention_mask'].dtype
__UpperCamelCase = torch.cat(
[ordered_inputs['attention_mask'], torch.ones(__UpperCAmelCase , __UpperCAmelCase , dtype=__UpperCAmelCase )] , dim=1 )
return ordered_inputs
@property
def UpperCAmelCase ( self ):
'''simple docstring'''
return 13
| 293 | 1 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def lowerCamelCase__ ( __lowercase , __lowercase ):
# ===== initialization =====
snake_case : str = Mock()
snake_case : Tuple = conn, Mock()
snake_case : Optional[Any] = iter([1, None] )
snake_case : Optional[Any] = lambda __lowercase : next(__lowercase )
# ===== invoke =====
send_file(filename="""mytext.txt""" , testing=__lowercase )
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| 116 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _a (metaclass=a__ ):
'''simple docstring'''
lowerCAmelCase_ : Any = ["""flax"""]
def __init__( self ,*__a ,**__a ) -> Tuple:
requires_backends(self ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> Tuple:
requires_backends(cls ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> Dict:
requires_backends(cls ,["""flax"""] )
class _a (metaclass=a__ ):
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = ["""flax"""]
def __init__( self ,*__a ,**__a ) -> List[Any]:
requires_backends(self ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> List[str]:
requires_backends(cls ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> str:
requires_backends(cls ,["""flax"""] )
class _a (metaclass=a__ ):
'''simple docstring'''
lowerCAmelCase_ : Any = ["""flax"""]
def __init__( self ,*__a ,**__a ) -> Any:
requires_backends(self ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> List[Any]:
requires_backends(cls ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> Any:
requires_backends(cls ,["""flax"""] )
class _a (metaclass=a__ ):
'''simple docstring'''
lowerCAmelCase_ : List[str] = ["""flax"""]
def __init__( self ,*__a ,**__a ) -> Optional[Any]:
requires_backends(self ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> Dict:
requires_backends(cls ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> int:
requires_backends(cls ,["""flax"""] )
class _a (metaclass=a__ ):
'''simple docstring'''
lowerCAmelCase_ : int = ["""flax"""]
def __init__( self ,*__a ,**__a ) -> Optional[int]:
requires_backends(self ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> Optional[Any]:
requires_backends(cls ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> List[str]:
requires_backends(cls ,["""flax"""] )
class _a (metaclass=a__ ):
'''simple docstring'''
lowerCAmelCase_ : Dict = ["""flax"""]
def __init__( self ,*__a ,**__a ) -> Optional[Any]:
requires_backends(self ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> List[Any]:
requires_backends(cls ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> int:
requires_backends(cls ,["""flax"""] )
class _a (metaclass=a__ ):
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = ["""flax"""]
def __init__( self ,*__a ,**__a ) -> List[str]:
requires_backends(self ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> Optional[Any]:
requires_backends(cls ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> str:
requires_backends(cls ,["""flax"""] )
class _a (metaclass=a__ ):
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = ["""flax"""]
def __init__( self ,*__a ,**__a ) -> str:
requires_backends(self ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> str:
requires_backends(cls ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> Union[str, Any]:
requires_backends(cls ,["""flax"""] )
class _a (metaclass=a__ ):
'''simple docstring'''
lowerCAmelCase_ : List[Any] = ["""flax"""]
def __init__( self ,*__a ,**__a ) -> Union[str, Any]:
requires_backends(self ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> Any:
requires_backends(cls ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> Optional[Any]:
requires_backends(cls ,["""flax"""] )
class _a (metaclass=a__ ):
'''simple docstring'''
lowerCAmelCase_ : Dict = ["""flax"""]
def __init__( self ,*__a ,**__a ) -> Dict:
requires_backends(self ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> List[str]:
requires_backends(cls ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> Optional[Any]:
requires_backends(cls ,["""flax"""] )
class _a (metaclass=a__ ):
'''simple docstring'''
lowerCAmelCase_ : List[str] = ["""flax"""]
def __init__( self ,*__a ,**__a ) -> Tuple:
requires_backends(self ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> Tuple:
requires_backends(cls ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> Optional[int]:
requires_backends(cls ,["""flax"""] )
class _a (metaclass=a__ ):
'''simple docstring'''
lowerCAmelCase_ : Tuple = ["""flax"""]
def __init__( self ,*__a ,**__a ) -> List[str]:
requires_backends(self ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> Union[str, Any]:
requires_backends(cls ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> Dict:
requires_backends(cls ,["""flax"""] )
class _a (metaclass=a__ ):
'''simple docstring'''
lowerCAmelCase_ : Tuple = ["""flax"""]
def __init__( self ,*__a ,**__a ) -> Tuple:
requires_backends(self ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> List[str]:
requires_backends(cls ,["""flax"""] )
@classmethod
def snake_case_ ( cls ,*__a ,**__a ) -> int:
requires_backends(cls ,["""flax"""] )
| 116 | 1 |
from pathlib import Path
import numpy as np
from PIL import Image
def _A (UpperCamelCase : int ) ->str:
'''simple docstring'''
lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ : Tuple = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b
def _A (UpperCamelCase : Optional[int] ) ->Optional[Any]:
'''simple docstring'''
return (gray > 127) & (gray <= 255)
def _A (UpperCamelCase : Optional[int] , UpperCamelCase : Dict ) ->str:
'''simple docstring'''
lowerCamelCase__ : List[Any] = np.zeros_like(A_ )
lowerCamelCase__ : Tuple = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
# Copy image to padded image
lowerCamelCase__ : str = image
# Iterate over image & apply kernel
for x in range(image.shape[1] ):
for y in range(image.shape[0] ):
lowerCamelCase__ : Tuple = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
lowerCamelCase__ : List[Any] = int(summation > 0 )
return output
if __name__ == "__main__":
# read original image
_lowercase = Path(__file__).resolve().parent / 'image_data' / 'lena.jpg'
_lowercase = np.array(Image.open(lena_path))
# kernel to be applied
_lowercase = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
_lowercase = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
_lowercase = Image.fromarray(output).convert('''RGB''')
pil_img.save('''result_dilation.png''')
| 710 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask
_lowercase = logging.getLogger(__name__)
class __A ( A_ ):
UpperCamelCase :Optional[int] = '''token-classification'''
def __init__(self , __magic_name__ ):
if type(__magic_name__ ) == dict:
lowerCamelCase__ : Any = Namespace(**__magic_name__ )
lowerCamelCase__ : str = import_module("""tasks""" )
try:
lowerCamelCase__ : Optional[Any] = getattr(__magic_name__ , hparams.task_type )
lowerCamelCase__ : TokenClassificationTask = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f"Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
f"Available tasks classes are: {TokenClassificationTask.__subclasses__()}" )
lowerCamelCase__ : Any = self.token_classification_task.get_labels(hparams.labels )
lowerCamelCase__ : Tuple = CrossEntropyLoss().ignore_index
super().__init__(__magic_name__ , len(self.labels ) , self.mode )
def _snake_case (self , **__magic_name__ ):
return self.model(**__magic_name__ )
def _snake_case (self , __magic_name__ , __magic_name__ ):
lowerCamelCase__ : Tuple = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type != "distilbert":
lowerCamelCase__ : Union[str, Any] = (
batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
) # XLM and RoBERTa don"t use token_type_ids
lowerCamelCase__ : List[str] = self(**__magic_name__ )
lowerCamelCase__ : Dict = outputs[0]
# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
return {"loss": loss}
def _snake_case (self ):
lowerCamelCase__ : Dict = self.hparams
for mode in ["train", "dev", "test"]:
lowerCamelCase__ : List[str] = self._feature_file(__magic_name__ )
if os.path.exists(__magic_name__ ) and not args.overwrite_cache:
logger.info("""Loading features from cached file %s""" , __magic_name__ )
lowerCamelCase__ : Union[str, Any] = torch.load(__magic_name__ )
else:
logger.info("""Creating features from dataset file at %s""" , args.data_dir )
lowerCamelCase__ : int = self.token_classification_task.read_examples_from_file(args.data_dir , __magic_name__ )
lowerCamelCase__ : Tuple = self.token_classification_task.convert_examples_to_features(
__magic_name__ , self.labels , args.max_seq_length , self.tokenizer , cls_token_at_end=bool(self.config.model_type in ["""xlnet"""] ) , cls_token=self.tokenizer.cls_token , cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0 , sep_token=self.tokenizer.sep_token , sep_token_extra=__magic_name__ , pad_on_left=bool(self.config.model_type in ["""xlnet"""] ) , pad_token=self.tokenizer.pad_token_id , pad_token_segment_id=self.tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info("""Saving features into cached file %s""" , __magic_name__ )
torch.save(__magic_name__ , __magic_name__ )
def _snake_case (self , __magic_name__ , __magic_name__ , __magic_name__ = False ):
lowerCamelCase__ : Any = self._feature_file(__magic_name__ )
logger.info("""Loading features from cached file %s""" , __magic_name__ )
lowerCamelCase__ : Optional[Any] = torch.load(__magic_name__ )
lowerCamelCase__ : Tuple = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
lowerCamelCase__ : Optional[Any] = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
if features[0].token_type_ids is not None:
lowerCamelCase__ : str = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
else:
lowerCamelCase__ : int = torch.tensor([0 for f in features] , dtype=torch.long )
# HACK(we will not use this anymore soon)
lowerCamelCase__ : Any = torch.tensor([f.label_ids for f in features] , dtype=torch.long )
return DataLoader(
TensorDataset(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) , batch_size=__magic_name__ )
def _snake_case (self , __magic_name__ , __magic_name__ ):
"""Compute validation""" ""
lowerCamelCase__ : Optional[int] = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type != "distilbert":
lowerCamelCase__ : Tuple = (
batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
) # XLM and RoBERTa don"t use token_type_ids
lowerCamelCase__ : str = self(**__magic_name__ )
lowerCamelCase__ ,lowerCamelCase__ : List[Any] = outputs[:2]
lowerCamelCase__ : List[Any] = logits.detach().cpu().numpy()
lowerCamelCase__ : Dict = inputs["""labels"""].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def _snake_case (self , __magic_name__ ):
lowerCamelCase__ : List[str] = torch.stack([x["""val_loss"""] for x in outputs] ).mean()
lowerCamelCase__ : Optional[int] = np.concatenate([x["""pred"""] for x in outputs] , axis=0 )
lowerCamelCase__ : List[str] = np.argmax(__magic_name__ , axis=2 )
lowerCamelCase__ : Optional[Any] = np.concatenate([x["""target"""] for x in outputs] , axis=0 )
lowerCamelCase__ : Optional[int] = dict(enumerate(self.labels ) )
lowerCamelCase__ : List[str] = [[] for _ in range(out_label_ids.shape[0] )]
lowerCamelCase__ : Any = [[] for _ in range(out_label_ids.shape[0] )]
for i in range(out_label_ids.shape[0] ):
for j in range(out_label_ids.shape[1] ):
if out_label_ids[i, j] != self.pad_token_label_id:
out_label_list[i].append(label_map[out_label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
lowerCamelCase__ : Tuple = {
"""val_loss""": val_loss_mean,
"""accuracy_score""": accuracy_score(__magic_name__ , __magic_name__ ),
"""precision""": precision_score(__magic_name__ , __magic_name__ ),
"""recall""": recall_score(__magic_name__ , __magic_name__ ),
"""f1""": fa_score(__magic_name__ , __magic_name__ ),
}
lowerCamelCase__ : Dict = dict(results.items() )
lowerCamelCase__ : str = results
return ret, preds_list, out_label_list
def _snake_case (self , __magic_name__ ):
# when stable
lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ : str = self._eval_end(__magic_name__ )
lowerCamelCase__ : Union[str, Any] = ret["""log"""]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def _snake_case (self , __magic_name__ ):
# updating to test_epoch_end instead of deprecated test_end
lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ : Union[str, Any] = self._eval_end(__magic_name__ )
# Converting to the dict required by pl
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
# pytorch_lightning/trainer/logging.py#L139
lowerCamelCase__ : List[Any] = ret["""log"""]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def _snake_case (__magic_name__ , __magic_name__ ):
# Add NER specific options
BaseTransformer.add_model_specific_args(__magic_name__ , __magic_name__ )
parser.add_argument(
"""--task_type""" , default="""NER""" , type=__magic_name__ , help="""Task type to fine tune in training (e.g. NER, POS, etc)""" )
parser.add_argument(
"""--max_seq_length""" , default=128 , type=__magic_name__ , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--labels""" , default="""""" , type=__magic_name__ , help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.""" , )
parser.add_argument(
"""--gpus""" , default=0 , type=__magic_name__ , help="""The number of GPUs allocated for this, it is by default 0 meaning none""" , )
parser.add_argument(
"""--overwrite_cache""" , action="""store_true""" , help="""Overwrite the cached training and evaluation sets""" )
return parser
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
add_generic_args(parser, os.getcwd())
_lowercase = NERTransformer.add_model_specific_args(parser, os.getcwd())
_lowercase = parser.parse_args()
_lowercase = NERTransformer(args)
_lowercase = generic_train(model, args)
if args.do_predict:
# See https://github.com/huggingface/transformers/issues/3159
# pl use this default format to create a checkpoint:
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
# /pytorch_lightning/callbacks/model_checkpoint.py#L322
_lowercase = sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
_lowercase = model.load_from_checkpoint(checkpoints[-1])
trainer.test(model)
| 96 | 0 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase_ = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase ( lowercase__ , unittest.TestCase ):
A__ = XLNetTokenizer
A__ = XLNetTokenizerFast
A__ = True
A__ = True
def __magic_name__( self ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase__ : Any = XLNetTokenizer(SCREAMING_SNAKE_CASE__ , keep_accents=SCREAMING_SNAKE_CASE__ )
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__( self ):
lowerCAmelCase__ : List[Any] = '<s>'
lowerCAmelCase__ : 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 __magic_name__( self ):
lowerCAmelCase__ : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''<eod>''' )
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , 1006 )
def __magic_name__( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def __magic_name__( self ):
lowerCAmelCase__ : Optional[Any] = XLNetTokenizer(SCREAMING_SNAKE_CASE__ , keep_accents=SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ : 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] )
lowerCAmelCase__ : List[str] = 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''',
'''.''',
] , )
lowerCAmelCase__ : List[str] = 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] )
lowerCAmelCase__ : List[Any] = 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>''',
'''.''',
] , )
def __magic_name__( self ):
lowerCAmelCase__ : Union[str, Any] = XLNetTokenizer(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ : List[str] = 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''',
'''se''',
'''.''',
] , )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''▁he''', '''ll''', '''o'''] )
def __magic_name__( self ):
lowerCAmelCase__ : Tuple = XLNetTokenizer(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ : int = 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''',
'''se''',
'''.''',
] , )
@slow
def __magic_name__( self ):
lowerCAmelCase__ : Optional[int] = XLNetTokenizer.from_pretrained('''xlnet-base-cased''' )
lowerCAmelCase__ : Any = tokenizer.encode('''sequence builders''' , add_special_tokens=SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ : Dict = tokenizer.encode('''multi-sequence build''' , add_special_tokens=SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ : int = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ : Dict = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def __magic_name__( self ):
# fmt: off
lowerCAmelCase__ : Optional[Any] = {'input_ids': [[17, 2_1442, 270, 17, 10, 1_4645, 318, 34, 17, 4546, 3145, 787, 13, 7752, 2_2018, 23, 21, 17, 4546, 3145, 787, 13, 3352, 1_4431, 13, 5500, 11, 1176, 580, 13, 1_6819, 4797, 23, 17, 10, 1_7135, 658, 19, 457, 7932, 13, 184, 19, 3154, 1_7135, 6468, 19, 1404, 1_2269, 19, 4229, 5356, 1_6264, 46, 19, 17, 2_0545, 1_0395, 9, 9, 9, 11, 28, 6421, 9531, 2_0729, 17, 10, 353, 1_7022, 11, 21, 6421, 9531, 1_6949, 17, 10, 1_1509, 753, 11, 33, 95, 2421, 7385, 956, 1_4431, 2626, 25, 842, 7385, 4836, 21, 1429, 2272, 9855, 3120, 161, 2_4738, 19, 1_3203, 658, 218, 787, 21, 430, 1_8482, 847, 2637, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 322, 2_2178, 27, 1064, 22, 956, 13, 1_1101, 1429, 5854, 2_4313, 1_8953, 40, 422, 2_4366, 68, 1758, 37, 1_0483, 1_4257, 31, 207, 263, 21, 203, 3773, 25, 71, 9735, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 2049, 3442, 17, 1_3894, 3380, 23, 95, 18, 1_7634, 2288, 9, 4, 3]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], '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], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=SCREAMING_SNAKE_CASE__ , model_name='''xlnet-base-cased''' , revision='''c841166438c31ec7ca9a106dee7bb312b73ae511''' , )
| 678 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import (
TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaubertConfig,
TFFlaubertForMultipleChoice,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForSequenceClassification,
TFFlaubertForTokenClassification,
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
)
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE__ : Tuple , ) -> Optional[int]:
a_ : Any = parent
a_ : Tuple = 1_3
a_ : Any = 7
a_ : Any = True
a_ : Tuple = True
a_ : List[str] = True
a_ : List[Any] = True
a_ : Dict = True
a_ : Union[str, Any] = False
a_ : Tuple = False
a_ : Union[str, Any] = False
a_ : List[str] = 2
a_ : Union[str, Any] = 9_9
a_ : Any = 0
a_ : Dict = 3_2
a_ : Dict = 2
a_ : int = 4
a_ : List[str] = 0.1
a_ : str = 0.1
a_ : Optional[Any] = 5_1_2
a_ : Optional[Any] = 1_6
a_ : Optional[Any] = 2
a_ : str = 0.02
a_ : Tuple = 3
a_ : List[str] = 4
a_ : List[Any] = 'last'
a_ : Tuple = True
a_ : str = None
a_ : Optional[Any] = 0
def SCREAMING_SNAKE_CASE ( self : Any ) -> Optional[Any]:
a_ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a_ : Dict = random_attention_mask([self.batch_size, self.seq_length] , dtype=tf.floataa )
a_ : Tuple = None
if self.use_input_lengths:
a_ : List[Any] = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
a_ : Optional[int] = None
if self.use_token_type_ids:
a_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
a_ : str = None
a_ : Any = None
a_ : Optional[Any] = None
if self.use_labels:
a_ : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a_ : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a_ : Optional[Any] = ids_tensor([self.batch_size] , 2 , dtype=tf.floataa )
a_ : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
a_ : str = FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , bos_token_id=self.bos_token_id , )
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def SCREAMING_SNAKE_CASE ( self : List[str] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : List[Any] , ) -> int:
a_ : str = TFFlaubertModel(config=SCREAMING_SNAKE_CASE__ )
a_ : str = {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
a_ : str = model(SCREAMING_SNAKE_CASE__ )
a_ : List[Any] = [input_ids, input_mask]
a_ : List[str] = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Any , ) -> List[Any]:
a_ : Tuple = TFFlaubertWithLMHeadModel(SCREAMING_SNAKE_CASE__ )
a_ : Optional[Any] = {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
a_ : List[str] = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE ( self : Tuple , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[int] , ) -> List[str]:
a_ : Optional[Any] = TFFlaubertForQuestionAnsweringSimple(SCREAMING_SNAKE_CASE__ )
a_ : Any = {'input_ids': input_ids, 'lengths': input_lengths}
a_ : List[str] = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[int] , ) -> List[Any]:
a_ : List[str] = TFFlaubertForSequenceClassification(SCREAMING_SNAKE_CASE__ )
a_ : Dict = {'input_ids': input_ids, 'lengths': input_lengths}
a_ : Dict = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def SCREAMING_SNAKE_CASE ( self : str , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , ) -> Optional[Any]:
a_ : str = self.num_labels
a_ : Optional[int] = TFFlaubertForTokenClassification(config=SCREAMING_SNAKE_CASE__ )
a_ : List[str] = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
a_ : str = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE ( self : Tuple , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , ) -> Dict:
a_ : Tuple = self.num_choices
a_ : List[Any] = TFFlaubertForMultipleChoice(config=SCREAMING_SNAKE_CASE__ )
a_ : List[Any] = tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__ , 1 ) , (1, self.num_choices, 1) )
a_ : List[str] = tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__ , 1 ) , (1, self.num_choices, 1) )
a_ : List[str] = tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__ , 1 ) , (1, self.num_choices, 1) )
a_ : List[Any] = {
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
}
a_ : Tuple = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Optional[int]:
a_ : Optional[Any] = self.prepare_config_and_inputs()
(
(
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) ,
) : Any = config_and_inputs
a_ : Union[str, Any] = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'langs': token_type_ids,
'lengths': input_lengths,
}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE__ ( lowercase__ , lowercase__ , unittest.TestCase ):
snake_case__ : List[Any] = (
(
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
TFFlaubertForSequenceClassification,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForTokenClassification,
TFFlaubertForMultipleChoice,
)
if is_tf_available()
else ()
)
snake_case__ : Dict = (
(TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
snake_case__ : List[Any] = (
{
'''feature-extraction''': TFFlaubertModel,
'''fill-mask''': TFFlaubertWithLMHeadModel,
'''question-answering''': TFFlaubertForQuestionAnsweringSimple,
'''text-classification''': TFFlaubertForSequenceClassification,
'''token-classification''': TFFlaubertForTokenClassification,
'''zero-shot''': TFFlaubertForSequenceClassification,
}
if is_tf_available()
else {}
)
snake_case__ : Any = False
snake_case__ : Optional[Any] = False
def SCREAMING_SNAKE_CASE ( self : Any , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> Dict:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def SCREAMING_SNAKE_CASE ( self : Any ) -> Tuple:
a_ : Tuple = TFFlaubertModelTester(self )
a_ : Any = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE__ , emb_dim=3_7 )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Optional[Any]:
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> List[str]:
a_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : str ) -> Union[str, Any]:
a_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Optional[int]:
a_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> List[Any]:
a_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> str:
a_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_for_token_classification(*SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> Union[str, Any]:
a_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_for_multiple_choice(*SCREAMING_SNAKE_CASE__ )
@slow
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Tuple:
for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a_ : Any = TFFlaubertModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
@require_tf
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE ( self : Dict ) -> str:
a_ : List[str] = TFFlaubertModel.from_pretrained('jplu/tf-flaubert-small-cased' )
a_ : Optional[Any] = tf.convert_to_tensor(
[[0, 1_5_8, 7_3_5, 2_5_9_2, 1_4_2_4, 6_7_2_7, 8_2, 1]] , dtype=tf.intaa , ) # "J'aime flaubert !"
a_ : Tuple = model(SCREAMING_SNAKE_CASE__ )[0]
a_ : List[Any] = tf.TensorShape((1, 8, 5_1_2) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE__ )
# compare the actual values for a slice.
a_ : int = tf.convert_to_tensor(
[
[
[-1.8768773, -1.566555, 0.27072418],
[-1.6920038, -0.5873505, 1.9329599],
[-2.9563985, -1.6993835, 1.7972052],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 570 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import List, Optional
class __snake_case( __A ):
def __init__( self ):
'''simple docstring'''
self.test()
def A ( self ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = False
while not completed:
if counter == 1:
self.reset()
_SCREAMING_SNAKE_CASE = self.advance()
if not self.does_advance(A_ ):
raise Exception(
'''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.''' )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.update(A_ )
counter += 1
if counter > 10_000:
raise Exception('''update() does not fulfill the constraint.''' )
if self.remaining() != 0:
raise Exception('''Custom Constraint is not defined correctly.''' )
@abstractmethod
def A ( self ):
'''simple docstring'''
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def A ( self , A_ ):
'''simple docstring'''
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def A ( self , A_ ):
'''simple docstring'''
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def A ( self ):
'''simple docstring'''
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def A ( self ):
'''simple docstring'''
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def A ( self , A_=False ):
'''simple docstring'''
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class __snake_case( __A ):
def __init__( self , A_ ):
'''simple docstring'''
super(A_ , self ).__init__()
if not isinstance(A_ , A_ ) or len(A_ ) == 0:
raise ValueError(F'''`token_ids` has to be a non-empty list, but is {token_ids}.''' )
if any((not isinstance(A_ , A_ ) or token_id < 0) for token_id in token_ids ):
raise ValueError(F'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.''' )
_SCREAMING_SNAKE_CASE = token_ids
_SCREAMING_SNAKE_CASE = len(self.token_ids )
_SCREAMING_SNAKE_CASE = -1 # the index of the currently fulfilled step
_SCREAMING_SNAKE_CASE = False
def A ( self ):
'''simple docstring'''
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def A ( self , A_ ):
'''simple docstring'''
if not isinstance(A_ , A_ ):
raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(A_ )}''' )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def A ( self , A_ ):
'''simple docstring'''
if not isinstance(A_ , A_ ):
raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(A_ )}''' )
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
if self.does_advance(A_ ):
self.fulfilled_idx += 1
_SCREAMING_SNAKE_CASE = True
if self.fulfilled_idx == (self.seqlen - 1):
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = completed
else:
# failed to make progress.
_SCREAMING_SNAKE_CASE = True
self.reset()
return stepped, completed, reset
def A ( self ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = 0
def A ( self ):
'''simple docstring'''
return self.seqlen - (self.fulfilled_idx + 1)
def A ( self , A_=False ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = PhrasalConstraint(self.token_ids )
if stateful:
_SCREAMING_SNAKE_CASE = self.seqlen
_SCREAMING_SNAKE_CASE = self.fulfilled_idx
_SCREAMING_SNAKE_CASE = self.completed
return new_constraint
class __snake_case:
def __init__( self , A_ , A_=True ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = max([len(A_ ) for one in nested_token_ids] )
_SCREAMING_SNAKE_CASE = {}
for token_ids in nested_token_ids:
_SCREAMING_SNAKE_CASE = root
for tidx, token_id in enumerate(A_ ):
if token_id not in level:
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = level[token_id]
if no_subsets and self.has_subsets(A_ , A_ ):
raise ValueError(
'''Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'''
F''' {nested_token_ids}.''' )
_SCREAMING_SNAKE_CASE = root
def A ( self , A_ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.trie
for current_token in current_seq:
_SCREAMING_SNAKE_CASE = start[current_token]
_SCREAMING_SNAKE_CASE = list(start.keys() )
return next_tokens
def A ( self , A_ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.next_tokens(A_ )
return len(A_ ) == 0
def A ( self , A_ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = list(root.values() )
if len(A_ ) == 0:
return 1
else:
return sum([self.count_leaves(A_ ) for nn in next_nodes] )
def A ( self , A_ , A_ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.count_leaves(A_ )
return len(A_ ) != leaf_count
class __snake_case( __A ):
def __init__( self , A_ ):
'''simple docstring'''
super(A_ , self ).__init__()
if not isinstance(A_ , A_ ) or len(A_ ) == 0:
raise ValueError(F'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''' )
if any(not isinstance(A_ , A_ ) for token_ids in nested_token_ids ):
raise ValueError(F'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.''' )
if any(
any((not isinstance(A_ , A_ ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
F'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.''' )
_SCREAMING_SNAKE_CASE = DisjunctiveTrie(A_ )
_SCREAMING_SNAKE_CASE = nested_token_ids
_SCREAMING_SNAKE_CASE = self.trie.max_height
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = False
def A ( self ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.trie.next_tokens(self.current_seq )
if len(A_ ) == 0:
return None
else:
return token_list
def A ( self , A_ ):
'''simple docstring'''
if not isinstance(A_ , A_ ):
raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(A_ )}''' )
_SCREAMING_SNAKE_CASE = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def A ( self , A_ ):
'''simple docstring'''
if not isinstance(A_ , A_ ):
raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(A_ )}''' )
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
if self.does_advance(A_ ):
self.current_seq.append(A_ )
_SCREAMING_SNAKE_CASE = True
else:
_SCREAMING_SNAKE_CASE = True
self.reset()
_SCREAMING_SNAKE_CASE = self.trie.reached_leaf(self.current_seq )
_SCREAMING_SNAKE_CASE = completed
return stepped, completed, reset
def A ( self ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = []
def A ( self ):
'''simple docstring'''
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def A ( self , A_=False ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = DisjunctiveConstraint(self.token_ids )
if stateful:
_SCREAMING_SNAKE_CASE = self.seqlen
_SCREAMING_SNAKE_CASE = self.current_seq
_SCREAMING_SNAKE_CASE = self.completed
return new_constraint
class __snake_case:
def __init__( self , A_ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = constraints
# max # of steps required to fulfill a given constraint
_SCREAMING_SNAKE_CASE = max([c.seqlen for c in constraints] )
_SCREAMING_SNAKE_CASE = len(A_ )
_SCREAMING_SNAKE_CASE = False
self.init_state()
def A ( self ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = [constraint.copy(stateful=A_ ) for constraint in self.constraints]
def A ( self ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def A ( self ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
_SCREAMING_SNAKE_CASE = constraint.advance()
if isinstance(A_ , A_ ):
token_list.append(A_ )
elif isinstance(A_ , A_ ):
token_list.extend(A_ )
else:
_SCREAMING_SNAKE_CASE = self.inprogress_constraint.advance()
if isinstance(A_ , A_ ):
token_list.append(A_ )
elif isinstance(A_ , A_ ):
token_list.extend(A_ )
if len(A_ ) == 0:
return None
else:
return token_list
def A ( self , A_ ):
'''simple docstring'''
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.add(A_ )
# the entire list of constraints are fulfilled
if self.completed:
break
def A ( self , A_ ):
'''simple docstring'''
if not isinstance(A_ , A_ ):
raise ValueError(F'''`token_id` should be an `int`, but is `{token_id}`.''' )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False, False
if self.completed:
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.inprogress_constraint.update(A_ )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=A_ ) )
_SCREAMING_SNAKE_CASE = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
_SCREAMING_SNAKE_CASE = None
if len(self.pending_constraints ) == 0:
# we're done!
_SCREAMING_SNAKE_CASE = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(A_ ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = pending_constraint.update(A_ )
if not stepped:
raise Exception(
'''`constraint.update(token_id)` is not yielding incremental progress, '''
'''even though `constraint.does_advance(token_id)` is true.''' )
if complete:
self.complete_constraints.append(A_ )
_SCREAMING_SNAKE_CASE = None
if not complete and stepped:
_SCREAMING_SNAKE_CASE = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
_SCREAMING_SNAKE_CASE = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
_SCREAMING_SNAKE_CASE = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def A ( self , A_=True ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
_SCREAMING_SNAKE_CASE = [
constraint.copy(stateful=A_ ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
_SCREAMING_SNAKE_CASE = self.inprogress_constraint.copy(stateful=A_ )
_SCREAMING_SNAKE_CASE = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 168 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A__ ( UpperCamelCase__ ):
'''simple docstring'''
if (
(cp >= 0x4E00 and cp <= 0x9FFF)
or (cp >= 0x3400 and cp <= 0x4DBF) #
or (cp >= 0x20000 and cp <= 0x2A6DF) #
or (cp >= 0x2A700 and cp <= 0x2B73F) #
or (cp >= 0x2B740 and cp <= 0x2B81F) #
or (cp >= 0x2B820 and cp <= 0x2CEAF) #
or (cp >= 0xF900 and cp <= 0xFAFF)
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True
return False
def A__ ( UpperCamelCase__ ):
'''simple docstring'''
for char in word:
_SCREAMING_SNAKE_CASE = ord(UpperCamelCase__ )
if not _is_chinese_char(UpperCamelCase__ ):
return 0
return 1
def A__ ( UpperCamelCase__ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = set()
for token in tokens:
_SCREAMING_SNAKE_CASE = len(UpperCamelCase__ ) > 1 and is_chinese(UpperCamelCase__ )
if chinese_word:
word_set.add(UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = list(UpperCamelCase__ )
return word_list
def A__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if not chinese_word_set:
return bert_tokens
_SCREAMING_SNAKE_CASE = max([len(UpperCamelCase__ ) for w in chinese_word_set] )
_SCREAMING_SNAKE_CASE = bert_tokens
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0, len(UpperCamelCase__ )
while start < end:
_SCREAMING_SNAKE_CASE = True
if is_chinese(bert_word[start] ):
_SCREAMING_SNAKE_CASE = min(end - start , UpperCamelCase__ )
for i in range(UpperCamelCase__ , 1 , -1 ):
_SCREAMING_SNAKE_CASE = ''''''.join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
_SCREAMING_SNAKE_CASE = '''##''' + bert_word[j]
_SCREAMING_SNAKE_CASE = start + i
_SCREAMING_SNAKE_CASE = False
break
if single_word:
start += 1
return bert_word
def A__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = []
for i in range(0 , len(UpperCamelCase__ ) , 100 ):
_SCREAMING_SNAKE_CASE = ltp_tokenizer.pipeline(lines[i : i + 100] , tasks=['''cws'''] ).cws
_SCREAMING_SNAKE_CASE = [get_chinese_word(UpperCamelCase__ ) for r in res]
ltp_res.extend(UpperCamelCase__ )
assert len(UpperCamelCase__ ) == len(UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = []
for i in range(0 , len(UpperCamelCase__ ) , 100 ):
_SCREAMING_SNAKE_CASE = bert_tokenizer(lines[i : i + 100] , add_special_tokens=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=512 )
bert_res.extend(res['''input_ids'''] )
assert len(UpperCamelCase__ ) == len(UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = []
for input_ids, chinese_word in zip(UpperCamelCase__ , UpperCamelCase__ ):
_SCREAMING_SNAKE_CASE = []
for id in input_ids:
_SCREAMING_SNAKE_CASE = bert_tokenizer._convert_id_to_token(UpperCamelCase__ )
input_tokens.append(UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = add_sub_symbol(UpperCamelCase__ , UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(UpperCamelCase__ ):
if token[:2] == "##":
_SCREAMING_SNAKE_CASE = token[2:]
# save chinese tokens' pos
if len(UpperCamelCase__ ) == 1 and _is_chinese_char(ord(UpperCamelCase__ ) ):
ref_id.append(UpperCamelCase__ )
ref_ids.append(UpperCamelCase__ )
assert len(UpperCamelCase__ ) == len(UpperCamelCase__ )
return ref_ids
def A__ ( UpperCamelCase__ ):
'''simple docstring'''
with open(args.file_name , '''r''' , encoding='''utf-8''' ) as f:
_SCREAMING_SNAKE_CASE = f.readlines()
_SCREAMING_SNAKE_CASE = [line.strip() for line in data if len(UpperCamelCase__ ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
_SCREAMING_SNAKE_CASE = LTP(args.ltp ) # faster in GPU device
_SCREAMING_SNAKE_CASE = BertTokenizer.from_pretrained(args.bert )
_SCREAMING_SNAKE_CASE = prepare_ref(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
with open(args.save_path , '''w''' , encoding='''utf-8''' ) as f:
_SCREAMING_SNAKE_CASE = [json.dumps(UpperCamelCase__ ) + '''\n''' for ref in ref_ids]
f.writelines(UpperCamelCase__ )
if __name__ == "__main__":
lowerCamelCase : Optional[Any] = argparse.ArgumentParser(description="""prepare_chinese_ref""")
parser.add_argument(
"""--file_name""",
required=False,
type=str,
default="""./resources/chinese-demo.txt""",
help="""file need process, same as training data in lm""",
)
parser.add_argument(
"""--ltp""",
required=False,
type=str,
default="""./resources/ltp""",
help="""resources for LTP tokenizer, usually a path""",
)
parser.add_argument(
"""--bert""",
required=False,
type=str,
default="""./resources/robert""",
help="""resources for Bert tokenizer""",
)
parser.add_argument(
"""--save_path""",
required=False,
type=str,
default="""./resources/ref.txt""",
help="""path to save res""",
)
lowerCamelCase : str = parser.parse_args()
main(args)
| 168 | 1 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : int )-> list[str]:
'''simple docstring'''
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
if partitions > number_of_bytes:
raise ValueError('''partitions can not > number_of_bytes!''' )
__snake_case = number_of_bytes // partitions
__snake_case = []
for i in range(_lowerCamelCase ):
__snake_case = i * bytes_per_partition + 1
__snake_case = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(f'''{start_bytes}-{end_bytes}''' )
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| 24 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class lowerCAmelCase ( __lowerCAmelCase , unittest.TestCase):
__lowercase : int = BarthezTokenizer
__lowercase : Any = BarthezTokenizerFast
__lowercase : Dict = True
__lowercase : Optional[int] = True
def lowerCAmelCase ( self ) -> Dict:
'''simple docstring'''
super().setUp()
__snake_case = BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=__SCREAMING_SNAKE_CASE )
__snake_case = tokenizer
def lowerCAmelCase ( self ) -> Dict:
'''simple docstring'''
__snake_case = '''<pad>'''
__snake_case = 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 lowerCAmelCase ( self ) -> Optional[int]:
'''simple docstring'''
__snake_case = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , 10_1122 )
def lowerCAmelCase ( self ) -> Any:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 10_1122 )
@require_torch
def lowerCAmelCase ( self ) -> Tuple:
'''simple docstring'''
__snake_case = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
__snake_case = [0, 57, 3018, 7_0307, 91, 2]
__snake_case = self.tokenizer(
__SCREAMING_SNAKE_CASE , max_length=len(__SCREAMING_SNAKE_CASE ) , padding=__SCREAMING_SNAKE_CASE , truncation=__SCREAMING_SNAKE_CASE , return_tensors='''pt''' )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
__snake_case = batch.input_ids.tolist()[0]
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
__snake_case = self.get_tokenizer()
__snake_case = self.get_rust_tokenizer()
__snake_case = '''I was born in 92000, and this is falsé.'''
__snake_case = tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
__snake_case = rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__snake_case = tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
__snake_case = rust_tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__snake_case = self.get_rust_tokenizer()
__snake_case = tokenizer.encode(__SCREAMING_SNAKE_CASE )
__snake_case = rust_tokenizer.encode(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
@slow
def lowerCAmelCase ( self ) -> int:
'''simple docstring'''
__snake_case = {'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
__snake_case = [
'''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
'''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
'''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
'''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
'''telles que la traduction et la synthèse de texte.''',
]
self.tokenizer_integration_test_util(
expected_encoding=__SCREAMING_SNAKE_CASE , model_name='''moussaKam/mbarthez''' , revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' , sequences=__SCREAMING_SNAKE_CASE , )
| 24 | 1 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class _snake_case ( unittest.TestCase ):
@require_torch
def __UpperCamelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE:Optional[int] = pipeline(
task="zero-shot-audio-classification" ,model="hf-internal-testing/tiny-clap-htsat-unfused" )
SCREAMING_SNAKE_CASE:Optional[int] = load_dataset("ashraq/esc50" )
SCREAMING_SNAKE_CASE:Union[str, Any] = dataset["train"]["audio"][-1]["array"]
SCREAMING_SNAKE_CASE:Union[str, Any] = audio_classifier(SCREAMING_SNAKE_CASE__ ,candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ ) ,[{"score": 0.501, "label": "Sound of a dog"}, {"score": 0.499, "label": "Sound of vaccum cleaner"}] ,)
@unittest.skip("No models are available in TF" )
def __UpperCamelCase ( self : Tuple ):
pass
@slow
@require_torch
def __UpperCamelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE:Any = pipeline(
task="zero-shot-audio-classification" ,model="laion/clap-htsat-unfused" ,)
# This is an audio of a dog
SCREAMING_SNAKE_CASE:str = load_dataset("ashraq/esc50" )
SCREAMING_SNAKE_CASE:List[str] = dataset["train"]["audio"][-1]["array"]
SCREAMING_SNAKE_CASE:Any = audio_classifier(SCREAMING_SNAKE_CASE__ ,candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ ) ,[
{"score": 0.999, "label": "Sound of a dog"},
{"score": 0.001, "label": "Sound of vaccum cleaner"},
] ,)
SCREAMING_SNAKE_CASE:Tuple = audio_classifier([audio] * 5 ,candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ ) ,[
[
{"score": 0.999, "label": "Sound of a dog"},
{"score": 0.001, "label": "Sound of vaccum cleaner"},
],
]
* 5 ,)
SCREAMING_SNAKE_CASE:int = audio_classifier(
[audio] * 5 ,candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] ,batch_size=5 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ ) ,[
[
{"score": 0.999, "label": "Sound of a dog"},
{"score": 0.001, "label": "Sound of vaccum cleaner"},
],
]
* 5 ,)
@unittest.skip("No models are available in TF" )
def __UpperCamelCase ( self : str ):
pass
| 465 |
'''simple docstring'''
from ....utils import logging
A_ = logging.get_logger(__name__)
class _snake_case ( _a ):
def __init__( self : Optional[Any] ,SCREAMING_SNAKE_CASE__ : Optional[int] ,SCREAMING_SNAKE_CASE__ : Optional[Any]=None ,SCREAMING_SNAKE_CASE__ : Optional[int]=2_048 ):
SCREAMING_SNAKE_CASE:int = config.__dict__
SCREAMING_SNAKE_CASE:List[str] = modal_hidden_size
if num_labels:
SCREAMING_SNAKE_CASE:str = num_labels
| 465 | 1 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__A : List[Any] = 1.0_5457_1817e-34 # unit of ℏ : J * s
__A : List[str] = 3e8 # unit of c : m * s^-1
def __a ( A__ : float , A__ : float , A__ : float ):
if (force, area, distance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if force < 0:
raise ValueError("Magnitude of force can not be negative" )
if distance < 0:
raise ValueError("Distance can not be negative" )
if area < 0:
raise ValueError("Area can not be negative" )
if force == 0:
SCREAMING_SNAKE_CASE = (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
240 * (distance) ** 4
)
return {"force": force}
elif area == 0:
SCREAMING_SNAKE_CASE = (240 * force * (distance) ** 4) / (
REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
)
return {"area": area}
elif distance == 0:
SCREAMING_SNAKE_CASE = (
(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
) ** (1 / 4)
return {"distance": distance}
raise ValueError("One and only one argument must be 0" )
# Run doctest
if __name__ == "__main__":
import doctest
doctest.testmod() | 16 |
def __a ( A__ : float , A__ : float ):
if mass < 0:
raise ValueError("The mass of a body cannot be negative" )
return 0.5 * mass * abs(A__ ) * abs(A__ )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) | 16 | 1 |
from collections import namedtuple
UpperCamelCase__ = namedtuple("from_to", "from_ to")
UpperCamelCase__ = {
"cubicmeter": from_to(1, 1),
"litre": from_to(0.001, 10_00),
"kilolitre": from_to(1, 1),
"gallon": from_to(0.00_454, 264.172),
"cubicyard": from_to(0.76_455, 1.30_795),
"cubicfoot": from_to(0.028, 35.3_147),
"cup": from_to(0.000_236_588, 4_226.75),
}
def UpperCAmelCase__ ( lowercase__ , lowercase__ , lowercase__ ) -> float:
if from_type not in METRIC_CONVERSION:
raise ValueError(
F"Invalid 'from_type' value: {from_type!r} Supported values are:\n"
+ """, """.join(lowercase__ ) )
if to_type not in METRIC_CONVERSION:
raise ValueError(
F"Invalid 'to_type' value: {to_type!r}. Supported values are:\n"
+ """, """.join(lowercase__ ) )
return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to
if __name__ == "__main__":
import doctest
doctest.testmod()
| 634 |
import random
def UpperCAmelCase__ ( lowercase__ , lowercase__ , lowercase__ = False ) -> dict:
__lowercase = {i: [] for i in range(lowercase__ )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
return complete_graph(lowercase__ )
# if probability is lower or equal than 0, then return a graph without edges
if probability <= 0:
return graph
# for each couple of nodes, add an edge from u to v
# if the number randomly generated is greater than probability probability
for i in range(lowercase__ ):
for j in range(i + 1 , lowercase__ ):
if random.random() < probability:
graph[i].append(lowercase__ )
if not directed:
# if the graph is undirected, add an edge in from j to i, either
graph[j].append(lowercase__ )
return graph
def UpperCAmelCase__ ( lowercase__ ) -> dict:
return {
i: [j for j in range(lowercase__ ) if i != j] for i in range(lowercase__ )
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 634 | 1 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def __UpperCAmelCase ( lowerCamelCase_) -> Callable:
@wraps(lowerCamelCase_)
def _inner_fn(*lowerCamelCase_ , **lowerCamelCase_):
warnings.warn(
(f'\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.') , lowerCamelCase_ , )
return fn(*lowerCamelCase_ , **lowerCamelCase_)
return _inner_fn
| 596 |
'''simple docstring'''
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
# Throw an error if user passes both BYO and on-demand cluster args
# Otherwise, use default values
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument('--user', type=str, default='ubuntu')
parser.add_argument('--host', type=str, default='localhost')
parser.add_argument('--key_path', type=str, default=None)
parser.add_argument('--instance', type=str, default='V100:1')
parser.add_argument('--provider', type=str, default='cheapest')
parser.add_argument('--use_spot', type=bool, default=False)
parser.add_argument('--example', type=str, default='pytorch/text-generation/run_generation.py')
lowerCAmelCase__ , lowerCAmelCase__ = parser.parse_known_args()
if args.host != "localhost":
if args.instance != "V100:1" or args.provider != "cheapest":
raise ValueError('Cannot specify both BYO and on-demand cluster args')
lowerCAmelCase__ = rh.cluster(
name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
)
else:
lowerCAmelCase__ = rh.cluster(
name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
)
lowerCAmelCase__ = args.example.rsplit('/', 1)[0]
# Set up remote environment
cluster.install_packages(['pip:./']) # Installs transformers from local source
# Note transformers is copied into the home directory on the remote machine, so we can install from there
cluster.run([f'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
cluster.run(['pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'])
# Run example. You can bypass the CLI wrapper and paste your own code here.
cluster.run([f'''python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'''])
# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
# from my_script... import train
# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
# launch_train_gpu = rh.function(fn=train,
# system=gpu,
# reqs=reqs,
# name='train_bert_glue')
#
# We can pass in arguments just like we would to a function:
# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
# stream_logs=True)
| 596 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self , a_ ) -> None:
lowercase : int = num_of_nodes
lowercase : Any = []
lowercase : Tuple = {}
def a__ ( self , a_ , a_ , a_ ) -> None:
self.m_edges.append([u_node, v_node, weight] )
def a__ ( self , a_ ) -> int:
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def a__ ( self , a_ ) -> None:
if self.m_component[u_node] != u_node:
for k in self.m_component:
lowercase : str = self.find_component(_SCREAMING_SNAKE_CASE )
def a__ ( self , a_ , a_ , a_ ) -> None:
if component_size[u_node] <= component_size[v_node]:
lowercase : int = v_node
component_size[v_node] += component_size[u_node]
self.set_component(_SCREAMING_SNAKE_CASE )
elif component_size[u_node] >= component_size[v_node]:
lowercase : str = self.find_component(_SCREAMING_SNAKE_CASE )
component_size[u_node] += component_size[v_node]
self.set_component(_SCREAMING_SNAKE_CASE )
def a__ ( self ) -> None:
lowercase : List[Any] = []
lowercase : int = 0
lowercase : int = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
lowercase : Tuple = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
lowercase , lowercase , lowercase : str = edge
lowercase : List[Any] = self.m_component[u]
lowercase : Optional[Any] = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
lowercase : int = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
lowercase , lowercase , lowercase : str = edge
lowercase : Optional[int] = self.m_component[u]
lowercase : Any = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n''' )
num_of_components -= 1
lowercase : Tuple = [-1] * self.m_num_of_nodes
print(F'''The total weight of the minimal spanning tree is: {mst_weight}''' )
def _A ( ) -> None:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 713 |
'''simple docstring'''
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("""To use the rich extension, install rich with `pip install rich`""")
| 425 | 0 |
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 DetaImageProcessor
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=7 , lowerCamelCase__=3 , lowerCamelCase__=30 , lowerCamelCase__=400 , lowerCamelCase__=True , lowerCamelCase__=None , lowerCamelCase__=True , lowerCamelCase__=[0.5, 0.5, 0.5] , lowerCamelCase__=[0.5, 0.5, 0.5] , lowerCamelCase__=True , lowerCamelCase__=1 / 255 , lowerCamelCase__=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
lowerCAmelCase_: Dict = size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
lowerCAmelCase_: Optional[int] = parent
lowerCAmelCase_: Optional[Any] = batch_size
lowerCAmelCase_: Optional[Any] = num_channels
lowerCAmelCase_: Optional[int] = min_resolution
lowerCAmelCase_: Tuple = max_resolution
lowerCAmelCase_: Optional[Any] = do_resize
lowerCAmelCase_: List[str] = size
lowerCAmelCase_: Dict = do_normalize
lowerCAmelCase_: Tuple = image_mean
lowerCAmelCase_: Optional[int] = image_std
lowerCAmelCase_: Union[str, Any] = do_rescale
lowerCAmelCase_: Optional[int] = rescale_factor
lowerCAmelCase_: List[Any] = do_pad
def _a ( self ):
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 _a ( self , lowerCamelCase__ , lowerCamelCase__=False ):
if not batched:
lowerCAmelCase_: Optional[Any] = image_inputs[0]
if isinstance(lowerCamelCase__ , Image.Image ):
lowerCAmelCase_ , lowerCAmelCase_: Dict = image.size
else:
lowerCAmelCase_ , lowerCAmelCase_: Any = image.shape[1], image.shape[2]
if w < h:
lowerCAmelCase_: List[Any] = int(self.size["shortest_edge"] * h / w )
lowerCAmelCase_: Any = self.size["shortest_edge"]
elif w > h:
lowerCAmelCase_: int = self.size["shortest_edge"]
lowerCAmelCase_: Dict = int(self.size["shortest_edge"] * w / h )
else:
lowerCAmelCase_: Optional[int] = self.size["shortest_edge"]
lowerCAmelCase_: List[str] = self.size["shortest_edge"]
else:
lowerCAmelCase_: int = []
for image in image_inputs:
lowerCAmelCase_ , lowerCAmelCase_: Tuple = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowerCAmelCase_: Optional[Any] = max(lowerCamelCase__ , key=lambda lowerCamelCase__ : item[0] )[0]
lowerCAmelCase_: List[str] = max(lowerCamelCase__ , key=lambda lowerCamelCase__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class _lowercase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE: str = DetaImageProcessor if is_vision_available() else None
def _a ( self ):
lowerCAmelCase_: Union[str, Any] = DetaImageProcessingTester(self )
@property
def _a ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def _a ( self ):
lowerCAmelCase_: Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase__ , "image_mean" ) )
self.assertTrue(hasattr(lowerCamelCase__ , "image_std" ) )
self.assertTrue(hasattr(lowerCamelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(lowerCamelCase__ , "do_resize" ) )
self.assertTrue(hasattr(lowerCamelCase__ , "do_rescale" ) )
self.assertTrue(hasattr(lowerCamelCase__ , "do_pad" ) )
self.assertTrue(hasattr(lowerCamelCase__ , "size" ) )
def _a ( self ):
lowerCAmelCase_: List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 1_333} )
self.assertEqual(image_processor.do_pad , lowerCamelCase__ )
def _a ( self ):
pass
def _a ( self ):
# Initialize image_processing
lowerCAmelCase_: Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase_: Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase__ , Image.Image )
# Test not batched input
lowerCAmelCase_: str = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
lowerCAmelCase_ , lowerCAmelCase_: Any = self.image_processor_tester.get_expected_values(lowerCamelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCAmelCase_ , lowerCAmelCase_: Union[str, Any] = self.image_processor_tester.get_expected_values(lowerCamelCase__ , batched=lowerCamelCase__ )
lowerCAmelCase_: Tuple = image_processing(lowerCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _a ( self ):
# Initialize image_processing
lowerCAmelCase_: str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase_: str = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase__ , numpify=lowerCamelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase__ , np.ndarray )
# Test not batched input
lowerCAmelCase_: str = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
lowerCAmelCase_ , lowerCAmelCase_: List[str] = self.image_processor_tester.get_expected_values(lowerCamelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCAmelCase_: List[str] = image_processing(lowerCamelCase__ , return_tensors="pt" ).pixel_values
lowerCAmelCase_ , lowerCAmelCase_: List[str] = self.image_processor_tester.get_expected_values(lowerCamelCase__ , batched=lowerCamelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _a ( self ):
# Initialize image_processing
lowerCAmelCase_: Dict = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase_: Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase__ , torchify=lowerCamelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase__ , torch.Tensor )
# Test not batched input
lowerCAmelCase_: Tuple = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
lowerCAmelCase_ , lowerCAmelCase_: Any = self.image_processor_tester.get_expected_values(lowerCamelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCAmelCase_: List[Any] = image_processing(lowerCamelCase__ , return_tensors="pt" ).pixel_values
lowerCAmelCase_ , lowerCAmelCase_: Dict = self.image_processor_tester.get_expected_values(lowerCamelCase__ , batched=lowerCamelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def _a ( self ):
# prepare image and target
lowerCAmelCase_: Dict = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
lowerCAmelCase_: List[Any] = json.loads(f.read() )
lowerCAmelCase_: int = {"image_id": 39_769, "annotations": target}
# encode them
lowerCAmelCase_: List[str] = DetaImageProcessor()
lowerCAmelCase_: List[Any] = image_processing(images=lowerCamelCase__ , annotations=lowerCamelCase__ , return_tensors="pt" )
# verify pixel values
lowerCAmelCase_: Union[str, Any] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , lowerCamelCase__ )
lowerCAmelCase_: int = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , lowerCamelCase__ , atol=1E-4 ) )
# verify area
lowerCAmelCase_: Union[str, Any] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , lowerCamelCase__ ) )
# verify boxes
lowerCAmelCase_: Union[str, Any] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCamelCase__ )
lowerCAmelCase_: Tuple = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , lowerCamelCase__ , atol=1E-3 ) )
# verify image_id
lowerCAmelCase_: Optional[int] = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCamelCase__ ) )
# verify is_crowd
lowerCAmelCase_: int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCamelCase__ ) )
# verify class_labels
lowerCAmelCase_: List[Any] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCamelCase__ ) )
# verify orig_size
lowerCAmelCase_: Optional[int] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCamelCase__ ) )
# verify size
lowerCAmelCase_: Union[str, Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCamelCase__ ) )
@slow
def _a ( self ):
# prepare image, target and masks_path
lowerCAmelCase_: Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f:
lowerCAmelCase_: List[Any] = json.loads(f.read() )
lowerCAmelCase_: Optional[Any] = {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}
lowerCAmelCase_: Union[str, Any] = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
lowerCAmelCase_: Union[str, Any] = DetaImageProcessor(format="coco_panoptic" )
lowerCAmelCase_: List[Any] = image_processing(images=lowerCamelCase__ , annotations=lowerCamelCase__ , masks_path=lowerCamelCase__ , return_tensors="pt" )
# verify pixel values
lowerCAmelCase_: Tuple = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , lowerCamelCase__ )
lowerCAmelCase_: str = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , lowerCamelCase__ , atol=1E-4 ) )
# verify area
lowerCAmelCase_: Any = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , lowerCamelCase__ ) )
# verify boxes
lowerCAmelCase_: Optional[Any] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCamelCase__ )
lowerCAmelCase_: int = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , lowerCamelCase__ , atol=1E-3 ) )
# verify image_id
lowerCAmelCase_: Union[str, Any] = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCamelCase__ ) )
# verify is_crowd
lowerCAmelCase_: List[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCamelCase__ ) )
# verify class_labels
lowerCAmelCase_: Dict = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCamelCase__ ) )
# verify masks
lowerCAmelCase_: Optional[int] = 822_873
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , lowerCamelCase__ )
# verify orig_size
lowerCAmelCase_: Any = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCamelCase__ ) )
# verify size
lowerCAmelCase_: Union[str, Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCamelCase__ ) ) | 613 | import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
LevitForImageClassification,
LevitForImageClassificationWithTeacher,
LevitModel,
)
from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
def _a ( self ):
lowerCAmelCase_: Optional[int] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowerCamelCase__ , "hidden_sizes" ) )
self.parent.assertTrue(hasattr(lowerCamelCase__ , "num_attention_heads" ) )
class _lowercase :
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=13 , lowerCamelCase__=64 , lowerCamelCase__=3 , lowerCamelCase__=3 , lowerCamelCase__=2 , lowerCamelCase__=1 , lowerCamelCase__=16 , lowerCamelCase__=[128, 256, 384] , lowerCamelCase__=[4, 6, 8] , lowerCamelCase__=[2, 3, 4] , lowerCamelCase__=[16, 16, 16] , lowerCamelCase__=0 , lowerCamelCase__=[2, 2, 2] , lowerCamelCase__=[2, 2, 2] , lowerCamelCase__=0.0_2 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=2 , ):
lowerCAmelCase_: int = parent
lowerCAmelCase_: Tuple = batch_size
lowerCAmelCase_: List[str] = image_size
lowerCAmelCase_: Tuple = num_channels
lowerCAmelCase_: Optional[int] = kernel_size
lowerCAmelCase_: int = stride
lowerCAmelCase_: Optional[int] = padding
lowerCAmelCase_: Tuple = hidden_sizes
lowerCAmelCase_: Union[str, Any] = num_attention_heads
lowerCAmelCase_: Tuple = depths
lowerCAmelCase_: Optional[int] = key_dim
lowerCAmelCase_: Optional[Any] = drop_path_rate
lowerCAmelCase_: List[str] = patch_size
lowerCAmelCase_: Any = attention_ratio
lowerCAmelCase_: Tuple = mlp_ratio
lowerCAmelCase_: Any = initializer_range
lowerCAmelCase_: List[str] = [
["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
lowerCAmelCase_: Any = is_training
lowerCAmelCase_: Optional[int] = use_labels
lowerCAmelCase_: Dict = num_labels
lowerCAmelCase_: Any = initializer_range
def _a ( self ):
lowerCAmelCase_: Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_: Tuple = None
if self.use_labels:
lowerCAmelCase_: int = ids_tensor([self.batch_size] , self.num_labels )
lowerCAmelCase_: Tuple = self.get_config()
return config, pixel_values, labels
def _a ( self ):
return LevitConfig(
image_size=self.image_size , num_channels=self.num_channels , kernel_size=self.kernel_size , stride=self.stride , padding=self.padding , patch_size=self.patch_size , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , depths=self.depths , key_dim=self.key_dim , drop_path_rate=self.drop_path_rate , mlp_ratio=self.mlp_ratio , attention_ratio=self.attention_ratio , initializer_range=self.initializer_range , down_ops=self.down_ops , )
def _a ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCAmelCase_: int = LevitModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
lowerCAmelCase_: Optional[Any] = model(lowerCamelCase__ )
lowerCAmelCase_: Tuple = (self.image_size, self.image_size)
lowerCAmelCase_ , lowerCAmelCase_: Optional[int] = image_size[0], image_size[1]
for _ in range(4 ):
lowerCAmelCase_: Union[str, Any] = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
lowerCAmelCase_: str = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1]) , )
def _a ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCAmelCase_: Tuple = self.num_labels
lowerCAmelCase_: str = LevitForImageClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
lowerCAmelCase_: Optional[Any] = model(lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self ):
lowerCAmelCase_: Union[str, Any] = self.prepare_config_and_inputs()
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_: Dict = config_and_inputs
lowerCAmelCase_: Tuple = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE: Any = (
(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE: int = (
{
'feature-extraction': LevitModel,
'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE: Optional[int] = False
SCREAMING_SNAKE_CASE: Optional[Any] = False
SCREAMING_SNAKE_CASE: str = False
SCREAMING_SNAKE_CASE: str = False
SCREAMING_SNAKE_CASE: List[Any] = False
def _a ( self ):
lowerCAmelCase_: List[str] = LevitModelTester(self )
lowerCAmelCase_: Union[str, Any] = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ , hidden_size=37 )
def _a ( self ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self ):
return
@unittest.skip(reason="Levit does not use inputs_embeds" )
def _a ( self ):
pass
@unittest.skip(reason="Levit does not support input and output embeddings" )
def _a ( self ):
pass
@unittest.skip(reason="Levit does not output attentions" )
def _a ( self ):
pass
def _a ( self ):
lowerCAmelCase_ , lowerCAmelCase_: Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_: List[str] = model_class(lowerCamelCase__ )
lowerCAmelCase_: Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_: Any = [*signature.parameters.keys()]
lowerCAmelCase_: str = ["pixel_values"]
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def _a ( self ):
def check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCAmelCase_: int = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
with torch.no_grad():
lowerCAmelCase_: Dict = model(**self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
lowerCAmelCase_: str = outputs.hidden_states
lowerCAmelCase_: List[Any] = len(self.model_tester.depths ) + 1
self.assertEqual(len(lowerCamelCase__ ) , lowerCamelCase__ )
lowerCAmelCase_: List[Any] = (self.model_tester.image_size, self.model_tester.image_size)
lowerCAmelCase_ , lowerCAmelCase_: int = image_size[0], image_size[1]
for _ in range(4 ):
lowerCAmelCase_: Tuple = floor(
(
(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
lowerCAmelCase_: Optional[int] = floor(
(
(width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [
height * width,
self.model_tester.hidden_sizes[0],
] , )
lowerCAmelCase_ , lowerCAmelCase_: str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_: int = True
check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase_: Union[str, Any] = True
check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def _a ( self ):
pass
def _a ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ):
lowerCAmelCase_: List[str] = super()._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
if return_labels:
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def _a ( self ):
lowerCAmelCase_: Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def _a ( self ):
lowerCAmelCase_: str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )
def _a ( self ):
if not self.model_tester.is_training:
return
lowerCAmelCase_ , lowerCAmelCase_: Tuple = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase_: int = True
for model_class in self.all_model_classes:
# LevitForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(lowerCamelCase__ )
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
lowerCAmelCase_: Tuple = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.train()
lowerCAmelCase_: Optional[int] = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
lowerCAmelCase_: Tuple = model(**lowerCamelCase__ ).loss
loss.backward()
def _a ( self ):
lowerCAmelCase_ , lowerCAmelCase_: List[str] = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
lowerCAmelCase_: Any = False
lowerCAmelCase_: Tuple = True
for model_class in self.all_model_classes:
if model_class in get_values(lowerCamelCase__ ) or not model_class.supports_gradient_checkpointing:
continue
# LevitForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
continue
lowerCAmelCase_: Any = model_class(lowerCamelCase__ )
model.gradient_checkpointing_enable()
model.to(lowerCamelCase__ )
model.train()
lowerCAmelCase_: str = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
lowerCAmelCase_: List[str] = model(**lowerCamelCase__ ).loss
loss.backward()
def _a ( self ):
lowerCAmelCase_ , lowerCAmelCase_: int = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase_: Optional[int] = [
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
{"title": "regression", "num_labels": 1, "dtype": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(lowerCamelCase__ ),
]
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}''' ):
lowerCAmelCase_: Tuple = problem_type["title"]
lowerCAmelCase_: Optional[Any] = problem_type["num_labels"]
lowerCAmelCase_: List[str] = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.train()
lowerCAmelCase_: List[Any] = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
if problem_type["num_labels"] > 1:
lowerCAmelCase_: List[str] = inputs["labels"].unsqueeze(1 ).repeat(1 , problem_type["num_labels"] )
lowerCAmelCase_: List[Any] = inputs["labels"].to(problem_type["dtype"] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=lowerCamelCase__ ) as warning_list:
lowerCAmelCase_: Dict = model(**lowerCamelCase__ ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
F'''Something is going wrong in the regression problem: intercepted {w.message}''' )
loss.backward()
@slow
def _a ( self ):
for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_: Optional[int] = LevitModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def snake_case__ ( ):
lowerCAmelCase_: Optional[int] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _a ( self ):
return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def _a ( self ):
lowerCAmelCase_: Any = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
lowerCamelCase__ )
lowerCAmelCase_: str = self.default_image_processor
lowerCAmelCase_: int = prepare_img()
lowerCAmelCase_: Optional[int] = image_processor(images=lowerCamelCase__ , return_tensors="pt" ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
lowerCAmelCase_: Tuple = model(**lowerCamelCase__ )
# verify the logits
lowerCAmelCase_: str = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
lowerCAmelCase_: Union[str, Any] = torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase__ , atol=1E-4 ) ) | 613 | 1 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
from transformers import Trainer
class __SCREAMING_SNAKE_CASE ( lowercase):
def __init__( self : List[str] , __UpperCamelCase : int = 101 ):
_UpperCAmelCase = length
def __len__( self : Dict ):
return self.length
def __getitem__( self : List[Any] , __UpperCamelCase : List[str] ):
return i
class __SCREAMING_SNAKE_CASE :
def __call__( self : Any , __UpperCamelCase : str ):
return {"input_ids": torch.tensor(__UpperCamelCase ), "labels": torch.tensor(__UpperCamelCase )}
class __SCREAMING_SNAKE_CASE ( nn.Module):
def __init__( self : Optional[Any] ):
super().__init__()
# Add some (unused) params otherwise DDP will complain.
_UpperCAmelCase = nn.Linear(120 , 80 )
def UpperCAmelCase__ ( self : int , __UpperCamelCase : Tuple , __UpperCamelCase : str=None ):
if labels is not None:
return torch.tensor(0.0 , device=input_ids.device ), input_ids
else:
return input_ids
class __SCREAMING_SNAKE_CASE ( lowercase):
@require_torch_neuroncore
def UpperCAmelCase__ ( self : Dict ):
_UpperCAmelCase = F'''--nproc_per_node=2
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
'''.split()
_UpperCAmelCase = self.get_auto_remove_tmp_dir()
_UpperCAmelCase = F'''--output_dir {output_dir}'''.split()
_UpperCAmelCase = ["torchrun"] + distributed_args + args
execute_subprocess_async(__UpperCamelCase , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
class __SCREAMING_SNAKE_CASE ( lowercase):
@require_torch_multi_gpu
def UpperCAmelCase__ ( self : str ):
_UpperCAmelCase = F'''--nproc_per_node={torch.cuda.device_count()}
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
'''.split()
_UpperCAmelCase = self.get_auto_remove_tmp_dir()
_UpperCAmelCase = F'''--output_dir {output_dir}'''.split()
_UpperCAmelCase = ["torchrun"] + distributed_args + args
execute_subprocess_async(__UpperCamelCase , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
#
# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py
__lowerCAmelCase = HfArgumentParser((TrainingArguments,))
__lowerCAmelCase = parser.parse_args_into_dataclasses()[0]
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
F'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
)
# Essentially, what we want to verify in the distributed case is that we get all samples back,
# in the right order. (this is crucial for prediction for instance)
for dataset_length in [1_0_1, 4_0, 7]:
__lowerCAmelCase = DummyDataset(dataset_length)
def __lowerCamelCase ( _lowerCAmelCase ) -> Dict:
_UpperCAmelCase = list(range(len(_lowerCAmelCase ) ) )
_UpperCAmelCase = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
if not success and training_args.local_rank == 0:
logger.warning(
"Predictions and/or labels do not match expected results:\n - predictions: "
F'''{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}''' )
return {"success": success}
__lowerCAmelCase = Trainer(
model=DummyModel(),
args=training_args,
data_collator=DummyDataCollator(),
eval_dataset=dataset,
compute_metrics=compute_metrics,
)
__lowerCAmelCase = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
__lowerCAmelCase = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
__lowerCAmelCase = 2
__lowerCAmelCase = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
__lowerCAmelCase = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
__lowerCAmelCase = None
| 129 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = r"\n Args:\n input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n Indices of input sequence tokens in the vocabulary.\n\n Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details.\n\n [What are input IDs?](../glossary#input-ids)\n scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n or scores for each vocabulary token after SoftMax.\n kwargs (`Dict[str, Any]`, *optional*):\n Additional stopping criteria specific kwargs.\n\n Return:\n `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n"
class __SCREAMING_SNAKE_CASE ( lowercase):
@add_start_docstrings(__UpperCamelCase )
def __call__( self : Optional[int] , __UpperCamelCase : torch.LongTensor , __UpperCamelCase : torch.FloatTensor , **__UpperCamelCase : str ):
raise NotImplementedError("StoppingCriteria needs to be subclassed" )
class __SCREAMING_SNAKE_CASE ( lowercase):
def __init__( self : Optional[Any] , __UpperCamelCase : int , __UpperCamelCase : Optional[int] = None ):
_UpperCAmelCase = max_length
_UpperCAmelCase = max_position_embeddings
@add_start_docstrings(__UpperCamelCase )
def __call__( self : Union[str, Any] , __UpperCamelCase : torch.LongTensor , __UpperCamelCase : torch.FloatTensor , **__UpperCamelCase : Any ):
_UpperCAmelCase = input_ids.shape[-1]
_UpperCAmelCase = cur_len >= self.max_length
if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
logger.warning_once(
"This is a friendly reminder - the current text generation call will exceed the model's predefined "
F'''maximum length ({self.max_position_embeddings}). Depending on the model, you may observe '''
"exceptions, performance degradation, or nothing at all." )
return is_done
class __SCREAMING_SNAKE_CASE ( lowercase):
def __init__( self : Tuple , __UpperCamelCase : int , __UpperCamelCase : int ):
warnings.warn(
"The class `MaxNewTokensCriteria` is deprecated. "
F'''Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` '''
"with `max_length = start_length + max_new_tokens` instead." , __UpperCamelCase , )
_UpperCAmelCase = start_length
_UpperCAmelCase = max_new_tokens
_UpperCAmelCase = start_length + max_new_tokens
@add_start_docstrings(__UpperCamelCase )
def __call__( self : Union[str, Any] , __UpperCamelCase : torch.LongTensor , __UpperCamelCase : torch.FloatTensor , **__UpperCamelCase : List[str] ):
return input_ids.shape[-1] >= self.max_length
class __SCREAMING_SNAKE_CASE ( lowercase):
def __init__( self : Union[str, Any] , __UpperCamelCase : float , __UpperCamelCase : Optional[float] = None ):
_UpperCAmelCase = max_time
_UpperCAmelCase = time.time() if initial_timestamp is None else initial_timestamp
@add_start_docstrings(__UpperCamelCase )
def __call__( self : Union[str, Any] , __UpperCamelCase : torch.LongTensor , __UpperCamelCase : torch.FloatTensor , **__UpperCamelCase : Any ):
return time.time() - self.initial_timestamp > self.max_time
class __SCREAMING_SNAKE_CASE ( lowercase):
@add_start_docstrings(__UpperCamelCase )
def __call__( self : int , __UpperCamelCase : torch.LongTensor , __UpperCamelCase : torch.FloatTensor , **__UpperCamelCase : Optional[Any] ):
return any(criteria(__UpperCamelCase , __UpperCamelCase ) for criteria in self )
@property
def UpperCAmelCase__ ( self : Dict ):
for stopping_criterium in self:
if isinstance(__UpperCamelCase , __UpperCamelCase ):
return stopping_criterium.max_length
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
return stopping_criterium.max_length
return None
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> StoppingCriteriaList:
_UpperCAmelCase = stopping_criteria.max_length
_UpperCAmelCase = deepcopy(_lowerCAmelCase )
if stopping_max_length is not None and stopping_max_length != max_length:
warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter" , _lowerCAmelCase )
elif stopping_max_length is None:
new_stopping_criteria.append(MaxLengthCriteria(max_length=_lowerCAmelCase ) )
return new_stopping_criteria
| 129 | 1 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__magic_name__ : Optional[Any] = logging.get_logger(__name__)
__magic_name__ : Tuple = {
'''b0''': efficientnet.EfficientNetBa,
'''b1''': efficientnet.EfficientNetBa,
'''b2''': efficientnet.EfficientNetBa,
'''b3''': efficientnet.EfficientNetBa,
'''b4''': efficientnet.EfficientNetBa,
'''b5''': efficientnet.EfficientNetBa,
'''b6''': efficientnet.EfficientNetBa,
'''b7''': efficientnet.EfficientNetBa,
}
__magic_name__ : Dict = {
'''b0''': {
'''hidden_dim''': 1280,
'''width_coef''': 1.0,
'''depth_coef''': 1.0,
'''image_size''': 224,
'''dropout_rate''': 0.2,
'''dw_padding''': [],
},
'''b1''': {
'''hidden_dim''': 1280,
'''width_coef''': 1.0,
'''depth_coef''': 1.1,
'''image_size''': 240,
'''dropout_rate''': 0.2,
'''dw_padding''': [16],
},
'''b2''': {
'''hidden_dim''': 1408,
'''width_coef''': 1.1,
'''depth_coef''': 1.2,
'''image_size''': 260,
'''dropout_rate''': 0.3,
'''dw_padding''': [5, 8, 16],
},
'''b3''': {
'''hidden_dim''': 1536,
'''width_coef''': 1.2,
'''depth_coef''': 1.4,
'''image_size''': 300,
'''dropout_rate''': 0.3,
'''dw_padding''': [5, 18],
},
'''b4''': {
'''hidden_dim''': 1792,
'''width_coef''': 1.4,
'''depth_coef''': 1.8,
'''image_size''': 380,
'''dropout_rate''': 0.4,
'''dw_padding''': [6],
},
'''b5''': {
'''hidden_dim''': 2048,
'''width_coef''': 1.6,
'''depth_coef''': 2.2,
'''image_size''': 456,
'''dropout_rate''': 0.4,
'''dw_padding''': [13, 27],
},
'''b6''': {
'''hidden_dim''': 2304,
'''width_coef''': 1.8,
'''depth_coef''': 2.6,
'''image_size''': 528,
'''dropout_rate''': 0.5,
'''dw_padding''': [31],
},
'''b7''': {
'''hidden_dim''': 2560,
'''width_coef''': 2.0,
'''depth_coef''': 3.1,
'''image_size''': 600,
'''dropout_rate''': 0.5,
'''dw_padding''': [18],
},
}
def lowercase__ ( _UpperCamelCase) -> List[Any]:
"""simple docstring"""
UpperCamelCase = EfficientNetConfig()
UpperCamelCase = CONFIG_MAP[model_name]['hidden_dim']
UpperCamelCase = CONFIG_MAP[model_name]['width_coef']
UpperCamelCase = CONFIG_MAP[model_name]['depth_coef']
UpperCamelCase = CONFIG_MAP[model_name]['image_size']
UpperCamelCase = CONFIG_MAP[model_name]['dropout_rate']
UpperCamelCase = CONFIG_MAP[model_name]['dw_padding']
UpperCamelCase = 'huggingface/label-files'
UpperCamelCase = 'imagenet-1k-id2label.json'
UpperCamelCase = 10_00
UpperCamelCase = json.load(open(hf_hub_download(_lowercase , _lowercase , repo_type='dataset') , 'r'))
UpperCamelCase = {int(_lowercase): v for k, v in idalabel.items()}
UpperCamelCase = idalabel
UpperCamelCase = {v: k for k, v in idalabel.items()}
return config
def lowercase__ ( ) -> List[str]:
"""simple docstring"""
UpperCamelCase = 'http://images.cocodataset.org/val2017/000000039769.jpg'
UpperCamelCase = Image.open(requests.get(_lowercase , stream=_lowercase).raw)
return im
def lowercase__ ( _UpperCamelCase) -> int:
"""simple docstring"""
UpperCamelCase = CONFIG_MAP[model_name]['image_size']
UpperCamelCase = EfficientNetImageProcessor(
size={'height': size, 'width': size} , image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3] , do_center_crop=_lowercase , )
return preprocessor
def lowercase__ ( _UpperCamelCase) -> int:
"""simple docstring"""
UpperCamelCase = [v.split('_')[0].split('block')[1] for v in original_param_names if v.startswith('block')]
UpperCamelCase = sorted(set(_lowercase))
UpperCamelCase = len(_lowercase)
UpperCamelCase = {b: str(_lowercase) for b, i in zip(_lowercase , range(_lowercase))}
UpperCamelCase = []
rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight'))
rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight'))
rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias'))
rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean'))
rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var'))
for b in block_names:
UpperCamelCase = block_name_mapping[b]
rename_keys.append((F'block{b}_expand_conv/kernel:0', F'encoder.blocks.{hf_b}.expansion.expand_conv.weight'))
rename_keys.append((F'block{b}_expand_bn/gamma:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.weight'))
rename_keys.append((F'block{b}_expand_bn/beta:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.bias'))
rename_keys.append(
(F'block{b}_expand_bn/moving_mean:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean'))
rename_keys.append(
(F'block{b}_expand_bn/moving_variance:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_var'))
rename_keys.append(
(F'block{b}_dwconv/depthwise_kernel:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight'))
rename_keys.append((F'block{b}_bn/gamma:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight'))
rename_keys.append((F'block{b}_bn/beta:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias'))
rename_keys.append(
(F'block{b}_bn/moving_mean:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean'))
rename_keys.append(
(F'block{b}_bn/moving_variance:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var'))
rename_keys.append((F'block{b}_se_reduce/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight'))
rename_keys.append((F'block{b}_se_reduce/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias'))
rename_keys.append((F'block{b}_se_expand/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.weight'))
rename_keys.append((F'block{b}_se_expand/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.bias'))
rename_keys.append(
(F'block{b}_project_conv/kernel:0', F'encoder.blocks.{hf_b}.projection.project_conv.weight'))
rename_keys.append((F'block{b}_project_bn/gamma:0', F'encoder.blocks.{hf_b}.projection.project_bn.weight'))
rename_keys.append((F'block{b}_project_bn/beta:0', F'encoder.blocks.{hf_b}.projection.project_bn.bias'))
rename_keys.append(
(F'block{b}_project_bn/moving_mean:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_mean'))
rename_keys.append(
(F'block{b}_project_bn/moving_variance:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_var'))
rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight'))
rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight'))
rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias'))
rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean'))
rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var'))
UpperCamelCase = {}
for item in rename_keys:
if item[0] in original_param_names:
UpperCamelCase = 'efficientnet.' + item[1]
UpperCamelCase = 'classifier.weight'
UpperCamelCase = 'classifier.bias'
return key_mapping
def lowercase__ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase) -> Any:
"""simple docstring"""
for key, value in tf_params.items():
if "normalization" in key:
continue
UpperCamelCase = key_mapping[key]
if "_conv" in key and "kernel" in key:
UpperCamelCase = torch.from_numpy(_lowercase).permute(3 , 2 , 0 , 1)
elif "depthwise_kernel" in key:
UpperCamelCase = torch.from_numpy(_lowercase).permute(2 , 3 , 0 , 1)
elif "kernel" in key:
UpperCamelCase = torch.from_numpy(np.transpose(_lowercase))
else:
UpperCamelCase = torch.from_numpy(_lowercase)
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(_lowercase)
@torch.no_grad()
def lowercase__ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = model_classes[model_name](
include_top=_lowercase , weights='imagenet' , input_tensor=_lowercase , input_shape=_lowercase , pooling=_lowercase , classes=10_00 , classifier_activation='softmax' , )
UpperCamelCase = original_model.trainable_variables
UpperCamelCase = original_model.non_trainable_variables
UpperCamelCase = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
UpperCamelCase = param.numpy()
UpperCamelCase = list(tf_params.keys())
# Load HuggingFace model
UpperCamelCase = get_efficientnet_config(_lowercase)
UpperCamelCase = EfficientNetForImageClassification(_lowercase).eval()
UpperCamelCase = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('Converting parameters...')
UpperCamelCase = rename_keys(_lowercase)
replace_params(_lowercase , _lowercase , _lowercase)
# Initialize preprocessor and preprocess input image
UpperCamelCase = convert_image_processor(_lowercase)
UpperCamelCase = preprocessor(images=prepare_img() , return_tensors='pt')
# HF model inference
hf_model.eval()
with torch.no_grad():
UpperCamelCase = hf_model(**_lowercase)
UpperCamelCase = outputs.logits.detach().numpy()
# Original model inference
UpperCamelCase = False
UpperCamelCase = CONFIG_MAP[model_name]['image_size']
UpperCamelCase = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST)
UpperCamelCase = image.img_to_array(_lowercase)
UpperCamelCase = np.expand_dims(_lowercase , axis=0)
UpperCamelCase = original_model.predict(_lowercase)
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(_lowercase , _lowercase , atol=1e-3), "The predicted logits are not the same."
print('Model outputs match!')
if save_model:
# Create folder to save model
if not os.path.isdir(_lowercase):
os.mkdir(_lowercase)
# Save converted model and image processor
hf_model.save_pretrained(_lowercase)
preprocessor.save_pretrained(_lowercase)
if push_to_hub:
# Push model and image processor to hub
print(F'Pushing converted {model_name} to the hub...')
UpperCamelCase = F'efficientnet-{model_name}'
preprocessor.push_to_hub(_lowercase)
hf_model.push_to_hub(_lowercase)
if __name__ == "__main__":
__magic_name__ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''b0''',
type=str,
help='''Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''hf_model''',
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
__magic_name__ : str = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 280 |
"""simple docstring"""
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
lowercase_ = True
from torch.cuda.amp import autocast
lowercase_ = logging.getLogger(__name__)
def UpperCAmelCase ( _lowercase : Optional[Any]=None , _lowercase : str=None ) -> List[str]:
"""simple docstring"""
return field(default_factory=lambda: default , metadata=_lowercase )
@dataclass
class __a :
lowerCamelCase : str =field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowerCamelCase : Optional[str] =field(
default=__snake_case , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
lowerCamelCase : Optional[bool] =field(
default=__snake_case , metadata={'help': 'Whether to freeze the feature extractor layers of the model.'} )
lowerCamelCase : Optional[float] =field(
default=0.1 , metadata={'help': 'The dropout ratio for the attention probabilities.'} )
lowerCamelCase : Optional[float] =field(
default=0.1 , metadata={'help': 'The dropout ratio for activations inside the fully connected layer.'} )
lowerCamelCase : Optional[float] =field(
default=0.1 , metadata={
'help': 'The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.'
} , )
lowerCamelCase : Optional[float] =field(
default=0.1 , metadata={'help': 'The dropout probabilitiy for all 1D convolutional layers in feature extractor.'} , )
lowerCamelCase : Optional[float] =field(
default=0.05 , metadata={
'help': (
'Propability of each feature vector along the time axis to be chosen as the start of the vector'
'span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature'
'vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``.'
)
} , )
lowerCamelCase : Optional[float] =field(default=0.0 , metadata={'help': 'The LayerDrop probability.'} )
@dataclass
class __a :
lowerCamelCase : Optional[str] =field(
default=__snake_case , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
lowerCamelCase : Optional[str] =field(
default='train+validation' , metadata={
'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
} , )
lowerCamelCase : bool =field(
default=__snake_case , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
lowerCamelCase : Optional[int] =field(
default=__snake_case , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
lowerCamelCase : Optional[int] =field(
default=__snake_case , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
lowerCamelCase : Optional[int] =field(
default=__snake_case , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of validation examples to this '
'value if set.'
)
} , )
lowerCamelCase : List[str] =list_field(
default=[',', '?', '.', '!', '-', ';', ':', '""', '%', '\'', '"', '�'] , metadata={'help': 'A list of characters to remove from the transcripts.'} , )
@dataclass
class __a :
lowerCamelCase : WavaVecaProcessor
lowerCamelCase : Union[bool, str] =True
lowerCamelCase : Optional[int] =None
lowerCamelCase : Optional[int] =None
lowerCamelCase : Optional[int] =None
lowerCamelCase : Optional[int] =None
def __call__( self , UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = [{'''input_values''': feature['''input_values''']} for feature in features]
lowerCAmelCase_ = [{'''input_ids''': feature['''labels''']} for feature in features]
lowerCAmelCase_ = self.processor.pad(
UpperCAmelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
lowerCAmelCase_ = self.processor.pad(
labels=UpperCAmelCase , padding=self.padding , max_length=self.max_length_labels , pad_to_multiple_of=self.pad_to_multiple_of_labels , return_tensors='''pt''' , )
# replace padding with -100 to ignore loss correctly
lowerCAmelCase_ = labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1 ) , -100 )
lowerCAmelCase_ = labels
return batch
class __a ( __snake_case ):
def lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase ):
'''simple docstring'''
model.train()
lowerCAmelCase_ = self._prepare_inputs(UpperCAmelCase )
if self.use_amp:
with autocast():
lowerCAmelCase_ = self.compute_loss(UpperCAmelCase , UpperCAmelCase )
else:
lowerCAmelCase_ = self.compute_loss(UpperCAmelCase , UpperCAmelCase )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
lowerCAmelCase_ = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
lowerCAmelCase_ = loss.sum() / (inputs['''labels'''] >= 0).sum()
else:
raise ValueError(F"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']""" )
if self.args.gradient_accumulation_steps > 1:
lowerCAmelCase_ = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(UpperCAmelCase ).backward()
elif self.use_apex:
with amp.scale_loss(UpperCAmelCase , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(UpperCAmelCase )
else:
loss.backward()
return loss.detach()
def UpperCAmelCase ( ) -> Tuple:
"""simple docstring"""
lowerCAmelCase_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
lowerCAmelCase_ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowerCAmelCase_ = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info('''Training/evaluation parameters %s''' , _lowercase )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
lowerCAmelCase_ = datasets.load_dataset(
'''common_voice''' , data_args.dataset_config_name , split=data_args.train_split_name )
lowerCAmelCase_ = datasets.load_dataset('''common_voice''' , data_args.dataset_config_name , split='''test''' )
# Create and save tokenizer
lowerCAmelCase_ = F"""[{"".join(data_args.chars_to_ignore )}]"""
def remove_special_characters(_lowercase : List[str] ):
lowerCAmelCase_ = re.sub(_lowercase , '''''' , batch['''sentence'''] ).lower() + ''' '''
return batch
lowerCAmelCase_ = train_dataset.map(_lowercase , remove_columns=['''sentence'''] )
lowerCAmelCase_ = eval_dataset.map(_lowercase , remove_columns=['''sentence'''] )
def extract_all_chars(_lowercase : str ):
lowerCAmelCase_ = ''' '''.join(batch['''text'''] )
lowerCAmelCase_ = list(set(_lowercase ) )
return {"vocab": [vocab], "all_text": [all_text]}
lowerCAmelCase_ = train_dataset.map(
_lowercase , batched=_lowercase , batch_size=-1 , keep_in_memory=_lowercase , remove_columns=train_dataset.column_names , )
lowerCAmelCase_ = train_dataset.map(
_lowercase , batched=_lowercase , batch_size=-1 , keep_in_memory=_lowercase , remove_columns=eval_dataset.column_names , )
lowerCAmelCase_ = list(set(vocab_train['''vocab'''][0] ) | set(vocab_test['''vocab'''][0] ) )
lowerCAmelCase_ = {v: k for k, v in enumerate(_lowercase )}
lowerCAmelCase_ = vocab_dict[''' ''']
del vocab_dict[" "]
lowerCAmelCase_ = len(_lowercase )
lowerCAmelCase_ = len(_lowercase )
with open('''vocab.json''' , '''w''' ) as vocab_file:
json.dump(_lowercase , _lowercase )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowerCAmelCase_ = WavaVecaCTCTokenizer(
'''vocab.json''' , unk_token='''[UNK]''' , pad_token='''[PAD]''' , word_delimiter_token='''|''' , )
lowerCAmelCase_ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6_0_0_0 , padding_value=0.0 , do_normalize=_lowercase , return_attention_mask=_lowercase )
lowerCAmelCase_ = WavaVecaProcessor(feature_extractor=_lowercase , tokenizer=_lowercase )
lowerCAmelCase_ = WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , activation_dropout=model_args.activation_dropout , attention_dropout=model_args.attention_dropout , hidden_dropout=model_args.hidden_dropout , feat_proj_dropout=model_args.feat_proj_dropout , mask_time_prob=model_args.mask_time_prob , gradient_checkpointing=training_args.gradient_checkpointing , layerdrop=model_args.layerdrop , ctc_loss_reduction='''mean''' , pad_token_id=processor.tokenizer.pad_token_id , vocab_size=len(processor.tokenizer ) , )
if data_args.max_train_samples is not None:
lowerCAmelCase_ = min(len(_lowercase ) , data_args.max_train_samples )
lowerCAmelCase_ = train_dataset.select(range(_lowercase ) )
if data_args.max_val_samples is not None:
lowerCAmelCase_ = eval_dataset.select(range(data_args.max_val_samples ) )
lowerCAmelCase_ = torchaudio.transforms.Resample(4_8_0_0_0 , 1_6_0_0_0 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(_lowercase : Union[str, Any] ):
lowerCAmelCase_ , lowerCAmelCase_ = torchaudio.load(batch['''path'''] )
lowerCAmelCase_ = resampler(_lowercase ).squeeze().numpy()
lowerCAmelCase_ = 1_6_0_0_0
lowerCAmelCase_ = batch['''text''']
return batch
lowerCAmelCase_ = train_dataset.map(
_lowercase , remove_columns=train_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
lowerCAmelCase_ = eval_dataset.map(
_lowercase , remove_columns=eval_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
def prepare_dataset(_lowercase : str ):
# check that all files have the correct sampling rate
assert (
len(set(batch['''sampling_rate'''] ) ) == 1
), F"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""
lowerCAmelCase_ = processor(
audio=batch['''speech'''] , text=batch['''target_text'''] , sampling_rate=batch['''sampling_rate'''][0] )
batch.update(_lowercase )
return batch
lowerCAmelCase_ = train_dataset.map(
_lowercase , remove_columns=train_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=_lowercase , num_proc=data_args.preprocessing_num_workers , )
lowerCAmelCase_ = eval_dataset.map(
_lowercase , remove_columns=eval_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=_lowercase , num_proc=data_args.preprocessing_num_workers , )
# Metric
lowerCAmelCase_ = datasets.load_metric('''wer''' )
def compute_metrics(_lowercase : Optional[int] ):
lowerCAmelCase_ = pred.predictions
lowerCAmelCase_ = np.argmax(_lowercase , axis=-1 )
lowerCAmelCase_ = processor.tokenizer.pad_token_id
lowerCAmelCase_ = processor.batch_decode(_lowercase )
# we do not want to group tokens when computing the metrics
lowerCAmelCase_ = processor.batch_decode(pred.label_ids , group_tokens=_lowercase )
lowerCAmelCase_ = wer_metric.compute(predictions=_lowercase , references=_lowercase )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
lowerCAmelCase_ = DataCollatorCTCWithPadding(processor=_lowercase , padding=_lowercase )
# Initialize our Trainer
lowerCAmelCase_ = CTCTrainer(
model=_lowercase , data_collator=_lowercase , args=_lowercase , compute_metrics=_lowercase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=processor.feature_extractor , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
lowerCAmelCase_ = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
lowerCAmelCase_ = model_args.model_name_or_path
else:
lowerCAmelCase_ = None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
lowerCAmelCase_ = trainer.train(resume_from_checkpoint=_lowercase )
trainer.save_model()
lowerCAmelCase_ = train_result.metrics
lowerCAmelCase_ = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_lowercase )
)
lowerCAmelCase_ = min(_lowercase , len(_lowercase ) )
trainer.log_metrics('''train''' , _lowercase )
trainer.save_metrics('''train''' , _lowercase )
trainer.save_state()
# Evaluation
lowerCAmelCase_ = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowerCAmelCase_ = trainer.evaluate()
lowerCAmelCase_ = data_args.max_val_samples if data_args.max_val_samples is not None else len(_lowercase )
lowerCAmelCase_ = min(_lowercase , len(_lowercase ) )
trainer.log_metrics('''eval''' , _lowercase )
trainer.save_metrics('''eval''' , _lowercase )
return results
if __name__ == "__main__":
main() | 552 | 0 |
"""simple docstring"""
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase_ : Dict = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
class UpperCamelCase_ ( a_ , unittest.TestCase ):
_A : str = XLMProphetNetTokenizer
_A : List[Any] = False
_A : Tuple = True
def UpperCamelCase_ ( self ) -> Any:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase = XLMProphetNetTokenizer(snake_case__ , keep_accents=snake_case__ )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase = """[PAD]"""
UpperCAmelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__ ) , snake_case__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__ ) , snake_case__ )
def UpperCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """[PAD]""" )
self.assertEqual(vocab_keys[1] , """[CLS]""" )
self.assertEqual(vocab_keys[-1] , """j""" )
self.assertEqual(len(snake_case__ ) , 10_12 )
def UpperCamelCase_ ( self ) -> Any:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 10_12 )
def UpperCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase = XLMProphetNetTokenizer(snake_case__ , keep_accents=snake_case__ )
UpperCAmelCase = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(snake_case__ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(snake_case__ ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
UpperCAmelCase = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
snake_case__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
UpperCAmelCase = tokenizer.convert_tokens_to_ids(snake_case__ )
self.assertListEqual(
snake_case__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, -9, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, -9, 4]
] , )
UpperCAmelCase = tokenizer.convert_ids_to_tokens(snake_case__ )
self.assertListEqual(
snake_case__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""[UNK]""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""[UNK]""",
""".""",
] , )
@cached_property
def UpperCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased""" )
@slow
def UpperCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase = """Hello World!"""
UpperCAmelCase = [3_53_89, 66_72, 49, 2]
self.assertListEqual(snake_case__ , self.big_tokenizer.encode(snake_case__ ) )
@slow
def UpperCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase = {"""input_ids""": [[1_10_73, 8_27_83, 18, 26, 8_27_83, 5_49, 5_15_40, 2_48, 1_72_09, 13_01, 2_17, 20, 21_51_86, 13_25, 1_47, 1_72_09, 13_01, 2_17, 20, 5_63_70, 53, 12_20_20, 20, 1_64_77, 27, 8_73_55, 45_48, 20, 47_28, 7_83_92, 17, 15_99_69, 18, 26, 2_44_91, 6_29, 15, 5_38, 2_27_04, 54_39, 15, 27_88, 2_44_91, 98_85, 15, 4_35_34, 6_05, 15, 8_14, 1_84_03, 3_32_00, 29, 15, 4_35_34, 2_44_58, 1_24_10, 1_11, 2_49_66, 8_36_69, 96_37, 14_40_68, 26, 8_50, 2_23_46, 27, 1_47, 2_49_66, 8_36_69, 8_34_90, 26, 3_91_13, 7_35, 27, 6_89, 6_56, 28_00, 13_39, 46_00, 53, 12_20_20, 11_57_85, 34, 8_16, 13_39, 4_68_87, 18, 1_47, 5_39_05, 19_51, 4_22_38, 4_11_70, 1_77_32, 8_34, 4_36, 15, 2_75_23, 9_87_33, 2_17, 1_47, 55_42, 49_81, 9_30, 1_73_47, 16, 2], [2_00_91, 6_29, 94, 8_27_86, 58, 4_90, 20, 15_28, 84, 5_39_05, 3_44, 8_05_92, 11_01_28, 1_88_22, 52_67, 13_06, 62, 15_25_37, 3_08, 79_97, 4_01, 12_44_27, 5_49, 3_54_42, 2_25, 1_09, 1_50_55, 2_57_48, 1_47, 71_19, 4_37_12, 34, 7_67, 13_53_66, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_92, 6_37_84, 11_94_66, 17, 14_78_08, 8_82_14, 18, 6_56, 81, 32, 32_96, 1_02_80, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=snake_case__ , model_name="""microsoft/xprophetnet-large-wiki100-cased""" , revision="""1acad1643ddd54a44df6a1b797ada8373685d90e""" , )
| 378 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Any = logging.get_logger(__name__)
lowerCAmelCase_ : Any = {
'''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
# See all CANINE models at https://huggingface.co/models?filter=canine
}
class UpperCamelCase_ ( a_ ):
_A : Tuple = 'canine'
def __init__( self , snake_case__=7_68 , snake_case__=12 , snake_case__=12 , snake_case__=30_72 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=1_63_84 , snake_case__=16 , snake_case__=0.02 , snake_case__=1e-12 , snake_case__=0 , snake_case__=0xE_0_0_0 , snake_case__=0xE_0_0_1 , snake_case__=4 , snake_case__=4 , snake_case__=8 , snake_case__=1_63_84 , snake_case__=1_28 , **snake_case__ , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , **snake_case__ )
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = initializer_range
UpperCAmelCase = type_vocab_size
UpperCAmelCase = layer_norm_eps
# Character config:
UpperCAmelCase = downsampling_rate
UpperCAmelCase = upsampling_kernel_size
UpperCAmelCase = num_hash_functions
UpperCAmelCase = num_hash_buckets
UpperCAmelCase = local_transformer_stride
| 378 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'RWKV/rwkv-4-169m-pile': 'https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json',
'RWKV/rwkv-4-430m-pile': 'https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json',
'RWKV/rwkv-4-1b5-pile': 'https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json',
'RWKV/rwkv-4-3b-pile': 'https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json',
'RWKV/rwkv-4-7b-pile': 'https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json',
'RWKV/rwkv-4-14b-pile': 'https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json',
'RWKV/rwkv-raven-1b5': 'https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json',
'RWKV/rwkv-raven-3b': 'https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json',
'RWKV/rwkv-raven-7b': 'https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json',
'RWKV/rwkv-raven-14b': 'https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json',
}
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Any = '''rwkv'''
UpperCamelCase : Optional[int] = {'''max_position_embeddings''': '''context_length'''}
def __init__( self : List[Any] , UpperCAmelCase__ : Optional[int]=50277 , UpperCAmelCase__ : List[Any]=1024 , UpperCAmelCase__ : Dict=4096 , UpperCAmelCase__ : Optional[Any]=32 , UpperCAmelCase__ : Optional[Any]=None , UpperCAmelCase__ : Any=None , UpperCAmelCase__ : int=1E-5 , UpperCAmelCase__ : Tuple=0 , UpperCAmelCase__ : Union[str, Any]=0 , UpperCAmelCase__ : Any=6 , UpperCAmelCase__ : Optional[Any]=False , UpperCAmelCase__ : Optional[int]=True , **UpperCAmelCase__ : List[Any] , ) -> Optional[int]:
_a : Dict = vocab_size
_a : List[Any] = context_length
_a : int = hidden_size
_a : Dict = num_hidden_layers
_a : List[Any] = attention_hidden_size if attention_hidden_size is not None else hidden_size
_a : Any = intermediate_size if intermediate_size is not None else 4 * hidden_size
_a : int = layer_norm_epsilon
_a : List[str] = rescale_every
_a : List[str] = use_cache
_a : List[str] = bos_token_id
_a : Optional[int] = eos_token_id
super().__init__(
tie_word_embeddings=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , **UpperCAmelCase__ )
| 389 |
"""simple docstring"""
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Tuple = 0
for i in range(1 , 1_0_0_1 ):
total += i**i
return str(UpperCamelCase__ )[-1_0:]
if __name__ == "__main__":
print(solution())
| 389 | 1 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCamelCase_ ( snake_case_ ):
'''simple docstring'''
def _UpperCamelCase ( self ) -> str:
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "col_2": "b"},
{"col_1": 1, "col_2": "c"},
{"col_1": 0, "col_2": "d"},
]
def _UpperCamelCase ( self ) -> Optional[int]:
snake_case_ = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
return Dataset.from_dict(a )
def _UpperCamelCase ( self ) -> Tuple:
snake_case_ = self._create_example_records()
snake_case_ = Dataset.from_list(a )
self.assertListEqual(dset.column_names , ['col_1', 'col_2'] )
for i, r in enumerate(a ):
self.assertDictEqual(a , example_records[i] )
def _UpperCamelCase ( self ) -> List[Any]:
snake_case_ = self._create_example_records()
snake_case_ = Dataset.from_list(a )
snake_case_ = Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
self.assertEqual(dset.info , dset_from_dict.info )
def _UpperCamelCase ( self ) -> str: # checks what happens with missing columns
snake_case_ = [{'col_1': 1}, {'col_2': 'x'}]
snake_case_ = Dataset.from_list(a )
self.assertDictEqual(dset[0] , {'col_1': 1} )
self.assertDictEqual(dset[1] , {'col_1': None} ) # NB: first record is used for columns
def _UpperCamelCase ( self ) -> Optional[int]: # checks if the type can be inferred from the second record
snake_case_ = [{'col_1': []}, {'col_1': [1, 2]}]
snake_case_ = Dataset.from_list(a )
self.assertEqual(dset.info.features['col_1'] , Sequence(Value('int64' ) ) )
def _UpperCamelCase ( self ) -> Optional[Any]:
snake_case_ = Dataset.from_list([] )
self.assertEqual(len(a ) , 0 )
self.assertListEqual(dset.column_names , [] )
| 718 |
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self , a ) -> None:
snake_case_ = set_counts
snake_case_ = max(a )
snake_case_ = len(a )
snake_case_ = [1] * num_sets
snake_case_ = list(range(a ) )
def _UpperCamelCase ( self , a , a ) -> bool:
snake_case_ = self.get_parent(a )
snake_case_ = self.get_parent(a )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
snake_case_ = 0
snake_case_ = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
snake_case_ = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
snake_case_ = 0
snake_case_ = src_parent
snake_case_ = self.set_counts[src_parent]
snake_case_ = max(self.max_set , a )
return True
def _UpperCamelCase ( self , a ) -> int:
if self.parents[disj_set] == disj_set:
return disj_set
snake_case_ = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 607 | 0 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
a : Optional[int] = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">": operator.gt,
}
def lowerCamelCase__ ( __lowerCamelCase : List[str] , __lowerCamelCase : List[str] , __lowerCamelCase : str , __lowerCamelCase : int , __lowerCamelCase : Optional[Any] , __lowerCamelCase : str ):
if got_ver is None or want_ver is None:
raise ValueError(
f"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
f""" reinstalling {pkg}.""" )
if not ops[op](version.parse(__lowerCamelCase ) , version.parse(__lowerCamelCase ) ):
raise ImportError(
f"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}""" )
def lowerCamelCase__ ( __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ):
__UpperCAmelCase : Optional[int] = f"""\n{hint}""" if hint is not None else """"""
# non-versioned check
if re.match(R"""^[\w_\-\d]+$""" , __lowerCamelCase ):
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = requirement, None, None
else:
__UpperCAmelCase : Optional[int] = re.findall(R"""^([^!=<>\s]+)([\s!=<>]{1,2}.+)""" , __lowerCamelCase )
if not match:
raise ValueError(
"""requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"""
f""" got {requirement}""" )
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = match[0]
__UpperCAmelCase : str = want_full.split(""",""" ) # there could be multiple requirements
__UpperCAmelCase : Dict = {}
for w in want_range:
__UpperCAmelCase : Optional[int] = re.findall(R"""^([\s!=<>]{1,2})(.+)""" , __lowerCamelCase )
if not match:
raise ValueError(
"""requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"""
f""" but got {requirement}""" )
__UpperCAmelCase , __UpperCAmelCase : List[str] = match[0]
__UpperCAmelCase : Optional[Any] = want_ver
if op not in ops:
raise ValueError(f"""{requirement}: need one of {list(ops.keys() )}, but got {op}""" )
# special case
if pkg == "python":
__UpperCAmelCase : Optional[Any] = """.""".join([str(__lowerCamelCase ) for x in sys.version_info[:3]] )
for op, want_ver in wanted.items():
_compare_versions(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return
# check if any version is installed
try:
__UpperCAmelCase : Union[str, Any] = importlib.metadata.version(__lowerCamelCase )
except importlib.metadata.PackageNotFoundError:
raise importlib.metadata.PackageNotFoundError(
f"""The '{requirement}' distribution was not found and is required by this application. {hint}""" )
# check that the right version is installed if version number or a range was provided
if want_ver is not None:
for op, want_ver in wanted.items():
_compare_versions(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
def lowerCamelCase__ ( __lowerCamelCase : List[Any] ):
__UpperCAmelCase : Union[str, Any] = """Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"""
return require_version(__lowerCamelCase , __lowerCamelCase )
| 63 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int) -> int:
'''simple docstring'''
__UpperCamelCase : Tuple = 1
for i in range(1 , num + 1):
fact *= i
return fact
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int) -> int:
'''simple docstring'''
__UpperCamelCase : Dict = 0
while number > 0:
__UpperCamelCase : List[Any] = number % 10
sum_of_digits += last_digit
__UpperCamelCase : List[Any] = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 100) -> int:
'''simple docstring'''
__UpperCamelCase : Optional[Any] = factorial(_lowerCamelCase)
__UpperCamelCase : Tuple = split_and_add(_lowerCamelCase)
return result
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip()))) | 557 | 0 |
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__ ( _a : List[str] ):
snake_case_ : int = filter(lambda _a : p.requires_grad , model.parameters() )
snake_case_ : str = sum([np.prod(p.size() ) for p in model_parameters] )
return params
lowercase : List[str] = logging.getLogger(__name__)
def lowerCAmelCase__ ( _a : Dict , _a : List[str] ):
if metric == "rouge2":
snake_case_ : List[str] = "{val_avg_rouge2:.4f}-{step_count}"
elif metric == "bleu":
snake_case_ : Any = "{val_avg_bleu:.4f}-{step_count}"
elif metric == "em":
snake_case_ : List[str] = "{val_avg_em:.4f}-{step_count}"
else:
raise NotImplementedError(
F'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
" function." )
snake_case_ : Optional[int] = ModelCheckpoint(
dirpath=_a , filename=_a , monitor=F'''val_{metric}''' , mode="max" , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def lowerCAmelCase__ ( _a : Optional[int] , _a : str ):
return EarlyStopping(
monitor=F'''val_{metric}''' , mode="min" if "loss" in metric else "max" , patience=_a , verbose=_a , )
class UpperCAmelCase_ ( pl.Callback ):
'''simple docstring'''
def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
snake_case_ : int = {f'''lr_group_{i}''': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(_SCREAMING_SNAKE_CASE )
@rank_zero_only
def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=True ) -> None:
logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
snake_case_ : Any = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]} )
# Log results
snake_case_ : str = Path(pl_module.hparams.output_dir )
if type_path == "test":
snake_case_ : Union[str, Any] = od / "test_results.txt"
snake_case_ : Tuple = 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.
snake_case_ : Optional[int] = od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
snake_case_ : Optional[int] = od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
results_file.parent.mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
generations_file.parent.mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
with open(_SCREAMING_SNAKE_CASE , "a+" ) as writer:
for key in sorted(_SCREAMING_SNAKE_CASE ):
if key in ["log", "progress_bar", "preds"]:
continue
snake_case_ : Union[str, Any] = metrics[key]
if isinstance(_SCREAMING_SNAKE_CASE , torch.Tensor ):
snake_case_ : Dict = val.item()
snake_case_ : Union[str, Any] = f'''{key}: {val:.6f}\n'''
writer.write(_SCREAMING_SNAKE_CASE )
if not save_generations:
return
if "preds" in metrics:
snake_case_ : Optional[Any] = "\n".join(metrics["preds"] )
generations_file.open("w+" ).write(_SCREAMING_SNAKE_CASE )
@rank_zero_only
def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
try:
snake_case_ : Optional[Any] = pl_module.model.model.num_parameters()
except AttributeError:
snake_case_ : Optional[Any] = pl_module.model.num_parameters()
snake_case_ : List[str] = count_trainable_parameters(_SCREAMING_SNAKE_CASE )
# 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 _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , "test" )
@rank_zero_only
def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 708 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
lowercase : int = {
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''ASTConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Optional[Any] = [
'''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ASTForAudioClassification''',
'''ASTModel''',
'''ASTPreTrainedModel''',
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Optional[int] = ['''ASTFeatureExtractor''']
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
lowercase : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 114 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _a ( snake_case_ , snake_case_ ):
"""simple docstring"""
@register_to_config
def __init__( self : Any , UpperCAmelCase : int = 768 , ):
super().__init__()
A_ = nn.Parameter(torch.zeros(1 , UpperCAmelCase ) )
A_ = nn.Parameter(torch.ones(1 , UpperCAmelCase ) )
def __A ( self : Optional[Any] , UpperCAmelCase : Optional[Union[str, torch.device]] = None , UpperCAmelCase : Optional[torch.dtype] = None , ):
A_ = nn.Parameter(self.mean.to(UpperCAmelCase ).to(UpperCAmelCase ) )
A_ = nn.Parameter(self.std.to(UpperCAmelCase ).to(UpperCAmelCase ) )
return self
def __A ( self : Any , UpperCAmelCase : int ):
A_ = (embeds - self.mean) * 1.0 / self.std
return embeds
def __A ( self : Dict , UpperCAmelCase : int ):
A_ = (embeds * self.std) + self.mean
return embeds | 86 |
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 _SCREAMING_SNAKE_CASE ( A , A , unittest.TestCase ):
__SCREAMING_SNAKE_CASE = IFPipeline
__SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
__SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_BATCH_PARAMS
__SCREAMING_SNAKE_CASE = PipelineTesterMixin.required_optional_params - {'''latents'''}
def __snake_case( self ):
return self._get_dummy_components()
def __snake_case( self , A_ , A_=0 ):
if str(A_ ).startswith("""mps""" ):
_UpperCAmelCase : Tuple = torch.manual_seed(A_ )
else:
_UpperCAmelCase : Optional[Any] = torch.Generator(device=A_ ).manual_seed(A_ )
_UpperCAmelCase : Any = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def __snake_case( self ):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""" )
def __snake_case( self ):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def __snake_case( self ):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def __snake_case( self ):
self._test_save_load_local()
def __snake_case( self ):
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 __snake_case( self ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __snake_case( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __snake_case( self ):
# if
_UpperCAmelCase : List[str] = IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0""" , variant="""fp16""" , torch_dtype=torch.floataa )
_UpperCAmelCase : Dict = IFSuperResolutionPipeline.from_pretrained(
"""DeepFloyd/IF-II-L-v1.0""" , variant="""fp16""" , torch_dtype=torch.floataa , text_encoder=A_ , tokenizer=A_ )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to("""cuda""" )
_UpperCAmelCase,_UpperCAmelCase : Dict = pipe_a.encode_prompt("""anime turtle""" , device="""cuda""" )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
_UpperCAmelCase : List[Any] = None
_UpperCAmelCase : Any = 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(A_ , A_ , A_ , A_ )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
_UpperCAmelCase : Any = IFImgaImgPipeline(**pipe_a.components )
_UpperCAmelCase : Union[str, Any] = 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(A_ , A_ , A_ , A_ )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
_UpperCAmelCase : Optional[int] = IFInpaintingPipeline(**pipe_a.components )
_UpperCAmelCase : Union[str, Any] = 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(A_ , A_ , A_ , A_ )
def __snake_case( self , A_ , A_ , A_ , A_ ):
# pipeline 1
_start_torch_memory_measurement()
_UpperCAmelCase : Optional[Any] = torch.Generator(device="""cpu""" ).manual_seed(0 )
_UpperCAmelCase : List[str] = pipe_a(
prompt_embeds=A_ , negative_prompt_embeds=A_ , num_inference_steps=2 , generator=A_ , output_type="""np""" , )
_UpperCAmelCase : int = output.images[0]
assert image.shape == (64, 64, 3)
_UpperCAmelCase : Dict = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
_UpperCAmelCase : str = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy""" )
assert_mean_pixel_difference(A_ , A_ )
# pipeline 2
_start_torch_memory_measurement()
_UpperCAmelCase : List[Any] = torch.Generator(device="""cpu""" ).manual_seed(0 )
_UpperCAmelCase : str = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(A_ )
_UpperCAmelCase : str = pipe_a(
prompt_embeds=A_ , negative_prompt_embeds=A_ , image=A_ , generator=A_ , num_inference_steps=2 , output_type="""np""" , )
_UpperCAmelCase : int = output.images[0]
assert image.shape == (2_56, 2_56, 3)
_UpperCAmelCase : Any = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_UpperCAmelCase : List[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(A_ , A_ )
def __snake_case( self , A_ , A_ , A_ , A_ ):
# pipeline 1
_start_torch_memory_measurement()
_UpperCAmelCase : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(A_ )
_UpperCAmelCase : Dict = torch.Generator(device="""cpu""" ).manual_seed(0 )
_UpperCAmelCase : List[str] = pipe_a(
prompt_embeds=A_ , negative_prompt_embeds=A_ , image=A_ , num_inference_steps=2 , generator=A_ , output_type="""np""" , )
_UpperCAmelCase : int = output.images[0]
assert image.shape == (64, 64, 3)
_UpperCAmelCase : str = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_UpperCAmelCase : str = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy""" )
assert_mean_pixel_difference(A_ , A_ )
# pipeline 2
_start_torch_memory_measurement()
_UpperCAmelCase : List[str] = torch.Generator(device="""cpu""" ).manual_seed(0 )
_UpperCAmelCase : Union[str, Any] = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(0 ) ).to(A_ )
_UpperCAmelCase : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(A_ )
_UpperCAmelCase : Optional[int] = pipe_a(
prompt_embeds=A_ , negative_prompt_embeds=A_ , image=A_ , original_image=A_ , generator=A_ , num_inference_steps=2 , output_type="""np""" , )
_UpperCAmelCase : Dict = output.images[0]
assert image.shape == (2_56, 2_56, 3)
_UpperCAmelCase : Any = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_UpperCAmelCase : int = 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(A_ , A_ )
def __snake_case( self , A_ , A_ , A_ , A_ ):
# pipeline 1
_start_torch_memory_measurement()
_UpperCAmelCase : List[Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(A_ )
_UpperCAmelCase : Dict = floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(A_ )
_UpperCAmelCase : Optional[Any] = torch.Generator(device="""cpu""" ).manual_seed(0 )
_UpperCAmelCase : str = pipe_a(
prompt_embeds=A_ , negative_prompt_embeds=A_ , image=A_ , mask_image=A_ , num_inference_steps=2 , generator=A_ , output_type="""np""" , )
_UpperCAmelCase : Optional[Any] = output.images[0]
assert image.shape == (64, 64, 3)
_UpperCAmelCase : List[str] = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_UpperCAmelCase : Dict = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy""" )
assert_mean_pixel_difference(A_ , A_ )
# pipeline 2
_start_torch_memory_measurement()
_UpperCAmelCase : str = torch.Generator(device="""cpu""" ).manual_seed(0 )
_UpperCAmelCase : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(A_ )
_UpperCAmelCase : Tuple = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(0 ) ).to(A_ )
_UpperCAmelCase : str = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(1 ) ).to(A_ )
_UpperCAmelCase : Union[str, Any] = pipe_a(
prompt_embeds=A_ , negative_prompt_embeds=A_ , image=A_ , mask_image=A_ , original_image=A_ , generator=A_ , num_inference_steps=2 , output_type="""np""" , )
_UpperCAmelCase : Any = output.images[0]
assert image.shape == (2_56, 2_56, 3)
_UpperCAmelCase : Optional[int] = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_UpperCAmelCase : Tuple = 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(A_ , A_ )
def a__ ( ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 643 | 0 |
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
| 709 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class __UpperCAmelCase (_UpperCAmelCase ):
__snake_case : int = "ctrl"
__snake_case : Dict = ["past_key_values"]
__snake_case : List[str] = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self: Optional[Any] , UpperCAmelCase_: int=246_534 , UpperCAmelCase_: List[Any]=256 , UpperCAmelCase_: int=1_280 , UpperCAmelCase_: str=8_192 , UpperCAmelCase_: Optional[Any]=48 , UpperCAmelCase_: Optional[Any]=16 , UpperCAmelCase_: Optional[int]=0.1 , UpperCAmelCase_: Dict=0.1 , UpperCAmelCase_: Union[str, Any]=1E-6 , UpperCAmelCase_: Optional[Any]=0.02 , UpperCAmelCase_: Dict=True , **UpperCAmelCase_: str , ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = n_positions
_SCREAMING_SNAKE_CASE = n_embd
_SCREAMING_SNAKE_CASE = n_layer
_SCREAMING_SNAKE_CASE = n_head
_SCREAMING_SNAKE_CASE = dff
_SCREAMING_SNAKE_CASE = resid_pdrop
_SCREAMING_SNAKE_CASE = embd_pdrop
_SCREAMING_SNAKE_CASE = layer_norm_epsilon
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = use_cache
super().__init__(**UpperCAmelCase_ )
| 569 | 0 |
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 ( _UpperCAmelCase ):
"""simple docstring"""
lowerCAmelCase__ = 42
lowerCAmelCase__ = 42
class _lowercase ( nn.Module ):
"""simple docstring"""
lowerCAmelCase__ = 42
lowerCAmelCase__ = (16, 32, 96, 256)
lowerCAmelCase__ = jnp.floataa
def _UpperCAmelCase ( self ):
'''simple docstring'''
_lowercase = nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_lowercase = []
for i in range(len(self.block_out_channels ) - 1 ):
_lowercase = self.block_out_channels[i]
_lowercase = self.block_out_channels[i + 1]
_lowercase = nn.Conv(
_snake_case , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(_snake_case )
_lowercase = nn.Conv(
_snake_case , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(_snake_case )
_lowercase = blocks
_lowercase = 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 , UpperCAmelCase ):
'''simple docstring'''
_lowercase = self.conv_in(_snake_case )
_lowercase = nn.silu(_snake_case )
for block in self.blocks:
_lowercase = block(_snake_case )
_lowercase = nn.silu(_snake_case )
_lowercase = self.conv_out(_snake_case )
return embedding
@flax_register_to_config
class _lowercase ( nn.Module , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
lowerCAmelCase__ = 32
lowerCAmelCase__ = 4
lowerCAmelCase__ = (
'CrossAttnDownBlock2D',
'CrossAttnDownBlock2D',
'CrossAttnDownBlock2D',
'DownBlock2D',
)
lowerCAmelCase__ = False
lowerCAmelCase__ = (320, 640, 1_280, 1_280)
lowerCAmelCase__ = 2
lowerCAmelCase__ = 8
lowerCAmelCase__ = None
lowerCAmelCase__ = 1_280
lowerCAmelCase__ = 0.0
lowerCAmelCase__ = False
lowerCAmelCase__ = jnp.floataa
lowerCAmelCase__ = True
lowerCAmelCase__ = 0
lowerCAmelCase__ = 'rgb'
lowerCAmelCase__ = (16, 32, 96, 256)
def _UpperCAmelCase ( self , UpperCAmelCase ):
'''simple docstring'''
_lowercase = (1, self.in_channels, self.sample_size, self.sample_size)
_lowercase = jnp.zeros(_snake_case , dtype=jnp.floataa )
_lowercase = jnp.ones((1,) , dtype=jnp.intaa )
_lowercase = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
_lowercase = (1, 3, self.sample_size * 8, self.sample_size * 8)
_lowercase = jnp.zeros(_snake_case , dtype=jnp.floataa )
_lowercase , _lowercase = jax.random.split(_snake_case )
_lowercase = {"""params""": params_rng, """dropout""": dropout_rng}
return self.init(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )["params"]
def _UpperCAmelCase ( self ):
'''simple docstring'''
_lowercase = self.block_out_channels
_lowercase = 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.
_lowercase = self.num_attention_heads or self.attention_head_dim
# input
_lowercase = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
_lowercase = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
_lowercase = FlaxTimestepEmbedding(_snake_case , dtype=self.dtype )
_lowercase = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
_lowercase = self.only_cross_attention
if isinstance(_snake_case , _snake_case ):
_lowercase = (only_cross_attention,) * len(self.down_block_types )
if isinstance(_snake_case , _snake_case ):
_lowercase = (num_attention_heads,) * len(self.down_block_types )
# down
_lowercase = []
_lowercase = []
_lowercase = block_out_channels[0]
_lowercase = nn.Conv(
_snake_case , kernel_size=(1, 1) , padding="""VALID""" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(_snake_case )
for i, down_block_type in enumerate(self.down_block_types ):
_lowercase = output_channel
_lowercase = block_out_channels[i]
_lowercase = i == len(_snake_case ) - 1
if down_block_type == "CrossAttnDownBlock2D":
_lowercase = FlaxCrossAttnDownBlockaD(
in_channels=_snake_case , out_channels=_snake_case , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
_lowercase = FlaxDownBlockaD(
in_channels=_snake_case , out_channels=_snake_case , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(_snake_case )
for _ in range(self.layers_per_block ):
_lowercase = nn.Conv(
_snake_case , kernel_size=(1, 1) , padding="""VALID""" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(_snake_case )
if not is_final_block:
_lowercase = nn.Conv(
_snake_case , kernel_size=(1, 1) , padding="""VALID""" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(_snake_case )
_lowercase = down_blocks
_lowercase = controlnet_down_blocks
# mid
_lowercase = block_out_channels[-1]
_lowercase = FlaxUNetMidBlockaDCrossAttn(
in_channels=_snake_case , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
_lowercase = nn.Conv(
_snake_case , kernel_size=(1, 1) , padding="""VALID""" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 1.0 , UpperCAmelCase = True , UpperCAmelCase = False , ):
'''simple docstring'''
_lowercase = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
_lowercase = jnp.flip(_snake_case , axis=1 )
# 1. time
if not isinstance(_snake_case , jnp.ndarray ):
_lowercase = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(_snake_case , jnp.ndarray ) and len(timesteps.shape ) == 0:
_lowercase = timesteps.astype(dtype=jnp.floataa )
_lowercase = jnp.expand_dims(_snake_case , 0 )
_lowercase = self.time_proj(_snake_case )
_lowercase = self.time_embedding(_snake_case )
# 2. pre-process
_lowercase = jnp.transpose(_snake_case , (0, 2, 3, 1) )
_lowercase = self.conv_in(_snake_case )
_lowercase = jnp.transpose(_snake_case , (0, 2, 3, 1) )
_lowercase = self.controlnet_cond_embedding(_snake_case )
sample += controlnet_cond
# 3. down
_lowercase = (sample,)
for down_block in self.down_blocks:
if isinstance(_snake_case , _snake_case ):
_lowercase , _lowercase = down_block(_snake_case , _snake_case , _snake_case , deterministic=not train )
else:
_lowercase , _lowercase = down_block(_snake_case , _snake_case , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
_lowercase = self.mid_block(_snake_case , _snake_case , _snake_case , deterministic=not train )
# 5. contronet blocks
_lowercase = ()
for down_block_res_sample, controlnet_block in zip(_snake_case , self.controlnet_down_blocks ):
_lowercase = controlnet_block(_snake_case )
controlnet_down_block_res_samples += (down_block_res_sample,)
_lowercase = controlnet_down_block_res_samples
_lowercase = self.controlnet_mid_block(_snake_case )
# 6. scaling
_lowercase = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=_snake_case , mid_block_res_sample=_snake_case )
| 398 |
'''simple docstring'''
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _SCREAMING_SNAKE_CASE :
"""simple docstring"""
pass
| 316 | 0 |
"""simple docstring"""
from ... import PretrainedConfig
__UpperCamelCase = {
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class lowerCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Any = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
SCREAMING_SNAKE_CASE_ : int = """nezha"""
def __init__( self , lowerCAmelCase__=21_128 , lowerCAmelCase__=768 , lowerCAmelCase__=12 , lowerCAmelCase__=12 , lowerCAmelCase__=3_072 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=512 , lowerCAmelCase__=64 , lowerCAmelCase__=2 , lowerCAmelCase__=0.02 , lowerCAmelCase__=1e-12 , lowerCAmelCase__=0.1 , lowerCAmelCase__=0 , lowerCAmelCase__=2 , lowerCAmelCase__=3 , lowerCAmelCase__=True , **lowerCAmelCase__ , ) -> Any:
super().__init__(pad_token_id=lowerCAmelCase__ , bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
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 = hidden_act
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = max_relative_position
SCREAMING_SNAKE_CASE = type_vocab_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = classifier_dropout
SCREAMING_SNAKE_CASE = use_cache
| 327 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __A ( self ) -> Optional[int]:
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE = BlipImageProcessor()
SCREAMING_SNAKE_CASE = BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel' )
SCREAMING_SNAKE_CASE = BlipProcessor(lowerCAmelCase__ , lowerCAmelCase__ )
processor.save_pretrained(self.tmpdirname )
def __A ( self , **lowerCAmelCase__ ) -> Tuple:
return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase__ ).tokenizer
def __A ( self , **lowerCAmelCase__ ) -> int:
return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase__ ).image_processor
def __A ( self ) -> str:
shutil.rmtree(self.tmpdirname )
def __A ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE = [Image.fromarray(np.moveaxis(lowerCAmelCase__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __A ( self ) -> List[str]:
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
SCREAMING_SNAKE_CASE = self.get_image_processor(do_normalize=lowerCAmelCase__ , padding_value=1.0 )
SCREAMING_SNAKE_CASE = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=lowerCAmelCase__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowerCAmelCase__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCAmelCase__ )
def __A ( self ) -> Any:
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = image_processor(lowerCAmelCase__ , return_tensors='np' )
SCREAMING_SNAKE_CASE = processor(images=lowerCAmelCase__ , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __A ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = 'lower newer'
SCREAMING_SNAKE_CASE = processor(text=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = tokenizer(lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __A ( self ) -> int:
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = 'lower newer'
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = processor(text=lowerCAmelCase__ , images=lowerCAmelCase__ )
self.assertListEqual(list(inputs.keys() ) , ['pixel_values', 'input_ids', 'attention_mask'] )
# test if it raises when no input is passed
with pytest.raises(lowerCAmelCase__ ):
processor()
def __A ( self ) -> Tuple:
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE = processor.batch_decode(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def __A ( self ) -> str:
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = 'lower newer'
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = processor(text=lowerCAmelCase__ , images=lowerCAmelCase__ )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ['pixel_values', 'input_ids', 'attention_mask'] )
| 327 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
@dataclass
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[List[PIL.Image.Image], np.ndarray]
SCREAMING_SNAKE_CASE : 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_cycle_diffusion import CycleDiffusionPipeline
from .pipeline_stable_diffusion import StableDiffusionPipeline
from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from .pipeline_stable_unclip import StableUnCLIPPipeline
from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
from .safety_checker import StableDiffusionSafetyChecker
from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
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 StableDiffusionImageVariationPipeline
else:
from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.26.0')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionPixaPixZeroPipeline,
)
else:
from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline
try:
if not (
is_torch_available()
and is_transformers_available()
and is_k_diffusion_available()
and is_k_diffusion_version('>=', '0.0.12')
):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline
try:
if not (is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_onnx_objects import * # noqa F403
else:
from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline
if is_transformers_available() and is_flax_available():
import flax
@flax.struct.dataclass
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : np.ndarray
SCREAMING_SNAKE_CASE : List[bool]
from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
| 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if len(UpperCAmelCase__ ) == 0:
return array
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = min(UpperCAmelCase__ ), max(UpperCAmelCase__ )
# Compute the variables
_SCREAMING_SNAKE_CASE = _max - _min + 1
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = [0] * holes_range, [0] * holes_range
# Make the sorting.
for i in array:
_SCREAMING_SNAKE_CASE = i - _min
_SCREAMING_SNAKE_CASE = i
holes_repeat[index] += 1
# Makes the array back by replacing the numbers.
_SCREAMING_SNAKE_CASE = 0
for i in range(UpperCAmelCase__ ):
while holes_repeat[i] > 0:
_SCREAMING_SNAKE_CASE = holes[i]
index += 1
holes_repeat[i] -= 1
# Returns the sorted array.
return array
if __name__ == "__main__":
import doctest
doctest.testmod()
snake_case : Dict = input('Enter numbers separated by comma:\n')
snake_case : List[Any] = [int(x) for x in user_input.split(',')]
print(pigeon_sort(unsorted))
| 605 | 1 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
SCREAMING_SNAKE_CASE__:List[str] = False
class snake_case__ ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
def a__ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a__ ( self ):
__a = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion" , torch_dtype=torch.floataa )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
__a = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
__a = torch.manual_seed(0 )
__a = pipe.dual_guided(
prompt="first prompt" , image=a_ , text_to_image_strength=0.75 , generator=a_ , guidance_scale=7.5 , num_inference_steps=2 , output_type="numpy" , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(a_ )
__a = VersatileDiffusionPipeline.from_pretrained(a_ , torch_dtype=torch.floataa )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
__a = generator.manual_seed(0 )
__a = pipe.dual_guided(
prompt="first prompt" , image=a_ , text_to_image_strength=0.75 , generator=a_ , guidance_scale=7.5 , num_inference_steps=2 , output_type="numpy" , ).images
assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"
def a__ ( self ):
__a = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion" , torch_dtype=torch.floataa )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
__a = """cyberpunk 2077"""
__a = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
__a = torch.manual_seed(0 )
__a = pipe.dual_guided(
prompt=a_ , image=a_ , text_to_image_strength=0.75 , generator=a_ , guidance_scale=7.5 , num_inference_steps=50 , output_type="numpy" , ).images
__a = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__a = np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
__a = """A painting of a squirrel eating a burger """
__a = torch.manual_seed(0 )
__a = pipe.text_to_image(
prompt=a_ , generator=a_ , guidance_scale=7.5 , num_inference_steps=50 , output_type="numpy" ).images
__a = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__a = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
__a = pipe.image_variation(a_ , generator=a_ , output_type="numpy" ).images
__a = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__a = np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
| 706 | """simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class snake_case__ :
def __init__( self , lowerCamelCase , lowerCamelCase=99 , lowerCamelCase=13 , lowerCamelCase=7 , lowerCamelCase=9 , lowerCamelCase=True , lowerCamelCase=True , lowerCamelCase=False , lowerCamelCase=32 , lowerCamelCase=5 , lowerCamelCase=4 , lowerCamelCase=37 , lowerCamelCase=8 , lowerCamelCase=0.1 , lowerCamelCase=0.002 , lowerCamelCase=1 , lowerCamelCase=0 , lowerCamelCase=0 , lowerCamelCase=None , lowerCamelCase=None , ):
__a = parent
__a = batch_size
__a = encoder_seq_length
__a = decoder_seq_length
# For common tests
__a = self.decoder_seq_length
__a = is_training
__a = use_attention_mask
__a = use_labels
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = d_ff
__a = relative_attention_num_buckets
__a = dropout_rate
__a = initializer_factor
__a = eos_token_id
__a = pad_token_id
__a = decoder_start_token_id
__a = None
__a = decoder_layers
def a__ ( self ):
return TaConfig.from_pretrained("google/umt5-base" )
def a__ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , ):
if attention_mask is None:
__a = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
__a = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
__a = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=lowerCamelCase )
if decoder_head_mask is None:
__a = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=lowerCamelCase )
if cross_attn_head_mask is None:
__a = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=lowerCamelCase )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def a__ ( self ):
__a = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
__a = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
__a = input_ids.clamp(self.pad_token_id + 1 )
__a = decoder_input_ids.clamp(self.pad_token_id + 1 )
__a = self.get_config()
__a = config.num_attention_heads
__a = self.prepare_inputs_dict(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return config, input_dict
def a__ ( self ):
__a , __a = self.prepare_config_and_inputs()
return config, inputs_dict
def a__ ( self ):
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def a__ ( self ):
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def a__ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , ):
__a = UMTaModel(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__a = model(
input_ids=lowerCamelCase , decoder_input_ids=lowerCamelCase , attention_mask=lowerCamelCase , decoder_attention_mask=lowerCamelCase , )
__a = model(input_ids=lowerCamelCase , decoder_input_ids=lowerCamelCase )
__a = result.last_hidden_state
__a = result.past_key_values
__a = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(lowerCamelCase ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def a__ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , ):
__a = UMTaModel(config=lowerCamelCase ).get_decoder().to(lowerCamelCase ).eval()
# first forward pass
__a = model(lowerCamelCase , use_cache=lowerCamelCase )
__a = model(lowerCamelCase )
__a = model(lowerCamelCase , use_cache=lowerCamelCase )
self.parent.assertTrue(len(lowerCamelCase ) == len(lowerCamelCase ) )
self.parent.assertTrue(len(lowerCamelCase ) == len(lowerCamelCase ) + 1 )
__a , __a = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__a = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
__a = torch.cat([input_ids, next_tokens] , dim=-1 )
__a = model(lowerCamelCase )["last_hidden_state"]
__a = model(lowerCamelCase , past_key_values=lowerCamelCase )["last_hidden_state"]
# select random slice
__a = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__a = output_from_no_past[:, -1, random_slice_idx].detach()
__a = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCamelCase , lowerCamelCase , atol=1E-3 ) )
def a__ ( self , lowerCamelCase , lowerCamelCase , ):
__a = UMTaModel(config=lowerCamelCase ).to(lowerCamelCase ).half().eval()
__a = model(**lowerCamelCase )["last_hidden_state"]
self.parent.assertFalse(torch.isnan(lowerCamelCase ).any().item() )
@require_torch
class snake_case__ ( snake_case_, snake_case_, snake_case_, unittest.TestCase ):
_snake_case : Union[str, Any] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
_snake_case : int = (UMTaForConditionalGeneration,) if is_torch_available() else ()
_snake_case : Optional[int] = (
{
"""conversational""": UMTaForConditionalGeneration,
"""feature-extraction""": UMTaModel,
"""summarization""": UMTaForConditionalGeneration,
"""text2text-generation""": UMTaForConditionalGeneration,
"""translation""": UMTaForConditionalGeneration,
"""question-answering""": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
_snake_case : List[Any] = True
_snake_case : Union[str, Any] = False
_snake_case : Union[str, Any] = False
_snake_case : Tuple = True
_snake_case : List[str] = True
# The small UMT5 model needs higher percentages for CPU/MP tests
_snake_case : Optional[Any] = [0.8, 0.9]
def a__ ( self ):
__a = UMTaModelTester(self )
@unittest.skip("Test has a segmentation fault on torch 1.8.0" )
def a__ ( self ):
__a = self.model_tester.prepare_config_and_inputs()
__a = UMTaModel(config_and_inputs[0] ).to(lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
lowerCamelCase , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , F"{tmpdirname}/t5_test.onnx" , export_params=lowerCamelCase , opset_version=9 , input_names=["input_ids", "decoder_input_ids"] , )
@unittest.skipIf(torch_device == "cpu" , "Cant do half precision" )
def a__ ( self ):
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*lowerCamelCase )
def a__ ( self ):
__a = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
__a = self.model_tester.prepare_config_and_inputs()
__a = config_and_inputs[0]
__a = UMTaForConditionalGeneration(lowerCamelCase ).eval()
model.to(lowerCamelCase )
__a = {
"head_mask": torch.zeros(config.num_layers , config.num_heads , device=lowerCamelCase ),
"decoder_head_mask": torch.zeros(config.num_decoder_layers , config.num_heads , device=lowerCamelCase ),
"cross_attn_head_mask": torch.zeros(config.num_decoder_layers , config.num_heads , device=lowerCamelCase ),
}
for attn_name, (name, mask) in zip(lowerCamelCase , head_masking.items() ):
__a = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
__a = torch.ones(
config.num_decoder_layers , config.num_heads , device=lowerCamelCase )
__a = model.generate(
config_and_inputs[1]["input_ids"] , num_beams=1 , max_length=3 , output_attentions=lowerCamelCase , return_dict_in_generate=lowerCamelCase , **lowerCamelCase , )
# We check the state of decoder_attentions and cross_attentions just from the last step
__a = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("Does not work on the tiny model as we keep hitting edge cases." )
def a__ ( self ):
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class snake_case__ ( unittest.TestCase ):
@slow
@unittest.skip(
"Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged" )
def a__ ( self ):
__a = UMTaForConditionalGeneration.from_pretrained("google/umt5-small" , return_dict=lowerCamelCase ).to(lowerCamelCase )
__a = AutoTokenizer.from_pretrained("google/umt5-small" , use_fast=lowerCamelCase , legacy=lowerCamelCase )
__a = [
"Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
"No se como puedo <extra_id_0>.",
"This is the reason why we <extra_id_0> them.",
"The <extra_id_0> walks in <extra_id_1>, seats",
"A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
]
__a = tokenizer(lowerCamelCase , return_tensors="pt" , padding=lowerCamelCase ).input_ids
# fmt: off
__a = torch.tensor(
[
[ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(lowerCamelCase , lowerCamelCase )
__a = model.generate(input_ids.to(lowerCamelCase ) )
__a = [
"<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
"<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
"<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
"<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
"<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
]
__a = tokenizer.batch_decode(lowerCamelCase )
self.assertEqual(lowerCamelCase , lowerCamelCase )
| 67 | 0 |
"""simple docstring"""
from __future__ import annotations
def __A (_SCREAMING_SNAKE_CASE ) ->list[int]:
"""simple docstring"""
lowerCAmelCase__ :str = [True] * limit
lowerCAmelCase__ :Optional[int] = False
lowerCAmelCase__ :str = False
lowerCAmelCase__ :Dict = True
for i in range(3 , int(limit**0.5 + 1 ) , 2 ):
lowerCAmelCase__ :Union[str, Any] = i * 2
while index < limit:
lowerCAmelCase__ :Dict = False
lowerCAmelCase__ :Any = index + i
lowerCAmelCase__ :List[Any] = [2]
for i in range(3 , _SCREAMING_SNAKE_CASE , 2 ):
if is_prime[i]:
primes.append(_SCREAMING_SNAKE_CASE )
return primes
def __A (_SCREAMING_SNAKE_CASE = 100_0000 ) ->int:
"""simple docstring"""
lowerCAmelCase__ :Tuple = prime_sieve(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ :List[str] = 0
lowerCAmelCase__ :Any = 0
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
for j in range(i + length , len(_SCREAMING_SNAKE_CASE ) ):
lowerCAmelCase__ :Any = sum(primes[i:j] )
if sol >= ceiling:
break
if sol in primes:
lowerCAmelCase__ :List[str] = j - i
lowerCAmelCase__ :Tuple = sol
return largest
if __name__ == "__main__":
print(F'''{solution() = }''')
| 93 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
_A = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["""memory_attention""", """encoder_attn"""],
["""attention""", """attn"""],
["""/""", """."""],
[""".LayerNorm.gamma""", """_layer_norm.weight"""],
[""".LayerNorm.beta""", """_layer_norm.bias"""],
["""r.layer_""", """r.layers."""],
["""output_proj""", """out_proj"""],
["""ffn.dense_1.""", """fc2."""],
["""ffn.dense.""", """fc1."""],
["""ffn_layer_norm""", """final_layer_norm"""],
["""kernel""", """weight"""],
["""encoder_layer_norm.""", """encoder.layer_norm."""],
["""decoder_layer_norm.""", """decoder.layer_norm."""],
["""embeddings.weights""", """shared.weight"""],
]
def A_ ( __SCREAMING_SNAKE_CASE : Dict ) -> List[Any]:
for pegasus_name, hf_name in PATTERNS:
__SCREAMING_SNAKE_CASE : List[str] = k.replace(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return k
def A_ ( __SCREAMING_SNAKE_CASE : dict , __SCREAMING_SNAKE_CASE : dict ) -> PegasusForConditionalGeneration:
__SCREAMING_SNAKE_CASE : Tuple = DEFAULTS.copy()
cfg_kwargs.update(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE : Union[str, Any] = PegasusConfig(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE : List[Any] = PegasusForConditionalGeneration(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE : Tuple = torch_model.model.state_dict()
__SCREAMING_SNAKE_CASE : Dict = {}
for k, v in tf_weights.items():
__SCREAMING_SNAKE_CASE : List[str] = rename_state_dict_key(__SCREAMING_SNAKE_CASE )
if new_k not in sd:
raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})""" )
if "dense" in k or "proj" in new_k:
__SCREAMING_SNAKE_CASE : Dict = v.T
__SCREAMING_SNAKE_CASE : Any = torch.tensor(__SCREAMING_SNAKE_CASE , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, f"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
# make sure embedding.padding_idx is respected
__SCREAMING_SNAKE_CASE : int = torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1] )
__SCREAMING_SNAKE_CASE : Optional[Any] = mapping['''shared.weight''']
__SCREAMING_SNAKE_CASE : Tuple = mapping['''shared.weight''']
__SCREAMING_SNAKE_CASE : List[Any] = {k: torch.zeros_like(__SCREAMING_SNAKE_CASE ) for k, v in sd.items() if k.endswith('''bias''' ) and k not in mapping}
mapping.update(**__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = torch_model.model.load_state_dict(__SCREAMING_SNAKE_CASE , strict=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE : List[Any] = [
k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
]
assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], f"""no matches found for the following tf keys {extra}"""
return torch_model
def A_ ( __SCREAMING_SNAKE_CASE : Union[str, Any]="./ckpt/aeslc/model.ckpt-32000" ) -> Dict:
__SCREAMING_SNAKE_CASE : Any = tf.train.list_variables(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE : Optional[int] = {}
__SCREAMING_SNAKE_CASE : Union[str, Any] = ['''Adafactor''', '''global_step''']
for name, shape in tqdm(__SCREAMING_SNAKE_CASE , desc='''converting tf checkpoint to dict''' ):
__SCREAMING_SNAKE_CASE : Tuple = any(pat in name for pat in ignore_name )
if skip_key:
continue
__SCREAMING_SNAKE_CASE : Any = tf.train.load_variable(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE : Dict = array
return tf_weights
def A_ ( __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str ) -> Optional[int]:
# save tokenizer first
__SCREAMING_SNAKE_CASE : List[str] = Path(__SCREAMING_SNAKE_CASE ).parent.name
__SCREAMING_SNAKE_CASE : Optional[int] = task_specific_params[f"""summarization_{dataset}"""]['''max_position_embeddings''']
__SCREAMING_SNAKE_CASE : List[str] = PegasusTokenizer.from_pretrained('''sshleifer/pegasus''' , model_max_length=__SCREAMING_SNAKE_CASE )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(__SCREAMING_SNAKE_CASE )
# convert model
__SCREAMING_SNAKE_CASE : Any = get_tf_weights_as_numpy(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE : str = task_specific_params[f"""summarization_{dataset}"""]
if dataset == "large":
__SCREAMING_SNAKE_CASE : Dict = task_specific_params
__SCREAMING_SNAKE_CASE : Optional[int] = convert_pegasus(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
torch_model.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE : List[Any] = torch_model.state_dict()
sd.pop('''model.decoder.embed_positions.weight''' )
sd.pop('''model.encoder.embed_positions.weight''' )
torch.save(__SCREAMING_SNAKE_CASE , Path(__SCREAMING_SNAKE_CASE ) / '''pytorch_model.bin''' )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
parser.add_argument("""save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
_A = parser.parse_args()
if args.save_dir is None:
_A = Path(args.tf_ckpt_path).parent.name
_A = os.path.join("""pegasus""", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 158 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCamelCase__ : List[Any] = {
'''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
# See all Dinat models at https://huggingface.co/models?filter=dinat
}
class _UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
_A : Optional[int] = '''dinat'''
_A : Optional[Any] = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self : Optional[Any] , lowerCAmelCase__ : Dict=4 , lowerCAmelCase__ : List[Any]=3 , lowerCAmelCase__ : List[str]=6_4 , lowerCAmelCase__ : Tuple=[3, 4, 6, 5] , lowerCAmelCase__ : Optional[Any]=[2, 4, 8, 1_6] , lowerCAmelCase__ : str=7 , lowerCAmelCase__ : Optional[Any]=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]] , lowerCAmelCase__ : Tuple=3.0 , lowerCAmelCase__ : str=True , lowerCAmelCase__ : Union[str, Any]=0.0 , lowerCAmelCase__ : Dict=0.0 , lowerCAmelCase__ : List[Any]=0.1 , lowerCAmelCase__ : Union[str, Any]="gelu" , lowerCAmelCase__ : int=0.02 , lowerCAmelCase__ : Any=1E-5 , lowerCAmelCase__ : int=0.0 , lowerCAmelCase__ : Union[str, Any]=None , lowerCAmelCase__ : Optional[int]=None , **lowerCAmelCase__ : Union[str, Any] , ):
"""simple docstring"""
super().__init__(**lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = patch_size
__SCREAMING_SNAKE_CASE : Any = num_channels
__SCREAMING_SNAKE_CASE : Any = embed_dim
__SCREAMING_SNAKE_CASE : Optional[int] = depths
__SCREAMING_SNAKE_CASE : Dict = len(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Dict = num_heads
__SCREAMING_SNAKE_CASE : Optional[int] = kernel_size
__SCREAMING_SNAKE_CASE : str = dilations
__SCREAMING_SNAKE_CASE : List[Any] = mlp_ratio
__SCREAMING_SNAKE_CASE : Optional[Any] = qkv_bias
__SCREAMING_SNAKE_CASE : Any = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : Dict = drop_path_rate
__SCREAMING_SNAKE_CASE : str = hidden_act
__SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
__SCREAMING_SNAKE_CASE : Tuple = initializer_range
# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__SCREAMING_SNAKE_CASE : int = int(embed_dim * 2 ** (len(lowerCAmelCase__ ) - 1) )
__SCREAMING_SNAKE_CASE : str = layer_scale_init_value
__SCREAMING_SNAKE_CASE : int = ["""stem"""] + [F"stage{idx}" for idx in range(1 , len(lowerCAmelCase__ ) + 1 )]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Union[str, Any] = get_aligned_output_features_output_indices(
out_features=lowerCAmelCase__ , out_indices=lowerCAmelCase__ , stage_names=self.stage_names ) | 178 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def lowerCAmelCase_ ( _lowerCamelCase: int ):
if hor == 1_28:
__SCREAMING_SNAKE_CASE : Any = ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
__SCREAMING_SNAKE_CASE : List[Any] = (32, 1_28, 2_56)
__SCREAMING_SNAKE_CASE : str = ("""UpResnetBlock1D""", """UpResnetBlock1D""")
elif hor == 32:
__SCREAMING_SNAKE_CASE : Union[str, Any] = ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
__SCREAMING_SNAKE_CASE : str = (32, 64, 1_28, 2_56)
__SCREAMING_SNAKE_CASE : Tuple = ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.load(F"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch" )
__SCREAMING_SNAKE_CASE : Any = model.state_dict()
__SCREAMING_SNAKE_CASE : Optional[Any] = {
"""down_block_types""": down_block_types,
"""block_out_channels""": block_out_channels,
"""up_block_types""": up_block_types,
"""layers_per_block""": 1,
"""use_timestep_embedding""": True,
"""out_block_type""": """OutConv1DBlock""",
"""norm_num_groups""": 8,
"""downsample_each_block""": False,
"""in_channels""": 14,
"""out_channels""": 14,
"""extra_in_channels""": 0,
"""time_embedding_type""": """positional""",
"""flip_sin_to_cos""": False,
"""freq_shift""": 1,
"""sample_size""": 6_55_36,
"""mid_block_type""": """MidResTemporalBlock1D""",
"""act_fn""": """mish""",
}
__SCREAMING_SNAKE_CASE : int = UNetaDModel(**_lowerCamelCase )
print(F"length of state dict: {len(state_dict.keys() )}" )
print(F"length of value function dict: {len(hf_value_function.state_dict().keys() )}" )
__SCREAMING_SNAKE_CASE : Optional[int] = dict(zip(model.state_dict().keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
__SCREAMING_SNAKE_CASE : Union[str, Any] = state_dict.pop(_lowerCamelCase )
hf_value_function.load_state_dict(_lowerCamelCase )
torch.save(hf_value_function.state_dict() , F"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin" )
with open(F"hub/hopper-medium-v2/unet/hor{hor}/config.json" , """w""" ) as f:
json.dump(_lowerCamelCase , _lowerCamelCase )
def lowerCAmelCase_ ( ):
__SCREAMING_SNAKE_CASE : Dict = {
"""in_channels""": 14,
"""down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
"""up_block_types""": (),
"""out_block_type""": """ValueFunction""",
"""mid_block_type""": """ValueFunctionMidBlock1D""",
"""block_out_channels""": (32, 64, 1_28, 2_56),
"""layers_per_block""": 1,
"""downsample_each_block""": True,
"""sample_size""": 6_55_36,
"""out_channels""": 14,
"""extra_in_channels""": 0,
"""time_embedding_type""": """positional""",
"""use_timestep_embedding""": True,
"""flip_sin_to_cos""": False,
"""freq_shift""": 1,
"""norm_num_groups""": 8,
"""act_fn""": """mish""",
}
__SCREAMING_SNAKE_CASE : Optional[int] = torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch""" )
__SCREAMING_SNAKE_CASE : Dict = model
__SCREAMING_SNAKE_CASE : List[Any] = UNetaDModel(**_lowerCamelCase )
print(F"length of state dict: {len(state_dict.keys() )}" )
print(F"length of value function dict: {len(hf_value_function.state_dict().keys() )}" )
__SCREAMING_SNAKE_CASE : Optional[Any] = dict(zip(state_dict.keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
__SCREAMING_SNAKE_CASE : str = state_dict.pop(_lowerCamelCase )
hf_value_function.load_state_dict(_lowerCamelCase )
torch.save(hf_value_function.state_dict() , """hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin""" )
with open("""hub/hopper-medium-v2/value_function/config.json""" , """w""" ) as f:
json.dump(_lowerCamelCase , _lowerCamelCase )
if __name__ == "__main__":
unet(32)
# unet(128)
value_function() | 178 | 1 |
'''simple docstring'''
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 import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
from transformers.models.esm.modeling_esm import (
ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
EsmEmbeddings,
create_position_ids_from_input_ids,
)
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase , _lowercase=13 , _lowercase=7 , _lowercase=False , _lowercase=True , _lowercase=False , _lowercase=True , _lowercase=33 , _lowercase=32 , _lowercase=5 , _lowercase=4 , _lowercase=37 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=512 , _lowercase=16 , _lowercase=2 , _lowercase=0.02 , _lowercase=3 , _lowercase=4 , _lowercase=None , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = seq_length
_lowerCAmelCase = is_training
_lowerCAmelCase = use_input_mask
_lowerCAmelCase = use_token_type_ids
_lowerCAmelCase = use_labels
_lowerCAmelCase = vocab_size
_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 = max_position_embeddings
_lowerCAmelCase = type_vocab_size
_lowerCAmelCase = type_sequence_label_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = num_labels
_lowerCAmelCase = num_choices
_lowerCAmelCase = scope
def _lowercase ( 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] )
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowerCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
_lowerCAmelCase = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowercase ( self ):
"""simple docstring"""
return EsmConfig(
vocab_size=self.vocab_size , 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 , )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = EsmModel(config=_lowercase )
model.to(_lowercase )
model.eval()
_lowerCAmelCase = model(_lowercase , attention_mask=_lowercase )
_lowerCAmelCase = model(_lowercase )
_lowerCAmelCase = model(_lowercase )
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 _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = EsmForMaskedLM(config=_lowercase )
model.to(_lowercase )
model.eval()
_lowerCAmelCase = model(_lowercase , attention_mask=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = EsmForTokenClassification(config=_lowercase )
model.to(_lowercase )
model.eval()
_lowerCAmelCase = model(_lowercase , attention_mask=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = config_and_inputs
_lowerCAmelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
_lowercase : List[str] = False
_lowercase : Tuple = (
(
EsmForMaskedLM,
EsmModel,
EsmForSequenceClassification,
EsmForTokenClassification,
)
if is_torch_available()
else ()
)
_lowercase : Tuple = ()
_lowercase : Optional[int] = (
{
'''feature-extraction''': EsmModel,
'''fill-mask''': EsmForMaskedLM,
'''text-classification''': EsmForSequenceClassification,
'''token-classification''': EsmForTokenClassification,
'''zero-shot''': EsmForSequenceClassification,
}
if is_torch_available()
else {}
)
_lowercase : int = True
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = EsmModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=_lowercase , hidden_size=37 )
def _lowercase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_lowerCAmelCase = type
self.model_tester.create_and_check_model(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_lowercase )
@slow
def _lowercase ( self ):
"""simple docstring"""
for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase = EsmModel.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()[0]
_lowerCAmelCase = EsmEmbeddings(config=_lowercase )
_lowerCAmelCase = torch.as_tensor([[12, 31, 13, model.padding_idx]] )
_lowerCAmelCase = torch.as_tensor(
[
[
0 + model.padding_idx + 1,
1 + model.padding_idx + 1,
2 + model.padding_idx + 1,
model.padding_idx,
]
] )
_lowerCAmelCase = create_position_ids_from_input_ids(_lowercase , model.padding_idx )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(_lowercase , _lowercase ) ) )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()[0]
_lowerCAmelCase = EsmEmbeddings(config=_lowercase )
_lowerCAmelCase = torch.empty(2 , 4 , 30 )
_lowerCAmelCase = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
_lowerCAmelCase = torch.as_tensor([expected_single_positions, expected_single_positions] )
_lowerCAmelCase = embeddings.create_position_ids_from_inputs_embeds(_lowercase )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(_lowercase , _lowercase ) ) )
@unittest.skip("""Esm does not support embedding resizing""" )
def _lowercase ( self ):
"""simple docstring"""
pass
@unittest.skip("""Esm does not support embedding resizing""" )
def _lowercase ( self ):
"""simple docstring"""
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _lowercase ( self ):
"""simple docstring"""
pass
@require_torch
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@slow
def _lowercase ( self ):
"""simple docstring"""
with torch.no_grad():
_lowerCAmelCase = EsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
model.eval()
_lowerCAmelCase = torch.tensor([[0, 1, 2, 3, 4, 5]] )
_lowerCAmelCase = model(_lowercase )[0]
_lowerCAmelCase = 33
_lowerCAmelCase = torch.Size((1, 6, vocab_size) )
self.assertEqual(output.shape , _lowercase )
_lowerCAmelCase = torch.tensor(
[[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _lowercase , atol=1e-4 ) )
@slow
def _lowercase ( self ):
"""simple docstring"""
with torch.no_grad():
_lowerCAmelCase = EsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
model.eval()
_lowerCAmelCase = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
_lowerCAmelCase = model(_lowercase )[0]
# compare the actual values for a slice.
_lowerCAmelCase = torch.tensor(
[[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _lowercase , atol=1e-4 ) )
| 5 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __UpperCAmelCase ( lowerCamelCase_ : np.ndarray , lowerCamelCase_ : np.ndarray ) -> float:
"""simple docstring"""
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(lowerCamelCase_ , lowerCamelCase_ ) ) )
def __UpperCAmelCase ( lowerCamelCase_ : np.ndarray , lowerCamelCase_ : np.ndarray ) -> list[list[list[float] | float]]:
"""simple docstring"""
if dataset.ndim != value_array.ndim:
SCREAMING_SNAKE_CASE_ : Optional[int] = (
'Wrong input data\'s dimensions... '
F'dataset : {dataset.ndim}, value_array : {value_array.ndim}'
)
raise ValueError(lowerCamelCase_ )
try:
if dataset.shape[1] != value_array.shape[1]:
SCREAMING_SNAKE_CASE_ : List[Any] = (
'Wrong input data\'s shape... '
F'dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'
)
raise ValueError(lowerCamelCase_ )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError('Wrong shape' )
if dataset.dtype != value_array.dtype:
SCREAMING_SNAKE_CASE_ : Tuple = (
'Input data have different datatype... '
F'dataset : {dataset.dtype}, value_array : {value_array.dtype}'
)
raise TypeError(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[Any] = []
for value in value_array:
SCREAMING_SNAKE_CASE_ : str = euclidean(lowerCamelCase_ , dataset[0] )
SCREAMING_SNAKE_CASE_ : List[Any] = dataset[0].tolist()
for dataset_value in dataset[1:]:
SCREAMING_SNAKE_CASE_ : Optional[Any] = euclidean(lowerCamelCase_ , lowerCamelCase_ )
if dist > temp_dist:
SCREAMING_SNAKE_CASE_ : Optional[int] = temp_dist
SCREAMING_SNAKE_CASE_ : Union[str, Any] = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def __UpperCAmelCase ( lowerCamelCase_ : np.ndarray , lowerCamelCase_ : np.ndarray ) -> float:
"""simple docstring"""
return np.dot(lowerCamelCase_ , lowerCamelCase_ ) / (norm(lowerCamelCase_ ) * norm(lowerCamelCase_ ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 105 | 0 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
lowercase : Optional[Any] = {
"169M": 12,
"430M": 24,
"1B5": 24,
"3B": 32,
"7B": 32,
"14B": 40,
}
lowercase : List[str] = {
"169M": 7_68,
"430M": 10_24,
"1B5": 20_48,
"3B": 25_60,
"7B": 40_96,
"14B": 51_20,
}
def snake_case__ ( lowerCamelCase_ ):
A : Optional[Any] = list(state_dict.keys() )
for name in state_dict_keys:
A : int = state_dict.pop(lowerCamelCase_ )
# emb -> embedding
if name.startswith('''emb.''' ):
A : Optional[int] = name.replace('''emb.''' , '''embeddings.''' )
# ln_0 -> pre_ln (only present at block 0)
if name.startswith('''blocks.0.ln0''' ):
A : int = name.replace('''blocks.0.ln0''' , '''blocks.0.pre_ln''' )
# att -> attention
A : Tuple = re.sub(r'''blocks\.(\d+)\.att''' , r'''blocks.\1.attention''' , lowerCamelCase_ )
# ffn -> feed_forward
A : Union[str, Any] = re.sub(r'''blocks\.(\d+)\.ffn''' , r'''blocks.\1.feed_forward''' , lowerCamelCase_ )
# time_mix_k -> time_mix_key and reshape
if name.endswith('''.time_mix_k''' ):
A : str = name.replace('''.time_mix_k''' , '''.time_mix_key''' )
# time_mix_v -> time_mix_value and reshape
if name.endswith('''.time_mix_v''' ):
A : Any = name.replace('''.time_mix_v''' , '''.time_mix_value''' )
# time_mix_r -> time_mix_key and reshape
if name.endswith('''.time_mix_r''' ):
A : List[Any] = name.replace('''.time_mix_r''' , '''.time_mix_receptance''' )
if name != "head.weight":
A : Dict = '''rwkv.''' + name
A : str = weight
return state_dict
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_=False , lowerCamelCase_=None ):
# 1. If possible, build the tokenizer.
if tokenizer_file is None:
print('''No `--tokenizer_file` provided, we will use the default tokenizer.''' )
A : Tuple = 50277
A : str = AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b''' )
else:
A : Optional[Any] = PreTrainedTokenizerFast(tokenizer_file=lowerCamelCase_ )
A : Optional[Any] = len(lowerCamelCase_ )
tokenizer.save_pretrained(lowerCamelCase_ )
# 2. Build the config
A : Optional[int] = list(NUM_HIDDEN_LAYERS_MAPPING.keys() )
if size is None:
# Try to infer size from the checkpoint name
for candidate in possible_sizes:
if candidate in checkpoint_file:
A : Any = candidate
break
if size is None:
raise ValueError('''Could not infer the size, please provide it with the `--size` argument.''' )
if size not in possible_sizes:
raise ValueError(F'`size` should be one of {possible_sizes}, got {size}.' )
A : Any = RwkvConfig(
vocab_size=lowerCamelCase_ , num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] , hidden_size=HIDEN_SIZE_MAPPING[size] , )
config.save_pretrained(lowerCamelCase_ )
# 3. Download model file then convert state_dict
A : Tuple = hf_hub_download(lowerCamelCase_ , lowerCamelCase_ )
A : Union[str, Any] = torch.load(lowerCamelCase_ , map_location='''cpu''' )
A : List[Any] = convert_state_dict(lowerCamelCase_ )
# 4. Split in shards and save
A , A : List[Any] = shard_checkpoint(lowerCamelCase_ )
for shard_file, shard in shards.items():
torch.save(lowerCamelCase_ , os.path.join(lowerCamelCase_ , lowerCamelCase_ ) )
if index is not None:
A : Optional[Any] = os.path.join(lowerCamelCase_ , lowerCamelCase_ )
# Save the index as well
with open(lowerCamelCase_ , '''w''' , encoding='''utf-8''' ) as f:
A : List[Any] = json.dumps(lowerCamelCase_ , indent=2 , sort_keys=lowerCamelCase_ ) + '''\n'''
f.write(lowerCamelCase_ )
# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
print(
'''Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.''' )
A : List[str] = list(shards.keys() )
del state_dict
del shards
gc.collect()
for shard_file in shard_files:
A : Dict = torch.load(os.path.join(lowerCamelCase_ , lowerCamelCase_ ) )
torch.save({k: v.cpu().clone() for k, v in state_dict.items()} , os.path.join(lowerCamelCase_ , lowerCamelCase_ ) )
del state_dict
gc.collect()
if push_to_hub:
if model_name is None:
raise ValueError('''Please provide a `model_name` to push the model to the Hub.''' )
A : List[Any] = AutoModelForCausalLM.from_pretrained(lowerCamelCase_ )
model.push_to_hub(lowerCamelCase_ , max_shard_size='''2GB''' )
tokenizer.push_to_hub(lowerCamelCase_ )
if __name__ == "__main__":
lowercase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--repo_id", default=None, type=str, required=True, help="Repo ID from which to pull the checkpoint."
)
parser.add_argument(
"--checkpoint_file", default=None, type=str, required=True, help="Name of the checkpoint file in the repo."
)
parser.add_argument(
"--output_dir", default=None, type=str, required=True, help="Where to save the converted model."
)
parser.add_argument(
"--tokenizer_file",
default=None,
type=str,
help="Path to the tokenizer file to use (if not provided, only the model is converted).",
)
parser.add_argument(
"--size",
default=None,
type=str,
help="Size of the model. Will be inferred from the `checkpoint_file` if not passed.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Push to the Hub the converted model.",
)
parser.add_argument(
"--model_name",
default=None,
type=str,
help="Name of the pushed model on the Hub, including the username / organization.",
)
lowercase : List[Any] = parser.parse_args()
convert_rmkv_checkpoint_to_hf_format(
args.repo_id,
args.checkpoint_file,
args.output_dir,
size=args.size,
tokenizer_file=args.tokenizer_file,
push_to_hub=args.push_to_hub,
model_name=args.model_name,
)
| 423 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowercase : Any = abspath(join(dirname(dirname(__file__)), "src"))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def snake_case__ ( lowerCamelCase_ ):
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(lowerCamelCase_ )
def snake_case__ ( lowerCamelCase_ ):
from diffusers.utils.testing_utils import pytest_terminal_summary_main
A : Union[str, Any] = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(lowerCamelCase_ , id=lowerCamelCase_ )
| 423 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : int = """gpt_neox_japanese"""
def __init__( self , __lowerCAmelCase=32000 , __lowerCAmelCase=2560 , __lowerCAmelCase=32 , __lowerCAmelCase=32 , __lowerCAmelCase=4 , __lowerCAmelCase="gelu" , __lowerCAmelCase=1.00 , __lowerCAmelCase=10000 , __lowerCAmelCase=2048 , __lowerCAmelCase=0.02 , __lowerCAmelCase=1E-5 , __lowerCAmelCase=True , __lowerCAmelCase=31996 , __lowerCAmelCase=31999 , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.0 , **__lowerCAmelCase , ):
super().__init__(bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
UpperCamelCase__ = vocab_size
UpperCamelCase__ = max_position_embeddings
UpperCamelCase__ = hidden_size
UpperCamelCase__ = num_hidden_layers
UpperCamelCase__ = num_attention_heads
UpperCamelCase__ = intermediate_multiple_size
UpperCamelCase__ = hidden_act
UpperCamelCase__ = rotary_pct
UpperCamelCase__ = rotary_emb_base
UpperCamelCase__ = initializer_range
UpperCamelCase__ = layer_norm_eps
UpperCamelCase__ = use_cache
UpperCamelCase__ = attention_dropout
UpperCamelCase__ = hidden_dropout
| 619 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( _a , unittest.TestCase ):
snake_case : Dict = CodeGenTokenizer
snake_case : Dict = CodeGenTokenizerFast
snake_case : Tuple = True
snake_case : Optional[int] = {"""add_prefix_space""": True}
snake_case : int = False
def _lowerCamelCase ( self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCamelCase__ = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
"""<|endoftext|>""",
]
UpperCamelCase__ = dict(zip(__lowerCAmelCase , range(len(__lowerCAmelCase ) ) ) )
UpperCamelCase__ = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
UpperCamelCase__ = {"""unk_token""": """<unk>"""}
UpperCamelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCamelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(__lowerCAmelCase ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(__lowerCAmelCase ) )
def _lowerCamelCase ( self , **__lowerCAmelCase ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def _lowerCamelCase ( self , **__lowerCAmelCase ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def _lowerCamelCase ( self , __lowerCAmelCase ):
UpperCamelCase__ = """lower newer"""
UpperCamelCase__ = """lower newer"""
return input_text, output_text
def _lowerCamelCase ( self ):
UpperCamelCase__ = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
UpperCamelCase__ = """lower newer"""
UpperCamelCase__ = ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
UpperCamelCase__ = tokenizer.tokenize(__lowerCAmelCase , add_prefix_space=__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
UpperCamelCase__ = tokens + [tokenizer.unk_token]
UpperCamelCase__ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase ) , __lowerCAmelCase )
def _lowerCamelCase ( self ):
if not self.test_rust_tokenizer:
return
UpperCamelCase__ = self.get_tokenizer()
UpperCamelCase__ = self.get_rust_tokenizer(add_prefix_space=__lowerCAmelCase )
UpperCamelCase__ = """lower newer"""
# Testing tokenization
UpperCamelCase__ = tokenizer.tokenize(__lowerCAmelCase , add_prefix_space=__lowerCAmelCase )
UpperCamelCase__ = rust_tokenizer.tokenize(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
# Testing conversion to ids without special tokens
UpperCamelCase__ = tokenizer.encode(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase , add_prefix_space=__lowerCAmelCase )
UpperCamelCase__ = rust_tokenizer.encode(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
# Testing conversion to ids with special tokens
UpperCamelCase__ = self.get_rust_tokenizer(add_prefix_space=__lowerCAmelCase )
UpperCamelCase__ = tokenizer.encode(__lowerCAmelCase , add_prefix_space=__lowerCAmelCase )
UpperCamelCase__ = rust_tokenizer.encode(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
# Testing the unknown token
UpperCamelCase__ = tokens + [rust_tokenizer.unk_token]
UpperCamelCase__ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__lowerCAmelCase ) , __lowerCAmelCase )
def _lowerCamelCase ( self , *__lowerCAmelCase , **__lowerCAmelCase ):
# It's very difficult to mix/test pretokenization with byte-level
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def _lowerCamelCase ( self , __lowerCAmelCase=15 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
UpperCamelCase__ = self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase , **__lowerCAmelCase )
# Simple input
UpperCamelCase__ = """This is a simple input"""
UpperCamelCase__ = ["""This is a simple input 1""", """This is a simple input 2"""]
UpperCamelCase__ = ("""This is a simple input""", """This is a pair""")
UpperCamelCase__ = [
("""This is a simple input 1""", """This is a simple input 2"""),
("""This is a simple pair 1""", """This is a simple pair 2"""),
]
# Simple input tests
self.assertRaises(__lowerCAmelCase , tokenizer_r.encode , __lowerCAmelCase , max_length=__lowerCAmelCase , padding="""max_length""" )
# Simple input
self.assertRaises(__lowerCAmelCase , tokenizer_r.encode_plus , __lowerCAmelCase , max_length=__lowerCAmelCase , padding="""max_length""" )
# Simple input
self.assertRaises(
__lowerCAmelCase , tokenizer_r.batch_encode_plus , __lowerCAmelCase , max_length=__lowerCAmelCase , padding="""max_length""" , )
# Pair input
self.assertRaises(__lowerCAmelCase , tokenizer_r.encode , __lowerCAmelCase , max_length=__lowerCAmelCase , padding="""max_length""" )
# Pair input
self.assertRaises(__lowerCAmelCase , tokenizer_r.encode_plus , __lowerCAmelCase , max_length=__lowerCAmelCase , padding="""max_length""" )
# Pair input
self.assertRaises(
__lowerCAmelCase , tokenizer_r.batch_encode_plus , __lowerCAmelCase , max_length=__lowerCAmelCase , padding="""max_length""" , )
def _lowerCamelCase ( self ):
UpperCamelCase__ = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token="""<pad>""" )
# Simple input
UpperCamelCase__ = """This is a simple input"""
UpperCamelCase__ = ["""This is a simple input looooooooong""", """This is a simple input"""]
UpperCamelCase__ = ("""This is a simple input""", """This is a pair""")
UpperCamelCase__ = [
("""This is a simple input loooooong""", """This is a simple input"""),
("""This is a simple pair loooooong""", """This is a simple pair"""),
]
UpperCamelCase__ = tokenizer.pad_token_id
UpperCamelCase__ = tokenizer(__lowerCAmelCase , padding="""max_length""" , max_length=30 , return_tensors="""np""" )
UpperCamelCase__ = tokenizer(__lowerCAmelCase , padding=__lowerCAmelCase , truncate=__lowerCAmelCase , return_tensors="""np""" )
UpperCamelCase__ = tokenizer(*__lowerCAmelCase , padding="""max_length""" , max_length=60 , return_tensors="""np""" )
UpperCamelCase__ = tokenizer(__lowerCAmelCase , padding=__lowerCAmelCase , truncate=__lowerCAmelCase , return_tensors="""np""" )
# s
# test single string max_length padding
self.assertEqual(out_s["""input_ids"""].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s["""input_ids"""] )
self.assertTrue(0 in out_s["""attention_mask"""] )
# s2
# test automatic padding
self.assertEqual(out_sa["""input_ids"""].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["""input_ids"""][0] )
self.assertFalse(0 in out_sa["""attention_mask"""][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["""input_ids"""][1] )
self.assertTrue(0 in out_sa["""attention_mask"""][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["""input_ids"""].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p["""input_ids"""] )
self.assertTrue(0 in out_p["""attention_mask"""] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["""input_ids"""].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["""input_ids"""][0] )
self.assertFalse(0 in out_pa["""attention_mask"""][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["""input_ids"""][1] )
self.assertTrue(0 in out_pa["""attention_mask"""][1] )
def _lowerCamelCase ( self ):
UpperCamelCase__ = """$$$"""
UpperCamelCase__ = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=__lowerCAmelCase , add_bos_token=__lowerCAmelCase )
UpperCamelCase__ = """This is a simple input"""
UpperCamelCase__ = ["""This is a simple input 1""", """This is a simple input 2"""]
UpperCamelCase__ = tokenizer.bos_token_id
UpperCamelCase__ = tokenizer(__lowerCAmelCase )
UpperCamelCase__ = tokenizer(__lowerCAmelCase )
self.assertEqual(out_s.input_ids[0] , __lowerCAmelCase )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
UpperCamelCase__ = tokenizer.decode(out_s.input_ids )
UpperCamelCase__ = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , __lowerCAmelCase )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def _lowerCamelCase ( self ):
UpperCamelCase__ = CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono""" )
UpperCamelCase__ = """\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"""
UpperCamelCase__ = """\nif len_a > len_b: result = a\nelse: result = b"""
UpperCamelCase__ = tokenizer.encode(__lowerCAmelCase )
UpperCamelCase__ = ["""^#""", re.escape("""<|endoftext|>""" ), """^'''""", """^\"\"\"""", """\n\n\n"""]
UpperCamelCase__ = tokenizer.decode(__lowerCAmelCase , truncate_before_pattern=__lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
def _lowerCamelCase ( self ):
pass
| 619 | 1 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowerCamelCase_() -> Any:
UpperCAmelCase = ArgumentParser("Diffusers CLI tool" , usage="diffusers-cli <command> [<args>]" )
UpperCAmelCase = parser.add_subparsers(help="diffusers-cli command helpers" )
# Register commands
EnvironmentCommand.register_subcommand(lowerCamelCase_ )
# Let's go
UpperCAmelCase = parser.parse_args()
if not hasattr(lowerCamelCase_ , "func" ):
parser.print_help()
exit(1 )
# Run
UpperCAmelCase = args.func(lowerCamelCase_ )
service.run()
if __name__ == "__main__":
main()
| 457 |
from __future__ import annotations
def lowerCamelCase_(lowerCamelCase_ ) -> int:
UpperCAmelCase = len(lowerCamelCase_ ) // 2
# choose the middle 3 elements
UpperCAmelCase = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m] ) == 2:
m -= 1
return peak(lst[m:] )
# decreasing
else:
if len(lst[:m] ) == 2:
m += 1
return peak(lst[:m] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 457 | 1 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
_a = True
except (ImportError, ModuleNotFoundError):
_a = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def lowerCamelCase__ ( __snake_case ) -> str:
"""simple docstring"""
re.sub('''<n>''', '''''', __snake_case ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__snake_case ) )
| 19 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase = ViTImageProcessor if is_vision_available() else None
@property
def __snake_case ( self : List[str]) -> Optional[int]:
return self.image_processor_tester.prepare_image_processor_dict()
def __snake_case ( self : Optional[Any]) -> List[str]:
A_ = (3, 32, 128)
A_ = tempfile.mkdtemp()
# fmt: off
A_ = ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
# fmt: on
A_ = dict(zip(_lowercase , range(len(_lowercase))))
A_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'])
with open(self.vocab_file , 'w' , encoding='utf-8') as fp:
fp.write(json.dumps(_lowercase) + '\n')
A_ = {
'do_normalize': False,
'do_resize': True,
'image_processor_type': 'ViTImageProcessor',
'resample': 3,
'size': {'height': 32, 'width': 128},
}
A_ = os.path.join(self.tmpdirname , _lowercase)
with open(self.image_processor_file , 'w' , encoding='utf-8') as fp:
json.dump(_lowercase , _lowercase)
def __snake_case ( self : int , **_lowercase : Optional[int]) -> int:
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_lowercase)
def __snake_case ( self : Optional[int] , **_lowercase : Optional[int]) -> Union[str, Any]:
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_lowercase)
def __snake_case ( self : Dict) -> str:
shutil.rmtree(self.tmpdirname)
def __snake_case ( self : Union[str, Any]) -> Any:
A_ = np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta)
A_ = Image.fromarray(np.moveaxis(_lowercase , 0 , -1))
return image_input
def __snake_case ( self : Optional[Any]) -> List[Any]:
A_ = self.get_tokenizer()
A_ = self.get_image_processor()
A_ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase)
processor.save_pretrained(self.tmpdirname)
A_ = MgpstrProcessor.from_pretrained(self.tmpdirname , use_fast=_lowercase)
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer.get_vocab())
self.assertIsInstance(processor.char_tokenizer , _lowercase)
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string())
self.assertIsInstance(processor.image_processor , _lowercase)
def __snake_case ( self : Union[str, Any]) -> Optional[Any]:
A_ = self.get_tokenizer()
A_ = self.get_image_processor()
A_ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase)
processor.save_pretrained(self.tmpdirname)
A_ = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)')
A_ = self.get_image_processor(do_normalize=_lowercase , padding_value=1.0)
A_ = MgpstrProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=_lowercase , padding_value=1.0)
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.char_tokenizer , _lowercase)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , _lowercase)
def __snake_case ( self : List[Any]) -> str:
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase)
A_ = self.prepare_image_inputs()
A_ = image_processor(_lowercase , return_tensors='np')
A_ = processor(images=_lowercase , return_tensors='np')
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2)
def __snake_case ( self : Any) -> str:
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase)
A_ = 'test'
A_ = processor(text=_lowercase)
A_ = tokenizer(_lowercase)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key])
def __snake_case ( self : str) -> Dict:
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase)
A_ = 'test'
A_ = self.prepare_image_inputs()
A_ = processor(text=_lowercase , images=_lowercase)
self.assertListEqual(list(inputs.keys()) , ['pixel_values', 'labels'])
# test if it raises when no input is passed
with pytest.raises(_lowercase):
processor()
def __snake_case ( self : Union[str, Any]) -> Optional[int]:
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase)
A_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]
A_ = processor.char_decode(_lowercase)
A_ = tokenizer.batch_decode(_lowercase)
A_ = [seq.replace(' ' , '') for seq in decoded_tok]
self.assertListEqual(_lowercase , _lowercase)
def __snake_case ( self : List[str]) -> str:
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase)
A_ = None
A_ = self.prepare_image_inputs()
A_ = processor(text=_lowercase , images=_lowercase)
self.assertListEqual(list(inputs.keys()) , processor.model_input_names)
def __snake_case ( self : List[str]) -> Any:
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase)
A_ = torch.randn(1 , 27 , 38)
A_ = torch.randn(1 , 27 , 50_257)
A_ = torch.randn(1 , 27 , 30_522)
A_ = processor.batch_decode([char_input, bpe_input, wp_input])
self.assertListEqual(list(results.keys()) , ['generated_text', 'scores', 'char_preds', 'bpe_preds', 'wp_preds'])
| 366 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class __a ( unittest.TestCase ):
def __init__( self : Optional[int] ,lowerCamelCase : str ,lowerCamelCase : List[str]=13 ,lowerCamelCase : Optional[Any]=30 ,lowerCamelCase : Dict=2 ,lowerCamelCase : List[Any]=3 ,lowerCamelCase : List[str]=True ,lowerCamelCase : str=True ,lowerCamelCase : Optional[int]=32 ,lowerCamelCase : Dict=5 ,lowerCamelCase : Optional[int]=4 ,lowerCamelCase : List[Any]=37 ,lowerCamelCase : Union[str, Any]="gelu" ,lowerCamelCase : List[Any]=0.1 ,lowerCamelCase : Any=0.1 ,lowerCamelCase : str=10 ,lowerCamelCase : Dict=0.02 ,):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = patch_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_labels
__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 = type_sequence_label_size
__SCREAMING_SNAKE_CASE = initializer_range
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
__SCREAMING_SNAKE_CASE = (image_size // patch_size) ** 2
__SCREAMING_SNAKE_CASE = num_patches + 1
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE = ViTConfig(
image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,is_decoder=lowerCamelCase ,initializer_range=self.initializer_range ,)
return config, pixel_values
def UpperCAmelCase__ ( self : Tuple ,lowerCamelCase : int ,lowerCamelCase : Optional[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = FlaxViTModel(config=lowerCamelCase )
__SCREAMING_SNAKE_CASE = model(lowerCamelCase )
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
__SCREAMING_SNAKE_CASE = (self.image_size, self.image_size)
__SCREAMING_SNAKE_CASE = (self.patch_size, self.patch_size)
__SCREAMING_SNAKE_CASE = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, num_patches + 1, self.hidden_size) )
def UpperCAmelCase__ ( self : Union[str, Any] ,lowerCamelCase : Optional[int] ,lowerCamelCase : Dict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.type_sequence_label_size
__SCREAMING_SNAKE_CASE = FlaxViTForImageClassification(config=lowerCamelCase )
__SCREAMING_SNAKE_CASE = model(lowerCamelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = FlaxViTForImageClassification(lowerCamelCase )
__SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE = model(lowerCamelCase )
def UpperCAmelCase__ ( self : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) ,
) = config_and_inputs
__SCREAMING_SNAKE_CASE = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_flax
class __a ( _snake_case, unittest.TestCase ):
__UpperCamelCase : Any = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def UpperCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = FlaxViTModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self ,config_class=lowerCamelCase ,has_text_modality=lowerCamelCase ,hidden_size=37 )
def UpperCAmelCase__ ( self : Tuple ):
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self : List[str] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase )
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase )
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class(lowerCamelCase )
__SCREAMING_SNAKE_CASE = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,lowerCamelCase )
def UpperCAmelCase__ ( self : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__SCREAMING_SNAKE_CASE = self._prepare_for_class(lowerCamelCase ,lowerCamelCase )
__SCREAMING_SNAKE_CASE = model_class(lowerCamelCase )
@jax.jit
def model_jitted(lowerCamelCase : int ,**lowerCamelCase : Union[str, Any] ):
return model(pixel_values=lowerCamelCase ,**lowerCamelCase )
with self.subTest("""JIT Enabled""" ):
__SCREAMING_SNAKE_CASE = model_jitted(**lowerCamelCase ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
__SCREAMING_SNAKE_CASE = model_jitted(**lowerCamelCase ).to_tuple()
self.assertEqual(len(lowerCamelCase ) ,len(lowerCamelCase ) )
for jitted_output, output in zip(lowerCamelCase ,lowerCamelCase ):
self.assertEqual(jitted_output.shape ,output.shape )
@slow
def UpperCAmelCase__ ( self : Optional[Any] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class_name.from_pretrained("""google/vit-base-patch16-224""" )
__SCREAMING_SNAKE_CASE = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(lowerCamelCase )
| 13 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a = list[list[float | int]]
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Matrix:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = len(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE = [[0 for _ in range(size + 1 )] for _ in range(__UpperCAmelCase )]
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for row in range(__UpperCAmelCase ):
for col in range(__UpperCAmelCase ):
__SCREAMING_SNAKE_CASE = matrix[row][col]
__SCREAMING_SNAKE_CASE = vector[row][0]
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
while row < size and col < size:
# pivoting
__SCREAMING_SNAKE_CASE = max((abs(augmented[rowa][col] ), rowa) for rowa in range(__UpperCAmelCase , __UpperCAmelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , __UpperCAmelCase ):
__SCREAMING_SNAKE_CASE = augmented[rowa][col] / augmented[row][col]
__SCREAMING_SNAKE_CASE = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , __UpperCAmelCase ):
for row in range(__UpperCAmelCase ):
__SCREAMING_SNAKE_CASE = augmented[row][col] / augmented[col][col]
for cola in range(__UpperCAmelCase , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(__UpperCAmelCase )
]
def __magic_name__ ( __UpperCAmelCase ) -> Callable[[int], int]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = len(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE = [[0 for _ in range(__UpperCAmelCase )] for _ in range(__UpperCAmelCase )]
__SCREAMING_SNAKE_CASE = [[0] for _ in range(__UpperCAmelCase )]
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for x_val, y_val in enumerate(__UpperCAmelCase ):
for col in range(__UpperCAmelCase ):
__SCREAMING_SNAKE_CASE = (x_val + 1) ** (size - col - 1)
__SCREAMING_SNAKE_CASE = y_val
__SCREAMING_SNAKE_CASE = solve(__UpperCAmelCase , __UpperCAmelCase )
def interpolated_func(__UpperCAmelCase ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(__UpperCAmelCase ) )
return interpolated_func
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def __magic_name__ ( __UpperCAmelCase = question_function , __UpperCAmelCase = 10 ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [func(__UpperCAmelCase ) for x_val in range(1 , order + 1 )]
__SCREAMING_SNAKE_CASE = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
for poly in polynomials:
__SCREAMING_SNAKE_CASE = 1
while func(__UpperCAmelCase ) == poly(__UpperCAmelCase ):
x_val += 1
ret += poly(__UpperCAmelCase )
return ret
if __name__ == "__main__":
print(F'''{solution() = }''')
| 13 | 1 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : List[Any] = ''''''
UpperCamelCase__ : str = (
None # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
)
UpperCamelCase__ : str = None # compression type in fsspec. ex: "gzip"
UpperCamelCase__ : str = None # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz
def __init__( self , _A = "" , _A = None , _A = None , **_A ):
'''simple docstring'''
super().__init__(self , **_A )
# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
__SCREAMING_SNAKE_CASE = fsspec.open(
_A , mode='rb' , protocol=_A , compression=self.compression , client_kwargs={
'requote_redirect_url': False, # see https://github.com/huggingface/datasets/pull/5459
'trust_env': True, # Enable reading proxy env variables.
**(target_options or {}).pop('client_kwargs' , {} ), # To avoid issues if it was already passed.
} , **(target_options or {}) , )
__SCREAMING_SNAKE_CASE = os.path.basename(self.file.path.split('::' )[0] )
__SCREAMING_SNAKE_CASE = (
self.compressed_name[: self.compressed_name.rindex('.' )]
if '.' in self.compressed_name
else self.compressed_name
)
__SCREAMING_SNAKE_CASE = None
@classmethod
def _A ( cls , _A ):
'''simple docstring'''
return super()._strip_protocol(_A ).lstrip('/' )
def _A ( self ):
'''simple docstring'''
if self.dir_cache is None:
__SCREAMING_SNAKE_CASE = {**self.file.fs.info(self.file.path ), 'name': self.uncompressed_name}
__SCREAMING_SNAKE_CASE = {f['name']: f}
def _A ( self , _A ):
'''simple docstring'''
return self.file.open().read()
def _A ( self , _A , _A = "rb" , _A=None , _A=True , _A=None , **_A , ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self._strip_protocol(_A )
if mode != "rb":
raise ValueError(f"""Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'""" )
return self.file.open()
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : List[Any] = '''bz2'''
UpperCamelCase__ : Tuple = '''bz2'''
UpperCamelCase__ : List[Any] = '''.bz2'''
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : int = '''gzip'''
UpperCamelCase__ : Optional[Any] = '''gzip'''
UpperCamelCase__ : Any = '''.gz'''
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : List[Any] = '''lz4'''
UpperCamelCase__ : int = '''lz4'''
UpperCamelCase__ : Union[str, Any] = '''.lz4'''
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = '''xz'''
UpperCamelCase__ : Any = '''xz'''
UpperCamelCase__ : Any = '''.xz'''
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : Dict = '''zstd'''
UpperCamelCase__ : Any = '''zstd'''
UpperCamelCase__ : Optional[int] = '''.zst'''
def __init__( self , _A , _A = "rb" , _A = None , _A = None , _A = DEFAULT_BLOCK_SIZE , **_A , ):
'''simple docstring'''
super().__init__(
fo=_A , mode=_A , target_protocol=_A , target_options=_A , block_size=_A , **_A , )
# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
#
# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
# out.close = close
# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
#
# see https://github.com/intake/filesystem_spec/issues/725
__SCREAMING_SNAKE_CASE = self.file.__enter__
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _A ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = file_
def __enter__( self ):
'''simple docstring'''
self._file.__enter__()
return self
def __exit__( self , *_A , **_A ):
'''simple docstring'''
self._file.__exit__(*_A , **_A )
def __iter__( self ):
'''simple docstring'''
return iter(self._file )
def _A ( self ):
'''simple docstring'''
return next(self._file )
def __getattr__( self , _A ):
'''simple docstring'''
return getattr(self._file , _A )
def fixed_enter(*_A , **_A ):
return WrappedFile(_enter(*_A , **_A ) )
__SCREAMING_SNAKE_CASE = fixed_enter
| 148 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ : List[str] =logging.get_logger(__name__)
lowerCAmelCase__ : List[Any] ={
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
lowerCAmelCase__ : Dict ={
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
lowerCAmelCase__ : Dict ={'''facebook/blenderbot_small-90M''': 512}
def __lowercase ( a__ ) -> str:
__SCREAMING_SNAKE_CASE = set()
__SCREAMING_SNAKE_CASE = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__SCREAMING_SNAKE_CASE = char
__SCREAMING_SNAKE_CASE = set(a__ )
return pairs
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : int = VOCAB_FILES_NAMES
UpperCamelCase__ : List[str] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ : Any = ['''input_ids''', '''attention_mask''']
def __init__( self , _A , _A , _A="__start__" , _A="__end__" , _A="__unk__" , _A="__null__" , **_A , ):
'''simple docstring'''
super().__init__(unk_token=_A , bos_token=_A , eos_token=_A , pad_token=_A , **_A )
with open(_A , encoding='utf-8' ) as vocab_handle:
__SCREAMING_SNAKE_CASE = json.load(_A )
__SCREAMING_SNAKE_CASE = {v: k for k, v in self.encoder.items()}
with open(_A , encoding='utf-8' ) as merges_handle:
__SCREAMING_SNAKE_CASE = merges_handle.read().split('\n' )[1:-1]
__SCREAMING_SNAKE_CASE = [tuple(merge.split() ) for merge in merges]
__SCREAMING_SNAKE_CASE = dict(zip(_A , range(len(_A ) ) ) )
__SCREAMING_SNAKE_CASE = {}
@property
def _A ( self ):
'''simple docstring'''
return len(self.encoder )
def _A ( self ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def _A ( self , _A ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
__SCREAMING_SNAKE_CASE = re.sub('([.,!?()])' , r' \1' , _A )
__SCREAMING_SNAKE_CASE = re.sub('(\')' , r' \1 ' , _A )
__SCREAMING_SNAKE_CASE = re.sub(r'\s{2,}' , ' ' , _A )
if "\n" in token:
__SCREAMING_SNAKE_CASE = token.replace('\n' , ' __newln__' )
__SCREAMING_SNAKE_CASE = token.split(' ' )
__SCREAMING_SNAKE_CASE = []
for token in tokens:
if not len(_A ):
continue
__SCREAMING_SNAKE_CASE = token.lower()
__SCREAMING_SNAKE_CASE = tuple(_A )
__SCREAMING_SNAKE_CASE = tuple(list(word[:-1] ) + [word[-1] + '</w>'] )
__SCREAMING_SNAKE_CASE = get_pairs(_A )
if not pairs:
words.append(_A )
continue
while True:
__SCREAMING_SNAKE_CASE = min(_A , key=lambda _A : self.bpe_ranks.get(_A , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = bigram
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = 0
while i < len(_A ):
try:
__SCREAMING_SNAKE_CASE = word.index(_A , _A )
new_word.extend(word[i:j] )
__SCREAMING_SNAKE_CASE = j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(_A ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__SCREAMING_SNAKE_CASE = tuple(_A )
__SCREAMING_SNAKE_CASE = new_word
if len(_A ) == 1:
break
else:
__SCREAMING_SNAKE_CASE = get_pairs(_A )
__SCREAMING_SNAKE_CASE = '@@ '.join(_A )
__SCREAMING_SNAKE_CASE = word[:-4]
__SCREAMING_SNAKE_CASE = word
words.append(_A )
return " ".join(_A )
def _A ( self , _A ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = re.findall(r'\S+\n?' , _A )
for token in words:
split_tokens.extend(list(self.bpe(_A ).split(' ' ) ) )
return split_tokens
def _A ( self , _A ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = token.lower()
return self.encoder.get(_A , self.encoder.get(self.unk_token ) )
def _A ( self , _A ):
'''simple docstring'''
return self.decoder.get(_A , self.unk_token )
def _A ( self , _A ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = ' '.join(_A ).replace('@@ ' , '' ).strip()
return out_string
def _A ( self , _A , _A = None ):
'''simple docstring'''
if not os.path.isdir(_A ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__SCREAMING_SNAKE_CASE = os.path.join(
_A , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
__SCREAMING_SNAKE_CASE = os.path.join(
_A , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(_A , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_A , ensure_ascii=_A ) + '\n' )
__SCREAMING_SNAKE_CASE = 0
with open(_A , 'w' , encoding='utf-8' ) as writer:
writer.write('#version: 0.2\n' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _A : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
' Please check that the tokenizer is not corrupted!' )
__SCREAMING_SNAKE_CASE = token_index
writer.write(' '.join(_A ) + '\n' )
index += 1
return vocab_file, merge_file
| 148 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
snake_case__ = """roc_bert"""
def __init__( self : List[str] , SCREAMING_SNAKE_CASE__ : List[Any]=30_522 , SCREAMING_SNAKE_CASE__ : List[str]=768 , SCREAMING_SNAKE_CASE__ : str=12 , SCREAMING_SNAKE_CASE__ : Optional[int]=12 , SCREAMING_SNAKE_CASE__ : Any=3_072 , SCREAMING_SNAKE_CASE__ : str="gelu" , SCREAMING_SNAKE_CASE__ : Dict=0.1 , SCREAMING_SNAKE_CASE__ : List[str]=0.1 , SCREAMING_SNAKE_CASE__ : Optional[Any]=512 , SCREAMING_SNAKE_CASE__ : Tuple=2 , SCREAMING_SNAKE_CASE__ : Optional[int]=0.02 , SCREAMING_SNAKE_CASE__ : Tuple=1e-1_2 , SCREAMING_SNAKE_CASE__ : Dict=True , SCREAMING_SNAKE_CASE__ : Tuple=0 , SCREAMING_SNAKE_CASE__ : int="absolute" , SCREAMING_SNAKE_CASE__ : List[Any]=None , SCREAMING_SNAKE_CASE__ : Union[str, Any]=True , SCREAMING_SNAKE_CASE__ : int=True , SCREAMING_SNAKE_CASE__ : int=768 , SCREAMING_SNAKE_CASE__ : List[Any]=910 , SCREAMING_SNAKE_CASE__ : Optional[Any]=512 , SCREAMING_SNAKE_CASE__ : Dict=24_858 , SCREAMING_SNAKE_CASE__ : Dict=True , **SCREAMING_SNAKE_CASE__ : int , ) -> Union[str, Any]:
lowerCAmelCase__ = vocab_size
lowerCAmelCase__ = max_position_embeddings
lowerCAmelCase__ = hidden_size
lowerCAmelCase__ = num_hidden_layers
lowerCAmelCase__ = num_attention_heads
lowerCAmelCase__ = intermediate_size
lowerCAmelCase__ = hidden_act
lowerCAmelCase__ = hidden_dropout_prob
lowerCAmelCase__ = attention_probs_dropout_prob
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = type_vocab_size
lowerCAmelCase__ = layer_norm_eps
lowerCAmelCase__ = use_cache
lowerCAmelCase__ = enable_pronunciation
lowerCAmelCase__ = enable_shape
lowerCAmelCase__ = pronunciation_embed_dim
lowerCAmelCase__ = pronunciation_vocab_size
lowerCAmelCase__ = shape_embed_dim
lowerCAmelCase__ = shape_vocab_size
lowerCAmelCase__ = concat_input
lowerCAmelCase__ = position_embedding_type
lowerCAmelCase__ = classifier_dropout
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
| 716 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def _A ( lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[int] ):
"""simple docstring"""
lowerCAmelCase__ = BigBirdConfig.from_json_file(lowerCAmelCase_ )
print(F'Building PyTorch model from configuration: {config}' )
if is_trivia_qa:
lowerCAmelCase__ = BigBirdForQuestionAnswering(lowerCAmelCase_ )
else:
lowerCAmelCase__ = BigBirdForPreTraining(lowerCAmelCase_ )
# Load weights from tf checkpoint
load_tf_weights_in_big_bird(lowerCAmelCase_ , lowerCAmelCase_ , is_trivia_qa=lowerCAmelCase_ )
# Save pytorch-model
print(F'Save PyTorch model to {pytorch_dump_path}' )
model.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--big_bird_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained BERT 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.'
)
parser.add_argument(
'--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
)
UpperCamelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
)
| 125 | 0 |
def UpperCAmelCase__ ( lowerCamelCase_ : List[Any] , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : Dict , lowerCamelCase_ : Tuple ):
# Return True if there is node that has not iterated.
__a : int = [False] * len(lowerCamelCase_ )
__a : Optional[int] = []
queue.append(lowerCamelCase_ )
__a : List[str] = True
while queue:
__a : Optional[Any] = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(lowerCamelCase_ )
__a : Dict = True
__a : int = u
return visited[t]
def UpperCAmelCase__ ( lowerCamelCase_ : Dict , lowerCamelCase_ : Tuple , lowerCamelCase_ : Any ):
# This array is filled by BFS and to store path
__a : List[str] = [-1] * (len(lowerCamelCase_ ))
__a : int = 0
while bfs(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
__a : List[Any] = float('Inf' )
__a : List[Any] = sink
while s != source:
# Find the minimum value in select path
__a : List[str] = min(lowerCamelCase_ , graph[parent[s]][s] )
__a : List[str] = parent[s]
max_flow += path_flow
__a : int = sink
while v != source:
__a : Optional[Any] = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
__a : Optional[Any] = parent[v]
return max_flow
SCREAMING_SNAKE_CASE__ = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 0, 5
print(ford_fulkerson(graph, source, sink))
| 47 | from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A = input('Enter image url: ').strip()
print(F'''Downloading image from {url} ...''')
A = BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL is in the content field of the first meta tag with property og:image
A = soup.find('meta', {'property': 'og:image'})['content']
A = requests.get(image_url).content
A = F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
with open(file_name, 'wb') as fp:
fp.write(image_data)
print(F'''Done. Image saved to disk as {file_name}.''') | 544 | 0 |
from math import isclose, sqrt
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> tuple[float, float, float]:
lowerCamelCase__ : str = point_y / 4 / point_x
lowerCamelCase__ : Dict = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
lowerCamelCase__ : Optional[int] = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
lowerCamelCase__ : int = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
lowerCamelCase__ : Union[str, Any] = outgoing_gradient**2 + 4
lowerCamelCase__ : Any = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
lowerCamelCase__ : int = (point_y - outgoing_gradient * point_x) ** 2 - 100
lowerCamelCase__ : List[Any] = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
lowerCamelCase__ : int = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
lowerCamelCase__ : List[str] = x_minus if isclose(UpperCamelCase , UpperCamelCase ) else x_plus
lowerCamelCase__ : Tuple = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def SCREAMING_SNAKE_CASE_ (UpperCamelCase = 1.4 , UpperCamelCase = -9.6 ) -> int:
lowerCamelCase__ : int = 0
lowerCamelCase__ : float = first_x_coord
lowerCamelCase__ : float = first_y_coord
lowerCamelCase__ : float = (10.1 - point_y) / (0.0 - point_x)
while not (-0.01 <= point_x <= 0.01 and point_y > 0):
lowerCamelCase__ : int = next_point(UpperCamelCase , UpperCamelCase , UpperCamelCase )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(F'{solution() = }')
| 715 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : str =logging.get_logger(__name__)
_A : int ={
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class _lowercase ( _lowercase ):
a = """roc_bert"""
def __init__( self: Optional[Any] , UpperCamelCase__: Any=30_522 , UpperCamelCase__: Optional[Any]=768 , UpperCamelCase__: Union[str, Any]=12 , UpperCamelCase__: Tuple=12 , UpperCamelCase__: Tuple=3_072 , UpperCamelCase__: str="gelu" , UpperCamelCase__: List[Any]=0.1 , UpperCamelCase__: List[str]=0.1 , UpperCamelCase__: Dict=512 , UpperCamelCase__: str=2 , UpperCamelCase__: str=0.02 , UpperCamelCase__: Tuple=1e-12 , UpperCamelCase__: Any=True , UpperCamelCase__: Union[str, Any]=0 , UpperCamelCase__: List[Any]="absolute" , UpperCamelCase__: Any=None , UpperCamelCase__: Any=True , UpperCamelCase__: Optional[int]=True , UpperCamelCase__: Union[str, Any]=768 , UpperCamelCase__: int=910 , UpperCamelCase__: Tuple=512 , UpperCamelCase__: int=24_858 , UpperCamelCase__: Optional[Any]=True , **UpperCamelCase__: Optional[Any] , ):
lowerCamelCase__ : Optional[Any] = vocab_size
lowerCamelCase__ : Tuple = max_position_embeddings
lowerCamelCase__ : List[Any] = hidden_size
lowerCamelCase__ : int = num_hidden_layers
lowerCamelCase__ : Tuple = num_attention_heads
lowerCamelCase__ : Any = intermediate_size
lowerCamelCase__ : List[str] = hidden_act
lowerCamelCase__ : str = hidden_dropout_prob
lowerCamelCase__ : Dict = attention_probs_dropout_prob
lowerCamelCase__ : Union[str, Any] = initializer_range
lowerCamelCase__ : Tuple = type_vocab_size
lowerCamelCase__ : Optional[Any] = layer_norm_eps
lowerCamelCase__ : List[Any] = use_cache
lowerCamelCase__ : Tuple = enable_pronunciation
lowerCamelCase__ : Union[str, Any] = enable_shape
lowerCamelCase__ : Union[str, Any] = pronunciation_embed_dim
lowerCamelCase__ : Any = pronunciation_vocab_size
lowerCamelCase__ : int = shape_embed_dim
lowerCamelCase__ : Tuple = shape_vocab_size
lowerCamelCase__ : Optional[Any] = concat_input
lowerCamelCase__ : str = position_embedding_type
lowerCamelCase__ : Dict = classifier_dropout
super().__init__(pad_token_id=UpperCamelCase__ , **UpperCamelCase__ )
| 631 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {
"""configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
"""processing_git""": ["""GitProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"""GIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GitForCausalLM""",
"""GitModel""",
"""GitPreTrainedModel""",
"""GitVisionModel""",
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
A_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 29 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a : Optional[Any] = logging.get_logger(__name__)
__a : Dict = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"
),
"google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json",
"google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json",
"google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json",
"google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json",
# See all REALM models at https://huggingface.co/models?filter=realm
}
class __lowercase ( lowercase_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = "realm"
def __init__( self : str , UpperCamelCase_ : List[Any]=30_522 , UpperCamelCase_ : Dict=768 , UpperCamelCase_ : Union[str, Any]=128 , UpperCamelCase_ : Tuple=12 , UpperCamelCase_ : Optional[Any]=12 , UpperCamelCase_ : Optional[int]=8 , UpperCamelCase_ : str=3_072 , UpperCamelCase_ : List[str]="gelu_new" , UpperCamelCase_ : Optional[int]=0.1 , UpperCamelCase_ : Optional[int]=0.1 , UpperCamelCase_ : str=512 , UpperCamelCase_ : int=2 , UpperCamelCase_ : str=0.02 , UpperCamelCase_ : List[Any]=1e-12 , UpperCamelCase_ : str=256 , UpperCamelCase_ : List[str]=10 , UpperCamelCase_ : int=1e-3 , UpperCamelCase_ : List[str]=5 , UpperCamelCase_ : Tuple=320 , UpperCamelCase_ : List[str]=13_353_718 , UpperCamelCase_ : Tuple=5_000 , UpperCamelCase_ : Optional[int]=1 , UpperCamelCase_ : List[str]=0 , UpperCamelCase_ : Union[str, Any]=2 , **UpperCamelCase_ : List[Any] , ):
"""simple docstring"""
super().__init__(pad_token_id=UpperCamelCase_ , bos_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , **UpperCamelCase_ )
# Common config
__A = vocab_size
__A = max_position_embeddings
__A = hidden_size
__A = retriever_proj_size
__A = num_hidden_layers
__A = num_attention_heads
__A = num_candidates
__A = intermediate_size
__A = hidden_act
__A = hidden_dropout_prob
__A = attention_probs_dropout_prob
__A = initializer_range
__A = type_vocab_size
__A = layer_norm_eps
# Reader config
__A = span_hidden_size
__A = max_span_width
__A = reader_layer_norm_eps
__A = reader_beam_size
__A = reader_seq_len
# Retrieval config
__A = num_block_records
__A = searcher_beam_size
| 637 | 0 |
def lowercase__( A , A ):
snake_case__ : Optional[Any] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowercase__( A , A , A ):
snake_case__ : Optional[Any] = 0
while b > 0:
if b & 1:
snake_case__ : List[Any] = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res
| 303 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class snake_case__ :
def __init__( self : Union[str, Any] , _lowerCamelCase : Dict , _lowerCamelCase : Optional[Any]=2 , _lowerCamelCase : Dict=True , _lowerCamelCase : Optional[Any]=False , _lowerCamelCase : int=1_0 , _lowerCamelCase : Dict=3 , _lowerCamelCase : List[str]=3_2 * 8 , _lowerCamelCase : Tuple=3_2 * 8 , _lowerCamelCase : Optional[int]=4 , _lowerCamelCase : Optional[Any]=6_4 , ):
snake_case__ : Dict = parent
snake_case__ : Optional[Any] = batch_size
snake_case__ : str = is_training
snake_case__ : List[str] = use_auxiliary_loss
snake_case__ : Union[str, Any] = num_queries
snake_case__ : List[Any] = num_channels
snake_case__ : Dict = min_size
snake_case__ : str = max_size
snake_case__ : Any = num_labels
snake_case__ : int = hidden_dim
snake_case__ : List[Any] = hidden_dim
def UpperCAmelCase__ ( self : str ):
snake_case__ : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_lowerCamelCase )
snake_case__ : List[Any] = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_lowerCamelCase )
snake_case__ : Optional[Any] = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_lowerCamelCase ) > 0.5
).float()
snake_case__ : str = (torch.rand((self.batch_size, self.num_labels) , device=_lowerCamelCase ) > 0.5).long()
snake_case__ : int = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def UpperCAmelCase__ ( self : Optional[int] ):
snake_case__ : Optional[Any] = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
snake_case__ : Optional[Any] = self.num_queries
snake_case__ : int = self.num_labels
snake_case__ : Any = [1, 1, 1, 1]
snake_case__ : str = self.num_channels
snake_case__ : List[str] = 6_4
snake_case__ : Optional[int] = 1_2_8
snake_case__ : Optional[int] = self.hidden_dim
snake_case__ : Optional[int] = self.hidden_dim
snake_case__ : Union[str, Any] = self.hidden_dim
return config
def UpperCAmelCase__ ( self : str ):
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ : Dict = self.prepare_config_and_inputs()
snake_case__ : Tuple = {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
return config, inputs_dict
def UpperCAmelCase__ ( self : Optional[int] , _lowerCamelCase : Tuple , _lowerCamelCase : Dict ):
snake_case__ : Optional[Any] = output.encoder_hidden_states
snake_case__ : Optional[int] = output.pixel_decoder_hidden_states
snake_case__ : Union[str, Any] = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , config.decoder_layers )
def UpperCAmelCase__ ( self : List[str] , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Dict , _lowerCamelCase : int , _lowerCamelCase : List[str]=False ):
with torch.no_grad():
snake_case__ : Union[str, Any] = MaskaFormerModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
snake_case__ : List[Any] = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
snake_case__ : Optional[Any] = model(_lowerCamelCase , output_hidden_states=_lowerCamelCase )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(_lowerCamelCase , _lowerCamelCase )
def UpperCAmelCase__ ( self : List[str] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : str , _lowerCamelCase : Optional[int] , _lowerCamelCase : Dict , _lowerCamelCase : Optional[Any] ):
snake_case__ : int = MaskaFormerForUniversalSegmentation(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
def comm_check_on_output(_lowerCamelCase : int ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
snake_case__ : Optional[Any] = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
snake_case__ : Any = model(_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
snake_case__ : List[Any] = model(
pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class snake_case__ ( UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
_lowerCAmelCase =(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
_lowerCAmelCase ={'feature-extraction': MaskaFormerModel} if is_torch_available() else {}
_lowerCAmelCase =False
_lowerCAmelCase =False
_lowerCAmelCase =False
_lowerCAmelCase =False
def UpperCAmelCase__ ( self : Tuple ):
snake_case__ : Optional[Any] = MaskaFormerModelTester(self )
snake_case__ : Optional[int] = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def UpperCAmelCase__ ( self : Tuple ):
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self : Dict ):
snake_case__ , snake_case__ : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def UpperCAmelCase__ ( self : str ):
snake_case__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_lowerCamelCase )
@unittest.skip(reason='Mask2Former does not use inputs_embeds' )
def UpperCAmelCase__ ( self : Union[str, Any] ):
pass
@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method' )
def UpperCAmelCase__ ( self : Optional[int] ):
pass
@unittest.skip(reason='Mask2Former is not a generative model' )
def UpperCAmelCase__ ( self : List[Any] ):
pass
@unittest.skip(reason='Mask2Former does not use token embeddings' )
def UpperCAmelCase__ ( self : Optional[Any] ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def UpperCAmelCase__ ( self : Optional[Any] ):
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def UpperCAmelCase__ ( self : Optional[int] ):
pass
def UpperCAmelCase__ ( self : int ):
snake_case__ , snake_case__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ : List[str] = model_class(_lowerCamelCase )
snake_case__ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case__ : Dict = [*signature.parameters.keys()]
snake_case__ : Dict = ['pixel_values']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
@slow
def UpperCAmelCase__ ( self : Tuple ):
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
snake_case__ : Tuple = MaskaFormerModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def UpperCAmelCase__ ( self : List[Any] ):
snake_case__ : int = (self.model_tester.min_size,) * 2
snake_case__ : Tuple = {
'pixel_values': torch.randn((2, 3, *size) , device=_lowerCamelCase ),
'mask_labels': torch.randn((2, 1_0, *size) , device=_lowerCamelCase ),
'class_labels': torch.zeros(2 , 1_0 , device=_lowerCamelCase ).long(),
}
snake_case__ : str = self.model_tester.get_config()
snake_case__ : Optional[int] = MaskaFormerForUniversalSegmentation(_lowerCamelCase ).to(_lowerCamelCase )
snake_case__ : Tuple = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
def UpperCAmelCase__ ( self : str ):
snake_case__ , snake_case__ : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def UpperCAmelCase__ ( self : Tuple ):
snake_case__ , snake_case__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ : Dict = model_class(_lowerCamelCase ).to(_lowerCamelCase )
snake_case__ : List[str] = model(**_lowerCamelCase , output_attentions=_lowerCamelCase )
self.assertTrue(outputs.attentions is not None )
def UpperCAmelCase__ ( self : Any ):
if not self.model_tester.is_training:
return
snake_case__ : Tuple = self.all_model_classes[1]
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ : List[str] = self.model_tester.prepare_config_and_inputs()
snake_case__ : List[Any] = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
snake_case__ : int = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase ).loss
loss.backward()
def UpperCAmelCase__ ( self : Any ):
snake_case__ : int = self.all_model_classes[1]
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ : Any = self.model_tester.prepare_config_and_inputs()
snake_case__ : Union[str, Any] = True
snake_case__ : Optional[Any] = True
snake_case__ : Tuple = model_class(_lowerCamelCase ).to(_lowerCamelCase )
model.train()
snake_case__ : Union[str, Any] = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
snake_case__ : Tuple = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
snake_case__ : Any = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
snake_case__ : Dict = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
snake_case__ : List[Any] = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_lowerCamelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
lowerCamelCase : int = 1e-4
def lowercase__( ):
snake_case__ : Optional[Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_vision
@slow
class snake_case__ ( unittest.TestCase ):
@cached_property
def UpperCAmelCase__ ( self : Union[str, Any] ):
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def UpperCAmelCase__ ( self : str ):
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def UpperCAmelCase__ ( self : int ):
snake_case__ : Dict = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(_lowerCamelCase )
snake_case__ : List[str] = self.default_image_processor
snake_case__ : Union[str, Any] = prepare_img()
snake_case__ : Any = image_processor(_lowerCamelCase , return_tensors='pt' ).to(_lowerCamelCase )
snake_case__ : Dict = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 3_8_4, 3_8_4) )
with torch.no_grad():
snake_case__ : List[Any] = model(**_lowerCamelCase )
snake_case__ : Optional[Any] = torch.tensor(
[[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
snake_case__ : List[Any] = torch.tensor(
[[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
snake_case__ : str = torch.tensor(
[[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def UpperCAmelCase__ ( self : List[Any] ):
snake_case__ : Dict = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(_lowerCamelCase ).eval()
snake_case__ : Union[str, Any] = self.default_image_processor
snake_case__ : Union[str, Any] = prepare_img()
snake_case__ : Tuple = image_processor(_lowerCamelCase , return_tensors='pt' ).to(_lowerCamelCase )
snake_case__ : str = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 3_8_4, 3_8_4) )
with torch.no_grad():
snake_case__ : Dict = model(**_lowerCamelCase )
# masks_queries_logits
snake_case__ : Optional[Any] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
snake_case__ : Optional[Any] = [
[-8.7839, -9.0056, -8.8121],
[-7.4104, -7.0313, -6.5401],
[-6.6105, -6.3427, -6.4675],
]
snake_case__ : Optional[Any] = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
snake_case__ : int = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
snake_case__ : List[str] = torch.tensor(
[
[1.8324, -8.0835, -4.1922],
[0.8450, -9.0050, -3.6053],
[0.3045, -7.7293, -3.0275],
] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def UpperCAmelCase__ ( self : int ):
snake_case__ : Any = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(_lowerCamelCase ).eval()
snake_case__ : Any = self.default_image_processor
snake_case__ : Optional[int] = image_processor(
[np.zeros((3, 8_0_0, 1_3_3_3) ), np.zeros((3, 8_0_0, 1_3_3_3) )] , segmentation_maps=[np.zeros((3_8_4, 3_8_4) ).astype(np.floataa ), np.zeros((3_8_4, 3_8_4) ).astype(np.floataa )] , return_tensors='pt' , )
snake_case__ : Dict = inputs['pixel_values'].to(_lowerCamelCase )
snake_case__ : Optional[Any] = [el.to(_lowerCamelCase ) for el in inputs['mask_labels']]
snake_case__ : Union[str, Any] = [el.to(_lowerCamelCase ) for el in inputs['class_labels']]
with torch.no_grad():
snake_case__ : Tuple = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
| 303 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"""kssteven/ibert-roberta-base""": """https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json""",
"""kssteven/ibert-roberta-large""": """https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json""",
"""kssteven/ibert-roberta-large-mnli""": (
"""https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"""
),
}
class _SCREAMING_SNAKE_CASE ( __a ):
__SCREAMING_SNAKE_CASE :Optional[Any] = """ibert"""
def __init__( self : Optional[Any] , a__ : List[str]=3_0522 , a__ : Tuple=768 , a__ : str=12 , a__ : Any=12 , a__ : List[Any]=3072 , a__ : Optional[int]="gelu" , a__ : Optional[int]=0.1 , a__ : List[str]=0.1 , a__ : Optional[int]=512 , a__ : List[Any]=2 , a__ : Optional[Any]=0.02 , a__ : int=1E-12 , a__ : List[Any]=1 , a__ : Optional[int]=0 , a__ : List[str]=2 , a__ : Tuple="absolute" , a__ : Union[str, Any]=False , a__ : str="none" , **a__ : List[Any] , ):
super().__init__(pad_token_id=lowercase__ , bos_token_id=lowercase__ , eos_token_id=lowercase__ , **lowercase__ )
__magic_name__ = vocab_size
__magic_name__ = hidden_size
__magic_name__ = num_hidden_layers
__magic_name__ = num_attention_heads
__magic_name__ = hidden_act
__magic_name__ = intermediate_size
__magic_name__ = hidden_dropout_prob
__magic_name__ = attention_probs_dropout_prob
__magic_name__ = max_position_embeddings
__magic_name__ = type_vocab_size
__magic_name__ = initializer_range
__magic_name__ = layer_norm_eps
__magic_name__ = position_embedding_type
__magic_name__ = quant_mode
__magic_name__ = force_dequant
class _SCREAMING_SNAKE_CASE ( __a ):
@property
def snake_case__ ( self : Any ):
if self.task == "multiple-choice":
__magic_name__ = {0: "batch", 1: "choice", 2: "sequence"}
else:
__magic_name__ = {0: "batch", 1: "sequence"}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 432 | '''simple docstring'''
import numpy as np
def __A ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase = 1E-12 ,UpperCAmelCase = 1_0_0 ,) -> tuple[float, np.ndarray]:
'''simple docstring'''
assert np.shape(UpperCAmelCase )[0] == np.shape(UpperCAmelCase )[1]
# Ensure proper dimensionality.
assert np.shape(UpperCAmelCase )[0] == np.shape(UpperCAmelCase )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(UpperCAmelCase ) == np.iscomplexobj(UpperCAmelCase )
_UpperCamelCase : int = np.iscomplexobj(UpperCAmelCase )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(UpperCAmelCase ,input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
_UpperCamelCase : str = False
_UpperCamelCase : Any = 0
_UpperCamelCase : List[Any] = 0
_UpperCamelCase : int = 1E12
while not convergence:
# Multiple matrix by the vector.
_UpperCamelCase : str = np.dot(UpperCAmelCase ,UpperCAmelCase )
# Normalize the resulting output vector.
_UpperCamelCase : int = w / np.linalg.norm(UpperCAmelCase )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
_UpperCamelCase : Any = vector.conj().T if is_complex else vector.T
_UpperCamelCase : Dict = np.dot(UpperCAmelCase ,np.dot(UpperCAmelCase ,UpperCAmelCase ) )
# Check convergence.
_UpperCamelCase : Optional[Any] = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
_UpperCamelCase : Union[str, Any] = True
_UpperCamelCase : int = lambda_
if is_complex:
_UpperCamelCase : List[Any] = np.real(lambda_ )
return lambda_, vector
def __A ( ) -> None:
'''simple docstring'''
_UpperCamelCase : Any = np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]] )
_UpperCamelCase : str = np.array([4_1, 4, 2_0] )
_UpperCamelCase : Union[str, Any] = real_input_matrix.astype(np.complexaaa )
_UpperCamelCase : List[str] = np.triu(1j * complex_input_matrix ,1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
_UpperCamelCase : Optional[int] = np.array([4_1, 4, 2_0] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
_UpperCamelCase : Tuple = real_input_matrix
_UpperCamelCase : List[Any] = real_vector
elif problem_type == "complex":
_UpperCamelCase : Optional[int] = complex_input_matrix
_UpperCamelCase : Any = complex_vector
# Our implementation.
_UpperCamelCase , _UpperCamelCase : Any = power_iteration(UpperCAmelCase ,UpperCAmelCase )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
_UpperCamelCase , _UpperCamelCase : Any = np.linalg.eigh(UpperCAmelCase )
# Last eigenvalue is the maximum one.
_UpperCamelCase : Union[str, Any] = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
_UpperCamelCase : Optional[int] = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(UpperCAmelCase ) - np.abs(UpperCAmelCase ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 435 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
SCREAMING_SNAKE_CASE = transforms.Compose(
[
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
if isinstance(__UpperCAmelCase ,torch.Tensor ):
return image
elif isinstance(__UpperCAmelCase ,PIL.Image.Image ):
_lowercase : Any = [image]
_lowercase : Dict = [trans(img.convert('RGB' ) ) for img in image]
_lowercase : Any = torch.stack(__UpperCAmelCase )
return image
class _lowerCamelCase (__lowerCamelCase ):
def __init__( self : Tuple , lowerCamelCase_ : int , lowerCamelCase_ : int ):
"""simple docstring"""
super().__init__()
# make sure scheduler can always be converted to DDIM
_lowercase : Union[str, Any] = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=lowerCamelCase_ , scheduler=lowerCamelCase_ )
def __UpperCAmelCase ( self : Any , lowerCamelCase_ : Optional[int] ):
"""simple docstring"""
if strength < 0 or strength > 1:
raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}''' )
def __UpperCAmelCase ( self : Optional[int] , lowerCamelCase_ : List[str] , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : Tuple ):
"""simple docstring"""
_lowercase : List[Any] = min(int(num_inference_steps * strength ) , lowerCamelCase_ )
_lowercase : Optional[int] = max(num_inference_steps - init_timestep , 0 )
_lowercase : Dict = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def __UpperCAmelCase ( self : Optional[int] , lowerCamelCase_ : str , lowerCamelCase_ : str , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : str , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : int=None ):
"""simple docstring"""
if not isinstance(lowerCamelCase_ , (torch.Tensor, PIL.Image.Image, list) ):
raise ValueError(
F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCamelCase_ )}''' )
_lowercase : Optional[Any] = image.to(device=lowerCamelCase_ , dtype=lowerCamelCase_ )
if isinstance(lowerCamelCase_ , lowerCamelCase_ ) and len(lowerCamelCase_ ) != batch_size:
raise ValueError(
F'''You have passed a list of generators of length {len(lowerCamelCase_ )}, but requested an effective batch'''
F''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
_lowercase : List[Any] = init_latents.shape
_lowercase : int = randn_tensor(lowerCamelCase_ , generator=lowerCamelCase_ , device=lowerCamelCase_ , dtype=lowerCamelCase_ )
# get latents
print('add noise to latents at timestep' , lowerCamelCase_ )
_lowercase : List[str] = self.scheduler.add_noise(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
_lowercase : str = init_latents
return latents
@torch.no_grad()
def __call__( self : str , lowerCamelCase_ : Union[torch.FloatTensor, PIL.Image.Image] = None , lowerCamelCase_ : float = 0.8 , lowerCamelCase_ : int = 1 , lowerCamelCase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowerCamelCase_ : float = 0.0 , lowerCamelCase_ : int = 5_0 , lowerCamelCase_ : Optional[bool] = None , lowerCamelCase_ : Optional[str] = "pil" , lowerCamelCase_ : bool = True , ):
"""simple docstring"""
self.check_inputs(lowerCamelCase_ )
# 2. Preprocess image
_lowercase : Union[str, Any] = preprocess(lowerCamelCase_ )
# 3. set timesteps
self.scheduler.set_timesteps(lowerCamelCase_ , device=self.device )
_lowercase : str = self.get_timesteps(lowerCamelCase_ , lowerCamelCase_ , self.device )
_lowercase : List[Any] = timesteps[:1].repeat(lowerCamelCase_ )
# 4. Prepare latent variables
_lowercase : Dict = self.prepare_latents(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , self.unet.dtype , self.device , lowerCamelCase_ )
_lowercase : str = latents
# 5. Denoising loop
for t in self.progress_bar(lowerCamelCase_ ):
# 1. predict noise model_output
_lowercase : str = self.unet(lowerCamelCase_ , lowerCamelCase_ ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
_lowercase : Optional[int] = self.scheduler.step(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , eta=lowerCamelCase_ , use_clipped_model_output=lowerCamelCase_ , generator=lowerCamelCase_ , ).prev_sample
_lowercase : Optional[Any] = (image / 2 + 0.5).clamp(0 , 1 )
_lowercase : Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_lowercase : Optional[int] = self.numpy_to_pil(lowerCamelCase_ )
if not return_dict:
return (image, latent_timestep.item())
return ImagePipelineOutput(images=lowerCamelCase_ )
| 702 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE = [
'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BioGptForCausalLM',
'BioGptForTokenClassification',
'BioGptForSequenceClassification',
'BioGptModel',
'BioGptPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
from .tokenization_biogpt import BioGptTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_biogpt import (
BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 283 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : Union[str, Any] ) -> List[Any]:
lowercase_ = tempfile.mkdtemp()
lowercase_ = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
lowercase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
lowercase_ = {
'''do_resize''': True,
'''size''': 2_0,
'''do_center_crop''': True,
'''crop_size''': 1_8,
'''do_normalize''': True,
'''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
}
lowercase_ = os.path.join(self.tmpdirname , SCREAMING_SNAKE_CASE_ )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Optional[Any] , **SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> Any:
return BertTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Optional[Any] , **SCREAMING_SNAKE_CASE_ : Tuple ) -> Tuple:
return BertTokenizerFast.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : List[str] , **SCREAMING_SNAKE_CASE_ : List[str] ) -> Optional[int]:
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Dict ) -> Optional[Any]:
shutil.rmtree(self.tmpdirname )
def _lowercase ( self : Dict ) -> Any:
lowercase_ = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
lowercase_ = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _lowercase ( self : Optional[Any] ) -> Union[str, Any]:
lowercase_ = self.get_tokenizer()
lowercase_ = self.get_rust_tokenizer()
lowercase_ = self.get_image_processor()
lowercase_ = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_ )
processor_slow.save_pretrained(self.tmpdirname )
lowercase_ = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=SCREAMING_SNAKE_CASE_ )
lowercase_ = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_ )
processor_fast.save_pretrained(self.tmpdirname )
lowercase_ = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(processor_fast.tokenizer , SCREAMING_SNAKE_CASE_ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(processor_fast.image_processor , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : List[Any] ) -> int:
lowercase_ = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowercase_ = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
lowercase_ = self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_ , padding_value=1.0 )
lowercase_ = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=SCREAMING_SNAKE_CASE_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , SCREAMING_SNAKE_CASE_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : List[Any] ) -> Union[str, Any]:
lowercase_ = self.get_image_processor()
lowercase_ = self.get_tokenizer()
lowercase_ = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_ )
lowercase_ = self.prepare_image_inputs()
lowercase_ = image_processor(SCREAMING_SNAKE_CASE_ , return_tensors='''np''' )
lowercase_ = processor(images=SCREAMING_SNAKE_CASE_ , return_tensors='''np''' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def _lowercase ( self : Optional[Any] ) -> int:
lowercase_ = self.get_image_processor()
lowercase_ = self.get_tokenizer()
lowercase_ = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_ )
lowercase_ = '''lower newer'''
lowercase_ = processor(text=SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer(SCREAMING_SNAKE_CASE_ , padding='''max_length''' , max_length=6_4 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _lowercase ( self : Dict ) -> str:
lowercase_ = self.get_image_processor()
lowercase_ = self.get_tokenizer()
lowercase_ = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_ )
lowercase_ = '''lower newer'''
lowercase_ = self.prepare_image_inputs()
lowercase_ = processor(text=SCREAMING_SNAKE_CASE_ , images=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
processor()
def _lowercase ( self : int ) -> List[str]:
lowercase_ = self.get_image_processor()
lowercase_ = self.get_tokenizer()
lowercase_ = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_ )
lowercase_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowercase_ = processor.batch_decode(SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer.batch_decode(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : str ) -> Any:
lowercase_ = self.get_image_processor()
lowercase_ = self.get_tokenizer()
lowercase_ = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_ )
lowercase_ = '''lower newer'''
lowercase_ = self.prepare_image_inputs()
lowercase_ = processor(text=SCREAMING_SNAKE_CASE_ , images=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 97 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase__ ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
a : int = CodeGenTokenizer
a : List[str] = CodeGenTokenizerFast
a : List[Any] = True
a : Optional[Any] = {"add_prefix_space": True}
a : Dict = False
def UpperCamelCase__ ( self ) -> int:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCamelCase__ : str = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
'''<|endoftext|>''',
]
UpperCamelCase__ : Dict = dict(zip(__magic_name__, range(len(__magic_name__ ) ) ) )
UpperCamelCase__ : Dict = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
UpperCamelCase__ : Dict = {'''unk_token''': '''<unk>'''}
UpperCamelCase__ : str = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
UpperCamelCase__ : Optional[int] = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__magic_name__ ) + '''\n''' )
with open(self.merges_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__magic_name__ ) )
def UpperCamelCase__ ( self, **__magic_name__ ) -> Union[str, Any]:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname, **__magic_name__ )
def UpperCamelCase__ ( self, **__magic_name__ ) -> Dict:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname, **__magic_name__ )
def UpperCamelCase__ ( self, __magic_name__ ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase__ : List[Any] = '''lower newer'''
UpperCamelCase__ : int = '''lower newer'''
return input_text, output_text
def UpperCamelCase__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase__ : Any = CodeGenTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map )
UpperCamelCase__ : List[Any] = '''lower newer'''
UpperCamelCase__ : Dict = ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
UpperCamelCase__ : int = tokenizer.tokenize(__magic_name__, add_prefix_space=__magic_name__ )
self.assertListEqual(__magic_name__, __magic_name__ )
UpperCamelCase__ : Dict = tokens + [tokenizer.unk_token]
UpperCamelCase__ : int = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__ ), __magic_name__ )
def UpperCamelCase__ ( self ) -> Any:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
UpperCamelCase__ : int = self.get_tokenizer()
UpperCamelCase__ : List[str] = self.get_rust_tokenizer(add_prefix_space=__magic_name__ )
UpperCamelCase__ : List[Any] = '''lower newer'''
# Testing tokenization
UpperCamelCase__ : Tuple = tokenizer.tokenize(__magic_name__, add_prefix_space=__magic_name__ )
UpperCamelCase__ : Tuple = rust_tokenizer.tokenize(__magic_name__ )
self.assertListEqual(__magic_name__, __magic_name__ )
# Testing conversion to ids without special tokens
UpperCamelCase__ : int = tokenizer.encode(__magic_name__, add_special_tokens=__magic_name__, add_prefix_space=__magic_name__ )
UpperCamelCase__ : str = rust_tokenizer.encode(__magic_name__, add_special_tokens=__magic_name__ )
self.assertListEqual(__magic_name__, __magic_name__ )
# Testing conversion to ids with special tokens
UpperCamelCase__ : Union[str, Any] = self.get_rust_tokenizer(add_prefix_space=__magic_name__ )
UpperCamelCase__ : Dict = tokenizer.encode(__magic_name__, add_prefix_space=__magic_name__ )
UpperCamelCase__ : List[str] = rust_tokenizer.encode(__magic_name__ )
self.assertListEqual(__magic_name__, __magic_name__ )
# Testing the unknown token
UpperCamelCase__ : Optional[int] = tokens + [rust_tokenizer.unk_token]
UpperCamelCase__ : Union[str, Any] = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__magic_name__ ), __magic_name__ )
def UpperCamelCase__ ( self, *__magic_name__, **__magic_name__ ) -> List[str]:
"""simple docstring"""
# It's very difficult to mix/test pretokenization with byte-level
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def UpperCamelCase__ ( self, __magic_name__=15 ) -> str:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
UpperCamelCase__ : List[Any] = self.rust_tokenizer_class.from_pretrained(__magic_name__, **__magic_name__ )
# Simple input
UpperCamelCase__ : int = '''This is a simple input'''
UpperCamelCase__ : Any = ['''This is a simple input 1''', '''This is a simple input 2''']
UpperCamelCase__ : Optional[int] = ('''This is a simple input''', '''This is a pair''')
UpperCamelCase__ : List[Any] = [
('''This is a simple input 1''', '''This is a simple input 2'''),
('''This is a simple pair 1''', '''This is a simple pair 2'''),
]
# Simple input tests
self.assertRaises(__magic_name__, tokenizer_r.encode, __magic_name__, max_length=__magic_name__, padding='''max_length''' )
# Simple input
self.assertRaises(__magic_name__, tokenizer_r.encode_plus, __magic_name__, max_length=__magic_name__, padding='''max_length''' )
# Simple input
self.assertRaises(
__magic_name__, tokenizer_r.batch_encode_plus, __magic_name__, max_length=__magic_name__, padding='''max_length''', )
# Pair input
self.assertRaises(__magic_name__, tokenizer_r.encode, __magic_name__, max_length=__magic_name__, padding='''max_length''' )
# Pair input
self.assertRaises(__magic_name__, tokenizer_r.encode_plus, __magic_name__, max_length=__magic_name__, padding='''max_length''' )
# Pair input
self.assertRaises(
__magic_name__, tokenizer_r.batch_encode_plus, __magic_name__, max_length=__magic_name__, padding='''max_length''', )
def UpperCamelCase__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase__ : Optional[int] = CodeGenTokenizer.from_pretrained(self.tmpdirname, pad_token='''<pad>''' )
# Simple input
UpperCamelCase__ : Union[str, Any] = '''This is a simple input'''
UpperCamelCase__ : List[Any] = ['''This is a simple input looooooooong''', '''This is a simple input''']
UpperCamelCase__ : Any = ('''This is a simple input''', '''This is a pair''')
UpperCamelCase__ : str = [
('''This is a simple input loooooong''', '''This is a simple input'''),
('''This is a simple pair loooooong''', '''This is a simple pair'''),
]
UpperCamelCase__ : str = tokenizer.pad_token_id
UpperCamelCase__ : int = tokenizer(__magic_name__, padding='''max_length''', max_length=30, return_tensors='''np''' )
UpperCamelCase__ : Union[str, Any] = tokenizer(__magic_name__, padding=__magic_name__, truncate=__magic_name__, return_tensors='''np''' )
UpperCamelCase__ : str = tokenizer(*__magic_name__, padding='''max_length''', max_length=60, return_tensors='''np''' )
UpperCamelCase__ : str = tokenizer(__magic_name__, padding=__magic_name__, truncate=__magic_name__, return_tensors='''np''' )
# s
# test single string max_length padding
self.assertEqual(out_s['''input_ids'''].shape[-1], 30 )
self.assertTrue(pad_token_id in out_s['''input_ids'''] )
self.assertTrue(0 in out_s['''attention_mask'''] )
# s2
# test automatic padding
self.assertEqual(out_sa['''input_ids'''].shape[-1], 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['''input_ids'''][0] )
self.assertFalse(0 in out_sa['''attention_mask'''][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['''input_ids'''][1] )
self.assertTrue(0 in out_sa['''attention_mask'''][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['''input_ids'''].shape[-1], 60 )
self.assertTrue(pad_token_id in out_p['''input_ids'''] )
self.assertTrue(0 in out_p['''attention_mask'''] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['''input_ids'''].shape[-1], 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['''input_ids'''][0] )
self.assertFalse(0 in out_pa['''attention_mask'''][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['''input_ids'''][1] )
self.assertTrue(0 in out_pa['''attention_mask'''][1] )
def UpperCamelCase__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase__ : Union[str, Any] = '''$$$'''
UpperCamelCase__ : List[Any] = CodeGenTokenizer.from_pretrained(self.tmpdirname, bos_token=__magic_name__, add_bos_token=__magic_name__ )
UpperCamelCase__ : Tuple = '''This is a simple input'''
UpperCamelCase__ : int = ['''This is a simple input 1''', '''This is a simple input 2''']
UpperCamelCase__ : List[Any] = tokenizer.bos_token_id
UpperCamelCase__ : Dict = tokenizer(__magic_name__ )
UpperCamelCase__ : Optional[Any] = tokenizer(__magic_name__ )
self.assertEqual(out_s.input_ids[0], __magic_name__ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
UpperCamelCase__ : Optional[int] = tokenizer.decode(out_s.input_ids )
UpperCamelCase__ : Union[str, Any] = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0], __magic_name__ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def UpperCamelCase__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase__ : List[Any] = CodeGenTokenizer.from_pretrained('''Salesforce/codegen-350M-mono''' )
UpperCamelCase__ : Any = '''\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#'''
UpperCamelCase__ : List[str] = '''\nif len_a > len_b: result = a\nelse: result = b'''
UpperCamelCase__ : int = tokenizer.encode(__magic_name__ )
UpperCamelCase__ : Optional[int] = ['''^#''', re.escape('''<|endoftext|>''' ), '''^\'\'\'''', '''^"""''', '''\n\n\n''']
UpperCamelCase__ : Tuple = tokenizer.decode(__magic_name__, truncate_before_pattern=__magic_name__ )
self.assertEqual(__magic_name__, __magic_name__ )
def UpperCamelCase__ ( self ) -> str:
"""simple docstring"""
pass
| 253 | 0 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
return "".join(chr(ord(_SCREAMING_SNAKE_CASE ) - 32 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 95 |
"""simple docstring"""
class _a :
"""simple docstring"""
def __init__( self : Tuple , __UpperCamelCase : list[int] )->None:
_UpperCAmelCase = len(__UpperCamelCase )
_UpperCAmelCase = [0] * len_array
if len_array > 0:
_UpperCAmelCase = array[0]
for i in range(1 , __UpperCamelCase ):
_UpperCAmelCase = self.prefix_sum[i - 1] + array[i]
def lowercase__ ( self : str , __UpperCamelCase : int , __UpperCamelCase : int )->int:
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def lowercase__ ( self : Any , __UpperCamelCase : int )->bool:
_UpperCAmelCase = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(__UpperCamelCase )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 95 | 1 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
__UpperCAmelCase = ['''\nclass''', '''\ndef''', '''\n#''', '''\n@''', '''\nprint''', '''\nif''']
class lowerCAmelCase_ ( a__ ):
def __init__( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=1 ) -> List[str]:
UpperCamelCase : int = tokenizer
UpperCamelCase : List[str] = dataset
UpperCamelCase : Optional[Any] = len(SCREAMING_SNAKE_CASE_ ) if n_tasks is None else n_tasks
UpperCamelCase : str = n_copies
def __iter__( self ) -> Tuple:
UpperCamelCase : List[str] = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['prompt'].strip() )
UpperCamelCase : Any = self.tokenizer(SCREAMING_SNAKE_CASE_, padding=SCREAMING_SNAKE_CASE_, return_tensors='pt' )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class lowerCAmelCase_ ( a__ ):
def __init__( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Tuple:
UpperCamelCase : Any = start_length
UpperCamelCase : Optional[int] = eof_strings
UpperCamelCase : List[Any] = tokenizer
def __call__( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
UpperCamelCase : str = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
UpperCamelCase : List[Any] = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase ( snake_case__ : Any ) -> List[str]:
UpperCamelCase : Tuple = re.split('(%s)' % '|'.join(snake_case__ ) , snake_case__ )
# last string should be ""
return "".join(string_list[:-2] )
def UpperCamelCase ( snake_case__ : List[str] , snake_case__ : List[str] , snake_case__ : Optional[int] , snake_case__ : Optional[int] , snake_case__ : Tuple , snake_case__ : Optional[Any]=20 , **snake_case__ : List[str] ) -> Tuple:
UpperCamelCase : Union[str, Any] = defaultdict(snake_case__ ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(snake_case__ ) ):
with torch.no_grad():
UpperCamelCase : Optional[int] = batch['ids'].shape[-1]
UpperCamelCase : Union[str, Any] = accelerator.unwrap_model(snake_case__ ).generate(
input_ids=batch['ids'][:, : batch['input_len']] , num_return_sequences=snake_case__ , **snake_case__ )
# each task is generated batch_size times
UpperCamelCase : Optional[Any] = batch['task_id'].repeat(snake_case__ )
UpperCamelCase : Any = accelerator.pad_across_processes(
snake_case__ , dim=1 , pad_index=tokenizer.pad_token_id )
UpperCamelCase , UpperCamelCase : Any = accelerator.gather((generated_tokens, generated_tasks) )
UpperCamelCase : Optional[int] = generated_tokens.cpu().numpy()
UpperCamelCase : str = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(snake_case__ , snake_case__ ):
gen_token_dict[task].append(snake_case__ )
UpperCamelCase : Tuple = [[] for _ in range(snake_case__ )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
UpperCamelCase : Optional[Any] = tokenizer.decode(snake_case__ , skip_special_tokens=snake_case__ , clean_up_tokenization_spaces=snake_case__ )
code_gens[task].append(remove_last_block(snake_case__ ) )
return code_gens
def UpperCamelCase ( ) -> Any:
# Setup configuration
UpperCamelCase : Optional[Any] = HfArgumentParser(snake_case__ )
UpperCamelCase : Optional[Any] = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
UpperCamelCase : Dict = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
UpperCamelCase : Optional[int] = 'false'
if args.num_workers is None:
UpperCamelCase : Union[str, Any] = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
UpperCamelCase : List[Any] = Accelerator()
set_seed(args.seed , device_specific=snake_case__ )
# Load model and tokenizer
UpperCamelCase : Dict = AutoTokenizer.from_pretrained(args.model_ckpt )
UpperCamelCase : List[str] = tokenizer.eos_token
UpperCamelCase : List[Any] = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
UpperCamelCase : Optional[Any] = {
'do_sample': args.do_sample,
'temperature': args.temperature,
'max_new_tokens': args.max_new_tokens,
'top_p': args.top_p,
'top_k': args.top_k,
'stopping_criteria': StoppingCriteriaList([EndOfFunctionCriteria(0 , snake_case__ , snake_case__ )] ),
}
# Load evaluation dataset and metric
UpperCamelCase : Dict = load_dataset('openai_humaneval' )
UpperCamelCase : List[str] = load_metric('code_eval' )
UpperCamelCase : Optional[int] = args.num_tasks if args.num_tasks is not None else len(human_eval['test'] )
UpperCamelCase : Union[str, Any] = args.n_samples // args.batch_size
UpperCamelCase : int = TokenizedDataset(snake_case__ , human_eval['test'] , n_copies=snake_case__ , n_tasks=snake_case__ )
# do not confuse args.batch_size, which is actually the num_return_sequences
UpperCamelCase : Tuple = DataLoader(snake_case__ , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
UpperCamelCase : int = code_eval_metric.compute(references=[''] , predictions=[['']] )
except ValueError as exception:
print(
'Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'
' flag to enable code evaluation.' )
raise exception
UpperCamelCase , UpperCamelCase : Tuple = accelerator.prepare(snake_case__ , snake_case__ )
UpperCamelCase : List[Any] = complete_code(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , n_tasks=snake_case__ , batch_size=args.batch_size , **snake_case__ , )
if accelerator.is_main_process:
UpperCamelCase : int = []
for task in tqdm(range(snake_case__ ) ):
UpperCamelCase : Tuple = human_eval['test'][task]['test']
UpperCamelCase : str = F"""check({human_eval["test"][task]["entry_point"]})"""
references.append('\n' + test_func + '\n' + entry_point )
# Evaluate completions with "code_eval" metric
UpperCamelCase , UpperCamelCase : str = code_eval_metric.compute(
references=snake_case__ , predictions=snake_case__ , num_workers=args.num_workers )
print(F"""Results: {pass_at_k}""" )
# Save results to json file
with open(args.output_file , 'w' ) as fp:
json.dump(snake_case__ , snake_case__ )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 40 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
def __magic_name__ ( lowercase , lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: str =namedtuple("""result""" , """name value""" )
if (voltage, current, power).count(0 ) != 1:
raise ValueError("""Only one argument must be 0""" )
elif power < 0:
raise ValueError(
"""Power cannot be negative in any electrical/electronics system""" )
elif voltage == 0:
return result("""voltage""" , power / current )
elif current == 0:
return result("""current""" , power / voltage )
elif power == 0:
return result("""power""" , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 409 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def snake_case_ ( _lowerCAmelCase : Callable[[int | float], int | float] , _lowerCAmelCase : int | float , _lowerCAmelCase : int | float , _lowerCAmelCase : int = 100 , ) -> float:
UpperCAmelCase : Optional[Any] = x_start
UpperCAmelCase : Dict = fnc(snake_case__ )
UpperCAmelCase : Union[str, Any] = 0.0
for _ in range(snake_case__ ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
UpperCAmelCase : int = (x_end - x_start) / steps + xa
UpperCAmelCase : Optional[int] = fnc(snake_case__ )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
UpperCAmelCase : str = xa
UpperCAmelCase : str = fxa
return area
if __name__ == "__main__":
def snake_case_ ( _lowerCAmelCase : Tuple ) -> List[str]:
return x**3 + x**2
print("f(x) = x^3 + x^2")
print("The area between the curve, x = -5, x = 5 and the x axis is:")
UpperCamelCase__: Union[str, Any] = 10
while i <= 100000:
print(F"with {i} steps: {trapezoidal_area(f, -5, 5, i)}")
i *= 10 | 717 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
UpperCamelCase__: Dict = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE( A__ ):
"""simple docstring"""
def __init__( self : Optional[Any] , *__snake_case : List[str] , **__snake_case : str ) -> None:
warnings.warn(
'''The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use BeitImageProcessor instead.''' , __snake_case , )
super().__init__(*__snake_case , **__snake_case )
| 528 | 0 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
_A : int = "docs/source/en/_toctree.yml"
def __magic_name__ ( __snake_case : List[str] ) -> Optional[Any]:
lowercase : List[str] = defaultdict(UpperCamelCase__ )
lowercase : Optional[Any] = []
lowercase : str = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"local": doc["local"], "title": doc["title"]} )
else:
new_doc_list.append(UpperCamelCase__ )
lowercase : str = new_doc_list
lowercase : List[str] = [key for key, value in counts.items() if value > 1]
lowercase : Optional[int] = []
for duplicate_key in duplicates:
lowercase : Optional[Any] = list({doc["title"] for doc in doc_list if doc["local"] == duplicate_key} )
if len(UpperCamelCase__ ) > 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 doc_list if "local" not in counts or counts[doc["local"]] == 1] )
lowercase : Tuple = sorted(UpperCamelCase__ , key=lambda __snake_case : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(UpperCamelCase__ ) > 1:
raise ValueError("{doc_list} has two \'overview\' docs which is not allowed." )
overview_doc.extend(UpperCamelCase__ )
# Sort
return overview_doc
def __magic_name__ ( __snake_case : Optional[Any]=False ) -> Optional[int]:
with open(UpperCamelCase__ , encoding="utf-8" ) as f:
lowercase : Union[str, Any] = yaml.safe_load(f.read() )
# Get to the API doc
lowercase : Optional[Any] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase : List[Any] = content[api_idx]["sections"]
# Then to the model doc
lowercase : List[str] = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
lowercase : Union[str, Any] = api_doc[scheduler_idx]["sections"]
lowercase : str = clean_doc_toc(UpperCamelCase__ )
lowercase : int = False
if new_scheduler_doc != scheduler_doc:
lowercase : Union[str, Any] = True
if overwrite:
lowercase : List[Any] = new_scheduler_doc
if diff:
if overwrite:
lowercase : List[str] = api_doc
with open(UpperCamelCase__ , "w" , encoding="utf-8" ) as f:
f.write(yaml.dump(UpperCamelCase__ , allow_unicode=UpperCamelCase__ ) )
else:
raise ValueError(
"The model doc part of the table of content is not properly sorted, run `make style` to fix this." )
def __magic_name__ ( __snake_case : Optional[int]=False ) -> List[str]:
with open(UpperCamelCase__ , encoding="utf-8" ) as f:
lowercase : List[Any] = yaml.safe_load(f.read() )
# Get to the API doc
lowercase : Optional[int] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase : Tuple = content[api_idx]["sections"]
# Then to the model doc
lowercase : Tuple = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
lowercase : List[Any] = False
lowercase : Tuple = api_doc[pipeline_idx]["sections"]
lowercase : int = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
lowercase : Dict = pipeline_doc["section"]
lowercase : Optional[Any] = clean_doc_toc(UpperCamelCase__ )
if overwrite:
lowercase : List[str] = new_sub_pipeline_doc
new_pipeline_docs.append(UpperCamelCase__ )
# sort overall pipeline doc
lowercase : int = clean_doc_toc(UpperCamelCase__ )
if new_pipeline_docs != pipeline_docs:
lowercase : Union[str, Any] = True
if overwrite:
lowercase : Union[str, Any] = new_pipeline_docs
if diff:
if overwrite:
lowercase : Tuple = api_doc
with open(UpperCamelCase__ , "w" , encoding="utf-8" ) as f:
f.write(yaml.dump(UpperCamelCase__ , allow_unicode=UpperCamelCase__ ) )
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 : Tuple = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
_A : Tuple = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 361 | '''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
__lowerCAmelCase : List[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class A ( UpperCAmelCase ):
def __init__( self : Dict , __a : List[Any] , __a : Optional[Any] ) -> Dict:
super().__init__()
self.register_modules(unet=__a , scheduler=__a )
@torch.no_grad()
def __call__( self : Tuple , __a : int = 1 , __a : int = 1_0_0 , __a : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __a : Optional[float] = None , __a : bool = True , ) -> Union[AudioPipelineOutput, Tuple]:
if audio_length_in_s is None:
__UpperCAmelCase = self.unet.config.sample_size / self.unet.config.sample_rate
__UpperCAmelCase = audio_length_in_s * self.unet.config.sample_rate
__UpperCAmelCase = 2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
f""" {3 * down_scale_factor / self.unet.config.sample_rate}.""" )
__UpperCAmelCase = int(__a )
if sample_size % down_scale_factor != 0:
__UpperCAmelCase = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
''' process.''' )
__UpperCAmelCase = int(__a )
__UpperCAmelCase = next(iter(self.unet.parameters() ) ).dtype
__UpperCAmelCase = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(__a , __a ) and len(__a ) != batch_size:
raise ValueError(
f"""You have passed a list of generators of length {len(__a )}, but requested an effective batch"""
f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )
__UpperCAmelCase = randn_tensor(__a , generator=__a , device=self.device , dtype=__a )
# set step values
self.scheduler.set_timesteps(__a , device=audio.device )
__UpperCAmelCase = self.scheduler.timesteps.to(__a )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
__UpperCAmelCase = self.unet(__a , __a ).sample
# 2. compute previous image: x_t -> t_t-1
__UpperCAmelCase = self.scheduler.step(__a , __a , __a ).prev_sample
__UpperCAmelCase = audio.clamp(-1 , 1 ).float().cpu().numpy()
__UpperCAmelCase = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=__a )
| 262 | 0 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@slow
def __a ( self :str ):
UpperCamelCase__ :Any = AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""" )
UpperCamelCase__ :str = AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""" )
model.to(_UpperCAmelCase )
from datasets import load_dataset
UpperCamelCase__ :int = load_dataset("""nielsr/rvlcdip-demo""" )
UpperCamelCase__ :str = dataset['''train'''][0]['''image'''].convert("""RGB""" )
UpperCamelCase__ :Optional[int] = image_processor(_UpperCAmelCase , return_tensors="""pt""" ).to(_UpperCAmelCase )
# forward pass
with torch.no_grad():
UpperCamelCase__ :Optional[Any] = model(**_UpperCAmelCase )
UpperCamelCase__ :List[str] = outputs.logits
UpperCamelCase__ :Any = torch.Size((1, 16) )
self.assertEqual(logits.shape , _UpperCAmelCase )
UpperCamelCase__ :str = torch.tensor(
[-0.4158, -0.4092, -0.4347] , device=_UpperCAmelCase , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , _UpperCAmelCase , atol=1e-4 ) ) | 713 |
from manim import *
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
def __a ( self :Optional[int] ):
UpperCamelCase__ :Union[str, Any] = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase__ :int = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
UpperCamelCase__ :Dict = [mem.copy() for i in range(6 )]
UpperCamelCase__ :Any = [mem.copy() for i in range(6 )]
UpperCamelCase__ :List[str] = VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__ , buff=0 )
UpperCamelCase__ :Any = VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__ , buff=0 )
UpperCamelCase__ :Dict = VGroup(lowerCamelCase__ , lowerCamelCase__ ).arrange(lowerCamelCase__ , buff=0 )
UpperCamelCase__ :Union[str, Any] = Text("""CPU""" , font_size=24 )
UpperCamelCase__ :str = Group(lowerCamelCase__ , lowerCamelCase__ ).arrange(lowerCamelCase__ , buff=0.5 , aligned_edge=lowerCamelCase__ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(lowerCamelCase__ )
UpperCamelCase__ :List[str] = [mem.copy() for i in range(1 )]
UpperCamelCase__ :Optional[int] = VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__ , buff=0 )
UpperCamelCase__ :Optional[Any] = Text("""GPU""" , font_size=24 )
UpperCamelCase__ :Any = Group(lowerCamelCase__ , lowerCamelCase__ ).arrange(lowerCamelCase__ , buff=0.5 , aligned_edge=lowerCamelCase__ )
gpu.align_to(lowerCamelCase__ , lowerCamelCase__ )
gpu.set_x(gpu.get_x() - 1 )
self.add(lowerCamelCase__ )
UpperCamelCase__ :Optional[int] = [mem.copy() for i in range(6 )]
UpperCamelCase__ :Optional[Any] = VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__ , buff=0 )
UpperCamelCase__ :str = Text("""Model""" , font_size=24 )
UpperCamelCase__ :Optional[Any] = Group(lowerCamelCase__ , lowerCamelCase__ ).arrange(lowerCamelCase__ , buff=0.5 , aligned_edge=lowerCamelCase__ )
model.move_to([3, -1.0, 0] )
self.play(
Create(lowerCamelCase__ , run_time=1 ) , Create(lowerCamelCase__ , run_time=1 ) , Create(lowerCamelCase__ , run_time=1 ) , )
UpperCamelCase__ :Tuple = MarkupText(
f"""First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM.""" , font_size=24 , )
UpperCamelCase__ :Union[str, Any] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
UpperCamelCase__ :Tuple = MarkupText(
f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(lowerCamelCase__ , run_time=2.5 ) , Write(lowerCamelCase__ ) , Write(lowerCamelCase__ ) )
self.add(lowerCamelCase__ )
UpperCamelCase__ :Any = []
UpperCamelCase__ :List[Any] = []
UpperCamelCase__ :int = []
for i, rect in enumerate(lowerCamelCase__ ):
UpperCamelCase__ :int = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(lowerCamelCase__ , opacity=0.7 )
cpu_target.move_to(lowerCamelCase__ )
cpu_target.generate_target()
UpperCamelCase__ :Any = 0.46 / 4
UpperCamelCase__ :Optional[Any] = 0.46 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=lowerCamelCase__ )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=lowerCamelCase__ , buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=lowerCamelCase__ , buff=0.0 )
cpu_targs.append(lowerCamelCase__ )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(lowerCamelCase__ ) )
second_animations.append(MoveToTarget(lowerCamelCase__ , run_time=1.5 ) )
self.play(*lowerCamelCase__ )
self.play(*lowerCamelCase__ )
self.wait() | 383 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.