code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
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"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class __lowerCAmelCase :
'''simple docstring'''
def __UpperCAmelCase ( self ):
torch.manual_seed(0 )
__a = TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
__a = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
__a = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
'''ResnetDownsampleBlock2D''',
'''SimpleCrossAttnDownBlock2D''',
] , mid_block_type='''UNetMidBlock2DSimpleCrossAttn''' , up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type='''text''' , addition_embed_type_num_heads=2 , cross_attention_norm='''group_norm''' , resnet_time_scale_shift='''scale_shift''' , act_fn='''gelu''' , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
__a = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule='''squaredcos_cap_v2''' , beta_start=0.0001 , beta_end=0.02 , thresholding=_a , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type='''epsilon''' , variance_type='''learned_range''' , )
torch.manual_seed(0 )
__a = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def __UpperCAmelCase ( self ):
torch.manual_seed(0 )
__a = TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
__a = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
__a = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
'''ResnetDownsampleBlock2D''',
'''SimpleCrossAttnDownBlock2D''',
] , mid_block_type='''UNetMidBlock2DSimpleCrossAttn''' , up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type='''text''' , addition_embed_type_num_heads=2 , cross_attention_norm='''group_norm''' , resnet_time_scale_shift='''scale_shift''' , act_fn='''gelu''' , class_embed_type='''timestep''' , mid_block_scale_factor=1.414 , time_embedding_act_fn='''gelu''' , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
__a = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule='''squaredcos_cap_v2''' , beta_start=0.0001 , beta_end=0.02 , thresholding=_a , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type='''epsilon''' , variance_type='''learned_range''' , )
torch.manual_seed(0 )
__a = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule='''squaredcos_cap_v2''' , beta_start=0.0001 , beta_end=0.02 , )
torch.manual_seed(0 )
__a = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def __UpperCAmelCase ( self ):
__a = self.get_dummy_components()
__a = self.pipeline_class(**_a )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
__a = self.get_dummy_inputs(_a )
__a = inputs['''prompt''']
__a = inputs['''generator''']
__a = inputs['''num_inference_steps''']
__a = inputs['''output_type''']
if "image" in inputs:
__a = inputs['''image''']
else:
__a = None
if "mask_image" in inputs:
__a = inputs['''mask_image''']
else:
__a = None
if "original_image" in inputs:
__a = inputs['''original_image''']
else:
__a = None
__a , __a = pipe.encode_prompt(_a )
# inputs with prompt converted to embeddings
__a = {
'''prompt_embeds''': prompt_embeds,
'''negative_prompt_embeds''': negative_prompt_embeds,
'''generator''': generator,
'''num_inference_steps''': num_inference_steps,
'''output_type''': output_type,
}
if image is not None:
__a = image
if mask_image is not None:
__a = mask_image
if original_image is not None:
__a = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(_a , _a , _a )
__a = pipe(**_a )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_a )
__a = self.pipeline_class.from_pretrained(_a )
pipe_loaded.to(_a )
pipe_loaded.set_progress_bar_config(disable=_a )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(_a , _a ) is None , f'''`{optional_component}` did not stay set to None after loading.''' , )
__a = self.get_dummy_inputs(_a )
__a = inputs['''generator''']
__a = inputs['''num_inference_steps''']
__a = inputs['''output_type''']
# inputs with prompt converted to embeddings
__a = {
'''prompt_embeds''': prompt_embeds,
'''negative_prompt_embeds''': negative_prompt_embeds,
'''generator''': generator,
'''num_inference_steps''': num_inference_steps,
'''output_type''': output_type,
}
if image is not None:
__a = image
if mask_image is not None:
__a = mask_image
if original_image is not None:
__a = original_image
__a = pipe_loaded(**_a )[0]
__a = np.abs(to_np(_a ) - to_np(_a ) ).max()
self.assertLess(_a , 1E-4 )
def __UpperCAmelCase ( self ):
__a = self.get_dummy_components()
__a = self.pipeline_class(**_a )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
__a = self.get_dummy_inputs(_a )
__a = pipe(**_a )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_a )
__a = self.pipeline_class.from_pretrained(_a )
pipe_loaded.to(_a )
pipe_loaded.set_progress_bar_config(disable=_a )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
__a = self.get_dummy_inputs(_a )
__a = pipe_loaded(**_a )[0]
__a = np.abs(to_np(_a ) - to_np(_a ) ).max()
self.assertLess(_a , 1E-4 )
| 45 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__a = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
def __init__( self : List[str] , *snake_case_ : str , **snake_case_ : List[str] ):
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_ )
| 35 | 0 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class a_ ( tf.keras.layers.Layer ):
def __init__( self :str , _lowercase :Dict[str, int] , _lowercase :List[str] , _lowercase :int = None , _lowercase :int = None) -> Optional[Any]:
super().__init__()
UpperCAmelCase_ = pad_token_id
UpperCAmelCase_ = max_length
UpperCAmelCase_ = vocab
UpperCAmelCase_ = merges
UpperCAmelCase_ = BytePairTokenizer(_lowercase , _lowercase , sequence_length=_lowercase)
@classmethod
def __a ( cls :Tuple , _lowercase :GPTaTokenizer , *_lowercase :Union[str, Any] , **_lowercase :str) -> List[Any]:
UpperCAmelCase_ = [''' '''.join(_lowercase) for m in tokenizer.bpe_ranks.keys()]
UpperCAmelCase_ = tokenizer.get_vocab()
return cls(_lowercase , _lowercase , *_lowercase , **_lowercase)
@classmethod
def __a ( cls :Optional[int] , _lowercase :Union[str, os.PathLike] , *_lowercase :str , **_lowercase :int) -> Union[str, Any]:
UpperCAmelCase_ = GPTaTokenizer.from_pretrained(_lowercase , *_lowercase , **_lowercase)
return cls.from_tokenizer(_lowercase , *_lowercase , **_lowercase)
@classmethod
def __a ( cls :List[str] , _lowercase :Any) -> Optional[int]:
return cls(**_lowercase)
def __a ( self :Dict) -> Optional[int]:
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def __a ( self :Tuple , _lowercase :Union[str, Any] , _lowercase :int = None) -> Optional[int]:
UpperCAmelCase_ = self.tf_tokenizer(_lowercase)
UpperCAmelCase_ = tf.ones_like(_lowercase)
if self.pad_token_id is not None:
# pad the tokens up to max length
UpperCAmelCase_ = max_length if max_length is not None else self.max_length
if max_length is not None:
UpperCAmelCase_ , UpperCAmelCase_ = pad_model_inputs(
_lowercase , max_seq_length=_lowercase , pad_value=self.pad_token_id)
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 344 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class a_ ( _snake_case ):
UpperCamelCase__ : List[Any] =(PNDMScheduler,)
UpperCamelCase__ : Optional[Any] =(("num_inference_steps", 50),)
def __a ( self :Union[str, Any] , **_lowercase :Any) -> Union[str, Any]:
UpperCAmelCase_ = {
'''num_train_timesteps''': 1000,
'''beta_start''': 0.0_001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
}
config.update(**_lowercase)
return config
def __a ( self :str , _lowercase :List[Any]=0 , **_lowercase :str) -> Union[str, Any]:
UpperCAmelCase_ = dict(self.forward_default_kwargs)
UpperCAmelCase_ = kwargs.pop('''num_inference_steps''' , _lowercase)
UpperCAmelCase_ = self.dummy_sample
UpperCAmelCase_ = 0.1 * sample
UpperCAmelCase_ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ = self.get_scheduler_config(**_lowercase)
UpperCAmelCase_ = scheduler_class(**_lowercase)
scheduler.set_timesteps(_lowercase)
# copy over dummy past residuals
UpperCAmelCase_ = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_lowercase)
UpperCAmelCase_ = scheduler_class.from_pretrained(_lowercase)
new_scheduler.set_timesteps(_lowercase)
# copy over dummy past residuals
UpperCAmelCase_ = dummy_past_residuals[:]
UpperCAmelCase_ = scheduler.step_prk(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
UpperCAmelCase_ = new_scheduler.step_prk(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
UpperCAmelCase_ = scheduler.step_plms(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
UpperCAmelCase_ = new_scheduler.step_plms(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
def __a ( self :Any) -> Optional[Any]:
pass
def __a ( self :str , _lowercase :int=0 , **_lowercase :Union[str, Any]) -> List[Any]:
UpperCAmelCase_ = dict(self.forward_default_kwargs)
UpperCAmelCase_ = kwargs.pop('''num_inference_steps''' , _lowercase)
UpperCAmelCase_ = self.dummy_sample
UpperCAmelCase_ = 0.1 * sample
UpperCAmelCase_ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ = self.get_scheduler_config()
UpperCAmelCase_ = scheduler_class(**_lowercase)
scheduler.set_timesteps(_lowercase)
# copy over dummy past residuals (must be after setting timesteps)
UpperCAmelCase_ = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_lowercase)
UpperCAmelCase_ = scheduler_class.from_pretrained(_lowercase)
# copy over dummy past residuals
new_scheduler.set_timesteps(_lowercase)
# copy over dummy past residual (must be after setting timesteps)
UpperCAmelCase_ = dummy_past_residuals[:]
UpperCAmelCase_ = scheduler.step_prk(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
UpperCAmelCase_ = new_scheduler.step_prk(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
UpperCAmelCase_ = scheduler.step_plms(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
UpperCAmelCase_ = new_scheduler.step_plms(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
def __a ( self :int , **_lowercase :str) -> Optional[Any]:
UpperCAmelCase_ = self.scheduler_classes[0]
UpperCAmelCase_ = self.get_scheduler_config(**_lowercase)
UpperCAmelCase_ = scheduler_class(**_lowercase)
UpperCAmelCase_ = 10
UpperCAmelCase_ = self.dummy_model()
UpperCAmelCase_ = self.dummy_sample_deter
scheduler.set_timesteps(_lowercase)
for i, t in enumerate(scheduler.prk_timesteps):
UpperCAmelCase_ = model(_lowercase , _lowercase)
UpperCAmelCase_ = scheduler.step_prk(_lowercase , _lowercase , _lowercase).prev_sample
for i, t in enumerate(scheduler.plms_timesteps):
UpperCAmelCase_ = model(_lowercase , _lowercase)
UpperCAmelCase_ = scheduler.step_plms(_lowercase , _lowercase , _lowercase).prev_sample
return sample
def __a ( self :Union[str, Any]) -> int:
UpperCAmelCase_ = dict(self.forward_default_kwargs)
UpperCAmelCase_ = kwargs.pop('''num_inference_steps''' , _lowercase)
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ = self.get_scheduler_config()
UpperCAmelCase_ = scheduler_class(**_lowercase)
UpperCAmelCase_ = self.dummy_sample
UpperCAmelCase_ = 0.1 * sample
if num_inference_steps is not None and hasattr(_lowercase , '''set_timesteps'''):
scheduler.set_timesteps(_lowercase)
elif num_inference_steps is not None and not hasattr(_lowercase , '''set_timesteps'''):
UpperCAmelCase_ = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
UpperCAmelCase_ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
UpperCAmelCase_ = dummy_past_residuals[:]
UpperCAmelCase_ = scheduler.step_prk(_lowercase , 0 , _lowercase , **_lowercase).prev_sample
UpperCAmelCase_ = scheduler.step_prk(_lowercase , 1 , _lowercase , **_lowercase).prev_sample
self.assertEqual(output_a.shape , sample.shape)
self.assertEqual(output_a.shape , output_a.shape)
UpperCAmelCase_ = scheduler.step_plms(_lowercase , 0 , _lowercase , **_lowercase).prev_sample
UpperCAmelCase_ = scheduler.step_plms(_lowercase , 1 , _lowercase , **_lowercase).prev_sample
self.assertEqual(output_a.shape , sample.shape)
self.assertEqual(output_a.shape , output_a.shape)
def __a ( self :Any) -> Dict:
for timesteps in [100, 1000]:
self.check_over_configs(num_train_timesteps=_lowercase)
def __a ( self :List[Any]) -> Any:
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=_lowercase)
UpperCAmelCase_ = self.scheduler_classes[0]
UpperCAmelCase_ = self.get_scheduler_config(steps_offset=1)
UpperCAmelCase_ = scheduler_class(**_lowercase)
scheduler.set_timesteps(10)
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]) , )
def __a ( self :Optional[int]) -> str:
for beta_start, beta_end in zip([0.0_001, 0.001] , [0.002, 0.02]):
self.check_over_configs(beta_start=_lowercase , beta_end=_lowercase)
def __a ( self :Any) -> List[str]:
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_lowercase)
def __a ( self :List[Any]) -> Dict:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_lowercase)
def __a ( self :Any) -> Tuple:
for t in [1, 5, 10]:
self.check_over_forward(time_step=_lowercase)
def __a ( self :Tuple) -> Dict:
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100]):
self.check_over_forward(num_inference_steps=_lowercase)
def __a ( self :str) -> List[Any]:
# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
UpperCAmelCase_ = 27
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ = self.dummy_sample
UpperCAmelCase_ = 0.1 * sample
UpperCAmelCase_ = self.get_scheduler_config()
UpperCAmelCase_ = scheduler_class(**_lowercase)
scheduler.set_timesteps(_lowercase)
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2]):
UpperCAmelCase_ = scheduler.step_prk(_lowercase , _lowercase , _lowercase).prev_sample
def __a ( self :List[str]) -> int:
with self.assertRaises(_lowercase):
UpperCAmelCase_ = self.scheduler_classes[0]
UpperCAmelCase_ = self.get_scheduler_config()
UpperCAmelCase_ = scheduler_class(**_lowercase)
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample).prev_sample
def __a ( self :List[str]) -> Dict:
UpperCAmelCase_ = self.full_loop()
UpperCAmelCase_ = torch.sum(torch.abs(_lowercase))
UpperCAmelCase_ = torch.mean(torch.abs(_lowercase))
assert abs(result_sum.item() - 198.1_318) < 1E-2
assert abs(result_mean.item() - 0.2_580) < 1E-3
def __a ( self :Any) -> Tuple:
UpperCAmelCase_ = self.full_loop(prediction_type='''v_prediction''')
UpperCAmelCase_ = torch.sum(torch.abs(_lowercase))
UpperCAmelCase_ = torch.mean(torch.abs(_lowercase))
assert abs(result_sum.item() - 67.3_986) < 1E-2
assert abs(result_mean.item() - 0.0_878) < 1E-3
def __a ( self :int) -> Any:
# We specify different beta, so that the first alpha is 0.99
UpperCAmelCase_ = self.full_loop(set_alpha_to_one=_lowercase , beta_start=0.01)
UpperCAmelCase_ = torch.sum(torch.abs(_lowercase))
UpperCAmelCase_ = torch.mean(torch.abs(_lowercase))
assert abs(result_sum.item() - 230.0_399) < 1E-2
assert abs(result_mean.item() - 0.2_995) < 1E-3
def __a ( self :Any) -> Dict:
# We specify different beta, so that the first alpha is 0.99
UpperCAmelCase_ = self.full_loop(set_alpha_to_one=_lowercase , beta_start=0.01)
UpperCAmelCase_ = torch.sum(torch.abs(_lowercase))
UpperCAmelCase_ = torch.mean(torch.abs(_lowercase))
assert abs(result_sum.item() - 186.9_482) < 1E-2
assert abs(result_mean.item() - 0.2_434) < 1E-3
| 344 | 1 |
'''simple docstring'''
from __future__ import annotations
def __a(SCREAMING_SNAKE_CASE_ : str ):
'''simple docstring'''
return [ord(SCREAMING_SNAKE_CASE_ ) - 96 for elem in plain]
def __a(SCREAMING_SNAKE_CASE_ : list[int] ):
'''simple docstring'''
return "".join(chr(elem + 96 ) for elem in encoded )
def __a():
'''simple docstring'''
_lowerCAmelCase = encode(input("-> " ).strip().lower() )
print("Encoded: " , SCREAMING_SNAKE_CASE_ )
print("Decoded:" , decode(SCREAMING_SNAKE_CASE_ ) )
if __name__ == "__main__":
main()
| 158 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def __a(SCREAMING_SNAKE_CASE_ : float ):
'''simple docstring'''
if num <= 0:
raise ValueError("math domain error" )
return quad(SCREAMING_SNAKE_CASE_ , 0 , SCREAMING_SNAKE_CASE_ , args=(SCREAMING_SNAKE_CASE_) )[0]
def __a(SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float ):
'''simple docstring'''
return math.pow(SCREAMING_SNAKE_CASE_ , z - 1 ) * math.exp(-x )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 158 | 1 |
'''simple docstring'''
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
UpperCAmelCase = Mapping[str, np.ndarray]
UpperCAmelCase = Mapping[str, Any] # Is a nested dict.
UpperCAmelCase = 0.01
@dataclasses.dataclass(frozen=_lowerCAmelCase )
class __snake_case:
'''simple docstring'''
UpperCAmelCase : np.ndarray # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
UpperCAmelCase : np.ndarray # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
UpperCAmelCase : np.ndarray # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
UpperCAmelCase : np.ndarray # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
UpperCAmelCase : np.ndarray # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
UpperCAmelCase : Optional[np.ndarray] = None
# Optional remark about the protein. Included as a comment in output PDB
# files
UpperCAmelCase : Optional[str] = None
# Templates used to generate this protein (prediction-only)
UpperCAmelCase : Optional[Sequence[str]] = None
# Chain corresponding to each parent
UpperCAmelCase : Optional[Sequence[int]] = None
def _snake_case ( _SCREAMING_SNAKE_CASE : str ) -> Protein:
"""simple docstring"""
lowerCAmelCase = R"""(\[[A-Z]+\]\n)"""
lowerCAmelCase = [tag.strip() for tag in re.split(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) if len(_SCREAMING_SNAKE_CASE ) > 0]
lowerCAmelCase = zip(tags[0::2] , [l.split("""\n""" ) for l in tags[1::2]] )
lowerCAmelCase = ["N", "CA", "C"]
lowerCAmelCase = None
lowerCAmelCase = None
lowerCAmelCase = None
for g in groups:
if "[PRIMARY]" == g[0]:
lowerCAmelCase = g[1][0].strip()
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
if seq[i] not in residue_constants.restypes:
lowerCAmelCase = """X""" # FIXME: strings are immutable
lowerCAmelCase = np.array(
[residue_constants.restype_order.get(_SCREAMING_SNAKE_CASE , residue_constants.restype_num ) for res_symbol in seq] )
elif "[TERTIARY]" == g[0]:
lowerCAmelCase = []
for axis in range(3 ):
tertiary.append(list(map(_SCREAMING_SNAKE_CASE , g[1][axis].split() ) ) )
lowerCAmelCase = np.array(_SCREAMING_SNAKE_CASE )
lowerCAmelCase = np.zeros((len(tertiary[0] ) // 3, residue_constants.atom_type_num, 3) ).astype(np.floataa )
for i, atom in enumerate(_SCREAMING_SNAKE_CASE ):
lowerCAmelCase = np.transpose(tertiary_np[:, i::3] )
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
lowerCAmelCase = np.array(list(map({"""-""": 0, """+""": 1}.get , g[1][0].strip() ) ) )
lowerCAmelCase = np.zeros(
(
len(_SCREAMING_SNAKE_CASE ),
residue_constants.atom_type_num,
) ).astype(np.floataa )
for i, atom in enumerate(_SCREAMING_SNAKE_CASE ):
lowerCAmelCase = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=_SCREAMING_SNAKE_CASE , atom_mask=_SCREAMING_SNAKE_CASE , aatype=_SCREAMING_SNAKE_CASE , residue_index=np.arange(len(_SCREAMING_SNAKE_CASE ) ) , b_factors=_SCREAMING_SNAKE_CASE , )
def _snake_case ( _SCREAMING_SNAKE_CASE : Protein , _SCREAMING_SNAKE_CASE : int = 0 ) -> List[str]:
"""simple docstring"""
lowerCAmelCase = []
lowerCAmelCase = prot.remark
if remark is not None:
pdb_headers.append(f'REMARK {remark}' )
lowerCAmelCase = prot.parents
lowerCAmelCase = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
lowerCAmelCase = [p for i, p in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) if i == chain_id]
if parents is None or len(_SCREAMING_SNAKE_CASE ) == 0:
lowerCAmelCase = ["""N/A"""]
pdb_headers.append(f'PARENT {" ".join(_SCREAMING_SNAKE_CASE )}' )
return pdb_headers
def _snake_case ( _SCREAMING_SNAKE_CASE : Protein , _SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
lowerCAmelCase = []
lowerCAmelCase = pdb_str.split("""\n""" )
lowerCAmelCase = prot.remark
if remark is not None:
out_pdb_lines.append(f'REMARK {remark}' )
lowerCAmelCase = 42
if prot.parents is not None and len(prot.parents ) > 0:
lowerCAmelCase = []
if prot.parents_chain_index is not None:
lowerCAmelCase = {}
for p, i in zip(prot.parents , prot.parents_chain_index ):
parent_dict.setdefault(str(_SCREAMING_SNAKE_CASE ) , [] )
parent_dict[str(_SCREAMING_SNAKE_CASE )].append(_SCREAMING_SNAKE_CASE )
lowerCAmelCase = max([int(_SCREAMING_SNAKE_CASE ) for chain_idx in parent_dict] )
for i in range(max_idx + 1 ):
lowerCAmelCase = parent_dict.get(str(_SCREAMING_SNAKE_CASE ) , ["""N/A"""] )
parents_per_chain.append(_SCREAMING_SNAKE_CASE )
else:
parents_per_chain.append(list(prot.parents ) )
else:
lowerCAmelCase = [["""N/A"""]]
def make_parent_line(_SCREAMING_SNAKE_CASE : Sequence[str] ) -> str:
return f'PARENT {" ".join(_SCREAMING_SNAKE_CASE )}'
out_pdb_lines.append(make_parent_line(parents_per_chain[0] ) )
lowerCAmelCase = 0
for i, l in enumerate(_SCREAMING_SNAKE_CASE ):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(_SCREAMING_SNAKE_CASE )
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(_SCREAMING_SNAKE_CASE ):
lowerCAmelCase = parents_per_chain[chain_counter]
else:
lowerCAmelCase = ["""N/A"""]
out_pdb_lines.append(make_parent_line(_SCREAMING_SNAKE_CASE ) )
return "\n".join(_SCREAMING_SNAKE_CASE )
def _snake_case ( _SCREAMING_SNAKE_CASE : Protein ) -> str:
"""simple docstring"""
lowerCAmelCase = residue_constants.restypes + ["""X"""]
def res_atoa(_SCREAMING_SNAKE_CASE : int ) -> str:
return residue_constants.restype_atoa.get(restypes[r] , """UNK""" )
lowerCAmelCase = residue_constants.atom_types
lowerCAmelCase = []
lowerCAmelCase = prot.atom_mask
lowerCAmelCase = prot.aatype
lowerCAmelCase = prot.atom_positions
lowerCAmelCase = prot.residue_index.astype(np.intaa )
lowerCAmelCase = prot.b_factors
lowerCAmelCase = prot.chain_index
if np.any(aatype > residue_constants.restype_num ):
raise ValueError("""Invalid aatypes.""" )
lowerCAmelCase = get_pdb_headers(_SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) > 0:
pdb_lines.extend(_SCREAMING_SNAKE_CASE )
lowerCAmelCase = aatype.shape[0]
lowerCAmelCase = 1
lowerCAmelCase = 0
lowerCAmelCase = string.ascii_uppercase
lowerCAmelCase = None
# Add all atom sites.
for i in range(_SCREAMING_SNAKE_CASE ):
lowerCAmelCase = res_atoa(aatype[i] )
for atom_name, pos, mask, b_factor in zip(_SCREAMING_SNAKE_CASE , atom_positions[i] , atom_mask[i] , b_factors[i] ):
if mask < 0.5:
continue
lowerCAmelCase = """ATOM"""
lowerCAmelCase = atom_name if len(_SCREAMING_SNAKE_CASE ) == 4 else f' {atom_name}'
lowerCAmelCase = """"""
lowerCAmelCase = """"""
lowerCAmelCase = 1.00
lowerCAmelCase = atom_name[0] # Protein supports only C, N, O, S, this works.
lowerCAmelCase = """"""
lowerCAmelCase = """A"""
if chain_index is not None:
lowerCAmelCase = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
lowerCAmelCase = (
f'{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'
f'{res_name_a:>3} {chain_tag:>1}'
f'{residue_index[i]:>4}{insertion_code:>1} '
f'{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'
f'{occupancy:>6.2f}{b_factor:>6.2f} '
f'{element:>2}{charge:>2}'
)
pdb_lines.append(_SCREAMING_SNAKE_CASE )
atom_index += 1
lowerCAmelCase = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
lowerCAmelCase = True
lowerCAmelCase = chain_index[i + 1]
if should_terminate:
# Close the chain.
lowerCAmelCase = """TER"""
lowerCAmelCase = (
f'{chain_end:<6}{atom_index:>5} {res_atoa(aatype[i] ):>3} {chain_tag:>1}{residue_index[i]:>4}'
)
pdb_lines.append(_SCREAMING_SNAKE_CASE )
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
pdb_lines.append("""END""" )
pdb_lines.append("""""" )
return "\n".join(_SCREAMING_SNAKE_CASE )
def _snake_case ( _SCREAMING_SNAKE_CASE : Protein ) -> np.ndarray:
"""simple docstring"""
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def _snake_case ( _SCREAMING_SNAKE_CASE : FeatureDict , _SCREAMING_SNAKE_CASE : ModelOutput , _SCREAMING_SNAKE_CASE : Optional[np.ndarray] = None , _SCREAMING_SNAKE_CASE : Optional[np.ndarray] = None , _SCREAMING_SNAKE_CASE : Optional[str] = None , _SCREAMING_SNAKE_CASE : Optional[Sequence[str]] = None , _SCREAMING_SNAKE_CASE : Optional[Sequence[int]] = None , ) -> Protein:
"""simple docstring"""
return Protein(
aatype=features["""aatype"""] , atom_positions=result["""final_atom_positions"""] , atom_mask=result["""final_atom_mask"""] , residue_index=features["""residue_index"""] + 1 , b_factors=b_factors if b_factors is not None else np.zeros_like(result["""final_atom_mask"""] ) , chain_index=_SCREAMING_SNAKE_CASE , remark=_SCREAMING_SNAKE_CASE , parents=_SCREAMING_SNAKE_CASE , parents_chain_index=_SCREAMING_SNAKE_CASE , ) | 187 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = '▁'
UpperCAmelCase = {
'vocab_file': 'vocab.json',
'spm_file': 'sentencepiece.bpe.model',
'tokenizer_config_file': 'tokenizer_config.json',
}
UpperCAmelCase = {
'vocab_file': {
'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
},
'spm_file': {
'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
},
'tokenizer_config_file': {
'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
},
}
UpperCAmelCase = {
'facebook/m2m100_418M': 1024,
}
# fmt: off
UpperCAmelCase = {
'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}
class __snake_case( _lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = VOCAB_FILES_NAMES
UpperCAmelCase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase : int = ["input_ids", "attention_mask"]
UpperCAmelCase : List[int] = []
UpperCAmelCase : List[int] = []
def __init__( self , A_ , A_ , A_=None , A_=None , A_="<s>" , A_="</s>" , A_="</s>" , A_="<pad>" , A_="<unk>" , A_="m2m100" , A_ = None , A_=8 , **A_ , ) -> None:
lowerCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
lowerCAmelCase = language_codes
lowerCAmelCase = FAIRSEQ_LANGUAGE_CODES[language_codes]
lowerCAmelCase = {lang_code: f'__{lang_code}__' for lang_code in fairseq_language_code}
lowerCAmelCase = kwargs.get("""additional_special_tokens""" , [] )
kwargs["additional_special_tokens"] += [
self.get_lang_token(A_ )
for lang_code in fairseq_language_code
if self.get_lang_token(A_ ) not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=A_ , tgt_lang=A_ , bos_token=A_ , eos_token=A_ , sep_token=A_ , unk_token=A_ , pad_token=A_ , language_codes=A_ , sp_model_kwargs=self.sp_model_kwargs , num_madeup_words=A_ , **A_ , )
lowerCAmelCase = vocab_file
lowerCAmelCase = load_json(A_ )
lowerCAmelCase = {v: k for k, v in self.encoder.items()}
lowerCAmelCase = spm_file
lowerCAmelCase = load_spm(A_ , self.sp_model_kwargs )
lowerCAmelCase = len(self.encoder )
lowerCAmelCase = {
self.get_lang_token(A_ ): self.encoder_size + i for i, lang_code in enumerate(A_ )
}
lowerCAmelCase = {lang_code: self.encoder_size + i for i, lang_code in enumerate(A_ )}
lowerCAmelCase = {v: k for k, v in self.lang_token_to_id.items()}
lowerCAmelCase = src_lang if src_lang is not None else """en"""
lowerCAmelCase = tgt_lang
lowerCAmelCase = self.get_lang_id(self._src_lang )
self.set_src_lang_special_tokens(self._src_lang )
lowerCAmelCase = num_madeup_words
@property
def __snake_case ( self ) -> int:
return len(self.encoder ) + len(self.lang_token_to_id )
@property
def __snake_case ( self ) -> str:
return self._src_lang
@src_lang.setter
def __snake_case ( self , A_ ) -> None:
lowerCAmelCase = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __snake_case ( self , A_ ) -> List[str]:
return self.sp_model.encode(A_ , out_type=A_ )
def __snake_case ( self , A_ ) -> Any:
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(A_ , self.encoder[self.unk_token] )
def __snake_case ( self , A_ ) -> str:
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(A_ , self.unk_token )
def __snake_case ( self , A_ ) -> List[str]:
lowerCAmelCase = []
lowerCAmelCase = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(A_ ) + token
lowerCAmelCase = []
else:
current_sub_tokens.append(A_ )
out_string += self.sp_model.decode(A_ )
return out_string.strip()
def __snake_case ( self , A_ , A_ = None , A_ = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A_ , token_ids_a=A_ , already_has_special_tokens=A_ )
lowerCAmelCase = [1] * len(self.prefix_tokens )
lowerCAmelCase = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(A_ )) + suffix_ones
return prefix_ones + ([0] * len(A_ )) + ([0] * len(A_ )) + suffix_ones
def __snake_case ( self , A_ , A_ = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def __snake_case ( self ) -> Dict:
lowerCAmelCase = {self.convert_ids_to_tokens(A_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Dict:
lowerCAmelCase = self.__dict__.copy()
lowerCAmelCase = None
return state
def __setstate__( self , A_ ) -> None:
lowerCAmelCase = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
lowerCAmelCase = {}
lowerCAmelCase = load_spm(self.spm_file , self.sp_model_kwargs )
def __snake_case ( self , A_ , A_ = None ) -> Tuple[str]:
lowerCAmelCase = Path(A_ )
if not save_dir.is_dir():
raise OSError(f'{save_directory} should be a directory' )
lowerCAmelCase = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
)
lowerCAmelCase = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
)
save_json(self.encoder , A_ )
if os.path.abspath(self.spm_file ) != os.path.abspath(A_ ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , A_ )
elif not os.path.isfile(self.spm_file ):
with open(A_ , """wb""" ) as fi:
lowerCAmelCase = self.sp_model.serialized_model_proto()
fi.write(A_ )
return (str(A_ ), str(A_ ))
def __snake_case ( self , A_ , A_ = "en" , A_ = None , A_ = "ro" , **A_ , ) -> BatchEncoding:
lowerCAmelCase = src_lang
lowerCAmelCase = tgt_lang
self.set_src_lang_special_tokens(self.src_lang )
return super().prepare_seqaseq_batch(A_ , A_ , **A_ )
def __snake_case ( self , A_ , A_ , A_ , **A_ ) -> str:
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
lowerCAmelCase = src_lang
lowerCAmelCase = self(A_ , add_special_tokens=A_ , **A_ )
lowerCAmelCase = self.get_lang_id(A_ )
lowerCAmelCase = tgt_lang_id
return inputs
def __snake_case ( self ) -> Any:
self.set_src_lang_special_tokens(self.src_lang )
def __snake_case ( self ) -> Optional[int]:
self.set_tgt_lang_special_tokens(self.tgt_lang )
def __snake_case ( self , A_ ) -> None:
lowerCAmelCase = self.get_lang_token(A_ )
lowerCAmelCase = self.lang_token_to_id[lang_token]
lowerCAmelCase = [self.cur_lang_id]
lowerCAmelCase = [self.eos_token_id]
def __snake_case ( self , A_ ) -> None:
lowerCAmelCase = self.get_lang_token(A_ )
lowerCAmelCase = self.lang_token_to_id[lang_token]
lowerCAmelCase = [self.cur_lang_id]
lowerCAmelCase = [self.eos_token_id]
def __snake_case ( self , A_ ) -> str:
return self.lang_code_to_token[lang]
def __snake_case ( self , A_ ) -> int:
lowerCAmelCase = self.get_lang_token(A_ )
return self.lang_token_to_id[lang_token]
def _snake_case ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Dict[str, Any] ) -> sentencepiece.SentencePieceProcessor:
"""simple docstring"""
lowerCAmelCase = sentencepiece.SentencePieceProcessor(**_SCREAMING_SNAKE_CASE )
spm.Load(str(_SCREAMING_SNAKE_CASE ) )
return spm
def _snake_case ( _SCREAMING_SNAKE_CASE : str ) -> Union[Dict, List]:
"""simple docstring"""
with open(_SCREAMING_SNAKE_CASE , """r""" ) as f:
return json.load(_SCREAMING_SNAKE_CASE )
def _snake_case ( _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : str ) -> None:
"""simple docstring"""
with open(_SCREAMING_SNAKE_CASE , """w""" ) as f:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , indent=2 ) | 187 | 1 |
from functools import lru_cache
def A ( lowercase ) -> set:
'''simple docstring'''
UpperCamelCase = 2
UpperCamelCase = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(lowercase )
if n > 1:
factors.add(lowercase )
return factors
@lru_cache
def A ( lowercase ) -> int:
'''simple docstring'''
return len(unique_prime_factors(lowercase ) )
def A ( lowercase ) -> bool:
'''simple docstring'''
return len(set(lowercase ) ) in (0, 1)
def A ( lowercase ) -> list:
'''simple docstring'''
UpperCamelCase = 2
while True:
# Increment each value of a generated range
UpperCamelCase = [base + i for i in range(lowercase )]
# Run elements through out unique_prime_factors function
# Append our target number to the end.
UpperCamelCase = [upf_len(lowercase ) for x in group]
checker.append(lowercase )
# If all numbers in the list are equal, return the group variable.
if equality(lowercase ):
return group
# Increment our base variable by 1
base += 1
def A ( lowercase = 4 ) -> int:
'''simple docstring'''
UpperCamelCase = run(lowercase )
return results[0] if len(lowercase ) else None
if __name__ == "__main__":
print(solution())
| 222 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
__lowercase : int = IFInpaintingPipeline
__lowercase : str = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
__lowercase : Optional[int] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__lowercase : Optional[int] = PipelineTesterMixin.required_optional_params - {"latents"}
def __UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
return self._get_dummy_components()
def __UpperCamelCase ( self , A_ , A_=0 ) -> List[Any]:
"""simple docstring"""
if str(A_ ).startswith('mps' ):
UpperCamelCase = torch.manual_seed(A_ )
else:
UpperCamelCase = torch.Generator(device=A_ ).manual_seed(A_ )
UpperCamelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(A_ ) ).to(A_ )
UpperCamelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(A_ ) ).to(A_ )
UpperCamelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'mask_image': mask_image,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def __UpperCamelCase ( self ) -> str:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def __UpperCamelCase ( self ) -> str:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def __UpperCamelCase ( self ) -> str:
"""simple docstring"""
# 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 __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def __UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
self._test_save_load_local()
def __UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 222 | 1 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
__lowercase = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class lowerCamelCase_ ( datasets.BuilderConfig ):
'''simple docstring'''
a__ : Optional[datasets.Features] = None
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , ):
'''simple docstring'''
import pyspark
def generate_fn():
__UpperCamelCase :Tuple = df.select('''*''' , pyspark.sql.functions.spark_partition_id().alias('''part_id''' ) )
for partition_id in partition_order:
__UpperCamelCase :str = df_with_partition_id.select('''*''' ).where(f"""part_id = {partition_id}""" ).drop('''part_id''' )
__UpperCamelCase :Optional[int] = partition_df.collect()
__UpperCamelCase :Tuple = 0
for row in rows:
yield f"""{partition_id}_{row_id}""", row.asDict()
row_id += 1
return generate_fn
class lowerCamelCase_ ( _BaseExamplesIterable ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase=None , ) -> int:
__UpperCamelCase :List[str] = df
__UpperCamelCase :Optional[Any] = partition_order or range(self.df.rdd.getNumPartitions())
__UpperCamelCase :int = _generate_iterable_examples(self.df , self.partition_order)
def __iter__( self) -> List[Any]:
yield from self.generate_examples_fn()
def UpperCamelCase__ ( self , __lowercase) -> "SparkExamplesIterable":
__UpperCamelCase :str = list(range(self.df.rdd.getNumPartitions()))
generator.shuffle(__lowercase)
return SparkExamplesIterable(self.df , partition_order=__lowercase)
def UpperCamelCase__ ( self , __lowercase , __lowercase) -> "SparkExamplesIterable":
__UpperCamelCase :Optional[int] = self.split_shard_indices_by_worker(__lowercase , __lowercase)
return SparkExamplesIterable(self.df , partition_order=__lowercase)
@property
def UpperCamelCase__ ( self) -> int:
return len(self.partition_order)
class lowerCamelCase_ ( datasets.DatasetBuilder ):
'''simple docstring'''
a__ : List[Any] = SparkConfig
def __init__( self , __lowercase , __lowercase = None , __lowercase = None , **__lowercase , ) -> Optional[int]:
import pyspark
__UpperCamelCase :Optional[int] = pyspark.sql.SparkSession.builder.getOrCreate()
__UpperCamelCase :Any = df
__UpperCamelCase :str = working_dir
super().__init__(
cache_dir=__lowercase , config_name=str(self.df.semanticHash()) , **__lowercase , )
def UpperCamelCase__ ( self) -> Tuple:
# Returns the path of the created file.
def create_cache_and_write_probe(__lowercase):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir , exist_ok=__lowercase)
__UpperCamelCase :List[str] = os.path.join(self._cache_dir , '''fs_test''' + uuid.uuida().hex)
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(__lowercase , '''a''')
return [probe_file]
if self._spark.conf.get('''spark.master''' , '''''').startswith('''local'''):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
__UpperCamelCase :Tuple = (
self._spark.sparkContext.parallelize(range(1) , 1).mapPartitions(__lowercase).collect()
)
if os.path.isfile(probe[0]):
return
raise ValueError(
'''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir''')
def UpperCamelCase__ ( self) -> int:
return datasets.DatasetInfo(features=self.config.features)
def UpperCamelCase__ ( self , __lowercase) -> Union[str, Any]:
return [datasets.SplitGenerator(name=datasets.Split.TRAIN)]
def UpperCamelCase__ ( self , __lowercase) -> Dict:
import pyspark
def get_arrow_batch_size(__lowercase):
for batch in it:
yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]})
__UpperCamelCase :int = self.df.count()
__UpperCamelCase :List[Any] = df_num_rows if df_num_rows <= 100 else 100
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
__UpperCamelCase :Union[str, Any] = (
self.df.limit(__lowercase)
.repartition(1)
.mapInArrow(__lowercase , '''batch_bytes: long''')
.agg(pyspark.sql.functions.sum('''batch_bytes''').alias('''sample_bytes'''))
.collect()[0]
.sample_bytes
/ sample_num_rows
)
__UpperCamelCase :Union[str, Any] = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
__UpperCamelCase :Dict = min(__lowercase , int(approx_total_size / max_shard_size))
__UpperCamelCase :List[str] = self.df.repartition(__lowercase)
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase , ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
import pyspark
__UpperCamelCase :Any = ParquetWriter if file_format == '''parquet''' else ArrowWriter
__UpperCamelCase :Optional[Any] = os.path.join(self._working_dir , os.path.basename(__lowercase)) if self._working_dir else fpath
__UpperCamelCase :List[str] = file_format == '''parquet'''
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
__UpperCamelCase :Optional[int] = self.config.features
__UpperCamelCase :int = self._writer_batch_size
__UpperCamelCase :Dict = self._fs.storage_options
def write_arrow(__lowercase):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
__UpperCamelCase :Tuple = pyspark.TaskContext().taskAttemptId()
__UpperCamelCase :str = next(__lowercase , __lowercase)
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
__UpperCamelCase :Optional[Any] = 0
__UpperCamelCase :Dict = writer_class(
features=__lowercase , path=working_fpath.replace('''SSSSS''' , f"""{shard_id:05d}""").replace('''TTTTT''' , f"""{task_id:05d}""") , writer_batch_size=__lowercase , storage_options=__lowercase , embed_local_files=__lowercase , )
__UpperCamelCase :str = pa.Table.from_batches([first_batch])
writer.write_table(__lowercase)
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
__UpperCamelCase , __UpperCamelCase :Dict = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
shard_id += 1
__UpperCamelCase :Optional[Any] = writer_class(
features=writer._features , path=working_fpath.replace('''SSSSS''' , f"""{shard_id:05d}""").replace('''TTTTT''' , f"""{task_id:05d}""") , writer_batch_size=__lowercase , storage_options=__lowercase , embed_local_files=__lowercase , )
__UpperCamelCase :Any = pa.Table.from_batches([batch])
writer.write_table(__lowercase)
if writer._num_bytes > 0:
__UpperCamelCase , __UpperCamelCase :int = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(__lowercase)):
__UpperCamelCase :Tuple = os.path.join(os.path.dirname(__lowercase) , os.path.basename(__lowercase))
shutil.move(__lowercase , __lowercase)
__UpperCamelCase :Tuple = (
self.df.mapInArrow(__lowercase , '''task_id: long, num_examples: long, num_bytes: long''')
.groupBy('''task_id''')
.agg(
pyspark.sql.functions.sum('''num_examples''').alias('''total_num_examples''') , pyspark.sql.functions.sum('''num_bytes''').alias('''total_num_bytes''') , pyspark.sql.functions.count('''num_bytes''').alias('''num_shards''') , pyspark.sql.functions.collect_list('''num_examples''').alias('''shard_lengths''') , )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def UpperCamelCase__ ( self , __lowercase , __lowercase = "arrow" , __lowercase = None , __lowercase = None , **__lowercase , ) -> Tuple:
self._validate_cache_dir()
__UpperCamelCase :Any = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE)
self._repartition_df_if_needed(__lowercase)
__UpperCamelCase :Any = not is_remote_filesystem(self._fs)
__UpperCamelCase :Optional[int] = os.path.join if is_local else posixpath.join
__UpperCamelCase :Dict = '''-TTTTT-SSSSS-of-NNNNN'''
__UpperCamelCase :Any = f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
__UpperCamelCase :str = path_join(self._output_dir , __lowercase)
__UpperCamelCase :str = 0
__UpperCamelCase :List[Any] = 0
__UpperCamelCase :str = 0
__UpperCamelCase :Union[str, Any] = []
__UpperCamelCase :Optional[Any] = []
for task_id, content in self._prepare_split_single(__lowercase , __lowercase , __lowercase):
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) :Union[str, Any] = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards))
all_shard_lengths.extend(__lowercase)
__UpperCamelCase :Any = total_num_examples
__UpperCamelCase :List[Any] = total_num_bytes
# should rename everything at the end
logger.debug(f"""Renaming {total_shards} shards.""")
if total_shards > 1:
__UpperCamelCase :List[str] = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
__UpperCamelCase :Optional[Any] = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
__lowercase , __lowercase , __lowercase , ):
rename(
__lowercase , fpath.replace('''SSSSS''' , f"""{shard_id:05d}""").replace('''TTTTT''' , f"""{task_id:05d}""") , fpath.replace('''TTTTT-SSSSS''' , f"""{global_shard_id:05d}""").replace('''NNNNN''' , f"""{total_shards:05d}""") , )
__UpperCamelCase :int = []
__UpperCamelCase :Optional[int] = 0
for i in range(len(__lowercase)):
__UpperCamelCase , __UpperCamelCase :List[str] = task_id_and_num_shards[i]
for shard_id in range(__lowercase):
args.append([task_id, shard_id, global_shard_id])
global_shard_id += 1
self._spark.sparkContext.parallelize(__lowercase , len(__lowercase)).map(lambda __lowercase: _rename_shard(*__lowercase)).collect()
else:
# don't use any pattern
__UpperCamelCase :Optional[int] = 0
__UpperCamelCase :Tuple = task_id_and_num_shards[0][0]
self._rename(
fpath.replace('''SSSSS''' , f"""{shard_id:05d}""").replace('''TTTTT''' , f"""{task_id:05d}""") , fpath.replace(__lowercase , '''''') , )
def UpperCamelCase__ ( self , __lowercase , ) -> SparkExamplesIterable:
return SparkExamplesIterable(self.df)
| 105 | import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Optional[int] = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase :List[str] = emb.weight.shape
__UpperCamelCase :str = nn.Linear(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , bias=SCREAMING_SNAKE_CASE )
__UpperCamelCase :Any = emb.weight.data
return lin_layer
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Dict = torch.load(SCREAMING_SNAKE_CASE , map_location='''cpu''' )
__UpperCamelCase :Tuple = mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
__UpperCamelCase :Dict = mam_aaa['''model''']
remove_ignore_keys_(SCREAMING_SNAKE_CASE )
__UpperCamelCase :Dict = state_dict['''encoder.embed_tokens.weight'''].shape[0]
__UpperCamelCase :Dict = MaMaaaConfig(
vocab_size=SCREAMING_SNAKE_CASE , max_position_embeddings=1_024 , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='''relu''' , )
__UpperCamelCase :Tuple = state_dict['''decoder.embed_tokens.weight''']
__UpperCamelCase :int = MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE )
model.model.load_state_dict(SCREAMING_SNAKE_CASE , strict=SCREAMING_SNAKE_CASE )
__UpperCamelCase :Optional[Any] = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
__lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
__lowercase = parser.parse_args()
__lowercase = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 105 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase=False ) -> Dict:
A: Union[str, Any] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
('''cls_token''', '''vit.embeddings.cls_token'''),
('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
('''pos_embed''', '''vit.embeddings.position_embeddings'''),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
A: Union[str, Any] = [(pair[0], pair[1][4:]) if pair[1].startswith('''vit''' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('''norm.weight''', '''vit.layernorm.weight'''),
('''norm.bias''', '''vit.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
return rename_keys
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase=False ) -> int:
for i in range(config.num_hidden_layers ):
if base_model:
A: Dict = ''''''
else:
A: Union[str, Any] = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
A: Tuple = state_dict.pop(F"""blocks.{i}.attn.qkv.weight""" )
A: Any = state_dict.pop(F"""blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
A: Dict = in_proj_weight[
: config.hidden_size, :
]
A: Dict = in_proj_bias[: config.hidden_size]
A: Optional[int] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
A: List[str] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
A: str = in_proj_weight[
-config.hidden_size :, :
]
A: List[Any] = in_proj_bias[-config.hidden_size :]
def SCREAMING_SNAKE_CASE( __lowercase ) -> Dict:
A: Dict = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(__lowercase , __lowercase )
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase ) -> Optional[int]:
A: str = dct.pop(__lowercase )
A: List[str] = val
def SCREAMING_SNAKE_CASE( ) -> Tuple:
A: Tuple = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
A: str = Image.open(requests.get(__lowercase , stream=__lowercase ).raw )
return im
@torch.no_grad()
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase=True ) -> Tuple:
A: Tuple = ViTConfig()
# patch_size
if model_name[-1] == "8":
A: List[str] = 8
# set labels if required
if not base_model:
A: Dict = 1_0_0_0
A: Tuple = '''huggingface/label-files'''
A: Optional[int] = '''imagenet-1k-id2label.json'''
A: Any = json.load(open(hf_hub_download(__lowercase , __lowercase , repo_type='''dataset''' ) , '''r''' ) )
A: str = {int(__lowercase ): v for k, v in idalabel.items()}
A: int = idalabel
A: Tuple = {v: k for k, v in idalabel.items()}
# size of the architecture
if model_name in ["dino_vits8", "dino_vits16"]:
A: Tuple = 3_8_4
A: Union[str, Any] = 1_5_3_6
A: Tuple = 1_2
A: List[str] = 6
# load original model from torch hub
A: List[str] = torch.hub.load('''facebookresearch/dino:main''' , __lowercase )
original_model.eval()
# load state_dict of original model, remove and rename some keys
A: Union[str, Any] = original_model.state_dict()
if base_model:
remove_classification_head_(__lowercase )
A: Optional[Any] = create_rename_keys(__lowercase , base_model=__lowercase )
for src, dest in rename_keys:
rename_key(__lowercase , __lowercase , __lowercase )
read_in_q_k_v(__lowercase , __lowercase , __lowercase )
# load HuggingFace model
if base_model:
A: Dict = ViTModel(__lowercase , add_pooling_layer=__lowercase ).eval()
else:
A: Union[str, Any] = ViTForImageClassification(__lowercase ).eval()
model.load_state_dict(__lowercase )
# Check outputs on an image, prepared by ViTImageProcessor
A: Tuple = ViTImageProcessor()
A: Any = image_processor(images=prepare_img() , return_tensors='''pt''' )
A: str = encoding['''pixel_values''']
A: str = model(__lowercase )
if base_model:
A: Optional[Any] = original_model(__lowercase )
assert torch.allclose(__lowercase , outputs.last_hidden_state[:, 0, :] , atol=1E-1 )
else:
A: Tuple = original_model(__lowercase )
assert logits.shape == outputs.logits.shape
assert torch.allclose(__lowercase , outputs.logits , atol=1E-3 )
Path(__lowercase ).mkdir(exist_ok=__lowercase )
print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__lowercase )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(__lowercase )
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''dino_vitb16''',
type=str,
help='''Name of the model trained with DINO you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--base_model''',
action='''store_true''',
help='''Whether to only convert the base model (no projection head weights).''',
)
parser.set_defaults(base_model=True)
UpperCamelCase = parser.parse_args()
convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
| 319 |
'''simple docstring'''
import heapq
import sys
import numpy as np
UpperCamelCase = tuple[int, int]
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : List[Any] ) -> str:
'''simple docstring'''
A: Any = []
A: int = set()
def _snake_case ( self : Optional[Any] ) -> int:
'''simple docstring'''
if not self.empty():
return self.elements[0][0]
else:
return float('''inf''' )
def _snake_case ( self : List[str] ) -> List[Any]:
'''simple docstring'''
return len(self.elements ) == 0
def _snake_case ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Any ) -> List[Any]:
'''simple docstring'''
if item not in self.set:
heapq.heappush(self.elements , (priority, item) )
self.set.add(SCREAMING_SNAKE_CASE_ )
else:
# update
# print("update", item)
A: Optional[int] = []
((A) , (A)): str = heapq.heappop(self.elements )
while x != item:
temp.append((pri, x) )
((A) , (A)): int = heapq.heappop(self.elements )
temp.append((priority, item) )
for pro, xxx in temp:
heapq.heappush(self.elements , (pro, xxx) )
def _snake_case ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : str ) -> Any:
'''simple docstring'''
if item in self.set:
self.set.remove(SCREAMING_SNAKE_CASE_ )
A: str = []
((A) , (A)): List[str] = heapq.heappop(self.elements )
while x != item:
temp.append((pro, x) )
((A) , (A)): Any = heapq.heappop(self.elements )
for prito, yyy in temp:
heapq.heappush(self.elements , (prito, yyy) )
def _snake_case ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
return self.elements[0][1]
def _snake_case ( self : int ) -> Union[str, Any]:
'''simple docstring'''
((A) , (A)): Dict = heapq.heappop(self.elements )
self.set.remove(SCREAMING_SNAKE_CASE_ )
return (priority, item)
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> Union[str, Any]:
# euclidean distance
A: List[str] = np.array(__lowercase )
A: Optional[int] = np.array(__lowercase )
return np.linalg.norm(a - b )
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> int:
# integer division by time variable
return consistent_heuristic(__lowercase , __lowercase ) // t
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> Optional[Any]:
# manhattan distance
return abs(p[0] - goal[0] ) + abs(p[1] - goal[1] )
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase ) -> List[Any]:
A: int = g_function[start] + Wa * heuristics[i](__lowercase , __lowercase )
return ans
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase ) -> Optional[int]:
A: Union[str, Any] = np.chararray((n, n) )
for i in range(__lowercase ):
for j in range(__lowercase ):
A: Union[str, Any] = '''*'''
for i in range(__lowercase ):
for j in range(__lowercase ):
if (j, (n - 1) - i) in blocks:
A: Optional[Any] = '''#'''
A: Tuple = '''-'''
A: List[str] = back_pointer[goal]
while x != start:
((A) , (A)): Tuple = x
# print(x)
A: List[str] = '''-'''
A: str = back_pointer[x]
A: Dict = '''-'''
for i in range(__lowercase ):
for j in range(__lowercase ):
if (i, j) == (0, n - 1):
print(grid[i][j] , end=''' ''' )
print('''<-- End position''' , end=''' ''' )
else:
print(grid[i][j] , end=''' ''' )
print()
print('''^''' )
print('''Start position''' )
print()
print('''# is an obstacle''' )
print('''- is the path taken by algorithm''' )
print('''PATH TAKEN BY THE ALGORITHM IS:-''' )
A: List[str] = back_pointer[goal]
while x != start:
print(__lowercase , end=''' ''' )
A: Optional[int] = back_pointer[x]
print(__lowercase )
sys.exit()
def SCREAMING_SNAKE_CASE( __lowercase ) -> Optional[Any]:
if p[0] < 0 or p[0] > n - 1:
return False
if p[1] < 0 or p[1] > n - 1:
return False
return True
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , ) -> Union[str, Any]:
for itera in range(__lowercase ):
open_list[itera].remove_element(__lowercase )
# print("s", s)
# print("j", j)
((A) , (A)): Tuple = s
A: Optional[Any] = (x - 1, y)
A: str = (x + 1, y)
A: List[Any] = (x, y + 1)
A: int = (x, y - 1)
for neighbours in [left, right, up, down]:
if neighbours not in blocks:
if valid(__lowercase ) and neighbours not in visited:
# print("neighbour", neighbours)
visited.add(__lowercase )
A: int = -1
A: int = float('''inf''' )
if valid(__lowercase ) and g_function[neighbours] > g_function[s] + 1:
A: List[str] = g_function[s] + 1
A: List[str] = s
if neighbours not in close_list_anchor:
open_list[0].put(__lowercase , key(__lowercase , 0 , __lowercase , __lowercase ) )
if neighbours not in close_list_inad:
for var in range(1 , __lowercase ):
if key(__lowercase , __lowercase , __lowercase , __lowercase ) <= Wa * key(
__lowercase , 0 , __lowercase , __lowercase ):
open_list[j].put(
__lowercase , key(__lowercase , __lowercase , __lowercase , __lowercase ) )
def SCREAMING_SNAKE_CASE( ) -> Tuple:
A: str = []
for x in range(1 , 5 ):
for y in range(1 , 6 ):
some_list.append((x, y) )
for x in range(1_5 , 2_0 ):
some_list.append((x, 1_7) )
for x in range(1_0 , 1_9 ):
for y in range(1 , 1_5 ):
some_list.append((x, y) )
# L block
for x in range(1 , 4 ):
for y in range(1_2 , 1_9 ):
some_list.append((x, y) )
for x in range(3 , 1_3 ):
for y in range(1_6 , 1_9 ):
some_list.append((x, y) )
return some_list
UpperCamelCase = {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}
UpperCamelCase = [
(0, 1),
(1, 1),
(2, 1),
(3, 1),
(4, 1),
(5, 1),
(6, 1),
(7, 1),
(8, 1),
(9, 1),
(10, 1),
(11, 1),
(12, 1),
(13, 1),
(14, 1),
(15, 1),
(16, 1),
(17, 1),
(18, 1),
(19, 1),
]
UpperCamelCase = make_common_ground()
UpperCamelCase = blocks_blk
# hyper parameters
UpperCamelCase = 1
UpperCamelCase = 1
UpperCamelCase = 20
UpperCamelCase = 3 # one consistent and two other inconsistent
# start and end destination
UpperCamelCase = (0, 0)
UpperCamelCase = (n - 1, n - 1)
UpperCamelCase = 1
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase ) -> int:
A: int = {start: 0, goal: float('''inf''' )}
A: Union[str, Any] = {start: -1, goal: -1}
A: List[Any] = []
A: Union[str, Any] = set()
for i in range(__lowercase ):
open_list.append(PriorityQueue() )
open_list[i].put(__lowercase , key(__lowercase , __lowercase , __lowercase , __lowercase ) )
A: list[int] = []
A: list[int] = []
while open_list[0].minkey() < float('''inf''' ):
for i in range(1 , __lowercase ):
# print(open_list[0].minkey(), open_list[i].minkey())
if open_list[i].minkey() <= Wa * open_list[0].minkey():
global t
t += 1
if g_function[goal] <= open_list[i].minkey():
if g_function[goal] < float('''inf''' ):
do_something(__lowercase , __lowercase , __lowercase )
else:
A , A: Union[str, Any] = open_list[i].top_show()
visited.add(__lowercase )
expand_state(
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , )
close_list_inad.append(__lowercase )
else:
if g_function[goal] <= open_list[0].minkey():
if g_function[goal] < float('''inf''' ):
do_something(__lowercase , __lowercase , __lowercase )
else:
A: Union[str, Any] = open_list[0].top_show()
visited.add(__lowercase )
expand_state(
__lowercase , 0 , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , )
close_list_anchor.append(__lowercase )
print('''No path found to goal''' )
print()
for i in range(n - 1 , -1 , -1 ):
for j in range(__lowercase ):
if (j, i) in blocks:
print('''#''' , end=''' ''' )
elif (j, i) in back_pointer:
if (j, i) == (n - 1, n - 1):
print('''*''' , end=''' ''' )
else:
print('''-''' , end=''' ''' )
else:
print('''*''' , end=''' ''' )
if (j, i) == (n - 1, n - 1):
print('''<-- End position''' , end=''' ''' )
print()
print('''^''' )
print('''Start position''' )
print()
print('''# is an obstacle''' )
print('''- is the path taken by algorithm''' )
if __name__ == "__main__":
multi_a_star(start, goal, n_heuristic)
| 319 | 1 |
"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Union[str, Any] = logging.get_logger(__name__)
# TODO Update this
A__ : Union[str, Any] = {
'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.json',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class lowercase__ ( snake_case__ ):
_UpperCAmelCase :Union[str, Any] = "esm"
def __init__( self : Tuple , snake_case__ : Tuple=None , snake_case__ : Optional[Any]=None , snake_case__ : Tuple=None , snake_case__ : Optional[int]=768 , snake_case__ : List[str]=12 , snake_case__ : Union[str, Any]=12 , snake_case__ : List[Any]=3072 , snake_case__ : int=0.1 , snake_case__ : Union[str, Any]=0.1 , snake_case__ : Any=1026 , snake_case__ : str=0.02 , snake_case__ : Optional[int]=1E-12 , snake_case__ : Optional[Any]="absolute" , snake_case__ : Union[str, Any]=True , snake_case__ : List[Any]=None , snake_case__ : str=False , snake_case__ : Union[str, Any]=False , snake_case__ : List[Any]=None , snake_case__ : Dict=None , **snake_case__ : str , ):
super().__init__(pad_token_id=snake_case__ , mask_token_id=snake_case__ , **snake_case__ )
lowerCamelCase_ : Union[str, Any] =vocab_size
lowerCamelCase_ : Tuple =hidden_size
lowerCamelCase_ : Optional[Any] =num_hidden_layers
lowerCamelCase_ : Union[str, Any] =num_attention_heads
lowerCamelCase_ : Tuple =intermediate_size
lowerCamelCase_ : List[Any] =hidden_dropout_prob
lowerCamelCase_ : str =attention_probs_dropout_prob
lowerCamelCase_ : Dict =max_position_embeddings
lowerCamelCase_ : Optional[int] =initializer_range
lowerCamelCase_ : Union[str, Any] =layer_norm_eps
lowerCamelCase_ : int =position_embedding_type
lowerCamelCase_ : List[str] =use_cache
lowerCamelCase_ : List[Any] =emb_layer_norm_before
lowerCamelCase_ : Tuple =token_dropout
lowerCamelCase_ : int =is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("No esmfold_config supplied for folding model, using default values." )
lowerCamelCase_ : List[Any] =EsmFoldConfig()
elif isinstance(snake_case__ , snake_case__ ):
lowerCamelCase_ : Dict =EsmFoldConfig(**snake_case__ )
lowerCamelCase_ : Optional[Any] =esmfold_config
if vocab_list is None:
logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!" )
lowerCamelCase_ : Optional[int] =get_default_vocab_list()
else:
lowerCamelCase_ : List[Any] =vocab_list
else:
lowerCamelCase_ : str =None
lowerCamelCase_ : Optional[int] =None
if self.esmfold_config is not None and getattr(self.esmfold_config , "use_esm_attn_map" , snake_case__ ):
raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!" )
def UpperCAmelCase__ ( self : Union[str, Any] ):
lowerCamelCase_ : List[Any] =super().to_dict()
if isinstance(self.esmfold_config , snake_case__ ):
lowerCamelCase_ : List[Any] =self.esmfold_config.to_dict()
return output
@dataclass
class lowercase__ :
_UpperCAmelCase :str = None
_UpperCAmelCase :bool = True
_UpperCAmelCase :bool = False
_UpperCAmelCase :bool = False
_UpperCAmelCase :bool = False
_UpperCAmelCase :float = 0
_UpperCAmelCase :bool = True
_UpperCAmelCase :bool = False
_UpperCAmelCase :int = 128
_UpperCAmelCase :"TrunkConfig" = None
def UpperCAmelCase__ ( self : Optional[int] ):
if self.trunk is None:
lowerCamelCase_ : List[str] =TrunkConfig()
elif isinstance(self.trunk , snake_case__ ):
lowerCamelCase_ : Union[str, Any] =TrunkConfig(**self.trunk )
def UpperCAmelCase__ ( self : int ):
lowerCamelCase_ : List[Any] =asdict(self )
lowerCamelCase_ : Any =self.trunk.to_dict()
return output
@dataclass
class lowercase__ :
_UpperCAmelCase :int = 48
_UpperCAmelCase :int = 1024
_UpperCAmelCase :int = 128
_UpperCAmelCase :int = 32
_UpperCAmelCase :int = 32
_UpperCAmelCase :int = 32
_UpperCAmelCase :float = 0
_UpperCAmelCase :float = 0
_UpperCAmelCase :bool = False
_UpperCAmelCase :int = 4
_UpperCAmelCase :Optional[int] = 128
_UpperCAmelCase :"StructureModuleConfig" = None
def UpperCAmelCase__ ( self : List[Any] ):
if self.structure_module is None:
lowerCamelCase_ : Optional[int] =StructureModuleConfig()
elif isinstance(self.structure_module , snake_case__ ):
lowerCamelCase_ : Tuple =StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F"""`max_recycles` should be positive, got {self.max_recycles}.""" )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
F""" {self.sequence_state_dim} and {self.sequence_state_dim}.""" )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
F""" {self.pairwise_state_dim} and {self.pairwise_state_dim}.""" )
lowerCamelCase_ : Union[str, Any] =self.sequence_state_dim // self.sequence_head_width
lowerCamelCase_ : Optional[int] =self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
F""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.""" )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
F""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.""" )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.""" )
if self.dropout >= 0.4:
raise ValueError(F"""`dropout` should not be greater than 0.4, got {self.dropout}.""" )
def UpperCAmelCase__ ( self : Union[str, Any] ):
lowerCamelCase_ : List[str] =asdict(self )
lowerCamelCase_ : Union[str, Any] =self.structure_module.to_dict()
return output
@dataclass
class lowercase__ :
_UpperCAmelCase :int = 384
_UpperCAmelCase :int = 128
_UpperCAmelCase :int = 16
_UpperCAmelCase :int = 128
_UpperCAmelCase :int = 12
_UpperCAmelCase :int = 4
_UpperCAmelCase :int = 8
_UpperCAmelCase :float = 0.1
_UpperCAmelCase :int = 8
_UpperCAmelCase :int = 1
_UpperCAmelCase :int = 2
_UpperCAmelCase :int = 7
_UpperCAmelCase :int = 10
_UpperCAmelCase :float = 1e-8
_UpperCAmelCase :float = 1e5
def UpperCAmelCase__ ( self : Dict ):
return asdict(self )
def _snake_case ( ) -> Tuple:
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 209 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def _snake_case ( lowerCamelCase__ : int=None ) -> Union[str, Any]:
if subparsers is not None:
lowerCamelCase_ : List[Any] =subparsers.add_parser("test" )
else:
lowerCamelCase_ : List[str] =argparse.ArgumentParser("Accelerate test command" )
parser.add_argument(
"--config_file" , default=lowerCamelCase__ , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=lowerCamelCase__ )
return parser
def _snake_case ( lowerCamelCase__ : List[Any] ) -> Any:
lowerCamelCase_ : Optional[Any] =os.path.sep.join(__file__.split(os.path.sep )[:-2] + ["test_utils", "scripts", "test_script.py"] )
if args.config_file is None:
lowerCamelCase_ : List[Any] =script_name
else:
lowerCamelCase_ : Union[str, Any] =F"""--config_file={args.config_file} {script_name}"""
lowerCamelCase_ : List[str] =["accelerate-launch"] + test_args.split()
lowerCamelCase_ : Tuple =execute_subprocess_async(lowerCamelCase__ , env=os.environ.copy() )
if result.returncode == 0:
print("Test is a success! You are ready for your distributed training!" )
def _snake_case ( ) -> Tuple:
lowerCamelCase_ : Any =test_command_parser()
lowerCamelCase_ : List[Any] =parser.parse_args()
test_command(lowerCamelCase__ )
if __name__ == "__main__":
main()
| 209 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
lowerCAmelCase : List[Any] = XLMTokenizer
lowerCAmelCase : List[Any] = False
def lowerCAmelCase__ ( self : Tuple ) ->List[Any]:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_UpperCAmelCase : Optional[int] = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''w</w>''',
'''r</w>''',
'''t</w>''',
'''lo''',
'''low''',
'''er</w>''',
'''low</w>''',
'''lowest</w>''',
'''newer</w>''',
'''wider</w>''',
'''<unk>''',
]
_UpperCAmelCase : Union[str, Any] = dict(zip(lowerCamelCase__ , range(len(lowerCamelCase__ ) ) ) )
_UpperCAmelCase : Any = ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']
_UpperCAmelCase : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
_UpperCAmelCase : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" ) as fp:
fp.write(json.dumps(lowerCamelCase__ ) )
with open(self.merges_file , "w" ) as fp:
fp.write("\n".join(lowerCamelCase__ ) )
def lowerCAmelCase__ ( self : List[str] , lowerCamelCase__ : int ) ->Any:
'''simple docstring'''
_UpperCAmelCase : Any = '''lower newer'''
_UpperCAmelCase : Any = '''lower newer'''
return input_text, output_text
def lowerCAmelCase__ ( self : Optional[Any] ) ->Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Dict = XLMTokenizer(self.vocab_file , self.merges_file )
_UpperCAmelCase : List[Any] = '''lower'''
_UpperCAmelCase : Optional[int] = ['''low''', '''er</w>''']
_UpperCAmelCase : Union[str, Any] = tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
_UpperCAmelCase : int = tokens + ['''<unk>''']
_UpperCAmelCase : Union[str, Any] = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , lowerCamelCase__ )
@slow
def lowerCAmelCase__ ( self : int ) ->Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : int = XLMTokenizer.from_pretrained("xlm-mlm-en-2048" )
_UpperCAmelCase : Dict = tokenizer.encode("sequence builders" , add_special_tokens=lowerCamelCase__ )
_UpperCAmelCase : Dict = tokenizer.encode("multi-sequence build" , add_special_tokens=lowerCamelCase__ )
_UpperCAmelCase : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ )
_UpperCAmelCase : str = tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ , lowerCamelCase__ )
assert encoded_sentence == [0] + text + [1]
assert encoded_pair == [0] + text + [1] + text_a + [1]
| 234 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def _a ( UpperCAmelCase ) -> Dict:
"""simple docstring"""
lowerCamelCase__ : Dict = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(UpperCAmelCase , UpperCAmelCase )
def _a ( UpperCAmelCase ) -> Any:
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
lowerCamelCase__ : Any = s_dict.pop(UpperCAmelCase )
elif "subsample" in key:
lowerCamelCase__ : Any = s_dict.pop(UpperCAmelCase )
def _a ( UpperCAmelCase ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = emb.weight.shape
lowerCamelCase__ : str = nn.Linear(UpperCAmelCase , UpperCAmelCase , bias=UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = emb.weight.data
return lin_layer
def _a ( UpperCAmelCase , UpperCAmelCase ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase__ : List[Any] = torch.load(UpperCAmelCase , map_location='''cpu''' )
lowerCamelCase__ : List[Any] = mam_aaa['''args''']
lowerCamelCase__ : Dict = mam_aaa['''model''']
lowerCamelCase__ : Optional[Any] = state_dict['''decoder.output_projection.weight''']
remove_ignore_keys_(UpperCAmelCase )
rename_keys(UpperCAmelCase )
lowerCamelCase__ : Tuple = state_dict['''decoder.embed_tokens.weight'''].shape[0]
lowerCamelCase__ : Tuple = args.share_decoder_input_output_embed
lowerCamelCase__ : Dict = [int(UpperCAmelCase ) for i in args.conv_kernel_sizes.split(''',''' )]
lowerCamelCase__ : str = SpeechaTextConfig(
vocab_size=UpperCAmelCase , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='''relu''' , num_conv_layers=len(UpperCAmelCase ) , conv_channels=args.conv_channels , conv_kernel_sizes=UpperCAmelCase , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=UpperCAmelCase , num_beams=5 , max_length=200 , use_cache=UpperCAmelCase , decoder_start_token_id=2 , early_stopping=UpperCAmelCase , )
lowerCamelCase__ : Optional[int] = SpeechaTextForConditionalGeneration(UpperCAmelCase )
lowerCamelCase__ , lowerCamelCase__ : Dict = model.model.load_state_dict(UpperCAmelCase , strict=UpperCAmelCase )
if len(UpperCAmelCase ) > 0 and not set(UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
f" but all the following weights are missing {missing}" )
if tie_embeds:
lowerCamelCase__ : List[str] = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
lowerCamelCase__ : Tuple = lm_head_weights
model.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
_A : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
_A : str = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 142 | 0 |
from pathlib import Path
import numpy as np
from PIL import Image
def lowerCAmelCase( __lowerCamelCase ):
__a = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b
def lowerCAmelCase( __lowerCamelCase ):
return (gray > 127) & (gray <= 255)
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase ):
__a = np.zeros_like(__lowerCAmelCase )
__a = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
# Copy image to padded image
__a = image
# Iterate over image & apply kernel
for x in range(image.shape[1] ):
for y in range(image.shape[0] ):
__a = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
__a = int(summation > 0 )
return output
if __name__ == "__main__":
# read original image
lowerCamelCase_ : List[str] = Path(__file__).resolve().parent / """image_data""" / """lena.jpg"""
lowerCamelCase_ : Dict = np.array(Image.open(lena_path))
# kernel to be applied
lowerCamelCase_ : Optional[Any] = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
lowerCamelCase_ : Optional[int] = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
lowerCamelCase_ : Optional[Any] = Image.fromarray(output).convert("""RGB""")
pil_img.save("""result_dilation.png""")
| 362 | from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 197 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DonutImageProcessor
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __init__( self : str , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Optional[int]=7 , lowerCAmelCase_ : List[str]=3 , lowerCAmelCase_ : Union[str, Any]=1_8 , lowerCAmelCase_ : Any=3_0 , lowerCAmelCase_ : Tuple=4_0_0 , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : Dict=False , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : Optional[int]=True , lowerCAmelCase_ : Dict=[0.5, 0.5, 0.5] , lowerCAmelCase_ : int=[0.5, 0.5, 0.5] , ):
"""simple docstring"""
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = num_channels
lowercase_ = image_size
lowercase_ = min_resolution
lowercase_ = max_resolution
lowercase_ = do_resize
lowercase_ = size if size is not None else {"""height""": 1_8, """width""": 2_0}
lowercase_ = do_thumbnail
lowercase_ = do_align_axis
lowercase_ = do_pad
lowercase_ = do_normalize
lowercase_ = image_mean
lowercase_ = image_std
def _UpperCAmelCase ( self : str):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ , unittest.TestCase ):
lowercase__ = DonutImageProcessor if is_vision_available() else None
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = DonutImageProcessingTester(self)
@property
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(lowerCAmelCase_ , """do_resize"""))
self.assertTrue(hasattr(lowerCAmelCase_ , """size"""))
self.assertTrue(hasattr(lowerCAmelCase_ , """do_thumbnail"""))
self.assertTrue(hasattr(lowerCAmelCase_ , """do_align_long_axis"""))
self.assertTrue(hasattr(lowerCAmelCase_ , """do_pad"""))
self.assertTrue(hasattr(lowerCAmelCase_ , """do_normalize"""))
self.assertTrue(hasattr(lowerCAmelCase_ , """image_mean"""))
self.assertTrue(hasattr(lowerCAmelCase_ , """image_std"""))
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size , {"""height""": 1_8, """width""": 2_0})
lowercase_ = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2)
self.assertEqual(image_processor.size , {"""height""": 4_2, """width""": 4_2})
# Previous config had dimensions in (width, height) order
lowercase_ = self.image_processing_class.from_dict(self.image_processor_dict , size=(4_2, 8_4))
self.assertEqual(image_processor.size , {"""height""": 8_4, """width""": 4_2})
def _UpperCAmelCase ( self : int):
"""simple docstring"""
pass
@is_flaky()
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
lowercase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase_)
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase_ , Image.Image)
# Test not batched input
lowercase_ = image_processing(image_inputs[0] , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
lowercase_ = image_processing(lowerCAmelCase_ , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
@is_flaky()
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
lowercase_ = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
lowercase_ = 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
lowercase_ = image_processing(image_inputs[0] , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
lowercase_ = image_processing(lowerCAmelCase_ , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
@is_flaky()
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
lowercase_ = 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
lowercase_ = image_processing(image_inputs[0] , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
lowercase_ = image_processing(lowerCAmelCase_ , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
| 136 |
"""simple docstring"""
from maths.prime_factors import prime_factors
def __magic_name__ ( lowercase ):
if not isinstance(lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: int =f'''Input value of [number={number}] must be an integer'''
raise TypeError(lowercase )
if number < 1:
raise ValueError("""Input must be a positive integer""" )
return -1 if len(prime_factors(lowercase ) ) % 2 else 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 173 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase : Any = StableDiffusionInstructPixaPixPipeline
__UpperCamelCase : Optional[int] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
__UpperCamelCase : Optional[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__UpperCamelCase : Optional[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
__UpperCamelCase : Dict = IMAGE_TO_IMAGE_IMAGE_PARAMS
def __magic_name__ ( self : Optional[Any] ):
"""simple docstring"""
torch.manual_seed(0 )
_A: Optional[Any] = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=8 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=3_2 , )
_A: int = PNDMScheduler(skip_prk_steps=lowerCAmelCase_ )
torch.manual_seed(0 )
_A: Any = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
_A: List[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
_A: Any = CLIPTextModel(lowerCAmelCase_ )
_A: List[str] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
_A: Dict = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def __magic_name__ ( self : Optional[int] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Optional[Any]=0 ):
"""simple docstring"""
_A: List[str] = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(lowerCAmelCase_ ) ).to(lowerCAmelCase_ )
_A: Tuple = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_A: Dict = Image.fromarray(np.uinta(lowerCAmelCase_ ) ).convert('''RGB''' )
if str(lowerCAmelCase_ ).startswith('''mps''' ):
_A: str = torch.manual_seed(lowerCAmelCase_ )
else:
_A: Union[str, Any] = torch.Generator(device=lowerCAmelCase_ ).manual_seed(lowerCAmelCase_ )
_A: Optional[Any] = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''image_guidance_scale''': 1,
'''output_type''': '''numpy''',
}
return inputs
def __magic_name__ ( self : Optional[int] ):
"""simple docstring"""
_A: Dict = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_A: str = self.get_dummy_components()
_A: Any = StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_ )
_A: Union[str, Any] = sd_pipe.to(lowerCAmelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_A: Optional[int] = self.get_dummy_inputs(lowerCAmelCase_ )
_A: List[str] = sd_pipe(**lowerCAmelCase_ ).images
_A: str = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
_A: int = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __magic_name__ ( self : List[str] ):
"""simple docstring"""
_A: List[str] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_A: Tuple = self.get_dummy_components()
_A: Optional[Any] = StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_ )
_A: int = sd_pipe.to(lowerCAmelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_A: Any = self.get_dummy_inputs(lowerCAmelCase_ )
_A: Optional[Any] = '''french fries'''
_A: Union[str, Any] = sd_pipe(**lowerCAmelCase_ , negative_prompt=lowerCAmelCase_ )
_A: Optional[Any] = output.images
_A: Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
_A: int = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __magic_name__ ( self : int ):
"""simple docstring"""
_A: List[Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_A: Optional[int] = self.get_dummy_components()
_A: Optional[int] = StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_ )
_A: Tuple = sd_pipe.to(lowerCAmelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_A: Union[str, Any] = self.get_dummy_inputs(lowerCAmelCase_ )
_A: Union[str, Any] = [inputs['''prompt''']] * 2
_A: str = np.array(inputs['''image'''] ).astype(np.floataa ) / 255.0
_A: int = torch.from_numpy(lowerCAmelCase_ ).unsqueeze(0 ).to(lowerCAmelCase_ )
_A: List[Any] = image / 2 + 0.5
_A: Optional[int] = image.permute(0 , 3 , 1 , 2 )
_A: Union[str, Any] = image.repeat(2 , 1 , 1 , 1 )
_A: Optional[Any] = sd_pipe(**lowerCAmelCase_ ).images
_A: Any = image[-1, -3:, -3:, -1]
assert image.shape == (2, 3_2, 3_2, 3)
_A: Tuple = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __magic_name__ ( self : str ):
"""simple docstring"""
_A: int = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_A: Union[str, Any] = self.get_dummy_components()
_A: Tuple = EulerAncestralDiscreteScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='''scaled_linear''' )
_A: Optional[Any] = StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_ )
_A: Dict = sd_pipe.to(lowerCAmelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_A: int = self.get_dummy_inputs(lowerCAmelCase_ )
_A: Optional[Any] = sd_pipe(**lowerCAmelCase_ ).images
_A: Dict = image[0, -3:, -3:, -1]
_A: List[str] = [round(lowerCAmelCase_ , 4 ) for x in image_slice.flatten().tolist()]
print(''','''.join([str(lowerCAmelCase_ ) for x in slice] ) )
assert image.shape == (1, 3_2, 3_2, 3)
_A: Any = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __magic_name__ ( self : int ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def __magic_name__ ( self : Optional[Any] ):
"""simple docstring"""
_A: int = self.get_dummy_components()
_A: int = StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_ )
_A: List[Any] = VaeImageProcessor(do_resize=lowerCAmelCase_ , do_normalize=lowerCAmelCase_ )
_A: Dict = pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_A: List[Any] = pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase_ , input_image_type='''pt''' ) )[0]
_A: List[Any] = components['''vae''']
_A: Tuple = self.get_dummy_inputs_by_type(lowerCAmelCase_ , input_image_type='''pt''' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
_A: List[Any] = vae.encode(inputs[image_param] ).latent_dist.mode()
_A: Optional[int] = pipe(**lowerCAmelCase_ )[0]
_A: Any = np.abs(out - out_latents_inputs ).max()
self.assertLess(lowerCAmelCase_ , 1e-4 , '''passing latents as image input generate different result from passing image''' )
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __magic_name__ ( self : int ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : List[Any] , lowerCAmelCase_ : Union[str, Any]=0 ):
"""simple docstring"""
_A: str = torch.manual_seed(lowerCAmelCase_ )
_A: Dict = load_image(
'''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg''' )
_A: Any = {
'''prompt''': '''turn him into a cyborg''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''image_guidance_scale''': 1.0,
'''output_type''': '''numpy''',
}
return inputs
def __magic_name__ ( self : List[Any] ):
"""simple docstring"""
_A: List[str] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=lowerCAmelCase_ )
pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
pipe.enable_attention_slicing()
_A: Dict = self.get_inputs()
_A: Optional[Any] = pipe(**lowerCAmelCase_ ).images
_A: Any = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_A: int = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def __magic_name__ ( self : Dict ):
"""simple docstring"""
_A: List[Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=lowerCAmelCase_ )
_A: int = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
pipe.enable_attention_slicing()
_A: Union[str, Any] = self.get_inputs()
_A: Optional[int] = pipe(**lowerCAmelCase_ ).images
_A: List[str] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_A: Optional[Any] = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def __magic_name__ ( self : str ):
"""simple docstring"""
_A: List[str] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=lowerCAmelCase_ )
_A: List[Any] = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
pipe.enable_attention_slicing()
_A: str = self.get_inputs()
_A: Tuple = pipe(**lowerCAmelCase_ ).images
_A: int = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_A: str = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def __magic_name__ ( self : Optional[Any] ):
"""simple docstring"""
_A: Tuple = 0
def callback_fn(lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : torch.FloatTensor ) -> None:
_A: str = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
_A: int = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 6_4)
_A: Optional[int] = latents[0, -3:, -3:, -1]
_A: str = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
_A: Tuple = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 6_4)
_A: int = latents[0, -3:, -3:, -1]
_A: Optional[int] = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
_A: Optional[Any] = False
_A: Optional[int] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=lowerCAmelCase_ , torch_dtype=torch.floataa )
_A: List[str] = pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
pipe.enable_attention_slicing()
_A: Any = self.get_inputs()
pipe(**lowerCAmelCase_ , callback=lowerCAmelCase_ , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def __magic_name__ ( self : List[Any] ):
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_A: Any = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=lowerCAmelCase_ , torch_dtype=torch.floataa )
_A: List[str] = pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
_A: int = self.get_inputs()
_A: List[Any] = pipe(**lowerCAmelCase_ )
_A: Optional[int] = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 1_0**9
def __magic_name__ ( self : Any ):
"""simple docstring"""
_A: List[str] = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
_A: List[str] = inputs['''image'''].resize((5_0_4, 5_0_4) )
_A: str = '''timbrooks/instruct-pix2pix'''
_A: List[Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
lowerCAmelCase_ , safety_checker=lowerCAmelCase_ , )
pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
pipe.enable_attention_slicing()
_A: Optional[Any] = pipe(**lowerCAmelCase_ )
_A: Tuple = output.images[0]
_A: List[str] = image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]
assert image.shape == (5_0_4, 5_0_4, 3)
_A: Tuple = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 301 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class UpperCAmelCase :
'''simple docstring'''
__UpperCamelCase : Any = MBartConfig
__UpperCamelCase : Tuple = {}
__UpperCamelCase : Dict = '''gelu'''
def __init__( self : Dict , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[Any]=1_3 , lowerCAmelCase_ : Dict=7 , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : Optional[Any]=False , lowerCAmelCase_ : Union[str, Any]=9_9 , lowerCAmelCase_ : Dict=3_2 , lowerCAmelCase_ : str=2 , lowerCAmelCase_ : int=4 , lowerCAmelCase_ : Union[str, Any]=3_7 , lowerCAmelCase_ : Dict=0.1 , lowerCAmelCase_ : str=0.1 , lowerCAmelCase_ : List[str]=2_0 , lowerCAmelCase_ : str=2 , lowerCAmelCase_ : Optional[int]=1 , lowerCAmelCase_ : List[Any]=0 , ):
"""simple docstring"""
_A: Union[str, Any] = parent
_A: List[Any] = batch_size
_A: Dict = seq_length
_A: Dict = is_training
_A: str = use_labels
_A: int = vocab_size
_A: str = hidden_size
_A: Tuple = num_hidden_layers
_A: Optional[Any] = num_attention_heads
_A: Tuple = intermediate_size
_A: int = hidden_dropout_prob
_A: Tuple = attention_probs_dropout_prob
_A: Tuple = max_position_embeddings
_A: Dict = eos_token_id
_A: int = pad_token_id
_A: Any = bos_token_id
def __magic_name__ ( self : Dict ):
"""simple docstring"""
_A: Optional[int] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
_A: Dict = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
_A: List[Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
_A: Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A: int = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
_A: Any = prepare_mbart_inputs_dict(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
return config, inputs_dict
def __magic_name__ ( self : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[str] ):
"""simple docstring"""
_A: Tuple = TFMBartModel(config=lowerCAmelCase_ ).get_decoder()
_A: List[str] = inputs_dict['''input_ids''']
_A: Tuple = input_ids[:1, :]
_A: List[Any] = inputs_dict['''attention_mask'''][:1, :]
_A: str = inputs_dict['''head_mask''']
_A: Optional[Any] = 1
# first forward pass
_A: Any = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , use_cache=lowerCAmelCase_ )
_A , _A: List[str] = outputs.to_tuple()
_A: Dict = past_key_values[1]
def lowerCamelCase__ ( a , a , a , a=None , a=None , a=None , a=None , a=None , ) -> Tuple:
if attention_mask is None:
_A: Union[str, Any] = tf.cast(tf.math.not_equal(a , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
_A: Optional[int] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
_A: Tuple = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_A: Union[str, Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
_A: Optional[Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase : Union[str, Any] = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
__UpperCamelCase : int = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
__UpperCamelCase : Tuple = (
{
'''conversational''': TFMBartForConditionalGeneration,
'''feature-extraction''': TFMBartModel,
'''summarization''': TFMBartForConditionalGeneration,
'''text2text-generation''': TFMBartForConditionalGeneration,
'''translation''': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
__UpperCamelCase : List[Any] = True
__UpperCamelCase : int = False
__UpperCamelCase : Optional[Any] = False
def __magic_name__ ( self : int , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : int ):
"""simple docstring"""
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def __magic_name__ ( self : Any ):
"""simple docstring"""
_A: Dict = TFMBartModelTester(self )
_A: Tuple = ConfigTester(self , config_class=lowerCAmelCase_ )
def __magic_name__ ( self : List[str] ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __magic_name__ ( self : Optional[Any] ):
"""simple docstring"""
_A: str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_ )
@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase : Optional[int] = [
''' UN Chief Says There Is No Military Solution in Syria''',
]
__UpperCamelCase : List[str] = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
]
__UpperCamelCase : Union[str, Any] = '''facebook/mbart-large-en-ro'''
@cached_property
def __magic_name__ ( self : Tuple ):
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def __magic_name__ ( self : str ):
"""simple docstring"""
_A: Union[str, Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def __magic_name__ ( self : Union[str, Any] , **lowerCAmelCase_ : Tuple ):
"""simple docstring"""
_A: Optional[Any] = self.translate_src_text(**lowerCAmelCase_ )
self.assertListEqual(self.expected_text , lowerCAmelCase_ )
def __magic_name__ ( self : Dict , **lowerCAmelCase_ : Tuple ):
"""simple docstring"""
_A: Any = self.tokenizer(self.src_text , **lowerCAmelCase_ , return_tensors='''tf''' )
_A: Any = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
_A: Optional[Any] = self.tokenizer.batch_decode(lowerCAmelCase_ , skip_special_tokens=lowerCAmelCase_ )
return generated_words
@slow
def __magic_name__ ( self : List[str] ):
"""simple docstring"""
self._assert_generated_batch_equal_expected()
| 301 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
A__ = len(SCREAMING_SNAKE_CASE__ )
while cur > 1:
# Find the maximum number in arr
A__ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A__ = arr[mi::-1] + arr[mi + 1 : len(SCREAMING_SNAKE_CASE__ )]
# Reverse whole list
A__ = arr[cur - 1 :: -1] + arr[cur : len(SCREAMING_SNAKE_CASE__ )]
cur -= 1
return arr
if __name__ == "__main__":
lowercase_ = input("Enter numbers separated by a comma:\n").strip()
lowercase_ = [int(item) for item in user_input.split(",")]
print(pancake_sort(unsorted))
| 7 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class A :
"""simple docstring"""
def __init__( self : Union[str, Any],lowercase_ : Any,lowercase_ : Union[str, Any]=1_3,lowercase_ : Tuple=3_0,lowercase_ : List[Any]=2,lowercase_ : Optional[int]=3,lowercase_ : Union[str, Any]=True,lowercase_ : Tuple=True,lowercase_ : Any=3_2,lowercase_ : List[str]=2,lowercase_ : Optional[int]=4,lowercase_ : Union[str, Any]=3_7,lowercase_ : Tuple="gelu",lowercase_ : str=0.1,lowercase_ : Tuple=0.1,lowercase_ : Union[str, Any]=1_0,lowercase_ : int=0.02,lowercase_ : List[Any]=3,lowercase_ : Any=None,)-> Dict:
'''simple docstring'''
A__ = parent
A__ = batch_size
A__ = image_size
A__ = patch_size
A__ = num_channels
A__ = is_training
A__ = use_labels
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__ = type_sequence_label_size
A__ = initializer_range
A__ = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
A__ = (image_size // patch_size) ** 2
A__ = num_patches + 1
def snake_case__ ( self : int )-> List[str]:
'''simple docstring'''
A__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size],self.type_sequence_label_size )
A__ = self.get_config()
return config, pixel_values, labels
def snake_case__ ( self : Tuple )-> List[Any]:
'''simple docstring'''
return 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=lowercase_,initializer_range=self.initializer_range,)
def snake_case__ ( self : List[str],lowercase_ : int,lowercase_ : Union[str, Any],lowercase_ : Tuple )-> Optional[Any]:
'''simple docstring'''
A__ = TFViTModel(config=lowercase_ )
A__ = model(lowercase_,training=lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
A__ = self.image_size // 2
A__ = pixel_values[:, :, :image_size, :image_size]
A__ = model(lowercase_,interpolate_pos_encoding=lowercase_,training=lowercase_ )
A__ = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, seq_length, self.hidden_size) )
def snake_case__ ( self : List[Any],lowercase_ : List[Any],lowercase_ : List[Any],lowercase_ : List[Any] )-> Dict:
'''simple docstring'''
A__ = self.type_sequence_label_size
A__ = TFViTForImageClassification(lowercase_ )
A__ = model(lowercase_,labels=lowercase_,training=lowercase_ )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
A__ = self.image_size // 2
A__ = pixel_values[:, :, :image_size, :image_size]
A__ = model(lowercase_,interpolate_pos_encoding=lowercase_,training=lowercase_ )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
A__ = 1
A__ = TFViTForImageClassification(lowercase_ )
A__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A__ = model(lowercase_ )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size) )
def snake_case__ ( self : Any )-> Optional[Any]:
'''simple docstring'''
A__ = self.prepare_config_and_inputs()
A__ , A__ , A__ = config_and_inputs
A__ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_tf
class A ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
lowerCamelCase = (
{'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
if is_tf_available()
else {}
)
lowerCamelCase = False
lowerCamelCase = False
lowerCamelCase = False
def snake_case__ ( self : int )-> List[Any]:
'''simple docstring'''
A__ = TFViTModelTester(self )
A__ = ConfigTester(self,config_class=lowercase_,has_text_modality=lowercase_,hidden_size=3_7 )
def snake_case__ ( self : Any )-> Optional[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='ViT does not use inputs_embeds' )
def snake_case__ ( self : Optional[Any] )-> str:
'''simple docstring'''
pass
@unittest.skip(reason='ViT does not use inputs_embeds' )
def snake_case__ ( self : Any )-> int:
'''simple docstring'''
pass
def snake_case__ ( self : str )-> Dict:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(lowercase_ )
self.assertIsInstance(model.get_input_embeddings(),(tf.keras.layers.Layer) )
A__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase_,tf.keras.layers.Layer ) )
def snake_case__ ( self : int )-> List[str]:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(lowercase_ )
A__ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A__ = [*signature.parameters.keys()]
A__ = ['pixel_values']
self.assertListEqual(arg_names[:1],lowercase_ )
def snake_case__ ( self : Union[str, Any] )-> Optional[Any]:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def snake_case__ ( self : Optional[Any] )-> Optional[Any]:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase_ )
@slow
def snake_case__ ( self : Union[str, Any] )-> Union[str, Any]:
'''simple docstring'''
A__ = TFViTModel.from_pretrained('google/vit-base-patch16-224' )
self.assertIsNotNone(lowercase_ )
def _snake_case( ) -> str:
'''simple docstring'''
A__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
@require_vision
class A ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def snake_case__ ( self : List[Any] )-> str:
'''simple docstring'''
return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224' ) if is_vision_available() else None
@slow
def snake_case__ ( self : Any )-> Dict:
'''simple docstring'''
A__ = TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224' )
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(images=lowercase_,return_tensors='tf' )
# forward pass
A__ = model(**lowercase_ )
# verify the logits
A__ = tf.TensorShape((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape,lowercase_ )
A__ = tf.constant([-0.2_744, 0.8_215, -0.0_836] )
tf.debugging.assert_near(outputs.logits[0, :3],lowercase_,atol=1E-4 )
| 7 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DonutImageProcessor
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : List[Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Tuple=7 , __UpperCAmelCase : Dict=3 , __UpperCAmelCase : Union[str, Any]=18 , __UpperCAmelCase : Optional[int]=30 , __UpperCAmelCase : Any=400 , __UpperCAmelCase : int=True , __UpperCAmelCase : Optional[Any]=None , __UpperCAmelCase : str=True , __UpperCAmelCase : str=False , __UpperCAmelCase : List[Any]=True , __UpperCAmelCase : str=True , __UpperCAmelCase : List[Any]=[0.5, 0.5, 0.5] , __UpperCAmelCase : List[Any]=[0.5, 0.5, 0.5] , ) ->Dict:
"""simple docstring"""
a = parent
a = batch_size
a = num_channels
a = image_size
a = min_resolution
a = max_resolution
a = do_resize
a = size if size is not None else {'''height''': 18, '''width''': 20}
a = do_thumbnail
a = do_align_axis
a = do_pad
a = do_normalize
a = image_mean
a = image_std
def __lowerCAmelCase ( self : str ) ->Optional[Any]:
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class lowercase_ ( lowercase , unittest.TestCase ):
'''simple docstring'''
__snake_case = DonutImageProcessor if is_vision_available() else None
def __lowerCAmelCase ( self : Any ) ->Optional[int]:
"""simple docstring"""
a = DonutImageProcessingTester(self )
@property
def __lowerCAmelCase ( self : str ) ->Optional[int]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCAmelCase ( self : Dict ) ->Optional[Any]:
"""simple docstring"""
a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__UpperCAmelCase , '''do_resize''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''size''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''do_thumbnail''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''do_align_long_axis''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''do_pad''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''image_mean''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''image_std''' ) )
def __lowerCAmelCase ( self : Tuple ) ->str:
"""simple docstring"""
a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 18, '''width''': 20} )
a = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
# Previous config had dimensions in (width, height) order
a = self.image_processing_class.from_dict(self.image_processor_dict , size=(42, 84) )
self.assertEqual(image_processor.size , {'''height''': 84, '''width''': 42} )
def __lowerCAmelCase ( self : List[str] ) ->Union[str, Any]:
"""simple docstring"""
pass
@is_flaky()
def __lowerCAmelCase ( self : Optional[int] ) ->Union[str, Any]:
"""simple docstring"""
a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , Image.Image )
# Test not batched input
a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
a = image_processing(__UpperCAmelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
@is_flaky()
def __lowerCAmelCase ( self : Dict ) ->str:
"""simple docstring"""
a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase , numpify=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , np.ndarray )
# Test not batched input
a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
a = image_processing(__UpperCAmelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
@is_flaky()
def __lowerCAmelCase ( self : Optional[int] ) ->str:
"""simple docstring"""
a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase , torchify=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
# Test not batched input
a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
a = image_processing(__UpperCAmelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
| 26 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , __UpperCAmelCase : UNetaDModel , __UpperCAmelCase : UNetaDModel , __UpperCAmelCase : DDPMScheduler , __UpperCAmelCase : Optional[int] , ) ->List[str]:
"""simple docstring"""
super().__init__()
a = value_function
a = unet
a = scheduler
a = env
a = env.get_dataset()
a = {}
for key in self.data.keys():
try:
a = self.data[key].mean()
except: # noqa: E722
pass
a = {}
for key in self.data.keys():
try:
a = self.data[key].std()
except: # noqa: E722
pass
a = env.observation_space.shape[0]
a = env.action_space.shape[0]
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[int] ) ->Dict:
"""simple docstring"""
return (x_in - self.means[key]) / self.stds[key]
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Dict , __UpperCAmelCase : Dict ) ->List[str]:
"""simple docstring"""
return x_in * self.stds[key] + self.means[key]
def __lowerCAmelCase ( self : int , __UpperCAmelCase : int ) ->List[str]:
"""simple docstring"""
if type(__UpperCAmelCase ) is dict:
return {k: self.to_torch(__UpperCAmelCase ) for k, v in x_in.items()}
elif torch.is_tensor(__UpperCAmelCase ):
return x_in.to(self.unet.device )
return torch.tensor(__UpperCAmelCase , device=self.unet.device )
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : int , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Tuple ) ->int:
"""simple docstring"""
for key, val in cond.items():
a = val.clone()
return x_in
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : str , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[Any] ) ->Tuple:
"""simple docstring"""
a = x.shape[0]
a = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
a = torch.full((batch_size,) , __UpperCAmelCase , device=self.unet.device , dtype=torch.long )
for _ in range(__UpperCAmelCase ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
a = self.value_function(x.permute(0 , 2 , 1 ) , __UpperCAmelCase ).sample
a = torch.autograd.grad([y.sum()] , [x] )[0]
a = self.scheduler._get_variance(__UpperCAmelCase )
a = torch.exp(0.5 * posterior_variance )
a = model_std * grad
a = 0
a = x.detach()
a = x + scale * grad
a = self.reset_xa(__UpperCAmelCase , __UpperCAmelCase , self.action_dim )
a = self.unet(x.permute(0 , 2 , 1 ) , __UpperCAmelCase ).sample.permute(0 , 2 , 1 )
# TODO: verify deprecation of this kwarg
a = self.scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , predict_epsilon=__UpperCAmelCase )['''prev_sample''']
# apply conditions to the trajectory (set the initial state)
a = self.reset_xa(__UpperCAmelCase , __UpperCAmelCase , self.action_dim )
a = self.to_torch(__UpperCAmelCase )
return x, y
def __call__( self : Union[str, Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : Optional[int]=64 , __UpperCAmelCase : int=32 , __UpperCAmelCase : Optional[Any]=2 , __UpperCAmelCase : str=0.1 ) ->List[str]:
"""simple docstring"""
a = self.normalize(__UpperCAmelCase , '''observations''' )
a = obs[None].repeat(__UpperCAmelCase , axis=0 )
a = {0: self.to_torch(__UpperCAmelCase )}
a = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
a = randn_tensor(__UpperCAmelCase , device=self.unet.device )
a = self.reset_xa(__UpperCAmelCase , __UpperCAmelCase , self.action_dim )
a = self.to_torch(__UpperCAmelCase )
# run the diffusion process
a , a = self.run_diffusion(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# sort output trajectories by value
a = y.argsort(0 , descending=__UpperCAmelCase ).squeeze()
a = x[sorted_idx]
a = sorted_values[:, :, : self.action_dim]
a = actions.detach().cpu().numpy()
a = self.de_normalize(__UpperCAmelCase , key='''actions''' )
# select the action with the highest value
if y is not None:
a = 0
else:
# if we didn't run value guiding, select a random action
a = np.random.randint(0 , __UpperCAmelCase )
a = denorm_actions[selected_index, 0]
return denorm_actions
| 26 | 1 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler
from transformers import GPTaLMHeadModel
def lowerCAmelCase (__A=32 , __A=10 , __A=100 , __A=1_026 , __A=True , __A="data/tokenized_stories_train_wikitext103.jbl" , __A="igf_context_pairs.jbl" , ):
"""simple docstring"""
set_seed(3)
# generate train_data and objective_set
_a , _a = generate_datasets(
__A , __A , number=__A , min_len=1_026 , trim=__A)
# keeps model same across runs
set_seed(4)
# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
# can we train on GPU?
_a = torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''')
# load pretrained model
_a = load_gpta('''gpt2''').to(__A)
print('''computing perplexity on objective set''')
_a = compute_perplexity(__A , __A , __A).item()
print('''perplexity on objective set:''' , __A)
# collect igf pairs and save to file demo.jbl
collect_objective_set(__A , __A , __A , __A , __A , __A , __A , __A)
# clean up, delete model and data we don't need anymore
del model, train_data, objective_set
torch.cuda.empty_cache()
def lowerCAmelCase (__A , __A=15 , __A=128 , __A=100 , __A="igf_model.pt" , ):
"""simple docstring"""
set_seed(42)
# Load pre-trained model
_a = GPTaLMHeadModel.from_pretrained('''gpt2''')
# Initialize secondary learner to use embedding weights of model
_a = SecondaryLearner(__A)
# Train secondary learner
_a = train_secondary_learner(
__A , __A , max_epochs=__A , batch_size=__A , eval_freq=100 , igf_model_path=__A , )
del model, secondary_learner_train_data
torch.cuda.empty_cache()
return secondary_learner
def lowerCAmelCase (__A , __A , __A , __A=32 , __A=1_000 , __A=16 , __A=1.0 , __A=recopy_gpta , __A=None , __A=10 , __A="gpt2_finetuned.pt" , ):
"""simple docstring"""
_a = torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''')
_a = RandomSampler(__A)
_a = DataLoader(__A , sampler=__A)
_a = max_steps // (len(__A)) + 1
_a = 0
_a = torch.zeros((1, context_len) , dtype=torch.long , device=__A)
_a , _a , _a = recopy_model(__A , __A , __A)
model.train()
if secondary_learner is not None:
secondary_learner.to(__A)
secondary_learner.eval()
_a = []
_a = 0
_a = []
_a = []
# Compute the performance of the transformer model at the beginning
_a = compute_perplexity(__A , __A , __A)
test_perps.append(__A)
print('''Test perplexity, step''' , __A , ''':''' , __A)
for epoch in range(int(__A)):
for step, example in enumerate(__A):
torch.cuda.empty_cache()
_a = random.randint(0 , example.size(2) - context_len - 1)
_a = example[0, 0, start : start + context_len]
lm_optimizer.zero_grad()
_a = model(__A , labels=__A)
_a = True
if secondary_learner is not None:
_a = secondary_learner.forward(
torch.tensor(__A , dtype=torch.long , device=__A).unsqueeze(0))[0].item()
observed_qs.append(float(__A))
# Here we implement the simple non-constant threshold for the predicted IG(X) value
# We will decay the selectivity of our secondary learner filter from
# 1 standard deviation above average to 1 below average after 10 batches.
if global_step == 10:
_a = -1
if predicted_q < threshold:
_a = False
# If we passed the filter, add the context to the batch!
if do_backprop:
contexts.append(np.array(context.cpu()))
_a = outputs[0]
lm_loss.backward()
examples += 1
del outputs
# Once the batch is filled with enough contexts, backprop on the batch.
if examples == batch_size:
torch.cuda.empty_cache()
_a = 0
# Do LM backprop
torch.nn.utils.clip_grad_norm_(model.parameters() , 3.0)
lm_optimizer.step()
lm_scheduler.step() # Update learning rate schedule
global_step += 1
# Compute the performance of the transformer model at this batch
if global_step % eval_interval == 0:
_a = compute_perplexity(__A , __A , __A)
test_perps.append(__A)
print('''Test perplexity, step''' , __A , ''':''' , __A)
# Break out of the loop after 60 batches
if max_steps > 0 and global_step > 60:
break
if max_steps > 0 and global_step > 60:
break
# save finetuned transformer model
torch.save(model.state_dict() , __A)
torch.cuda.empty_cache()
# Do some cleaning up so we can reinitialize for the next run of this function
del lm_optimizer
del lm_scheduler
return model
def lowerCAmelCase ():
"""simple docstring"""
_a = argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task''')
# Required parameters
parser.add_argument(
'''--data_dir''' , default=__A , type=__A , required=__A , help='''The input data dir. Should contain data files for WikiText.''' , )
parser.add_argument(
'''--model_name_or_path''' , default=__A , type=__A , required=__A , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--data_file''' , type=__A , default=__A , help=(
'''A jbl file containing tokenized data which can be split as objective dataset, '''
'''train_dataset and test_dataset.'''
) , )
parser.add_argument(
'''--igf_data_file''' , type=__A , default=__A , help='''A jbl file containing the context and information gain pairs to train secondary learner.''' , )
parser.add_argument(
'''--output_dir''' , default=__A , type=__A , required=__A , help='''The output directory where the final fine-tuned model is stored.''' , )
parser.add_argument(
'''--tokenizer_name''' , default=__A , type=__A , help='''Pretrained tokenizer name or path if not the same as model_name''' , )
parser.add_argument('''--seed''' , type=__A , default=__A , help='''A seed for reproducible training.''')
parser.add_argument(
'''--context_len''' , default=32 , type=__A , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--size_objective_set''' , default=100 , type=__A , help='''number of articles that are long enough to be used as our objective set''' , )
parser.add_argument(
'''--eval_freq''' , default=100 , type=__A , help='''secondary model evaluation is triggered at eval_freq''')
parser.add_argument('''--max_steps''' , default=1_000 , type=__A , help='''To calculate training epochs''')
parser.add_argument(
'''--secondary_learner_batch_size''' , default=128 , type=__A , help='''batch size of training data for secondary learner''' , )
parser.add_argument(
'''--batch_size''' , default=16 , type=__A , help='''batch size of training data of language model(gpt2) ''')
parser.add_argument(
'''--eval_interval''' , default=10 , type=__A , help=(
'''decay the selectivity of our secondary learner filter from'''
'''1 standard deviation above average to 1 below average after 10 batches'''
) , )
parser.add_argument(
'''--number''' , default=100 , type=__A , help='''The number of examples split to be used as objective_set/test_data''')
parser.add_argument(
'''--min_len''' , default=1_026 , type=__A , help='''The minimum length of the article to be used as objective set''')
parser.add_argument(
'''--secondary_learner_max_epochs''' , default=15 , type=__A , help='''number of epochs to train secondary learner''')
parser.add_argument('''--trim''' , default=__A , type=__A , help='''truncate the example if it exceeds context length''')
parser.add_argument(
'''--threshold''' , default=1.0 , type=__A , help=(
'''The threshold value used by secondary learner to filter the train_data and allow only'''
''' informative data as input to the model'''
) , )
parser.add_argument('''--finetuned_model_name''' , default='''gpt2_finetuned.pt''' , type=__A , help='''finetuned_model_name''')
parser.add_argument(
'''--recopy_model''' , default=__A , type=__A , help='''Reset the model to the original pretrained GPT-2 weights after each iteration''' , )
# function calls
# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
generate_n_pairs(
context_len=32 , max_steps=10 , size_objective_set=100 , min_len=1_026 , trim=__A , data_file='''data/tokenized_stories_train_wikitext103.jbl''' , igf_data_file='''igf_context_pairs.jbl''' , )
# Load train data for secondary learner
_a = joblib.load('''data/IGF_values.jbl''')
# Train secondary learner
_a = training_secondary_learner(
__A , secondary_learner_max_epochs=15 , secondary_learner_batch_size=128 , eval_freq=100 , igf_model_path='''igf_model.pt''' , )
# load pretrained gpt2 model
_a = GPTaLMHeadModel.from_pretrained('''gpt2''')
set_seed(42)
# Generate train and test data to train and evaluate gpt2 model
_a , _a = generate_datasets(
context_len=32 , file='''data/tokenized_stories_train_wikitext103.jbl''' , number=100 , min_len=1_026 , trim=__A)
# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
finetune(
__A , __A , __A , context_len=32 , max_steps=1_000 , batch_size=16 , threshold=1.0 , recopy_model=__A , secondary_learner=__A , eval_interval=10 , finetuned_model_name='''gpt2_finetuned.pt''' , )
if __name__ == "__main__":
main()
| 211 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
lowercase_ = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n"
lowercase_ = "\nArgs:\n predictions (`List[ndarray]`):\n List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n references (`List[ndarray]`):\n List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n num_labels (`int`):\n Number of classes (categories).\n ignore_index (`int`):\n Index that will be ignored during evaluation.\n nan_to_num (`int`, *optional*):\n If specified, NaN values will be replaced by the number defined by the user.\n label_map (`dict`, *optional*):\n If specified, dictionary mapping old label indices to new label indices.\n reduce_labels (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n `Dict[str, float | ndarray]` comprising various elements:\n - *mean_iou* (`float`):\n Mean Intersection-over-Union (IoU averaged over all categories).\n - *mean_accuracy* (`float`):\n Mean accuracy (averaged over all categories).\n - *overall_accuracy* (`float`):\n Overall accuracy on all images.\n - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n Per category accuracy.\n - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n Per category IoU.\n\nExamples:\n\n >>> import numpy as np\n\n >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n >>> # suppose one has 3 different segmentation maps predicted\n >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n >>> predicted = [predicted_1, predicted_2, predicted_3]\n >>> ground_truth = [actual_1, actual_2, actual_3]\n\n >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), 'per_category_accuracy': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}\n"
lowercase_ = "\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}"
def lowerCAmelCase (__A , __A , __A , __A , __A = None , __A = False , ):
"""simple docstring"""
if label_map is not None:
for old_id, new_id in label_map.items():
_a = new_id
# turn into Numpy arrays
_a = np.array(__A)
_a = np.array(__A)
if reduce_labels:
_a = 255
_a = label - 1
_a = 255
_a = label != ignore_index
_a = np.not_equal(__A , __A)
_a = pred_label[mask]
_a = np.array(__A)[mask]
_a = pred_label[pred_label == label]
_a = np.histogram(__A , bins=__A , range=(0, num_labels - 1))[0]
_a = np.histogram(__A , bins=__A , range=(0, num_labels - 1))[0]
_a = np.histogram(__A , bins=__A , range=(0, num_labels - 1))[0]
_a = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def lowerCAmelCase (__A , __A , __A , __A , __A = None , __A = False , ):
"""simple docstring"""
_a = np.zeros((num_labels,) , dtype=np.floataa)
_a = np.zeros((num_labels,) , dtype=np.floataa)
_a = np.zeros((num_labels,) , dtype=np.floataa)
_a = np.zeros((num_labels,) , dtype=np.floataa)
for result, gt_seg_map in zip(__A , __A):
_a , _a , _a , _a = intersect_and_union(
__A , __A , __A , __A , __A , __A)
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def lowerCAmelCase (__A , __A , __A , __A , __A = None , __A = None , __A = False , ):
"""simple docstring"""
_a , _a , _a , _a = total_intersect_and_union(
__A , __A , __A , __A , __A , __A)
# compute metrics
_a = {}
_a = total_area_intersect.sum() / total_area_label.sum()
_a = total_area_intersect / total_area_union
_a = total_area_intersect / total_area_label
_a = np.nanmean(__A)
_a = np.nanmean(__A)
_a = all_acc
_a = iou
_a = acc
if nan_to_num is not None:
_a = {metric: np.nan_to_num(__A , nan=__A) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
'''simple docstring'''
def a__ (self ) -> List[str]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
'''predictions''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16''' ) ) ),
'''references''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16''' ) ) ),
} ) , reference_urls=[
'''https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'''
] , )
def a__ (self , A , A , A , A , A = None , A = None , A = False , ) -> List[Any]:
"""simple docstring"""
_a = mean_iou(
results=A , gt_seg_maps=A , num_labels=A , ignore_index=A , nan_to_num=A , label_map=A , reduce_labels=A , )
return iou_result
| 211 | 1 |
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
lowerCamelCase__ = parse(importlib.metadata.version('''torch'''))
def A(__a: Union[str, Version] , __a: str , __a: str ):
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(F"`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys() )}, received {operation}" )
lowerCAmelCase_ = STR_OPERATION_TO_FUNC[operation]
if isinstance(__a , __a ):
lowerCAmelCase_ = parse(importlib.metadata.version(__a ) )
return operation(__a , parse(__a ) )
def A(__a: str , __a: str ):
return compare_versions(__a , __a , __a )
| 22 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
lowerCamelCase__ = '''bert-base-cased'''
lowerCamelCase__ = '''google/pegasus-xsum'''
lowerCamelCase__ = [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowerCamelCase__ = ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowerCamelCase__ = '''patrickvonplaten/t5-tiny-random'''
lowerCamelCase__ = '''sshleifer/bart-tiny-random'''
lowerCamelCase__ = '''sshleifer/tiny-mbart'''
lowerCamelCase__ = '''sshleifer/tiny-marian-en-de'''
def A(__a: Path , __a: list ):
lowerCAmelCase_ = "\n".join(__a )
Path(__a ).open("w" ).writelines(__a )
def A(__a: str ):
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(__a , F"{split}.source" ) , __a )
_dump_articles(os.path.join(__a , F"{split}.target" ) , __a )
return tmp_dir
class __magic_name__ (__lowercase ):
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def __a ( self , _a ) -> Dict:
lowerCAmelCase_ = AutoTokenizer.from_pretrained(_a )
lowerCAmelCase_ = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
lowerCAmelCase_ = max(len(tokenizer.encode(_a ) ) for a in ARTICLES )
lowerCAmelCase_ = max(len(tokenizer.encode(_a ) ) for a in SUMMARIES )
lowerCAmelCase_ = 4
lowerCAmelCase_ = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
lowerCAmelCase_ , lowerCAmelCase_ = "ro_RO", "de_DE" # ignored for all but mbart, but never causes error.
lowerCAmelCase_ = SeqaSeqDataset(
_a , data_dir=_a , type_path="train" , max_source_length=_a , max_target_length=_a , src_lang=_a , tgt_lang=_a , )
lowerCAmelCase_ = DataLoader(_a , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert isinstance(_a , _a )
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
lowerCAmelCase_ = shift_tokens_right(batch["labels"] , tokenizer.pad_token_id )
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
def __a ( self , _a ) -> str:
lowerCAmelCase_ = AutoTokenizer.from_pretrained(_a )
lowerCAmelCase_ = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
lowerCAmelCase_ = max(len(tokenizer.encode(_a ) ) for a in ARTICLES )
lowerCAmelCase_ = max(len(tokenizer.encode(_a ) ) for a in SUMMARIES )
lowerCAmelCase_ = 4
lowerCAmelCase_ = LegacySeqaSeqDataset(
_a , data_dir=_a , type_path="train" , max_source_length=20 , max_target_length=_a , )
lowerCAmelCase_ = DataLoader(_a , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 20 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def __a ( self ) -> Union[str, Any]:
lowerCAmelCase_ = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25" )
lowerCAmelCase_ = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
lowerCAmelCase_ = tmp_dir.joinpath("train.source" ).open().readlines()
lowerCAmelCase_ = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(_a , _a , 128 , _a )
lowerCAmelCase_ = {x.name for x in tmp_dir.iterdir()}
lowerCAmelCase_ = {x.name for x in save_dir.iterdir()}
lowerCAmelCase_ = save_dir.joinpath("train.source" ).open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(_a ) < len(_a )
assert len(_a ) == 1
assert len(packed_examples[0] ) == sum(len(_a ) for x in orig_examples )
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE , reason="This test requires fairseq" )
def __a ( self ) -> Any:
if not FAIRSEQ_AVAILABLE:
return
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = self._get_dataset(max_len=64 )
lowerCAmelCase_ = 64
lowerCAmelCase_ = ds.make_dynamic_sampler(_a , required_batch_size_multiple=_a )
lowerCAmelCase_ = [len(_a ) for x in batch_sampler]
assert len(set(_a ) ) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(_a ) == len(_a ) # no dropped or added examples
lowerCAmelCase_ = DataLoader(_a , batch_sampler=_a , collate_fn=ds.collate_fn , num_workers=2 )
lowerCAmelCase_ = []
lowerCAmelCase_ = []
for batch in data_loader:
lowerCAmelCase_ = batch["input_ids"].shape
lowerCAmelCase_ = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
lowerCAmelCase_ = np.product(batch["input_ids"].shape )
num_src_per_batch.append(_a )
if num_src_tokens > (max_tokens * 1.1):
failures.append(_a )
assert num_src_per_batch[0] == max(_a )
if failures:
raise AssertionError(f"too many tokens in {len(_a )} batches" )
def __a ( self ) -> List[str]:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = self._get_dataset(max_len=512 )
lowerCAmelCase_ = 2
lowerCAmelCase_ = ds.make_sortish_sampler(_a , shuffle=_a )
lowerCAmelCase_ = DataLoader(_a , batch_size=_a , collate_fn=ds.collate_fn , num_workers=2 )
lowerCAmelCase_ = DataLoader(_a , batch_size=_a , collate_fn=ds.collate_fn , num_workers=2 , sampler=_a )
lowerCAmelCase_ = tokenizer.pad_token_id
def count_pad_tokens(_a , _a="input_ids" ):
return [batch[k].eq(_a ).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(_a , k="labels" ) ) < sum(count_pad_tokens(_a , k="labels" ) )
assert sum(count_pad_tokens(_a ) ) < sum(count_pad_tokens(_a ) )
assert len(_a ) == len(_a )
def __a ( self , _a=1000 , _a=128 ) -> str:
if os.getenv("USE_REAL_DATA" , _a ):
lowerCAmelCase_ = "examples/seq2seq/wmt_en_ro"
lowerCAmelCase_ = max_len * 2 * 64
if not Path(_a ).joinpath("train.len" ).exists():
save_len_file(_a , _a )
else:
lowerCAmelCase_ = "examples/seq2seq/test_data/wmt_en_ro"
lowerCAmelCase_ = max_len * 4
save_len_file(_a , _a )
lowerCAmelCase_ = AutoTokenizer.from_pretrained(_a )
lowerCAmelCase_ = SeqaSeqDataset(
_a , data_dir=_a , type_path="train" , max_source_length=_a , max_target_length=_a , n_obs=_a , )
return ds, max_tokens, tokenizer
def __a ( self ) -> Union[str, Any]:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = self._get_dataset()
lowerCAmelCase_ = set(DistributedSortishSampler(_a , 256 , num_replicas=2 , rank=0 , add_extra_examples=_a ) )
lowerCAmelCase_ = set(DistributedSortishSampler(_a , 256 , num_replicas=2 , rank=1 , add_extra_examples=_a ) )
assert idsa.intersection(_a ) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
def __a ( self , _a ) -> List[str]:
lowerCAmelCase_ = AutoTokenizer.from_pretrained(_a , use_fast=_a )
if tok_name == MBART_TINY:
lowerCAmelCase_ = SeqaSeqDataset(
_a , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path="train" , max_source_length=4 , max_target_length=8 , src_lang="EN" , tgt_lang="FR" , )
lowerCAmelCase_ = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
lowerCAmelCase_ = SeqaSeqDataset(
_a , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path="train" , max_source_length=4 , max_target_length=8 , )
lowerCAmelCase_ = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(_a ) == 1 if tok_name == BART_TINY else len(_a ) == 0
| 22 | 1 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class _lowerCAmelCase ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = None ) -> str:
super().__init__()
A_ : Optional[Any] = pad_token_id
A_ : List[Any] = max_length
A_ : str = vocab
A_ : Union[str, Any] = merges
A_ : List[Any] = BytePairTokenizer(_lowerCamelCase , _lowerCamelCase , sequence_length=_lowerCamelCase )
@classmethod
def UpperCAmelCase_ ( cls , _lowerCamelCase , *_lowerCamelCase , **_lowerCamelCase ) -> int:
A_ : Tuple = [""" """.join(_lowerCamelCase ) for m in tokenizer.bpe_ranks.keys()]
A_ : Dict = tokenizer.get_vocab()
return cls(_lowerCamelCase , _lowerCamelCase , *_lowerCamelCase , **_lowerCamelCase )
@classmethod
def UpperCAmelCase_ ( cls , _lowerCamelCase , *_lowerCamelCase , **_lowerCamelCase ) -> str:
A_ : Tuple = GPTaTokenizer.from_pretrained(_lowerCamelCase , *_lowerCamelCase , **_lowerCamelCase )
return cls.from_tokenizer(_lowerCamelCase , *_lowerCamelCase , **_lowerCamelCase )
@classmethod
def UpperCAmelCase_ ( cls , _lowerCamelCase ) -> List[Any]:
return cls(**_lowerCamelCase )
def UpperCAmelCase_ ( self ) -> Optional[Any]:
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase = None ) -> Any:
A_ : List[Any] = self.tf_tokenizer(_lowerCamelCase )
A_ : Any = tf.ones_like(_lowerCamelCase )
if self.pad_token_id is not None:
# pad the tokens up to max length
A_ : List[Any] = max_length if max_length is not None else self.max_length
if max_length is not None:
A_ , A_ : Tuple = pad_model_inputs(
_lowerCamelCase , max_seq_length=_lowerCamelCase , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 344 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
UpperCamelCase__ : Optional[int] = {
'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/config.json',
'distilbert-base-uncased-distilled-squad': (
'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'
),
'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/config.json',
'distilbert-base-cased-distilled-squad': (
'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'
),
'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json',
'distilbert-base-multilingual-cased': (
'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'
),
'distilbert-base-uncased-finetuned-sst-2-english': (
'https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'
),
}
class _lowerCAmelCase ( __A ):
"""simple docstring"""
lowerCamelCase = '''distilbert'''
lowerCamelCase = {
'''hidden_size''': '''dim''',
'''num_attention_heads''': '''n_heads''',
'''num_hidden_layers''': '''n_layers''',
}
def __init__( self , _lowerCamelCase=3_0522 , _lowerCamelCase=512 , _lowerCamelCase=False , _lowerCamelCase=6 , _lowerCamelCase=12 , _lowerCamelCase=768 , _lowerCamelCase=4 * 768 , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase="gelu" , _lowerCamelCase=0.02 , _lowerCamelCase=0.1 , _lowerCamelCase=0.2 , _lowerCamelCase=0 , **_lowerCamelCase , ) -> Optional[Any]:
A_ : Tuple = vocab_size
A_ : List[Any] = max_position_embeddings
A_ : int = sinusoidal_pos_embds
A_ : int = n_layers
A_ : str = n_heads
A_ : Optional[int] = dim
A_ : int = hidden_dim
A_ : Tuple = dropout
A_ : List[Any] = attention_dropout
A_ : int = activation
A_ : Dict = initializer_range
A_ : List[Any] = qa_dropout
A_ : int = seq_classif_dropout
super().__init__(**_lowerCamelCase , pad_token_id=_lowerCamelCase )
class _lowerCAmelCase ( __A ):
"""simple docstring"""
@property
def UpperCAmelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
A_ : Union[str, Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
A_ : int = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 344 | 1 |
from math import sqrt
def a_ ( __snake_case : int ) -> bool:
"""simple docstring"""
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(sqrt(__snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def a_ ( __snake_case : int = 1_0001 ) -> int:
"""simple docstring"""
lowerCamelCase_ =0
lowerCamelCase_ =1
while count != nth and number < 3:
number += 1
if is_prime(__snake_case ):
count += 1
while count != nth:
number += 2
if is_prime(__snake_case ):
count += 1
return number
if __name__ == "__main__":
print(F"""{solution() = }""")
| 366 |
'''simple docstring'''
from itertools import product
def a_ ( __snake_case : int , __snake_case : int ) -> list[int]:
"""simple docstring"""
lowerCamelCase_ =sides_number
lowerCamelCase_ =max_face_number * dice_number
lowerCamelCase_ =[0] * (max_total + 1)
lowerCamelCase_ =1
lowerCamelCase_ =range(__snake_case , max_face_number + 1 )
for dice_numbers in product(__snake_case , repeat=__snake_case ):
lowerCamelCase_ =sum(__snake_case )
totals_frequencies[total] += 1
return totals_frequencies
def a_ ( ) -> float:
"""simple docstring"""
lowerCamelCase_ =total_frequency_distribution(
sides_number=4 , dice_number=9 )
lowerCamelCase_ =total_frequency_distribution(
sides_number=6 , dice_number=6 )
lowerCamelCase_ =0
lowerCamelCase_ =9
lowerCamelCase_ =4 * 9
lowerCamelCase_ =6
for peter_total in range(__snake_case , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
lowerCamelCase_ =(4**9) * (6**6)
lowerCamelCase_ =peter_wins_count / total_games_number
lowerCamelCase_ =round(__snake_case , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(F"""{solution() = }""")
| 6 | 0 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
lowercase__ : List[str] = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
lowerCAmelCase_ = ['''input_features''']
def __init__( self : Optional[Any] , __lowercase : Optional[int]=80 , __lowercase : Any=1_60_00 , __lowercase : Any=1_60 , __lowercase : Dict=30 , __lowercase : Union[str, Any]=4_00 , __lowercase : Tuple=0.0 , __lowercase : int=False , **__lowercase : int , ):
"""simple docstring"""
super().__init__(
feature_size=__lowercase , sampling_rate=__lowercase , padding_value=__lowercase , return_attention_mask=__lowercase , **__lowercase , )
snake_case_ = n_fft
snake_case_ = hop_length
snake_case_ = chunk_length
snake_case_ = chunk_length * sampling_rate
snake_case_ = self.n_samples // hop_length
snake_case_ = sampling_rate
snake_case_ = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__lowercase , min_frequency=0.0 , max_frequency=8000.0 , sampling_rate=__lowercase , norm="slaney" , mel_scale="slaney" , )
def snake_case__ ( self : Union[str, Any] , __lowercase : np.array ):
"""simple docstring"""
snake_case_ = spectrogram(
__lowercase , window_function(self.n_fft , "hann" ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters , log_mel="log10" , )
snake_case_ = log_spec[:, :-1]
snake_case_ = np.maximum(__lowercase , log_spec.max() - 8.0 )
snake_case_ = (log_spec + 4.0) / 4.0
return log_spec
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def snake_case__ ( __lowercase : List[np.ndarray] , __lowercase : List[np.ndarray] , __lowercase : float = 0.0 ):
"""simple docstring"""
if attention_mask is not None:
snake_case_ = np.array(__lowercase , np.intaa )
snake_case_ = []
for vector, length in zip(__lowercase , attention_mask.sum(-1 ) ):
snake_case_ = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
snake_case_ = padding_value
normed_input_values.append(__lowercase )
else:
snake_case_ = [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def __call__( self : str , __lowercase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __lowercase : bool = True , __lowercase : Optional[int] = None , __lowercase : Optional[Union[str, TensorType]] = None , __lowercase : Optional[bool] = None , __lowercase : Optional[str] = "max_length" , __lowercase : Optional[int] = None , __lowercase : Optional[int] = None , __lowercase : Optional[bool] = None , **__lowercase : Optional[Any] , ):
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"The model corresponding to this feature extractor: {self.__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." )
snake_case_ = isinstance(__lowercase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"Only mono-channel audio is supported for input to {self}" )
snake_case_ = is_batched_numpy or (
isinstance(__lowercase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
snake_case_ = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__lowercase , np.ndarray ):
snake_case_ = np.asarray(__lowercase , dtype=np.floataa )
elif isinstance(__lowercase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
snake_case_ = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
snake_case_ = [np.asarray([raw_speech] ).T]
snake_case_ = BatchFeature({"input_features": raw_speech} )
# convert into correct format for padding
snake_case_ = self.pad(
__lowercase , padding=__lowercase , max_length=max_length if max_length else self.n_samples , truncation=__lowercase , pad_to_multiple_of=__lowercase , return_attention_mask=return_attention_mask or do_normalize , )
# zero-mean and unit-variance normalization
if do_normalize:
snake_case_ = self.zero_mean_unit_var_norm(
padded_inputs["input_features"] , attention_mask=padded_inputs["attention_mask"] , padding_value=self.padding_value , )
snake_case_ = np.stack(padded_inputs["input_features"] , axis=0 )
# make sure list is in array format
snake_case_ = padded_inputs.get("input_features" ).transpose(2 , 0 , 1 )
snake_case_ = [self._np_extract_fbank_features(__lowercase ) for waveform in input_features[0]]
if isinstance(input_features[0] , __lowercase ):
snake_case_ = [np.asarray(__lowercase , dtype=np.floataa ) for feature in input_features]
else:
snake_case_ = input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
snake_case_ = padded_inputs["attention_mask"][:, :: self.hop_length]
if return_tensors is not None:
snake_case_ = padded_inputs.convert_to_tensors(__lowercase )
return padded_inputs
def snake_case__ ( self : Union[str, Any] ):
"""simple docstring"""
snake_case_ = copy.deepcopy(self.__dict__ )
snake_case_ = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 187 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = CodeGenTokenizer
lowerCAmelCase_ = CodeGenTokenizerFast
lowerCAmelCase_ = True
lowerCAmelCase_ = {'''add_prefix_space''': True}
lowerCAmelCase_ = False
def snake_case__ ( self : List[Any] ):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case_ = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
snake_case_ = dict(zip(__lowercase , range(len(__lowercase ) ) ) )
snake_case_ = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
snake_case_ = {"unk_token": "<unk>"}
snake_case_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
snake_case_ = 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(__lowercase ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(__lowercase ) )
def snake_case__ ( self : Union[str, Any] , **__lowercase : List[str] ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **__lowercase )
def snake_case__ ( self : Optional[Any] , **__lowercase : Union[str, Any] ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **__lowercase )
def snake_case__ ( self : Optional[int] , __lowercase : List[str] ):
"""simple docstring"""
snake_case_ = "lower newer"
snake_case_ = "lower newer"
return input_text, output_text
def snake_case__ ( self : Optional[Any] ):
"""simple docstring"""
snake_case_ = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case_ = "lower newer"
snake_case_ = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
snake_case_ = tokenizer.tokenize(__lowercase , add_prefix_space=__lowercase )
self.assertListEqual(__lowercase , __lowercase )
snake_case_ = tokens + [tokenizer.unk_token]
snake_case_ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase ) , __lowercase )
def snake_case__ ( self : Optional[int] ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
snake_case_ = self.get_tokenizer()
snake_case_ = self.get_rust_tokenizer(add_prefix_space=__lowercase )
snake_case_ = "lower newer"
# Testing tokenization
snake_case_ = tokenizer.tokenize(__lowercase , add_prefix_space=__lowercase )
snake_case_ = rust_tokenizer.tokenize(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
# Testing conversion to ids without special tokens
snake_case_ = tokenizer.encode(__lowercase , add_special_tokens=__lowercase , add_prefix_space=__lowercase )
snake_case_ = rust_tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
self.assertListEqual(__lowercase , __lowercase )
# Testing conversion to ids with special tokens
snake_case_ = self.get_rust_tokenizer(add_prefix_space=__lowercase )
snake_case_ = tokenizer.encode(__lowercase , add_prefix_space=__lowercase )
snake_case_ = rust_tokenizer.encode(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
# Testing the unknown token
snake_case_ = tokens + [rust_tokenizer.unk_token]
snake_case_ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__lowercase ) , __lowercase )
def snake_case__ ( self : Any , *__lowercase : Union[str, Any] , **__lowercase : Tuple ):
"""simple docstring"""
pass
def snake_case__ ( self : int , __lowercase : str=15 ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
snake_case_ = self.rust_tokenizer_class.from_pretrained(__lowercase , **__lowercase )
# Simple input
snake_case_ = "This is a simple input"
snake_case_ = ["This is a simple input 1", "This is a simple input 2"]
snake_case_ = ("This is a simple input", "This is a pair")
snake_case_ = [
("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(__lowercase , tokenizer_r.encode , __lowercase , max_length=__lowercase , padding="max_length" )
# Simple input
self.assertRaises(__lowercase , tokenizer_r.encode_plus , __lowercase , max_length=__lowercase , padding="max_length" )
# Simple input
self.assertRaises(
__lowercase , tokenizer_r.batch_encode_plus , __lowercase , max_length=__lowercase , padding="max_length" , )
# Pair input
self.assertRaises(__lowercase , tokenizer_r.encode , __lowercase , max_length=__lowercase , padding="max_length" )
# Pair input
self.assertRaises(__lowercase , tokenizer_r.encode_plus , __lowercase , max_length=__lowercase , padding="max_length" )
# Pair input
self.assertRaises(
__lowercase , tokenizer_r.batch_encode_plus , __lowercase , max_length=__lowercase , padding="max_length" , )
def snake_case__ ( self : str ):
"""simple docstring"""
snake_case_ = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token="<pad>" )
# Simple input
snake_case_ = "This is a simple input"
snake_case_ = ["This is a simple input looooooooong", "This is a simple input"]
snake_case_ = ("This is a simple input", "This is a pair")
snake_case_ = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
snake_case_ = tokenizer.pad_token_id
snake_case_ = tokenizer(__lowercase , padding="max_length" , max_length=30 , return_tensors="np" )
snake_case_ = tokenizer(__lowercase , padding=__lowercase , truncate=__lowercase , return_tensors="np" )
snake_case_ = tokenizer(*__lowercase , padding="max_length" , max_length=60 , return_tensors="np" )
snake_case_ = tokenizer(__lowercase , padding=__lowercase , truncate=__lowercase , 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 snake_case__ ( self : Tuple ):
"""simple docstring"""
snake_case_ = "$$$"
snake_case_ = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=__lowercase , add_bos_token=__lowercase )
snake_case_ = "This is a simple input"
snake_case_ = ["This is a simple input 1", "This is a simple input 2"]
snake_case_ = tokenizer.bos_token_id
snake_case_ = tokenizer(__lowercase )
snake_case_ = tokenizer(__lowercase )
self.assertEqual(out_s.input_ids[0] , __lowercase )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
snake_case_ = tokenizer.decode(out_s.input_ids )
snake_case_ = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , __lowercase )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def snake_case__ ( self : Tuple ):
"""simple docstring"""
snake_case_ = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" )
snake_case_ = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
snake_case_ = "\nif len_a > len_b: result = a\nelse: result = b"
snake_case_ = tokenizer.encode(__lowercase )
snake_case_ = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"]
snake_case_ = tokenizer.decode(__lowercase , truncate_before_pattern=__lowercase )
self.assertEqual(__lowercase , __lowercase )
def snake_case__ ( self : Dict ):
"""simple docstring"""
pass
| 187 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class a ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = (DPMSolverSinglestepScheduler,)
UpperCAmelCase = (("num_inference_steps", 2_5),)
def UpperCamelCase ( self: Dict , **UpperCamelCase: Tuple ):
"""simple docstring"""
A__ = {
"""num_train_timesteps""": 10_00,
"""beta_start""": 0.0_001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""solver_order""": 2,
"""prediction_type""": """epsilon""",
"""thresholding""": False,
"""sample_max_value""": 1.0,
"""algorithm_type""": """dpmsolver++""",
"""solver_type""": """midpoint""",
"""lambda_min_clipped""": -float("""inf""" ),
"""variance_type""": None,
}
config.update(**UpperCamelCase )
return config
def UpperCamelCase ( self: int , UpperCamelCase: Optional[Any]=0 , **UpperCamelCase: Union[str, Any] ):
"""simple docstring"""
A__ = dict(self.forward_default_kwargs )
A__ = kwargs.pop("""num_inference_steps""" , UpperCamelCase )
A__ = self.dummy_sample
A__ = 0.1 * sample
A__ = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
A__ = self.get_scheduler_config(**UpperCamelCase )
A__ = scheduler_class(**UpperCamelCase )
scheduler.set_timesteps(UpperCamelCase )
# copy over dummy past residuals
A__ = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCamelCase )
A__ = scheduler_class.from_pretrained(UpperCamelCase )
new_scheduler.set_timesteps(UpperCamelCase )
# copy over dummy past residuals
A__ = dummy_past_residuals[: new_scheduler.config.solver_order]
A__ , A__ = sample, sample
for t in range(UpperCamelCase , time_step + scheduler.config.solver_order + 1 ):
A__ = scheduler.step(UpperCamelCase , UpperCamelCase , UpperCamelCase , **UpperCamelCase ).prev_sample
A__ = new_scheduler.step(UpperCamelCase , UpperCamelCase , UpperCamelCase , **UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def UpperCamelCase ( self: str ):
"""simple docstring"""
pass
def UpperCamelCase ( self: int , UpperCamelCase: Optional[int]=0 , **UpperCamelCase: Optional[Any] ):
"""simple docstring"""
A__ = dict(self.forward_default_kwargs )
A__ = kwargs.pop("""num_inference_steps""" , UpperCamelCase )
A__ = self.dummy_sample
A__ = 0.1 * sample
A__ = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
A__ = self.get_scheduler_config()
A__ = scheduler_class(**UpperCamelCase )
scheduler.set_timesteps(UpperCamelCase )
# copy over dummy past residuals (must be after setting timesteps)
A__ = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCamelCase )
A__ = scheduler_class.from_pretrained(UpperCamelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(UpperCamelCase )
# copy over dummy past residual (must be after setting timesteps)
A__ = dummy_past_residuals[: new_scheduler.config.solver_order]
A__ = scheduler.step(UpperCamelCase , UpperCamelCase , UpperCamelCase , **UpperCamelCase ).prev_sample
A__ = new_scheduler.step(UpperCamelCase , UpperCamelCase , UpperCamelCase , **UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def UpperCamelCase ( self: str , UpperCamelCase: Optional[Any]=None , **UpperCamelCase: int ):
"""simple docstring"""
if scheduler is None:
A__ = self.scheduler_classes[0]
A__ = self.get_scheduler_config(**UpperCamelCase )
A__ = scheduler_class(**UpperCamelCase )
A__ = self.scheduler_classes[0]
A__ = self.get_scheduler_config(**UpperCamelCase )
A__ = scheduler_class(**UpperCamelCase )
A__ = 10
A__ = self.dummy_model()
A__ = self.dummy_sample_deter
scheduler.set_timesteps(UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
A__ = model(UpperCamelCase , UpperCamelCase )
A__ = scheduler.step(UpperCamelCase , UpperCamelCase , UpperCamelCase ).prev_sample
return sample
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
A__ = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
A__ = 50
A__ = self.dummy_model()
A__ = self.dummy_sample_deter
scheduler.set_timesteps(UpperCamelCase )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
A__ = model(UpperCamelCase , UpperCamelCase )
A__ = scheduler.step(UpperCamelCase , UpperCamelCase , UpperCamelCase ).prev_sample
A__ = torch.mean(torch.abs(UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_574 ) < 1e-3
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
for timesteps in [25, 50, 1_00, 9_99, 10_00]:
self.check_over_configs(num_train_timesteps=UpperCamelCase )
def UpperCamelCase ( self: Dict ):
"""simple docstring"""
A__ = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
A__ = self.full_loop(scheduler=UpperCamelCase )
A__ = torch.mean(torch.abs(UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_791 ) < 1e-3
A__ = DEISMultistepScheduler.from_config(scheduler.config )
A__ = DPMSolverMultistepScheduler.from_config(scheduler.config )
A__ = UniPCMultistepScheduler.from_config(scheduler.config )
A__ = DPMSolverSinglestepScheduler.from_config(scheduler.config )
A__ = self.full_loop(scheduler=UpperCamelCase )
A__ = torch.mean(torch.abs(UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_791 ) < 1e-3
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
self.check_over_configs(thresholding=UpperCamelCase )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=UpperCamelCase , prediction_type=UpperCamelCase , sample_max_value=UpperCamelCase , algorithm_type="""dpmsolver++""" , solver_order=UpperCamelCase , solver_type=UpperCamelCase , )
def UpperCamelCase ( self: Any ):
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCamelCase )
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=UpperCamelCase , solver_type=UpperCamelCase , prediction_type=UpperCamelCase , algorithm_type=UpperCamelCase , )
A__ = self.full_loop(
solver_order=UpperCamelCase , solver_type=UpperCamelCase , prediction_type=UpperCamelCase , algorithm_type=UpperCamelCase , )
assert not torch.isnan(UpperCamelCase ).any(), "Samples have nan numbers"
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
self.check_over_configs(lower_order_final=UpperCamelCase )
self.check_over_configs(lower_order_final=UpperCamelCase )
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
self.check_over_configs(lambda_min_clipped=-float("""inf""" ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
self.check_over_configs(variance_type=UpperCamelCase )
self.check_over_configs(variance_type="""learned_range""" )
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
self.check_over_forward(num_inference_steps=UpperCamelCase , time_step=0 )
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
A__ = self.full_loop()
A__ = torch.mean(torch.abs(UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_791 ) < 1e-3
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
A__ = self.full_loop(use_karras_sigmas=UpperCamelCase )
A__ = torch.mean(torch.abs(UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_248 ) < 1e-3
def UpperCamelCase ( self: Any ):
"""simple docstring"""
A__ = self.full_loop(prediction_type="""v_prediction""" )
A__ = torch.mean(torch.abs(UpperCamelCase ) )
assert abs(result_mean.item() - 0.1_453 ) < 1e-3
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ = self.full_loop(prediction_type="""v_prediction""" , use_karras_sigmas=UpperCamelCase )
A__ = torch.mean(torch.abs(UpperCamelCase ) )
assert abs(result_mean.item() - 0.0_649 ) < 1e-3
def UpperCamelCase ( self: Any ):
"""simple docstring"""
A__ = self.scheduler_classes[0]
A__ = self.get_scheduler_config(thresholding=UpperCamelCase , dynamic_thresholding_ratio=0 )
A__ = scheduler_class(**UpperCamelCase )
A__ = 10
A__ = self.dummy_model()
A__ = self.dummy_sample_deter.half()
scheduler.set_timesteps(UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
A__ = model(UpperCamelCase , UpperCamelCase )
A__ = scheduler.step(UpperCamelCase , UpperCamelCase , UpperCamelCase ).prev_sample
assert sample.dtype == torch.floataa
| 69 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE_ : Union[str, Any] = get_tests_dir('fixtures/test_sentencepiece_bpe.model')
class a ( _lowerCamelCase, unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = BartphoTokenizer
UpperCAmelCase = False
UpperCAmelCase = True
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
super().setUp()
A__ = ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]
A__ = dict(zip(UpperCamelCase , range(len(UpperCamelCase ) ) ) )
A__ = {"""unk_token""": """<unk>"""}
A__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""monolingual_vocab_file"""] )
with open(self.monolingual_vocab_file , """w""" , encoding="""utf-8""" ) as fp:
for token in vocab_tokens:
fp.write(f"""{token} {vocab_tokens[token]}\n""" )
A__ = BartphoTokenizer(UpperCamelCase , self.monolingual_vocab_file , **self.special_tokens_map )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase ( self: Dict , **UpperCamelCase: Tuple ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return BartphoTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase )
def UpperCamelCase ( self: Union[str, Any] , UpperCamelCase: Tuple ):
"""simple docstring"""
A__ = """This is a là test"""
A__ = """This is a<unk><unk> test"""
return input_text, output_text
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
A__ = BartphoTokenizer(UpperCamelCase , self.monolingual_vocab_file , **self.special_tokens_map )
A__ = """This is a là test"""
A__ = """▁This ▁is ▁a ▁l à ▁t est""".split()
A__ = tokenizer.tokenize(UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
A__ = tokens + [tokenizer.unk_token]
A__ = [4, 5, 6, 3, 3, 7, 8, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase ) , UpperCamelCase )
| 69 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pi
def _SCREAMING_SNAKE_CASE ( _lowercase : float , _lowercase : float , _lowercase : float ) ->dict[str, float]:
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if inductance < 0:
raise ValueError("Inductance cannot be negative" )
if frequency < 0:
raise ValueError("Frequency cannot be negative" )
if reactance < 0:
raise ValueError("Inductive reactance cannot be negative" )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 105 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
a : List[str] = logging.get_logger(__name__)
a : List[Any] = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
a : str = {
'''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'''
},
}
a : Tuple = {'''allegro/herbert-base-cased''': 514}
a : Optional[int] = {}
class __UpperCamelCase ( a__ ):
lowerCamelCase : str =VOCAB_FILES_NAMES
lowerCamelCase : Dict =PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase : Dict =PRETRAINED_INIT_CONFIGURATION
lowerCamelCase : int =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase : Optional[Any] =HerbertTokenizer
def __init__( self , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__="<s>" , lowerCAmelCase__="<unk>" , lowerCAmelCase__="<pad>" , lowerCAmelCase__="<mask>" , lowerCAmelCase__="</s>" , **lowerCAmelCase__ , ) -> Optional[int]:
super().__init__(
lowerCAmelCase__ , lowerCAmelCase__ , tokenizer_file=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , **lowerCAmelCase__ , )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> List[int]:
a : Optional[Any] = [self.cls_token_id]
a : Any = [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 __a ( self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase__ , token_ids_a=lowerCAmelCase__ , already_has_special_tokens=lowerCAmelCase__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCAmelCase__ )) + [1]
return [1] + ([0] * len(lowerCAmelCase__ )) + [1] + ([0] * len(lowerCAmelCase__ )) + [1]
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> List[int]:
a : Dict = [self.sep_token_id]
a : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> Tuple[str]:
a : List[str] = self._tokenizer.model.save(lowerCAmelCase__ , name=lowerCAmelCase__ )
return tuple(lowerCAmelCase__ )
| 105 | 1 |
def lowerCamelCase__ ( UpperCamelCase__ : List[Any] ) -> Any:
'''simple docstring'''
_snake_case = []
_snake_case = set({'(', '[', '{'} )
_snake_case = set({')', ']', '}'} )
_snake_case = {"{": "}", "[": "]", "(": ")"}
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(_SCREAMING_SNAKE_CASE ) == 0 or (len(_SCREAMING_SNAKE_CASE ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(_SCREAMING_SNAKE_CASE ) == 0
def lowerCamelCase__ ( ) -> Optional[int]:
'''simple docstring'''
_snake_case = input('Enter sequence of brackets: ' )
if is_balanced(_SCREAMING_SNAKE_CASE ):
print(_SCREAMING_SNAKE_CASE , 'is balanced' )
else:
print(_SCREAMING_SNAKE_CASE , 'is not balanced' )
if __name__ == "__main__":
main()
| 351 |
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
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""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 UpperCamelCase_ ( _lowerCamelCase ):
lowerCAmelCase_ = '''gpt_neo'''
lowerCAmelCase_ = ['''past_key_values''']
lowerCAmelCase_ = {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self , lowerCAmelCase_=5_0257 , lowerCAmelCase_=2048 , lowerCAmelCase_=2048 , lowerCAmelCase_=24 , lowerCAmelCase_=[[["global", "local"], 12]] , lowerCAmelCase_=16 , lowerCAmelCase_=None , lowerCAmelCase_=256 , lowerCAmelCase_="gelu_new" , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.1 , lowerCAmelCase_=1E-5 , lowerCAmelCase_=0.02 , lowerCAmelCase_=True , lowerCAmelCase_=5_0256 , lowerCAmelCase_=5_0256 , **lowerCAmelCase_ , ) -> Tuple:
_snake_case = vocab_size
_snake_case = max_position_embeddings
_snake_case = hidden_size
_snake_case = num_layers
_snake_case = num_heads
_snake_case = intermediate_size
_snake_case = window_size
_snake_case = activation_function
_snake_case = resid_dropout
_snake_case = embed_dropout
_snake_case = attention_dropout
_snake_case = classifier_dropout
_snake_case = layer_norm_epsilon
_snake_case = initializer_range
_snake_case = use_cache
_snake_case = bos_token_id
_snake_case = eos_token_id
_snake_case = attention_types
_snake_case = self.expand_attention_types_params(lowerCAmelCase_ )
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=lowerCAmelCase_ , eos_token_id=lowerCAmelCase_ , **lowerCAmelCase_ )
@staticmethod
def lowerCAmelCase ( lowerCAmelCase_ ) -> Any:
_snake_case = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def lowerCamelCase__ ( UpperCamelCase__ : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int] ) -> Any:
'''simple docstring'''
import torch
_snake_case = input.size()
_snake_case = len(UpperCamelCase__ )
_snake_case = shape[dimension]
_snake_case = torch.arange(0 , UpperCamelCase__ , UpperCamelCase__ )
_snake_case = torch.div(sizedim - size , UpperCamelCase__ , rounding_mode='floor' ) + 1
_snake_case = torch.arange(UpperCamelCase__ ) + low_indices[:min_length][:, None]
_snake_case = [slice(UpperCamelCase__ )] * rank
_snake_case = indices
_snake_case = input[s]
_snake_case = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(UpperCamelCase__ )
def lowerCamelCase__ ( UpperCamelCase__ : Tuple , UpperCamelCase__ : Dict ) -> str:
'''simple docstring'''
import torch
_snake_case = torch.arange(1 , UpperCamelCase__ )
_snake_case = torch.remainder(UpperCamelCase__ , UpperCamelCase__ )
_snake_case = remainders == 0
_snake_case = candidates[divisor_indices]
_snake_case = torch.max(UpperCamelCase__ )
return largest_divisor, torch.div(UpperCamelCase__ , UpperCamelCase__ , rounding_mode='floor' )
class UpperCamelCase_ ( _lowerCamelCase ):
@property
def lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
_snake_case = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
if self.use_past:
self.fill_with_past_key_values_(lowerCAmelCase_ , direction='inputs' )
_snake_case = {0: 'batch', 1: 'past_sequence + sequence'}
else:
_snake_case = {0: 'batch', 1: 'sequence'}
return common_inputs
@property
def lowerCAmelCase ( self ) -> int:
return self._config.num_heads
def lowerCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = -1 , lowerCAmelCase_ = -1 , lowerCAmelCase_ = False , lowerCAmelCase_ = None , ) -> Mapping[str, Any]:
_snake_case = super(lowerCAmelCase_ , self ).generate_dummy_inputs(
lowerCAmelCase_ , batch_size=lowerCAmelCase_ , seq_length=lowerCAmelCase_ , is_pair=lowerCAmelCase_ , framework=lowerCAmelCase_ )
# We need to order the input in the way they appears in the forward()
_snake_case = OrderedDict({'input_ids': common_inputs['input_ids']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
_snake_case , _snake_case = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
_snake_case = seqlen + 2
_snake_case = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_snake_case = [
(torch.zeros(lowerCAmelCase_ ), torch.zeros(lowerCAmelCase_ )) for _ in range(self.num_layers )
]
_snake_case = common_inputs['attention_mask']
if self.use_past:
_snake_case = ordered_inputs['attention_mask'].dtype
_snake_case = torch.cat(
[ordered_inputs['attention_mask'], torch.ones(lowerCAmelCase_ , lowerCAmelCase_ , dtype=lowerCAmelCase_ )] , dim=1 )
return ordered_inputs
@property
def lowerCAmelCase ( self ) -> int:
return 13
| 295 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class __A ( lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = """wav2vec2"""
def __init__( self , __lowerCAmelCase=3_2 , __lowerCAmelCase=7_6_8 , __lowerCAmelCase=1_2 , __lowerCAmelCase=1_2 , __lowerCAmelCase=3_0_7_2 , __lowerCAmelCase="gelu" , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.0 , __lowerCAmelCase=0.0 , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.02 , __lowerCAmelCase=1E-5 , __lowerCAmelCase="group" , __lowerCAmelCase="gelu" , __lowerCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2) , __lowerCAmelCase=(5, 2, 2, 2, 2, 2, 2) , __lowerCAmelCase=(1_0, 3, 3, 3, 3, 2, 2) , __lowerCAmelCase=False , __lowerCAmelCase=1_2_8 , __lowerCAmelCase=1_6 , __lowerCAmelCase=False , __lowerCAmelCase=True , __lowerCAmelCase=0.05 , __lowerCAmelCase=1_0 , __lowerCAmelCase=2 , __lowerCAmelCase=0.0 , __lowerCAmelCase=1_0 , __lowerCAmelCase=0 , __lowerCAmelCase=3_2_0 , __lowerCAmelCase=2 , __lowerCAmelCase=0.1 , __lowerCAmelCase=1_0_0 , __lowerCAmelCase=2_5_6 , __lowerCAmelCase=2_5_6 , __lowerCAmelCase=0.1 , __lowerCAmelCase="sum" , __lowerCAmelCase=False , __lowerCAmelCase=False , __lowerCAmelCase=2_5_6 , __lowerCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0) , __lowerCAmelCase=(5, 3, 3, 1, 1) , __lowerCAmelCase=(1, 2, 3, 1, 1) , __lowerCAmelCase=5_1_2 , __lowerCAmelCase=0 , __lowerCAmelCase=1 , __lowerCAmelCase=2 , __lowerCAmelCase=False , __lowerCAmelCase=3 , __lowerCAmelCase=2 , __lowerCAmelCase=3 , __lowerCAmelCase=None , __lowerCAmelCase=None , **__lowerCAmelCase , ):
'''simple docstring'''
super().__init__(**__lowerCAmelCase , pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase )
lowerCamelCase__ = hidden_size
lowerCamelCase__ = feat_extract_norm
lowerCamelCase__ = feat_extract_activation
lowerCamelCase__ = list(__lowerCAmelCase )
lowerCamelCase__ = list(__lowerCAmelCase )
lowerCamelCase__ = list(__lowerCAmelCase )
lowerCamelCase__ = conv_bias
lowerCamelCase__ = num_conv_pos_embeddings
lowerCamelCase__ = num_conv_pos_embedding_groups
lowerCamelCase__ = len(self.conv_dim )
lowerCamelCase__ = num_hidden_layers
lowerCamelCase__ = intermediate_size
lowerCamelCase__ = hidden_act
lowerCamelCase__ = num_attention_heads
lowerCamelCase__ = hidden_dropout
lowerCamelCase__ = attention_dropout
lowerCamelCase__ = activation_dropout
lowerCamelCase__ = feat_proj_dropout
lowerCamelCase__ = final_dropout
lowerCamelCase__ = layerdrop
lowerCamelCase__ = layer_norm_eps
lowerCamelCase__ = initializer_range
lowerCamelCase__ = vocab_size
lowerCamelCase__ = do_stable_layer_norm
lowerCamelCase__ = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
F' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'
F' `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowerCamelCase__ = apply_spec_augment
lowerCamelCase__ = mask_time_prob
lowerCamelCase__ = mask_time_length
lowerCamelCase__ = mask_time_min_masks
lowerCamelCase__ = mask_feature_prob
lowerCamelCase__ = mask_feature_length
lowerCamelCase__ = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
lowerCamelCase__ = num_codevectors_per_group
lowerCamelCase__ = num_codevector_groups
lowerCamelCase__ = contrastive_logits_temperature
lowerCamelCase__ = feat_quantizer_dropout
lowerCamelCase__ = num_negatives
lowerCamelCase__ = codevector_dim
lowerCamelCase__ = proj_codevector_dim
lowerCamelCase__ = diversity_loss_weight
# ctc loss
lowerCamelCase__ = ctc_loss_reduction
lowerCamelCase__ = ctc_zero_infinity
# adapter
lowerCamelCase__ = add_adapter
lowerCamelCase__ = adapter_kernel_size
lowerCamelCase__ = adapter_stride
lowerCamelCase__ = num_adapter_layers
lowerCamelCase__ = output_hidden_size or hidden_size
lowerCamelCase__ = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
lowerCamelCase__ = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
lowerCamelCase__ = list(__lowerCAmelCase )
lowerCamelCase__ = list(__lowerCAmelCase )
lowerCamelCase__ = list(__lowerCAmelCase )
lowerCamelCase__ = xvector_output_dim
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 209 |
def lowerCAmelCase__(__snake_case ) -> list:
'''simple docstring'''
lowerCamelCase__ = len(__snake_case )
for _ in range(__snake_case ):
for i in range(_ % 2 ,arr_size - 1 ,2 ):
if arr[i + 1] < arr[i]:
lowerCamelCase__ , lowerCamelCase__ = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
_a = list(range(10, 0, -1))
print(f"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")
| 209 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=a__ )
class __UpperCamelCase ( a__ ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
lowerCamelCase : str =field(default="""question-answering-extractive""" , metadata={"""include_in_asdict_even_if_is_default""": True} )
lowerCamelCase : ClassVar[Features] =Features({"""question""": Value("""string""" ), """context""": Value("""string""" )} )
lowerCamelCase : ClassVar[Features] =Features(
{
"""answers""": Sequence(
{
"""text""": Value("""string""" ),
"""answer_start""": Value("""int32""" ),
} )
} )
lowerCamelCase : str ="question"
lowerCamelCase : str ="context"
lowerCamelCase : str ="answers"
@property
def __a ( self ) -> Dict[str, str]:
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 79 |
"""simple docstring"""
from __future__ import annotations
from math import pow, sqrt
def _SCREAMING_SNAKE_CASE ( _lowercase : float , _lowercase : float , _lowercase : float ) ->dict[str, float]:
'''simple docstring'''
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance == 0:
return {"resistance": sqrt(pow(_lowercase , 2 ) - pow(_lowercase , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(_lowercase , 2 ) - pow(_lowercase , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(_lowercase , 2 ) + pow(_lowercase , 2 ) )}
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 79 | 1 |
"""simple docstring"""
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
_lowercase = ''''''
if version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):
class lowerCAmelCase_ ( tr.AbstractTransform ):
'''simple docstring'''
def __init__( self : str ,A_ : str = " " ) -> Tuple:
A = sentence_delimiter
def _SCREAMING_SNAKE_CASE ( self : int ,A_ : str ) -> Optional[int]:
return list(A_ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ,A_ : List[str] ) -> Any:
A = []
for sent_idx, sentence in enumerate(A_ ):
chars.extend(self.process_string(A_ ) )
if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(A_ ) - 1:
chars.append(self.sentence_delimiter )
return chars
_lowercase = tr.Compose(
[tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
)
else:
_lowercase = tr.Compose(
[
tr.RemoveMultipleSpaces(),
tr.Strip(),
tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
tr.ReduceToListOfListOfChars(),
]
)
_lowercase = '''\
@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.}
}
'''
_lowercase = '''\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.
CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.
Character error rate can be computed as:
CER = (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 characters,
N is the number of characters in the reference (N=S+D+C).
CER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
'''
_lowercase = '''
Computes CER score of transcribed segments against references.
Args:
references: list of references for each speech input.
predictions: list of transcribtions to score.
concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
(float): the character error rate
Examples:
>>> predictions = ["this is the prediction", "there is an other sample"]
>>> references = ["this is the reference", "there is another one"]
>>> cer = datasets.load_metric("cer")
>>> cer_score = cer.compute(predictions=predictions, references=references)
>>> print(cer_score)
0.34146341463414637
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase_ ( datasets.Metric ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> List[Any]:
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',
'https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates',
] ,)
def _SCREAMING_SNAKE_CASE ( self : Any ,A_ : Optional[Any] ,A_ : Optional[int] ,A_ : str=False ) -> Tuple:
if concatenate_texts:
return jiwer.compute_measures(
A_ ,A_ ,truth_transform=A_ ,hypothesis_transform=A_ ,)["wer"]
A = 0
A = 0
for prediction, reference in zip(A_ ,A_ ):
A = jiwer.compute_measures(
A_ ,A_ ,truth_transform=A_ ,hypothesis_transform=A_ ,)
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total | 74 | """simple docstring"""
from ..utils import DummyObject, requires_backends
class _A ( metaclass=lowerCAmelCase ):
snake_case__ : Optional[int] = ['torch', 'torchsde']
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(self , ["""torch""", """torchsde"""] )
@classmethod
def A__ ( cls , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls , ["""torch""", """torchsde"""] )
@classmethod
def A__ ( cls , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls , ["""torch""", """torchsde"""] )
| 197 | 0 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( _a):
'''simple docstring'''
__UpperCamelCase : int = ["image_processor", "tokenizer"]
__UpperCamelCase : List[str] = "AutoImageProcessor"
__UpperCamelCase : Optional[Any] = "AutoTokenizer"
def __init__( self , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , **__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase : Optional[int] = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , __SCREAMING_SNAKE_CASE , )
UpperCamelCase : Any = kwargs.pop('''feature_extractor''' )
UpperCamelCase : str = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
UpperCamelCase : Optional[Any] = self.image_processor
UpperCamelCase : int = False
def __call__( self , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
UpperCamelCase : Union[str, Any] = kwargs.pop('''images''' , __SCREAMING_SNAKE_CASE )
UpperCamelCase : Any = kwargs.pop('''text''' , __SCREAMING_SNAKE_CASE )
if len(__SCREAMING_SNAKE_CASE ) > 0:
UpperCamelCase : Union[str, Any] = args[0]
UpperCamelCase : str = args[1:]
if images is None and text is None:
raise ValueError('''You need to specify either an `images` or `text` input to process.''' )
if images is not None:
UpperCamelCase : List[str] = self.image_processor(__SCREAMING_SNAKE_CASE , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
if text is not None:
UpperCamelCase : Optional[Any] = self.tokenizer(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
if text is None:
return inputs
elif images is None:
return encodings
else:
UpperCamelCase : List[str] = encodings['''input_ids''']
return inputs
def _lowercase ( self , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _lowercase ( self , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
@contextmanager
def _lowercase ( self ):
"""simple docstring"""
warnings.warn(
'''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
'''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
'''your images inputs, or in a separate call.''' )
UpperCamelCase : Any = True
UpperCamelCase : int = self.tokenizer
yield
UpperCamelCase : List[Any] = self.image_processor
UpperCamelCase : Tuple = False
def _lowercase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=None ):
"""simple docstring"""
if added_vocab is None:
UpperCamelCase : str = self.tokenizer.get_added_vocab()
UpperCamelCase : int = {}
while tokens:
UpperCamelCase : Dict = re.search(R'''<s_(.*?)>''' , __SCREAMING_SNAKE_CASE , re.IGNORECASE )
if start_token is None:
break
UpperCamelCase : List[str] = start_token.group(1 )
UpperCamelCase : Dict = re.search(Rf"""</s_{key}>""" , __SCREAMING_SNAKE_CASE , re.IGNORECASE )
UpperCamelCase : Any = start_token.group()
if end_token is None:
UpperCamelCase : Optional[int] = tokens.replace(__SCREAMING_SNAKE_CASE , '''''' )
else:
UpperCamelCase : Dict = end_token.group()
UpperCamelCase : int = re.escape(__SCREAMING_SNAKE_CASE )
UpperCamelCase : Dict = re.escape(__SCREAMING_SNAKE_CASE )
UpperCamelCase : str = re.search(f"""{start_token_escaped}(.*?){end_token_escaped}""" , __SCREAMING_SNAKE_CASE , re.IGNORECASE )
if content is not None:
UpperCamelCase : Dict = content.group(1 ).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
UpperCamelCase : Tuple = self.tokenajson(__SCREAMING_SNAKE_CASE , is_inner_value=__SCREAMING_SNAKE_CASE , added_vocab=__SCREAMING_SNAKE_CASE )
if value:
if len(__SCREAMING_SNAKE_CASE ) == 1:
UpperCamelCase : str = value[0]
UpperCamelCase : str = value
else: # leaf nodes
UpperCamelCase : Optional[int] = []
for leaf in content.split(R'''<sep/>''' ):
UpperCamelCase : Optional[int] = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
UpperCamelCase : int = leaf[1:-2] # for categorical special tokens
output[key].append(__SCREAMING_SNAKE_CASE )
if len(output[key] ) == 1:
UpperCamelCase : Tuple = output[key][0]
UpperCamelCase : List[Any] = tokens[tokens.find(__SCREAMING_SNAKE_CASE ) + len(__SCREAMING_SNAKE_CASE ) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:] , is_inner_value=__SCREAMING_SNAKE_CASE , added_vocab=__SCREAMING_SNAKE_CASE )
if len(__SCREAMING_SNAKE_CASE ):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def _lowercase ( self ):
"""simple docstring"""
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __SCREAMING_SNAKE_CASE , )
return self.image_processor_class
@property
def _lowercase ( self ):
"""simple docstring"""
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __SCREAMING_SNAKE_CASE , )
return self.image_processor
| 315 |
import collections
import os
import re
from pathlib import Path
__UpperCAmelCase : List[str] = "src/transformers"
# Matches is_xxx_available()
__UpperCAmelCase : int = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
__UpperCAmelCase : Optional[int] = re.compile(r"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
__UpperCAmelCase : List[Any] = re.compile(r"\s+\"\S*\":\s+\[([^\]]*)\]")
# Catches a line if not is_foo_available
__UpperCAmelCase : List[Any] = re.compile(r"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)")
# Catches a line _import_struct["bla"].append("foo")
__UpperCAmelCase : str = re.compile(r"^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
__UpperCAmelCase : Union[str, Any] = re.compile(r"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]")
# Catches a line with an object between quotes and a comma: "MyModel",
__UpperCAmelCase : Dict = re.compile(r"^\s+\"([^\"]+)\",")
# Catches a line with objects between brackets only: ["foo", "bar"],
__UpperCAmelCase : str = re.compile(r"^\s+\[([^\]]+)\]")
# Catches a line with from foo import bar, bla, boo
__UpperCAmelCase : str = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
# Catches a line with try:
__UpperCAmelCase : Any = re.compile(r"^\s*try:")
# Catches a line with else:
__UpperCAmelCase : List[Any] = re.compile(r"^\s*else:")
def a ( SCREAMING_SNAKE_CASE_ : Dict ):
"""simple docstring"""
if _re_test_backend.search(SCREAMING_SNAKE_CASE_ ) is None:
return None
UpperCamelCase : Union[str, Any] = [b[0] for b in _re_backend.findall(SCREAMING_SNAKE_CASE_ )]
backends.sort()
return "_and_".join(SCREAMING_SNAKE_CASE_ )
def a ( SCREAMING_SNAKE_CASE_ : Tuple ):
"""simple docstring"""
with open(SCREAMING_SNAKE_CASE_ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
UpperCamelCase : Tuple = f.readlines()
UpperCamelCase : Tuple = 0
while line_index < len(SCREAMING_SNAKE_CASE_ ) and not lines[line_index].startswith('''_import_structure = {''' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(SCREAMING_SNAKE_CASE_ ):
return None
# First grab the objects without a specific backend in _import_structure
UpperCamelCase : List[Any] = []
while not lines[line_index].startswith('''if TYPE_CHECKING''' ) and find_backend(lines[line_index] ) is None:
UpperCamelCase : Optional[int] = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = _re_one_line_import_struct.search(SCREAMING_SNAKE_CASE_ ).groups()[0]
UpperCamelCase : str = re.findall(R'''\[([^\]]+)\]''' , SCREAMING_SNAKE_CASE_ )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(''', ''' )] )
line_index += 1
continue
UpperCamelCase : List[Any] = _re_import_struct_key_value.search(SCREAMING_SNAKE_CASE_ )
if single_line_import_search is not None:
UpperCamelCase : List[str] = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', ''' ) if len(SCREAMING_SNAKE_CASE_ ) > 0]
objects.extend(SCREAMING_SNAKE_CASE_ )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
line_index += 1
UpperCamelCase : Dict = {'''none''': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('''if TYPE_CHECKING''' ):
# If the line is an if not is_backend_available, we grab all objects associated.
UpperCamelCase : Dict = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
UpperCamelCase : Optional[Any] = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
UpperCamelCase : str = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 4 ):
UpperCamelCase : str = lines[line_index]
if _re_import_struct_add_one.search(SCREAMING_SNAKE_CASE_ ) is not None:
objects.append(_re_import_struct_add_one.search(SCREAMING_SNAKE_CASE_ ).groups()[0] )
elif _re_import_struct_add_many.search(SCREAMING_SNAKE_CASE_ ) is not None:
UpperCamelCase : Union[str, Any] = _re_import_struct_add_many.search(SCREAMING_SNAKE_CASE_ ).groups()[0].split(''', ''' )
UpperCamelCase : List[Any] = [obj[1:-1] for obj in imports if len(SCREAMING_SNAKE_CASE_ ) > 0]
objects.extend(SCREAMING_SNAKE_CASE_ )
elif _re_between_brackets.search(SCREAMING_SNAKE_CASE_ ) is not None:
UpperCamelCase : str = _re_between_brackets.search(SCREAMING_SNAKE_CASE_ ).groups()[0].split(''', ''' )
UpperCamelCase : Dict = [obj[1:-1] for obj in imports if len(SCREAMING_SNAKE_CASE_ ) > 0]
objects.extend(SCREAMING_SNAKE_CASE_ )
elif _re_quote_object.search(SCREAMING_SNAKE_CASE_ ) is not None:
objects.append(_re_quote_object.search(SCREAMING_SNAKE_CASE_ ).groups()[0] )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
elif line.startswith(''' ''' * 1_2 + '''"''' ):
objects.append(line[1_3:-3] )
line_index += 1
UpperCamelCase : Tuple = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
UpperCamelCase : int = []
while (
line_index < len(SCREAMING_SNAKE_CASE_ )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('''else''' )
):
UpperCamelCase : Tuple = lines[line_index]
UpperCamelCase : Any = _re_import.search(SCREAMING_SNAKE_CASE_ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 8 ):
objects.append(line[8:-2] )
line_index += 1
UpperCamelCase : Any = {'''none''': objects}
# Let's continue with backend-specific objects
while line_index < len(SCREAMING_SNAKE_CASE_ ):
# If the line is an if is_backend_available, we grab all objects associated.
UpperCamelCase : Optional[Any] = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
UpperCamelCase : Dict = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
UpperCamelCase : Dict = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 8 ):
UpperCamelCase : Optional[Any] = lines[line_index]
UpperCamelCase : str = _re_import.search(SCREAMING_SNAKE_CASE_ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 1_2 ):
objects.append(line[1_2:-2] )
line_index += 1
UpperCamelCase : str = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def a ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : List[Any] ):
"""simple docstring"""
def find_duplicates(SCREAMING_SNAKE_CASE_ : Any ):
return [k for k, v in collections.Counter(SCREAMING_SNAKE_CASE_ ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
UpperCamelCase : Dict = []
for key in import_dict_objects.keys():
UpperCamelCase : Union[str, Any] = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F"""Duplicate _import_structure definitions for: {duplicate_imports}""" )
UpperCamelCase : Dict = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F"""Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}""" )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
UpperCamelCase : List[str] = '''base imports''' if key == '''none''' else F"""{key} backend"""
errors.append(F"""Differences for {name}:""" )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F""" {a} in TYPE_HINT but not in _import_structure.""" )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F""" {a} in _import_structure but not in TYPE_HINT.""" )
return errors
def a ( ):
"""simple docstring"""
UpperCamelCase : Any = []
for root, _, files in os.walk(SCREAMING_SNAKE_CASE_ ):
if "__init__.py" in files:
UpperCamelCase : int = os.path.join(SCREAMING_SNAKE_CASE_ , '''__init__.py''' )
UpperCamelCase : Optional[int] = parse_init(SCREAMING_SNAKE_CASE_ )
if objects is not None:
UpperCamelCase : str = analyze_results(*SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : List[Any] = F"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
failures.append('''\n'''.join(SCREAMING_SNAKE_CASE_ ) )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
raise ValueError('''\n\n'''.join(SCREAMING_SNAKE_CASE_ ) )
def a ( ):
"""simple docstring"""
UpperCamelCase : Dict = []
for path, directories, files in os.walk(SCREAMING_SNAKE_CASE_ ):
for folder in directories:
# Ignore private modules
if folder.startswith('''_''' ):
directories.remove(SCREAMING_SNAKE_CASE_ )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(SCREAMING_SNAKE_CASE_ ) / folder).glob('''*.py''' ) ) ) == 0:
continue
UpperCamelCase : List[str] = str((Path(SCREAMING_SNAKE_CASE_ ) / folder).relative_to(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : str = short_path.replace(os.path.sep , '''.''' )
submodules.append(SCREAMING_SNAKE_CASE_ )
for fname in files:
if fname == "__init__.py":
continue
UpperCamelCase : Tuple = str((Path(SCREAMING_SNAKE_CASE_ ) / fname).relative_to(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : int = short_path.replace('''.py''' , '''''' ).replace(os.path.sep , '''.''' )
if len(submodule.split('''.''' ) ) == 1:
submodules.append(SCREAMING_SNAKE_CASE_ )
return submodules
__UpperCAmelCase : Optional[int] = [
"convert_pytorch_checkpoint_to_tf2",
"modeling_flax_pytorch_utils",
"models.esm.openfold_utils",
]
def a ( ):
"""simple docstring"""
from transformers.utils import direct_transformers_import
UpperCamelCase : Tuple = direct_transformers_import(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(SCREAMING_SNAKE_CASE_ , '''__init__.py''' ) , '''r''' ) as f:
UpperCamelCase : List[Any] = f.read()
import_structure_keys.update(set(re.findall(R'''import_structure\[\"([^\"]*)\"\]''' , SCREAMING_SNAKE_CASE_ ) ) )
UpperCamelCase : Union[str, Any] = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : str = '''\n'''.join(F"""- {module}""" for module in module_not_registered )
raise ValueError(
'''The following submodules are not properly registed in the main init of Transformers:\n'''
F"""{list_of_modules}\n"""
'''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.''' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 315 | 1 |
"""simple docstring"""
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowercase (_lowerCAmelCase , _lowerCAmelCase = True , _lowerCAmelCase = math.inf , _lowerCAmelCase = -math.inf , _lowerCAmelCase = math.inf , _lowerCAmelCase = -math.inf , _lowerCAmelCase = False , _lowerCAmelCase = 100 , _lowerCAmelCase = 0.01 , _lowerCAmelCase = 1 , ):
__lowerCAmelCase = False
__lowerCAmelCase = search_prob
__lowerCAmelCase = start_temperate
__lowerCAmelCase = []
__lowerCAmelCase = 0
__lowerCAmelCase = None
while not search_end:
__lowerCAmelCase = current_state.score()
if best_state is None or current_score > best_state.score():
__lowerCAmelCase = current_state
scores.append(_lowerCAmelCase )
iterations += 1
__lowerCAmelCase = None
__lowerCAmelCase = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
__lowerCAmelCase = random.randint(0 , len(_lowerCAmelCase ) - 1 ) # picking a random neighbor
__lowerCAmelCase = neighbors.pop(_lowerCAmelCase )
__lowerCAmelCase = 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:
__lowerCAmelCase = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
__lowerCAmelCase = picked_neighbor
else:
__lowerCAmelCase = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
__lowerCAmelCase = picked_neighbor
__lowerCAmelCase = 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
__lowerCAmelCase = True
else:
__lowerCAmelCase = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(_lowerCAmelCase ) , _lowerCAmelCase )
plt.xlabel("""Iterations""" )
plt.ylabel("""Function values""" )
plt.show()
return best_state
if __name__ == "__main__":
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
SCREAMING_SNAKE_CASE_ = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
SCREAMING_SNAKE_CASE_ = 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)
SCREAMING_SNAKE_CASE_ = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
SCREAMING_SNAKE_CASE_ = 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 lowercase (_lowerCAmelCase , _lowerCAmelCase ):
return (3 * x**2) - (6 * y)
SCREAMING_SNAKE_CASE_ = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
SCREAMING_SNAKE_CASE_ = 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()}"
)
SCREAMING_SNAKE_CASE_ = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
SCREAMING_SNAKE_CASE_ = 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()}"
)
| 301 |
"""simple docstring"""
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
SCREAMING_SNAKE_CASE_ = getLogger(__name__)
SCREAMING_SNAKE_CASE_ = '''cuda''' if torch.cuda.is_available() else '''cpu'''
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 8 , _lowerCAmelCase = DEFAULT_DEVICE , _lowerCAmelCase=False , _lowerCAmelCase="summarization" , _lowerCAmelCase=None , **_lowerCAmelCase , ):
__lowerCAmelCase = Path(_lowerCAmelCase ).open("""w""" , encoding="""utf-8""" )
__lowerCAmelCase = str(_lowerCAmelCase )
__lowerCAmelCase = AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase ).to(_lowerCAmelCase )
if fpaa:
__lowerCAmelCase = model.half()
__lowerCAmelCase = AutoTokenizer.from_pretrained(_lowerCAmelCase )
logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}""" ) # if this is wrong, check config.model_type.
__lowerCAmelCase = time.time()
# update config with task specific params
use_task_specific_params(_lowerCAmelCase , _lowerCAmelCase )
if prefix is None:
__lowerCAmelCase = prefix or getattr(model.config , """prefix""" , """""" ) or """"""
for examples_chunk in tqdm(list(chunks(_lowerCAmelCase , _lowerCAmelCase ) ) ):
__lowerCAmelCase = [prefix + text for text in examples_chunk]
__lowerCAmelCase = tokenizer(_lowerCAmelCase , return_tensors="""pt""" , truncation=_lowerCAmelCase , padding="""longest""" ).to(_lowerCAmelCase )
__lowerCAmelCase = model.generate(
input_ids=batch.input_ids , attention_mask=batch.attention_mask , **_lowerCAmelCase , )
__lowerCAmelCase = tokenizer.batch_decode(_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase , clean_up_tokenization_spaces=_lowerCAmelCase )
for hypothesis in dec:
fout.write(hypothesis + """\n""" )
fout.flush()
fout.close()
__lowerCAmelCase = int(time.time() - start_time ) # seconds
__lowerCAmelCase = len(_lowerCAmelCase )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs , 4 )}
def lowercase ():
return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S""" )
def lowercase (_lowerCAmelCase=True ):
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument("""model_name""" , type=_lowerCAmelCase , help="""like facebook/bart-large-cnn,t5-base, etc.""" )
parser.add_argument("""input_path""" , type=_lowerCAmelCase , help="""like cnn_dm/test.source""" )
parser.add_argument("""save_path""" , type=_lowerCAmelCase , help="""where to save summaries""" )
parser.add_argument("""--reference_path""" , type=_lowerCAmelCase , required=_lowerCAmelCase , help="""like cnn_dm/test.target""" )
parser.add_argument("""--score_path""" , type=_lowerCAmelCase , required=_lowerCAmelCase , default="""metrics.json""" , help="""where to save metrics""" )
parser.add_argument("""--device""" , type=_lowerCAmelCase , required=_lowerCAmelCase , default=_lowerCAmelCase , help="""cuda, cuda:1, cpu etc.""" )
parser.add_argument(
"""--prefix""" , type=_lowerCAmelCase , required=_lowerCAmelCase , default=_lowerCAmelCase , help="""will be added to the begininng of src examples""" )
parser.add_argument("""--task""" , type=_lowerCAmelCase , default="""summarization""" , help="""used for task_specific_params + metrics""" )
parser.add_argument("""--bs""" , type=_lowerCAmelCase , default=8 , required=_lowerCAmelCase , help="""batch size""" )
parser.add_argument(
"""--n_obs""" , type=_lowerCAmelCase , default=-1 , required=_lowerCAmelCase , help="""How many observations. Defaults to all.""" )
parser.add_argument("""--fp16""" , action="""store_true""" )
parser.add_argument("""--dump-args""" , action="""store_true""" , help="""print the custom hparams with the results""" )
parser.add_argument(
"""--info""" , nargs="""?""" , type=_lowerCAmelCase , const=datetime_now() , help=(
"""use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."""
""" lang=en-ru. If no value is passed, the current datetime string will be used."""
) , )
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
__lowerCAmelCase , __lowerCAmelCase = parser.parse_known_args()
__lowerCAmelCase = parse_numeric_n_bool_cl_kwargs(_lowerCAmelCase )
if parsed_args and verbose:
print(f"""parsed the following generate kwargs: {parsed_args}""" )
__lowerCAmelCase = [""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
__lowerCAmelCase = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=_lowerCAmelCase )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c.""" )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError("""Can't mix --fp16 and --device cpu""" )
__lowerCAmelCase = generate_summaries_or_translations(
_lowerCAmelCase , args.save_path , args.model_name , batch_size=args.bs , device=args.device , fpaa=args.fpaa , task=args.task , prefix=args.prefix , **_lowerCAmelCase , )
if args.reference_path is None:
return {}
# Compute scores
__lowerCAmelCase = calculate_bleu if """translation""" in args.task else calculate_rouge
__lowerCAmelCase = [x.rstrip() for x in open(args.save_path ).readlines()]
__lowerCAmelCase = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_lowerCAmelCase )]
__lowerCAmelCase = score_fn(_lowerCAmelCase , _lowerCAmelCase )
scores.update(_lowerCAmelCase )
if args.dump_args:
scores.update(_lowerCAmelCase )
if args.info:
__lowerCAmelCase = args.info
if verbose:
print(_lowerCAmelCase )
if args.score_path is not None:
json.dump(_lowerCAmelCase , open(args.score_path , """w""" ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 301 | 1 |
"""simple docstring"""
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , ) ->str:
"""simple docstring"""
if attention_mask is None:
lowerCAmelCase__ :List[str] = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
lowerCAmelCase__ :Tuple = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
lowerCAmelCase__ :Union[str, Any] = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=_SCREAMING_SNAKE_CASE )
if decoder_head_mask is None:
lowerCAmelCase__ :List[str] = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=_SCREAMING_SNAKE_CASE )
if cross_attn_head_mask is None:
lowerCAmelCase__ :List[str] = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=_SCREAMING_SNAKE_CASE )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=1_3 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=9_9 , __UpperCAmelCase=1_6 , __UpperCAmelCase=2 , __UpperCAmelCase=4 , __UpperCAmelCase=4 , __UpperCAmelCase="relu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=2_0 , __UpperCAmelCase=2 , __UpperCAmelCase=1 , __UpperCAmelCase=0 , ):
'''simple docstring'''
lowerCAmelCase__ :List[Any] = parent
lowerCAmelCase__ :Any = batch_size
lowerCAmelCase__ :Optional[Any] = seq_length
lowerCAmelCase__ :int = is_training
lowerCAmelCase__ :Tuple = use_labels
lowerCAmelCase__ :Union[str, Any] = vocab_size
lowerCAmelCase__ :Tuple = hidden_size
lowerCAmelCase__ :Tuple = num_hidden_layers
lowerCAmelCase__ :Tuple = num_attention_heads
lowerCAmelCase__ :Dict = intermediate_size
lowerCAmelCase__ :Optional[int] = hidden_act
lowerCAmelCase__ :Any = hidden_dropout_prob
lowerCAmelCase__ :Dict = attention_probs_dropout_prob
lowerCAmelCase__ :Tuple = encoder_layerdrop
lowerCAmelCase__ :Tuple = decoder_layerdrop
lowerCAmelCase__ :Tuple = max_position_embeddings
lowerCAmelCase__ :Any = eos_token_id
lowerCAmelCase__ :str = pad_token_id
lowerCAmelCase__ :Tuple = bos_token_id
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase__ :Tuple = self.eos_token_id # Eos Token
lowerCAmelCase__ :Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 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
lowerCAmelCase__ :List[Any] = input_ids.clamp(self.pad_token_id + 1 )
lowerCAmelCase__ :Dict = decoder_input_ids.clamp(self.pad_token_id + 1 )
lowerCAmelCase__ :Optional[Any] = self.get_config()
lowerCAmelCase__ :Any = prepare_mam_aaa_inputs_dict(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
return config, inputs_dict
def snake_case ( self ):
'''simple docstring'''
return MaMaaaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ :List[Any] = self.prepare_config_and_inputs()
return config, inputs_dict
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :List[Any] = MaMaaaModel(config=__UpperCAmelCase ).get_decoder().to(__UpperCAmelCase ).eval()
lowerCAmelCase__ :Optional[int] = inputs_dict['input_ids']
lowerCAmelCase__ :Any = inputs_dict['attention_mask']
lowerCAmelCase__ :Tuple = inputs_dict['head_mask']
# first forward pass
lowerCAmelCase__ :int = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , head_mask=__UpperCAmelCase , use_cache=__UpperCAmelCase )
lowerCAmelCase__ , lowerCAmelCase__ :Union[str, Any] = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
lowerCAmelCase__ :Any = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowerCAmelCase__ :int = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
lowerCAmelCase__ :Tuple = torch.cat([input_ids, next_tokens] , dim=-1 )
lowerCAmelCase__ :Union[str, Any] = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
lowerCAmelCase__ :Dict = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase )['last_hidden_state']
lowerCAmelCase__ :Optional[Any] = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , past_key_values=__UpperCAmelCase )[
'last_hidden_state'
]
# select random slice
lowerCAmelCase__ :Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowerCAmelCase__ :List[Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCAmelCase__ :Union[str, Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-2 ) )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Tuple = MaMaaaModel(config=__UpperCAmelCase ).to(__UpperCAmelCase ).eval()
lowerCAmelCase__ :List[Any] = model(**__UpperCAmelCase )
lowerCAmelCase__ :int = outputs.encoder_last_hidden_state
lowerCAmelCase__ :Any = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ :Union[str, Any] = model.get_encoder()
encoder.save_pretrained(__UpperCAmelCase )
lowerCAmelCase__ :Any = MaMaaaEncoder.from_pretrained(__UpperCAmelCase ).to(__UpperCAmelCase )
lowerCAmelCase__ :Any = encoder(inputs_dict['input_ids'] , attention_mask=inputs_dict['attention_mask'] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ :Optional[int] = model.get_decoder()
decoder.save_pretrained(__UpperCAmelCase )
lowerCAmelCase__ :Dict = MaMaaaDecoder.from_pretrained(__UpperCAmelCase ).to(__UpperCAmelCase )
lowerCAmelCase__ :int = decoder(
input_ids=inputs_dict['decoder_input_ids'] , attention_mask=inputs_dict['decoder_attention_mask'] , encoder_hidden_states=__UpperCAmelCase , encoder_attention_mask=inputs_dict['attention_mask'] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class _lowerCAmelCase ( a , a , a , unittest.TestCase ):
"""simple docstring"""
__magic_name__ :Optional[int] = (
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
__magic_name__ :str = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
__magic_name__ :str = (
{
"""conversational""": MaMaaaForConditionalGeneration,
"""feature-extraction""": MaMaaaModel,
"""summarization""": MaMaaaForConditionalGeneration,
"""text2text-generation""": MaMaaaForConditionalGeneration,
"""translation""": MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
__magic_name__ :Any = True
__magic_name__ :Union[str, Any] = True
__magic_name__ :Tuple = False
__magic_name__ :List[str] = False
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :int = MaMaaaModelTester(self )
lowerCAmelCase__ :Tuple = ConfigTester(self , config_class=__UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ :str = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
lowerCAmelCase__ :str = model_class(__UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__UpperCAmelCase )
lowerCAmelCase__ , lowerCAmelCase__ :List[Any] = model_class.from_pretrained(__UpperCAmelCase , output_loading_info=__UpperCAmelCase )
self.assertEqual(info['missing_keys'] , [] )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*__UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :int = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*__UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ :List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
lowerCAmelCase__ :Optional[int] = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ :List[Any] = copy.deepcopy(self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
if not self.is_encoder_decoder:
lowerCAmelCase__ :List[str] = inputs['input_ids']
del inputs["input_ids"]
else:
lowerCAmelCase__ :int = inputs['input_ids']
lowerCAmelCase__ :str = inputs.get('decoder_input_ids' , __UpperCAmelCase )
del inputs["input_ids"]
inputs.pop('decoder_input_ids' , __UpperCAmelCase )
lowerCAmelCase__ :List[Any] = model.get_input_embeddings()
if not self.is_encoder_decoder:
lowerCAmelCase__ :Tuple = wte(__UpperCAmelCase )
else:
lowerCAmelCase__ :List[Any] = wte(__UpperCAmelCase )
lowerCAmelCase__ :Dict = wte(__UpperCAmelCase )
with torch.no_grad():
model(**__UpperCAmelCase )[0]
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ :int = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase__ :Any = input_dict['input_ids']
lowerCAmelCase__ :Optional[Any] = input_ids.ne(1 ).to(__UpperCAmelCase )
lowerCAmelCase__ :List[Any] = MaMaaaForConditionalGeneration(__UpperCAmelCase ).eval().to(__UpperCAmelCase )
if torch_device == "cuda":
model.half()
model.generate(__UpperCAmelCase , attention_mask=__UpperCAmelCase )
model.generate(num_beams=4 , do_sample=__UpperCAmelCase , early_stopping=__UpperCAmelCase , num_return_sequences=3 )
def __A (_SCREAMING_SNAKE_CASE ) ->Dict:
"""simple docstring"""
return torch.tensor(_SCREAMING_SNAKE_CASE , dtype=torch.long , device=_SCREAMING_SNAKE_CASE )
__A = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def snake_case ( self ):
'''simple docstring'''
return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :List[str] = MaMaaaModel.from_pretrained('facebook/m2m100_418M' ).to(__UpperCAmelCase )
lowerCAmelCase__ :str = _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]] )
lowerCAmelCase__ :Optional[int] = _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]] )
lowerCAmelCase__ :Union[str, Any] = prepare_mam_aaa_inputs_dict(model.config , __UpperCAmelCase , __UpperCAmelCase )
with torch.no_grad():
lowerCAmelCase__ :Any = model(**__UpperCAmelCase )[0]
lowerCAmelCase__ :List[str] = torch.Size((1, 1_1, 1_0_2_4) )
self.assertEqual(output.shape , __UpperCAmelCase )
# change to expected output here
lowerCAmelCase__ :int = torch.tensor(
[[-0.77_80, -0.16_76, 0.10_38], [-6.75_56, -1.39_92, 0.05_67], [-7.53_83, -0.59_20, -0.27_79]] , device=__UpperCAmelCase )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=__UpperCAmelCase ) )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(__UpperCAmelCase )
# change to intended input
lowerCAmelCase__ :str = _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]] )
lowerCAmelCase__ :Any = _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]] )
lowerCAmelCase__ :List[Any] = prepare_mam_aaa_inputs_dict(model.config , __UpperCAmelCase , __UpperCAmelCase )
with torch.no_grad():
lowerCAmelCase__ :List[Any] = model(**__UpperCAmelCase )[0]
lowerCAmelCase__ :Any = torch.Size((1, 1_1, model.config.vocab_size) )
self.assertEqual(output.shape , __UpperCAmelCase )
# change to expected output here
lowerCAmelCase__ :List[Any] = torch.tensor(
[[-1.04_48, -1.04_11, 3.79_92], [-3.21_91, -3.23_86, -1.34_51], [-3.62_10, -3.59_93, 0.49_25]] , device=__UpperCAmelCase )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCAmelCase , atol=__UpperCAmelCase ) )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Tuple = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(__UpperCAmelCase )
lowerCAmelCase__ :Union[str, Any] = MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' , src_lang='fr' , tgt_lang='en' )
lowerCAmelCase__ :Tuple = [
'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
lowerCAmelCase__ :Union[str, Any] = tokenizer(__UpperCAmelCase , padding=__UpperCAmelCase , return_tensors='pt' )
lowerCAmelCase__ :List[Any] = model.generate(
input_ids=dct['input_ids'].to(__UpperCAmelCase ) , attention_mask=dct['attention_mask'].to(__UpperCAmelCase ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id('en' ) , )
lowerCAmelCase__ :Optional[Any] = [
'The NSA case highlights the total absence of intelligence debate',
'I think there are two levels of response from the French government.',
'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
' communications in France.',
]
lowerCAmelCase__ :Any = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
assert generated == expected_en
| 254 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A = logging.get_logger(__name__)
__A = {
"""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 _lowerCAmelCase ( a , a ):
"""simple docstring"""
__magic_name__ :int = """swin"""
__magic_name__ :Tuple = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self , __UpperCAmelCase=2_2_4 , __UpperCAmelCase=4 , __UpperCAmelCase=3 , __UpperCAmelCase=9_6 , __UpperCAmelCase=[2, 2, 6, 2] , __UpperCAmelCase=[3, 6, 1_2, 2_4] , __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=3_2 , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(**__UpperCAmelCase )
lowerCAmelCase__ :Any = image_size
lowerCAmelCase__ :List[Any] = patch_size
lowerCAmelCase__ :Optional[int] = num_channels
lowerCAmelCase__ :str = embed_dim
lowerCAmelCase__ :Optional[int] = depths
lowerCAmelCase__ :List[str] = len(__UpperCAmelCase )
lowerCAmelCase__ :Optional[Any] = num_heads
lowerCAmelCase__ :List[Any] = window_size
lowerCAmelCase__ :List[Any] = mlp_ratio
lowerCAmelCase__ :int = qkv_bias
lowerCAmelCase__ :Optional[int] = hidden_dropout_prob
lowerCAmelCase__ :int = attention_probs_dropout_prob
lowerCAmelCase__ :List[Any] = drop_path_rate
lowerCAmelCase__ :Any = hidden_act
lowerCAmelCase__ :Dict = use_absolute_embeddings
lowerCAmelCase__ :int = layer_norm_eps
lowerCAmelCase__ :Dict = initializer_range
lowerCAmelCase__ :int = 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
lowerCAmelCase__ :str = int(embed_dim * 2 ** (len(__UpperCAmelCase ) - 1) )
lowerCAmelCase__ :str = ['stem'] + [F"stage{idx}" for idx in range(1 , len(__UpperCAmelCase ) + 1 )]
lowerCAmelCase__ , lowerCAmelCase__ :List[Any] = get_aligned_output_features_output_indices(
out_features=__UpperCAmelCase , out_indices=__UpperCAmelCase , stage_names=self.stage_names )
class _lowerCAmelCase ( a ):
"""simple docstring"""
__magic_name__ :int = version.parse("""1.11""" )
@property
def snake_case ( self ):
'''simple docstring'''
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def snake_case ( self ):
'''simple docstring'''
return 1E-4
| 254 | 1 |
import torch
from diffusers import DiffusionPipeline
class lowercase ( UpperCamelCase__ ):
def __init__( self , _a , _a ) -> Optional[Any]:
super().__init__()
self.register_modules(unet=_a , scheduler=_a )
def __call__( self ) -> Dict:
_A : str = torch.randn(
(1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , )
_A : Any = 1
_A : List[Any] = self.unet(_a , _a ).sample
_A : Dict = self.scheduler.step(_a , _a , _a ).prev_sample
_A : int = scheduler_output - scheduler_output + torch.ones_like(_a )
return result
| 26 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class lowercase ( unittest.TestCase ):
_a = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
_a = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def a__ ( self , _a , _a , _a ) -> int:
_A : str = TextaTextGenerationPipeline(model=_a , tokenizer=_a )
return generator, ["Something to write", "Something else"]
def a__ ( self , _a , _a ) -> Dict:
_A : Any = generator("""Something there""" )
self.assertEqual(_a , [{"""generated_text""": ANY(_a )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there""" ) )
_A : List[Any] = generator(["""This is great !""", """Something else"""] , num_return_sequences=2 , do_sample=_a )
self.assertEqual(
_a , [
[{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
[{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
] , )
_A : Optional[int] = generator(
["""This is great !""", """Something else"""] , num_return_sequences=2 , batch_size=2 , do_sample=_a )
self.assertEqual(
_a , [
[{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
[{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
] , )
with self.assertRaises(_a ):
generator(4 )
@require_torch
def a__ ( self ) -> List[str]:
_A : Any = pipeline("""text2text-generation""" , model="""patrickvonplaten/t5-tiny-random""" , framework="""pt""" )
# do_sample=False necessary for reproducibility
_A : Dict = generator("""Something there""" , do_sample=_a )
self.assertEqual(_a , [{"""generated_text""": """"""}] )
_A : Any = 3
_A : Any = generator(
"""Something there""" , num_return_sequences=_a , num_beams=_a , )
_A : Optional[int] = [
{"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
{"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
{"""generated_text""": """"""},
]
self.assertEqual(_a , _a )
_A : Dict = generator("""This is a test""" , do_sample=_a , num_return_sequences=2 , return_tensors=_a )
self.assertEqual(
_a , [
{"""generated_token_ids""": ANY(torch.Tensor )},
{"""generated_token_ids""": ANY(torch.Tensor )},
] , )
_A : Dict = generator.model.config.eos_token_id
_A : List[str] = """<pad>"""
_A : Dict = generator(
["""This is a test""", """This is a second test"""] , do_sample=_a , num_return_sequences=2 , batch_size=2 , return_tensors=_a , )
self.assertEqual(
_a , [
[
{"""generated_token_ids""": ANY(torch.Tensor )},
{"""generated_token_ids""": ANY(torch.Tensor )},
],
[
{"""generated_token_ids""": ANY(torch.Tensor )},
{"""generated_token_ids""": ANY(torch.Tensor )},
],
] , )
@require_tf
def a__ ( self ) -> int:
_A : Optional[Any] = pipeline("""text2text-generation""" , model="""patrickvonplaten/t5-tiny-random""" , framework="""tf""" )
# do_sample=False necessary for reproducibility
_A : str = generator("""Something there""" , do_sample=_a )
self.assertEqual(_a , [{"""generated_text""": """"""}] )
| 26 | 1 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class __magic_name__ ( unittest.TestCase):
def UpperCAmelCase__ ( self : Dict ) -> Any:
'''simple docstring'''
UpperCamelCase__ : Tuple = "hf-internal-testing/tiny-random-t5"
UpperCamelCase__ : Optional[Any] = AutoTokenizer.from_pretrained(lowercase_ )
UpperCamelCase__ : Optional[int] = AutoModelForSeqaSeqLM.from_pretrained(lowercase_ )
UpperCamelCase__ : List[Any] = tokenizer('''This is me''' , return_tensors='''pt''' )
UpperCamelCase__ : List[Any] = model.to_bettertransformer()
self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
UpperCamelCase__ : Dict = model.generate(**lowercase_ )
UpperCamelCase__ : Union[str, Any] = model.reverse_bettertransformer()
self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase_ )
UpperCamelCase__ : List[str] = AutoModelForSeqaSeqLM.from_pretrained(lowercase_ )
self.assertFalse(
any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
UpperCamelCase__ : Tuple = model_reloaded.generate(**lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , lowercase_ ) )
def UpperCAmelCase__ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ : Optional[int] = "hf-internal-testing/tiny-random-t5"
UpperCamelCase__ : Union[str, Any] = AutoModelForSeqaSeqLM.from_pretrained(lowercase_ )
UpperCamelCase__ : Union[str, Any] = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(lowercase_ ):
model.save_pretrained(lowercase_ )
UpperCamelCase__ : Optional[int] = model.reverse_bettertransformer()
model.save_pretrained(lowercase_ )
| 351 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __magic_name__ ( __lowerCAmelCase):
A: Optional[int] = ["image_processor", "tokenizer"]
A: int = "FlavaImageProcessor"
A: List[str] = ("BertTokenizer", "BertTokenizerFast")
def __init__( self : int , lowerCamelCase__ : Dict=None , lowerCamelCase__ : Union[str, Any]=None , **lowerCamelCase__ : int ) -> int:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , lowerCamelCase__ , )
UpperCamelCase__ : Union[str, Any] = kwargs.pop('''feature_extractor''' )
UpperCamelCase__ : str = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(lowerCamelCase__ , lowerCamelCase__ )
UpperCamelCase__ : List[str] = self.image_processor
def __call__( self : int , lowerCamelCase__ : Optional[ImageInput] = None , lowerCamelCase__ : Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None , lowerCamelCase__ : bool = True , lowerCamelCase__ : Union[bool, str, PaddingStrategy] = False , lowerCamelCase__ : Union[bool, str, TruncationStrategy] = False , lowerCamelCase__ : Optional[int] = None , lowerCamelCase__ : int = 0 , lowerCamelCase__ : Optional[int] = None , lowerCamelCase__ : Optional[bool] = None , lowerCamelCase__ : Optional[bool] = None , lowerCamelCase__ : Optional[bool] = None , lowerCamelCase__ : Optional[bool] = None , lowerCamelCase__ : bool = False , lowerCamelCase__ : bool = False , lowerCamelCase__ : bool = False , lowerCamelCase__ : bool = False , lowerCamelCase__ : bool = True , lowerCamelCase__ : Optional[Union[str, TensorType]] = None , **lowerCamelCase__ : List[str] , ) -> Any:
'''simple docstring'''
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
UpperCamelCase__ : Dict = self.tokenizer(
text=lowerCamelCase__ , add_special_tokens=lowerCamelCase__ , padding=lowerCamelCase__ , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ , stride=lowerCamelCase__ , pad_to_multiple_of=lowerCamelCase__ , return_token_type_ids=lowerCamelCase__ , return_attention_mask=lowerCamelCase__ , return_overflowing_tokens=lowerCamelCase__ , return_special_tokens_mask=lowerCamelCase__ , return_offsets_mapping=lowerCamelCase__ , return_length=lowerCamelCase__ , verbose=lowerCamelCase__ , return_tensors=lowerCamelCase__ , **lowerCamelCase__ , )
if images is not None:
UpperCamelCase__ : Optional[int] = self.image_processor(
lowerCamelCase__ , return_image_mask=lowerCamelCase__ , return_codebook_pixels=lowerCamelCase__ , return_tensors=lowerCamelCase__ , **lowerCamelCase__ , )
if text is not None and images is not None:
encoding.update(lowerCamelCase__ )
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowerCamelCase__ ) , tensor_type=lowerCamelCase__ )
def UpperCAmelCase__ ( self : Optional[Any] , *lowerCamelCase__ : str , **lowerCamelCase__ : int ) -> Optional[Any]:
'''simple docstring'''
return self.tokenizer.batch_decode(*lowerCamelCase__ , **lowerCamelCase__ )
def UpperCAmelCase__ ( self : Dict , *lowerCamelCase__ : Dict , **lowerCamelCase__ : int ) -> List[str]:
'''simple docstring'''
return self.tokenizer.decode(*lowerCamelCase__ , **lowerCamelCase__ )
@property
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = self.tokenizer.model_input_names
UpperCamelCase__ : Union[str, Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCAmelCase__ ( self : Tuple ) -> Dict:
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , lowerCamelCase__ , )
return self.image_processor_class
@property
def UpperCAmelCase__ ( self : List[Any] ) -> Any:
'''simple docstring'''
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , lowerCamelCase__ , )
return self.image_processor
| 51 | 0 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : list[int] ) -> list[list[int]]:
'''simple docstring'''
_UpperCAmelCase = []
if len(__lowercase ) == 1:
return [nums.copy()]
for _ in range(len(__lowercase ) ):
_UpperCAmelCase = nums.pop(0 )
_UpperCAmelCase = permute(__lowercase )
for perm in permutations:
perm.append(__lowercase )
result.extend(__lowercase )
nums.append(__lowercase )
return result
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
def backtrack(__lowercase : Dict ):
if start == len(__lowercase ) - 1:
output.append(nums[:] )
else:
for i in range(__lowercase , len(__lowercase ) ):
_UpperCAmelCase , _UpperCAmelCase = nums[i], nums[start]
backtrack(start + 1 )
_UpperCAmelCase , _UpperCAmelCase = nums[i], nums[start] # backtrack
_UpperCAmelCase = []
backtrack(0 )
return output
if __name__ == "__main__":
import doctest
# use res to print the data in permute2 function
__SCREAMING_SNAKE_CASE :Union[str, Any] = permutea([1, 2, 3])
print(res)
doctest.testmod()
| 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
return " ".join(
"".join(word[::-1] ) if len(__lowercase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wollef sroirraw'''))
| 22 | 1 |
'''simple docstring'''
def _UpperCAmelCase ( _UpperCamelCase : int = 3, _UpperCamelCase : int = 7, _UpperCamelCase : int = 1_00_00_00 ) -> int:
A_ = 0
A_ = 1
for current_denominator in range(1, limit + 1 ):
A_ = current_denominator * numerator // denominator
if current_denominator % denominator == 0:
current_numerator -= 1
if current_numerator * max_denominator > current_denominator * max_numerator:
A_ = current_numerator
A_ = current_denominator
return max_numerator
if __name__ == "__main__":
print(solution(numerator=3, denominator=7, limit=1_000_000))
| 18 | '''simple docstring'''
import math
def _UpperCAmelCase ( _UpperCamelCase : float, _UpperCamelCase : float ) -> float:
if initial_intensity < 0:
raise ValueError('''The value of intensity cannot be negative''' )
# handling of negative values of initial intensity
if angle < 0 or angle > 3_60:
raise ValueError('''In Malus Law, the angle is in the range 0-360 degrees''' )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(_UpperCamelCase ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name='malus_law')
| 18 | 1 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def __UpperCAmelCase ( a_ , a_ , a_):
# Initialise PyTorch model
snake_case_ = AlbertConfig.from_json_file(a__)
print(f'''Building PyTorch model from configuration: {config}''')
snake_case_ = AlbertForPreTraining(a__)
# Load weights from tf checkpoint
load_tf_weights_in_albert(a__ , a__ , a__)
# Save pytorch-model
print(f'''Save PyTorch model to {pytorch_dump_path}''')
torch.save(model.state_dict() , a__)
if __name__ == "__main__":
lowercase = 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(
"--albert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained ALBERT 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."
)
lowercase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 178 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Optional[int] = {
'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class __A( a ):
snake_case_ = '''levit'''
def __init__( self , _snake_case=224 , _snake_case=3 , _snake_case=3 , _snake_case=2 , _snake_case=1 , _snake_case=16 , _snake_case=[128, 256, 384] , _snake_case=[4, 8, 12] , _snake_case=[4, 4, 4] , _snake_case=[16, 16, 16] , _snake_case=0 , _snake_case=[2, 2, 2] , _snake_case=[2, 2, 2] , _snake_case=0.02 , **_snake_case , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**_snake_case )
__a = image_size
__a = num_channels
__a = kernel_size
__a = stride
__a = padding
__a = hidden_sizes
__a = num_attention_heads
__a = depths
__a = key_dim
__a = drop_path_rate
__a = patch_size
__a = attention_ratio
__a = mlp_ratio
__a = initializer_range
__a = [
['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class __A( a ):
snake_case_ = version.parse('''1.11''' )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> float:
'''simple docstring'''
return 1E-4 | 6 | 0 |
import logging
from transformers import PretrainedConfig
__a = logging.getLogger(__name__)
__a = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
A : Tuple = '''bertabs'''
def __init__( self , SCREAMING_SNAKE_CASE__=30522 , SCREAMING_SNAKE_CASE__=512 , SCREAMING_SNAKE_CASE__=6 , SCREAMING_SNAKE_CASE__=512 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=512 , SCREAMING_SNAKE_CASE__=0.2 , SCREAMING_SNAKE_CASE__=6 , SCREAMING_SNAKE_CASE__=768 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=0.2 , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(**_a )
lowercase : Any = vocab_size
lowercase : List[Any] = max_pos
lowercase : Union[str, Any] = enc_layers
lowercase : Optional[Any] = enc_hidden_size
lowercase : Dict = enc_heads
lowercase : List[Any] = enc_ff_size
lowercase : Any = enc_dropout
lowercase : Any = dec_layers
lowercase : List[str] = dec_hidden_size
lowercase : Dict = dec_heads
lowercase : Optional[int] = dec_ff_size
lowercase : Optional[int] = dec_dropout
| 355 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __lowercase ( _UpperCamelCase ) ->Tuple:
"""simple docstring"""
lowercase : List[str] = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(_UpperCamelCase, _UpperCamelCase )
def __lowercase ( _UpperCamelCase ) ->List[str]:
"""simple docstring"""
lowercase , lowercase : str = emb.weight.shape
lowercase : Optional[int] = nn.Linear(_UpperCamelCase, _UpperCamelCase, bias=_UpperCamelCase )
lowercase : Any = emb.weight.data
return lin_layer
def __lowercase ( _UpperCamelCase ) ->List[str]:
"""simple docstring"""
lowercase : Optional[int] = torch.load(_UpperCamelCase, map_location='''cpu''' )
lowercase : List[str] = mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
lowercase : int = mam_aaa['''model''']
remove_ignore_keys_(_UpperCamelCase )
lowercase : Any = state_dict['''encoder.embed_tokens.weight'''].shape[0]
lowercase : Dict = MaMaaaConfig(
vocab_size=_UpperCamelCase, max_position_embeddings=1024, encoder_layers=args.encoder_layers, decoder_layers=args.decoder_layers, encoder_attention_heads=args.encoder_attention_heads, decoder_attention_heads=args.decoder_attention_heads, encoder_ffn_dim=args.encoder_ffn_embed_dim, decoder_ffn_dim=args.decoder_ffn_embed_dim, d_model=args.encoder_embed_dim, encoder_layerdrop=args.encoder_layerdrop, decoder_layerdrop=args.decoder_layerdrop, dropout=args.dropout, attention_dropout=args.attention_dropout, activation_dropout=args.activation_dropout, activation_function='''relu''', )
lowercase : Union[str, Any] = state_dict['''decoder.embed_tokens.weight''']
lowercase : Dict = MaMaaaForConditionalGeneration(_UpperCamelCase )
model.model.load_state_dict(_UpperCamelCase, strict=_UpperCamelCase )
lowercase : Dict = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
__a = parser.parse_args()
__a = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 173 | 0 |
"""simple docstring"""
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
AlbertConfig,
BartConfig,
BertConfig,
CamembertConfig,
CTRLConfig,
DistilBertConfig,
DPRConfig,
ElectraConfig,
FlaubertConfig,
GPTaConfig,
LayoutLMConfig,
LxmertConfig,
OpenAIGPTConfig,
RobertaConfig,
TaConfig,
TFAlbertForPreTraining,
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFCamembertForMaskedLM,
TFCTRLLMHeadModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
TFElectraForPreTraining,
TFFlaubertWithLMHeadModel,
TFGPTaLMHeadModel,
TFLayoutLMForMaskedLM,
TFLxmertForPreTraining,
TFLxmertVisualFeatureEncoder,
TFOpenAIGPTLMHeadModel,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForSequenceClassification,
TFTaForConditionalGeneration,
TFTransfoXLLMHeadModel,
TFWavaVecaModel,
TFXLMRobertaForMaskedLM,
TFXLMWithLMHeadModel,
TFXLNetLMHeadModel,
TransfoXLConfig,
WavaVecaConfig,
WavaVecaModel,
XLMConfig,
XLMRobertaConfig,
XLNetConfig,
is_torch_available,
load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging
if is_torch_available():
import numpy as np
import torch
from . import (
AlbertForPreTraining,
BartForConditionalGeneration,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
CamembertForMaskedLM,
CTRLLMHeadModel,
DistilBertForMaskedLM,
DistilBertForQuestionAnswering,
DPRContextEncoder,
DPRQuestionEncoder,
DPRReader,
ElectraForPreTraining,
FlaubertWithLMHeadModel,
GPTaLMHeadModel,
LayoutLMForMaskedLM,
LxmertForPreTraining,
LxmertVisualFeatureEncoder,
OpenAIGPTLMHeadModel,
RobertaForMaskedLM,
RobertaForSequenceClassification,
TaForConditionalGeneration,
TransfoXLLMHeadModel,
XLMRobertaForMaskedLM,
XLMWithLMHeadModel,
XLNetLMHeadModel,
)
logging.set_verbosity_info()
__UpperCamelCase = {
'''bart''': (
BartConfig,
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
BartForConditionalGeneration,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'''bert''': (
BertConfig,
TFBertForPreTraining,
BertForPreTraining,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''bert-large-uncased-whole-word-masking-finetuned-squad''': (
BertConfig,
TFBertForQuestionAnswering,
BertForQuestionAnswering,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''bert-large-cased-whole-word-masking-finetuned-squad''': (
BertConfig,
TFBertForQuestionAnswering,
BertForQuestionAnswering,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''bert-base-cased-finetuned-mrpc''': (
BertConfig,
TFBertForSequenceClassification,
BertForSequenceClassification,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''dpr''': (
DPRConfig,
TFDPRQuestionEncoder,
TFDPRContextEncoder,
TFDPRReader,
DPRQuestionEncoder,
DPRContextEncoder,
DPRReader,
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'''gpt2''': (
GPTaConfig,
TFGPTaLMHeadModel,
GPTaLMHeadModel,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''xlnet''': (
XLNetConfig,
TFXLNetLMHeadModel,
XLNetLMHeadModel,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''xlm''': (
XLMConfig,
TFXLMWithLMHeadModel,
XLMWithLMHeadModel,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''xlm-roberta''': (
XLMRobertaConfig,
TFXLMRobertaForMaskedLM,
XLMRobertaForMaskedLM,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''transfo-xl''': (
TransfoXLConfig,
TFTransfoXLLMHeadModel,
TransfoXLLMHeadModel,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''openai-gpt''': (
OpenAIGPTConfig,
TFOpenAIGPTLMHeadModel,
OpenAIGPTLMHeadModel,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''roberta''': (
RobertaConfig,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
RobertaForMaskedLM,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''layoutlm''': (
LayoutLMConfig,
TFLayoutLMForMaskedLM,
LayoutLMForMaskedLM,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'''roberta-large-mnli''': (
RobertaConfig,
TFRobertaForSequenceClassification,
RobertaForSequenceClassification,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''camembert''': (
CamembertConfig,
TFCamembertForMaskedLM,
CamembertForMaskedLM,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''flaubert''': (
FlaubertConfig,
TFFlaubertWithLMHeadModel,
FlaubertWithLMHeadModel,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''distilbert''': (
DistilBertConfig,
TFDistilBertForMaskedLM,
DistilBertForMaskedLM,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''distilbert-base-distilled-squad''': (
DistilBertConfig,
TFDistilBertForQuestionAnswering,
DistilBertForQuestionAnswering,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''lxmert''': (
LxmertConfig,
TFLxmertForPreTraining,
LxmertForPreTraining,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''lxmert-visual-feature-encoder''': (
LxmertConfig,
TFLxmertVisualFeatureEncoder,
LxmertVisualFeatureEncoder,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''ctrl''': (
CTRLConfig,
TFCTRLLMHeadModel,
CTRLLMHeadModel,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''albert''': (
AlbertConfig,
TFAlbertForPreTraining,
AlbertForPreTraining,
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''t5''': (
TaConfig,
TFTaForConditionalGeneration,
TaForConditionalGeneration,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''electra''': (
ElectraConfig,
TFElectraForPreTraining,
ElectraForPreTraining,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''wav2vec2''': (
WavaVecaConfig,
TFWavaVecaModel,
WavaVecaModel,
WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
}
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=False , UpperCAmelCase=True ) -> int:
if model_type not in MODEL_CLASSES:
raise ValueError(f'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys() )}.' )
snake_case_ , snake_case_ , snake_case_ , snake_case_ = MODEL_CLASSES[model_type]
# Initialise TF model
if config_file in aws_config_map:
snake_case_ = cached_file(UpperCAmelCase , UpperCAmelCase , force_download=not use_cached_models )
snake_case_ = config_class.from_json_file(UpperCAmelCase )
snake_case_ = True
snake_case_ = True
print(f'Building TensorFlow model from configuration: {config}' )
snake_case_ = model_class(UpperCAmelCase )
# Load weights from tf checkpoint
if pytorch_checkpoint_path in aws_config_map.keys():
snake_case_ = cached_file(
UpperCAmelCase , UpperCAmelCase , force_download=not use_cached_models )
# Load PyTorch checkpoint in tf2 model:
snake_case_ = load_pytorch_checkpoint_in_tfa_model(UpperCAmelCase , UpperCAmelCase )
if compare_with_pt_model:
snake_case_ = tf_model(tf_model.dummy_inputs , training=UpperCAmelCase ) # build the network
snake_case_ = torch.load(UpperCAmelCase , map_location='cpu' )
snake_case_ = pt_model_class.from_pretrained(
pretrained_model_name_or_path=UpperCAmelCase , config=UpperCAmelCase , state_dict=UpperCAmelCase )
with torch.no_grad():
snake_case_ = pt_model(**pt_model.dummy_inputs )
snake_case_ = pto[0].numpy()
snake_case_ = tfo[0].numpy()
snake_case_ = np.amax(np.abs(np_pt - np_tf ) )
print(f'Max absolute difference between models outputs {diff}' )
assert diff <= 2e-2, f'Error, model absolute difference is >2e-2: {diff}'
# Save pytorch-model
print(f'Save TensorFlow model to {tf_dump_path}' )
tf_model.save_weights(UpperCAmelCase , save_format='h5' )
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=False , UpperCAmelCase=False , UpperCAmelCase=False , UpperCAmelCase=False , ) -> Union[str, Any]:
if args_model_type is None:
snake_case_ = list(MODEL_CLASSES.keys() )
else:
snake_case_ = [args_model_type]
for j, model_type in enumerate(UpperCAmelCase , start=1 ):
print('=' * 100 )
print(f' Converting model type {j}/{len(UpperCAmelCase )}: {model_type}' )
print('=' * 100 )
if model_type not in MODEL_CLASSES:
raise ValueError(f'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys() )}.' )
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ = MODEL_CLASSES[model_type]
if model_shortcut_names_or_path is None:
snake_case_ = list(aws_model_maps.keys() )
if config_shortcut_names_or_path is None:
snake_case_ = model_shortcut_names_or_path
for i, (model_shortcut_name, config_shortcut_name) in enumerate(
zip(UpperCAmelCase , UpperCAmelCase ) , start=1 ):
print('-' * 100 )
if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
if not only_convert_finetuned_models:
print(f' Skipping finetuned checkpoint {model_shortcut_name}' )
continue
snake_case_ = model_shortcut_name
elif only_convert_finetuned_models:
print(f' Skipping not finetuned checkpoint {model_shortcut_name}' )
continue
print(
f' Converting checkpoint {i}/{len(UpperCAmelCase )}: {model_shortcut_name} - model_type {model_type}' )
print('-' * 100 )
if config_shortcut_name in aws_config_map:
snake_case_ = cached_file(UpperCAmelCase , UpperCAmelCase , force_download=not use_cached_models )
else:
snake_case_ = config_shortcut_name
if model_shortcut_name in aws_model_maps:
snake_case_ = cached_file(UpperCAmelCase , UpperCAmelCase , force_download=not use_cached_models )
else:
snake_case_ = model_shortcut_name
if os.path.isfile(UpperCAmelCase ):
snake_case_ = 'converted_model'
convert_pt_checkpoint_to_tf(
model_type=UpperCAmelCase , pytorch_checkpoint_path=UpperCAmelCase , config_file=UpperCAmelCase , tf_dump_path=os.path.join(UpperCAmelCase , model_shortcut_name + '-tf_model.h5' ) , compare_with_pt_model=UpperCAmelCase , )
if remove_cached_files:
os.remove(UpperCAmelCase )
os.remove(UpperCAmelCase )
if __name__ == "__main__":
__UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.'''
)
parser.add_argument(
'''--model_type''',
default=None,
type=str,
help=(
F"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
'''convert all the models from AWS.'''
),
)
parser.add_argument(
'''--pytorch_checkpoint_path''',
default=None,
type=str,
help=(
'''Path to the PyTorch checkpoint path or shortcut name to download from AWS. '''
'''If not given, will download and convert all the checkpoints from AWS.'''
),
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
help=(
'''The config json file corresponding to the pre-trained model. \n'''
'''This specifies the model architecture. If not given and '''
'''--pytorch_checkpoint_path is not given or is a shortcut name '''
'''use the configuration associated to the shortcut name on the AWS'''
),
)
parser.add_argument(
'''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.'''
)
parser.add_argument(
'''--use_cached_models''',
action='''store_true''',
help='''Use cached models if possible instead of updating to latest checkpoint versions.''',
)
parser.add_argument(
'''--remove_cached_files''',
action='''store_true''',
help='''Remove pytorch models after conversion (save memory when converting in batches).''',
)
parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''')
__UpperCamelCase = parser.parse_args()
# if args.pytorch_checkpoint_path is not None:
# convert_pt_checkpoint_to_tf(args.model_type.lower(),
# args.pytorch_checkpoint_path,
# args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
# args.tf_dump_path,
# compare_with_pt_model=args.compare_with_pt_model,
# use_cached_models=args.use_cached_models)
# else:
convert_all_pt_checkpoints_to_tf(
args.model_type.lower() if args.model_type is not None else None,
args.tf_dump_path,
model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
if args.pytorch_checkpoint_path is not None
else None,
config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
compare_with_pt_model=args.compare_with_pt_model,
use_cached_models=args.use_cached_models,
remove_cached_files=args.remove_cached_files,
only_convert_finetuned_models=args.only_convert_finetuned_models,
)
| 69 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
'''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
'''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
'''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
'''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
'''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
'''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
'''self_attn.rotary_emb''': '''encoder.embed_positions''',
'''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
'''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
'''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
'''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
'''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
'''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
'''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
'''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
'''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
'''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
'''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
'''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
__UpperCamelCase = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Union[str, Any]:
for attribute in key.split('.' ):
snake_case_ = getattr(UpperCAmelCase , UpperCAmelCase )
if weight_type is not None:
snake_case_ = getattr(UpperCAmelCase , UpperCAmelCase ).shape
else:
snake_case_ = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}' )
if weight_type == "weight":
snake_case_ = value
elif weight_type == "weight_g":
snake_case_ = value
elif weight_type == "weight_v":
snake_case_ = value
elif weight_type == "bias":
snake_case_ = value
elif weight_type == "running_mean":
snake_case_ = value
elif weight_type == "running_var":
snake_case_ = value
elif weight_type == "num_batches_tracked":
snake_case_ = value
elif weight_type == "inv_freq":
snake_case_ = value
else:
snake_case_ = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> int:
snake_case_ = []
snake_case_ = fairseq_model.state_dict()
snake_case_ = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
snake_case_ = False
if "conv_layers" in name:
load_conv_layer(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , hf_model.config.feat_extract_norm == 'group' , )
snake_case_ = True
else:
for key, mapped_key in MAPPING.items():
snake_case_ = 'wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
snake_case_ = True
if "*" in mapped_key:
snake_case_ = name.split(UpperCAmelCase )[0].split('.' )[-2]
snake_case_ = mapped_key.replace('*' , UpperCAmelCase )
if "pos_bias_u" in name:
snake_case_ = None
elif "pos_bias_v" in name:
snake_case_ = None
elif "weight_g" in name:
snake_case_ = 'weight_g'
elif "weight_v" in name:
snake_case_ = 'weight_v'
elif "bias" in name:
snake_case_ = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
snake_case_ = 'weight'
elif "running_mean" in name:
snake_case_ = 'running_mean'
elif "inv_freq" in name:
snake_case_ = 'inv_freq'
elif "running_var" in name:
snake_case_ = 'running_var'
elif "num_batches_tracked" in name:
snake_case_ = 'num_batches_tracked'
else:
snake_case_ = None
set_recursively(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
continue
if not is_used:
unused_weights.append(UpperCAmelCase )
logger.warning(f'Unused weights: {unused_weights}' )
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Optional[Any]:
snake_case_ = full_name.split('conv_layers.' )[-1]
snake_case_ = name.split('.' )
snake_case_ = int(items[0] )
snake_case_ = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
snake_case_ = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
snake_case_ = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.' )
snake_case_ = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.' )
snake_case_ = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(UpperCAmelCase )
@torch.no_grad()
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=True ) -> str:
if config_path is not None:
snake_case_ = WavaVecaConformerConfig.from_pretrained(UpperCAmelCase , hidden_act='swish' )
else:
snake_case_ = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
snake_case_ = 'rotary'
if is_finetuned:
if dict_path:
snake_case_ = Dictionary.load(UpperCAmelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
snake_case_ = target_dict.pad_index
snake_case_ = target_dict.bos_index
snake_case_ = target_dict.eos_index
snake_case_ = len(target_dict.symbols )
snake_case_ = os.path.join(UpperCAmelCase , 'vocab.json' )
if not os.path.isdir(UpperCAmelCase ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(UpperCAmelCase ) )
return
os.makedirs(UpperCAmelCase , exist_ok=UpperCAmelCase )
snake_case_ = target_dict.indices
# fairseq has the <pad> and <s> switched
snake_case_ = 0
snake_case_ = 1
with open(UpperCAmelCase , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(UpperCAmelCase , UpperCAmelCase )
snake_case_ = WavaVecaCTCTokenizer(
UpperCAmelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='|' , do_lower_case=UpperCAmelCase , )
snake_case_ = True if config.feat_extract_norm == 'layer' else False
snake_case_ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=UpperCAmelCase , return_attention_mask=UpperCAmelCase , )
snake_case_ = WavaVecaProcessor(feature_extractor=UpperCAmelCase , tokenizer=UpperCAmelCase )
processor.save_pretrained(UpperCAmelCase )
snake_case_ = WavaVecaConformerForCTC(UpperCAmelCase )
else:
snake_case_ = WavaVecaConformerForPreTraining(UpperCAmelCase )
if is_finetuned:
snake_case_ , snake_case_ , snake_case_ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
snake_case_ = argparse.Namespace(task='audio_pretraining' )
snake_case_ = fairseq.tasks.setup_task(UpperCAmelCase )
snake_case_ , snake_case_ , snake_case_ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=UpperCAmelCase )
snake_case_ = model[0].eval()
recursively_load_weights(UpperCAmelCase , UpperCAmelCase , not is_finetuned )
hf_wavavec.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
__UpperCamelCase = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
__UpperCamelCase = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 69 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union
from packaging import version
from ..utils import is_torch_available, logging
if is_torch_available():
import torch
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
@dataclass
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Dict , __snake_case : List[str]=False , __snake_case : Optional[Any]=False , __snake_case : Union[str, Any]=6.0 , __snake_case : Union[str, Any]=None , __snake_case : Union[str, Any]=False , __snake_case : str=False , __snake_case : Optional[Any]=None , __snake_case : int="fp4" , __snake_case : int=False , **__snake_case : Optional[Any] , )-> List[Any]:
snake_case = load_in_abit
snake_case = load_in_abit
snake_case = llm_inta_threshold
snake_case = llm_inta_skip_modules
snake_case = llm_inta_enable_fpaa_cpu_offload
snake_case = llm_inta_has_fpaa_weight
snake_case = bnb_abit_quant_type
snake_case = bnb_abit_use_double_quant
if bnb_abit_compute_dtype is None:
snake_case = torch.floataa
elif isinstance(__snake_case , __snake_case ):
snake_case = getattr(__snake_case , __snake_case )
elif isinstance(__snake_case , torch.dtype ):
snake_case = bnb_abit_compute_dtype
else:
raise ValueError("""bnb_4bit_compute_dtype must be a string or a torch.dtype""" )
self.post_init()
def lowerCAmelCase ( self : str )-> str:
if not isinstance(self.llm_inta_threshold , __snake_case ):
raise ValueError("""llm_int8_threshold must be a float""" )
if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules , __snake_case ):
raise ValueError("""llm_int8_skip_modules must be a list of strings""" )
if not isinstance(self.llm_inta_enable_fpaa_cpu_offload , __snake_case ):
raise ValueError("""llm_int8_enable_fp32_cpu_offload must be a boolean""" )
if not isinstance(self.llm_inta_has_fpaa_weight , __snake_case ):
raise ValueError("""llm_int8_has_fp16_weight must be a boolean""" )
if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype , torch.dtype ):
raise ValueError("""bnb_4bit_compute_dtype must be torch.dtype""" )
if not isinstance(self.bnb_abit_quant_type , __snake_case ):
raise ValueError("""bnb_4bit_quant_type must be a string""" )
if not isinstance(self.bnb_abit_use_double_quant , __snake_case ):
raise ValueError("""bnb_4bit_use_double_quant must be a boolean""" )
if self.load_in_abit and not version.parse(importlib.metadata.version("""bitsandbytes""" ) ) >= version.parse(
"""0.39.0""" ):
raise ValueError(
"""4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version""" )
def lowerCAmelCase ( self : Union[str, Any] )-> Optional[Any]:
return self.load_in_abit or self.load_in_abit
def lowerCAmelCase ( self : List[Any] )-> List[Any]:
if self.load_in_abit:
return "llm_int8"
elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
return "fp4"
elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
return "nf4"
else:
return None
@classmethod
def lowerCAmelCase ( cls : List[str] , __snake_case : Dict , __snake_case : str , **__snake_case : Dict )-> Tuple:
snake_case = cls(**__snake_case )
snake_case = []
for key, value in kwargs.items():
if hasattr(__snake_case , __snake_case ):
setattr(__snake_case , __snake_case , __snake_case )
to_remove.append(__snake_case )
for key in to_remove:
kwargs.pop(__snake_case , __snake_case )
if return_unused_kwargs:
return config, kwargs
else:
return config
def lowerCAmelCase ( self : Tuple , __snake_case : Union[str, os.PathLike] )-> Optional[int]:
with open(__snake_case , """w""" , encoding="""utf-8""" ) as writer:
snake_case = self.to_dict()
snake_case = json.dumps(__snake_case , indent=2 , sort_keys=__snake_case ) + "\n"
writer.write(__snake_case )
def lowerCAmelCase ( self : List[str] )-> Union[str, Any]:
snake_case = copy.deepcopy(self.__dict__ )
snake_case = str(output["""bnb_4bit_compute_dtype"""] ).split(""".""" )[1]
return output
def __repr__( self : List[str] )-> Any:
return f'''{self.__class__.__name__} {self.to_json_string()}'''
def lowerCAmelCase ( self : List[str] , __snake_case : bool = True )-> str:
if use_diff is True:
snake_case = self.to_diff_dict()
else:
snake_case = self.to_dict()
return json.dumps(__snake_case , indent=2 , sort_keys=__snake_case ) + "\n"
def lowerCAmelCase ( self : Dict )-> Tuple:
snake_case = self.to_dict()
# get the default config dict
snake_case = BitsAndBytesConfig().to_dict()
snake_case = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
snake_case = value
return serializable_config_dict
| 353 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _lowerCAmelCase ( A__ ):
"""simple docstring"""
snake_case_ = "WhisperFeatureExtractor"
snake_case_ = "WhisperTokenizer"
def __init__( self : Dict , __snake_case : Any , __snake_case : int )-> List[Any]:
super().__init__(__snake_case , __snake_case )
snake_case = self.feature_extractor
snake_case = False
def lowerCAmelCase ( self : Union[str, Any] , __snake_case : str=None , __snake_case : List[str]=None , __snake_case : int=True )-> Union[str, Any]:
return self.tokenizer.get_decoder_prompt_ids(task=__snake_case , language=__snake_case , no_timestamps=__snake_case )
def __call__( self : str , *__snake_case : Tuple , **__snake_case : Union[str, Any] )-> Any:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*__snake_case , **__snake_case )
snake_case = kwargs.pop("""audio""" , __snake_case )
snake_case = kwargs.pop("""sampling_rate""" , __snake_case )
snake_case = kwargs.pop("""text""" , __snake_case )
if len(__snake_case ) > 0:
snake_case = args[0]
snake_case = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if audio is not None:
snake_case = self.feature_extractor(__snake_case , *__snake_case , sampling_rate=__snake_case , **__snake_case )
if text is not None:
snake_case = self.tokenizer(__snake_case , **__snake_case )
if text is None:
return inputs
elif audio is None:
return encodings
else:
snake_case = encodings["""input_ids"""]
return inputs
def lowerCAmelCase ( self : Union[str, Any] , *__snake_case : Union[str, Any] , **__snake_case : str )-> Optional[Any]:
return self.tokenizer.batch_decode(*__snake_case , **__snake_case )
def lowerCAmelCase ( self : Optional[int] , *__snake_case : Any , **__snake_case : Union[str, Any] )-> List[str]:
return self.tokenizer.decode(*__snake_case , **__snake_case )
def lowerCAmelCase ( self : Any , __snake_case : str , __snake_case : Dict="np" )-> Any:
return self.tokenizer.get_prompt_ids(__snake_case , return_tensors=__snake_case )
| 3 | 0 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def _A ( ):
"""simple docstring"""
a =ArgumentParser('''Accelerate CLI tool''' , usage='''accelerate <command> [<args>]''' , allow_abbrev=lowercase )
a =parser.add_subparsers(help='''accelerate command helpers''' )
# Register commands
get_config_parser(subparsers=lowercase )
env_command_parser(subparsers=lowercase )
launch_command_parser(subparsers=lowercase )
tpu_command_parser(subparsers=lowercase )
test_command_parser(subparsers=lowercase )
# Let's go
a =parser.parse_args()
if not hasattr(lowercase , '''func''' ):
parser.print_help()
exit(1 )
# Run
args.func(lowercase )
if __name__ == "__main__":
main() | 81 |
import os
import numpy
import onnx
def _lowerCamelCase( lowercase__ , lowercase__ ) -> Union[str, Any]:
'''simple docstring'''
__lowercase= a.name
__lowercase= b.name
__lowercase= ''
__lowercase= ''
__lowercase= a == b
__lowercase= name_a
__lowercase= name_b
return res
def _lowerCamelCase( lowercase__ , lowercase__ , lowercase__ ) -> Union[str, Any]:
'''simple docstring'''
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(lowercase__ , lowercase__ )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , lowercase__ , lowercase__ )
_graph_replace_input_with(node_proto.attribute[1].g , lowercase__ , lowercase__ )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , lowercase__ , lowercase__ )
def _lowerCamelCase( lowercase__ , lowercase__ , lowercase__ ) -> str:
'''simple docstring'''
for n in graph_proto.node:
_node_replace_input_with(lowercase__ , lowercase__ , lowercase__ )
def _lowerCamelCase( lowercase__ , lowercase__ , lowercase__ ) -> Any:
'''simple docstring'''
__lowercase= list(model.graph.initializer )
__lowercase= list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
__lowercase= inits[i].name
__lowercase= inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , lowercase__ , lowercase__ )
def _lowerCamelCase( lowercase__ ) -> Dict:
'''simple docstring'''
__lowercase= os.path.dirname(lowercase__ )
__lowercase= os.path.basename(lowercase__ )
__lowercase= onnx.load(os.path.join(lowercase__ , lowercase__ ) )
__lowercase= list(model.graph.initializer )
__lowercase= set()
__lowercase= {}
__lowercase= []
__lowercase= 0
for i in range(len(lowercase__ ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(lowercase__ ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(lowercase__ )
dup_set.add(lowercase__ )
__lowercase= inits[j].data_type
__lowercase= numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 1_1:
mem_size *= 8
else:
print('unexpected data type: ' , lowercase__ )
total_reduced_size += mem_size
__lowercase= inits[i].name
__lowercase= inits[j].name
if name_i in dup_map:
dup_map[name_i].append(lowercase__ )
else:
__lowercase= [name_j]
ind_to_replace.append((j, i) )
print('total reduced size: ' , total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4 , 'GB' )
__lowercase= sorted(lowercase__ )
_remove_dup_initializers_from_model(lowercase__ , lowercase__ , lowercase__ )
__lowercase= 'optimized_' + model_file_name
__lowercase= os.path.join(lowercase__ , lowercase__ )
onnx.save(lowercase__ , lowercase__ )
return new_model
| 295 | 0 |
'''simple docstring'''
import random
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
@staticmethod
def lowerCamelCase_ ( UpperCAmelCase_ : Any ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = [ord(_SCREAMING_SNAKE_CASE ) for i in text]
__UpperCAmelCase : str = []
__UpperCAmelCase : str = []
for i in plain:
__UpperCAmelCase : Tuple = random.randint(1 , 300 )
__UpperCAmelCase : Dict = (i + k) * k
cipher.append(_SCREAMING_SNAKE_CASE )
key.append(_SCREAMING_SNAKE_CASE )
return cipher, key
@staticmethod
def lowerCamelCase_ ( UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : List[str] ):
"""simple docstring"""
__UpperCAmelCase : str = []
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
__UpperCAmelCase : List[Any] = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(_SCREAMING_SNAKE_CASE ) )
return "".join(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowerCAmelCase__ : Optional[Any] = Onepad().encrypt("Hello")
print(c, k)
print(Onepad().decrypt(c, k))
| 368 |
'''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
lowerCAmelCase__ : Optional[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 __UpperCamelCase ( _UpperCAmelCase ):
# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
# the original model
__UpperCAmelCase : List[str] = list(s_dict.keys() )
for key in keys:
__UpperCAmelCase : int = R".*/layers_(\d+)"
__UpperCAmelCase : List[str] = key
if re.match(_UpperCAmelCase, _UpperCAmelCase ):
__UpperCAmelCase : Optional[int] = re.sub(R"layers_(\d+)", R"block/\1/layer", _UpperCAmelCase )
__UpperCAmelCase : Any = R"(encoder|decoder)\/"
if re.match(_UpperCAmelCase, _UpperCAmelCase ):
__UpperCAmelCase : List[Any] = re.match(_UpperCAmelCase, _UpperCAmelCase ).groups()
if groups[0] == "encoder":
__UpperCAmelCase : Optional[Any] = re.sub(R"/mlp/", R"/1/mlp/", _UpperCAmelCase )
__UpperCAmelCase : List[Any] = re.sub(R"/pre_mlp_layer_norm/", R"/1/layer_norm/", _UpperCAmelCase )
elif groups[0] == "decoder":
__UpperCAmelCase : List[Any] = re.sub(R"/mlp/", R"/2/mlp/", _UpperCAmelCase )
__UpperCAmelCase : Any = 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:
__UpperCAmelCase : List[str] = new_key.replace(_UpperCAmelCase, _UpperCAmelCase )
print(F"{key} -> {new_key}" )
__UpperCAmelCase : Any = s_dict.pop(_UpperCAmelCase )
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
__UpperCAmelCase : Tuple = s_dict[
"encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
__UpperCAmelCase : Optional[Any] = s_dict[
"decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys() ):
if "expert" in key:
__UpperCAmelCase : Any = s_dict[key].shape[0]
__UpperCAmelCase : str = s_dict[key]
for idx in range(_UpperCAmelCase ):
__UpperCAmelCase : Optional[Any] = expert_weihts[idx]
print(F"{key} -> {key.replace('expert/', 'nested fstring' )}" )
s_dict.pop(_UpperCAmelCase )
return s_dict
lowerCAmelCase__ : Optional[Any] = {
"NUM_ENCODER_LAYERS": "num_layers",
"NUM_DECODER_LAYERS": "num_decoder_layers",
"NUM_HEADS": "num_heads",
"HEAD_DIM": "d_kv",
"EMBED_DIM": "d_model",
"MLP_DIM": "d_ff",
"NUM_SELECTED_EXPERTS": "num_selected_experts",
"NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
"NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
"dense.MlpBlock.activations": "feed_forward_proj",
}
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase ):
# Convert a google style config to the hugging face fromat
import regex as re
with open(_UpperCAmelCase, "r" ) as f:
__UpperCAmelCase : List[Any] = f.read()
__UpperCAmelCase : Union[str, Any] = re.findall(R"(.*) = ([0-9.]*)", _UpperCAmelCase )
__UpperCAmelCase : Dict = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
__UpperCAmelCase : Tuple = float(_UpperCAmelCase ) if "." in value else int(_UpperCAmelCase )
__UpperCAmelCase : str = re.findall(R"(.*activations) = \(\'(.*)\',\)", _UpperCAmelCase )[0]
__UpperCAmelCase : int = str(activation[1] )
__UpperCAmelCase : int = num_experts
__UpperCAmelCase : List[str] = SwitchTransformersConfig(**_UpperCAmelCase )
return config
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase=None, _UpperCAmelCase="./", _UpperCAmelCase=8 ):
# Initialise PyTorch model
print(F"Loading flax weights from : {flax_checkpoint_path}" )
__UpperCAmelCase : Dict = checkpoints.load_tax_checkpoint(_UpperCAmelCase )
if gin_file is not None:
__UpperCAmelCase : int = convert_gin_to_config(_UpperCAmelCase, _UpperCAmelCase )
else:
__UpperCAmelCase : int = SwitchTransformersConfig.from_pretrained(_UpperCAmelCase )
__UpperCAmelCase : Any = SwitchTransformersForConditionalGeneration(_UpperCAmelCase )
__UpperCAmelCase : str = flax_params["target"]
__UpperCAmelCase : Any = flatten_dict(_UpperCAmelCase, sep="/" )
__UpperCAmelCase : Optional[Any] = rename_keys(_UpperCAmelCase )
__UpperCAmelCase : Any = 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__":
lowerCAmelCase__ : int = 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")
lowerCAmelCase__ : int = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 37 | 0 |
'''simple docstring'''
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Dict , __UpperCAmelCase : Any ):
'''simple docstring'''
_A = parent
def lowerCAmelCase ( self : int ):
'''simple docstring'''
return {}
def __lowercase ( ) -> Tuple:
'''simple docstring'''
_A = "<HTML>\n\n <HEAD>\n <TITLE>sample document</TITLE>\n </HEAD>\n\n <BODY BGCOLOR=\"FFFFFF\">\n <HR>\n <a href=\"http://google.com\">Goog</a>\n <H1>This is one header</H1>\n <H2>This is a another Header</H2>\n <P>Travel from\n <P>\n <B>SFO to JFK</B>\n <BR>\n <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n <HR>\n <div style=\"color:#0000FF\">\n <h3>Traveler <b> name </b> is\n <p> John Doe </p>\n </div>"
_A = "\n <!DOCTYPE html>\n <html>\n <body>\n\n <h1>My First Heading</h1>\n <p>My first paragraph.</p>\n\n </body>\n </html>\n "
return [html_string_a, html_string_a]
@require_bsa
class _UpperCAmelCase ( snake_case_ , unittest.TestCase ):
"""simple docstring"""
snake_case = MarkupLMFeatureExtractor if is_bsa_available() else None
def lowerCAmelCase ( self : Tuple ):
'''simple docstring'''
_A = MarkupLMFeatureExtractionTester(self )
@property
def lowerCAmelCase ( self : Tuple ):
'''simple docstring'''
return self.feature_extract_tester.prepare_feat_extract_dict()
def lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
_A = self.feature_extraction_class()
# Test not batched input
_A = get_html_strings()[0]
_A = feature_extractor(__UpperCAmelCase )
# fmt: off
_A = [["sample document", "Goog", "This is one header", "This is a another Header", "Travel from", "SFO to JFK", "on May 2, 2015 at 2:00 pm. For details go to confirm.com", "Traveler", "name", "is", "John Doe"]]
_A = [["/html/head/title", "/html/body/a", "/html/body/h1", "/html/body/h2", "/html/body/p", "/html/body/p/p/b[1]", "/html/body/p/p/b[2]/i", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/b", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/p"]]
# fmt: on
self.assertEqual(encoding.nodes , __UpperCAmelCase )
self.assertEqual(encoding.xpaths , __UpperCAmelCase )
# Test batched
_A = get_html_strings()
_A = feature_extractor(__UpperCAmelCase )
# fmt: off
_A = expected_nodes + [["My First Heading", "My first paragraph."]]
_A = expected_xpaths + [["/html/body/h1", "/html/body/p"]]
self.assertEqual(len(encoding.nodes ) , 2 )
self.assertEqual(len(encoding.xpaths ) , 2 )
self.assertEqual(encoding.nodes , __UpperCAmelCase )
self.assertEqual(encoding.xpaths , __UpperCAmelCase )
| 79 |
'''simple docstring'''
from __future__ import annotations
def __lowercase ( __lowercase , __lowercase = None , __lowercase = None ) -> None:
'''simple docstring'''
if start is None:
_A = 0
if end is None:
_A = len(__lowercase ) - 1
if start >= end:
return
_A = (start + end) // 2
slowsort(__lowercase , __lowercase , __lowercase )
slowsort(__lowercase , mid + 1 , __lowercase )
if sequence[end] < sequence[mid]:
_A , _A = sequence[mid], sequence[end]
slowsort(__lowercase , __lowercase , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 79 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''microsoft/beit-base-patch16-224-pt22k''': (
'''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class __snake_case ( _lowercase):
snake_case__ : Union[str, Any] = "beit"
def __init__( self : Dict , __lowerCAmelCase : str=8_1_9_2 , __lowerCAmelCase : Dict=7_6_8 , __lowerCAmelCase : Optional[Any]=1_2 , __lowerCAmelCase : Union[str, Any]=1_2 , __lowerCAmelCase : Union[str, Any]=3_0_7_2 , __lowerCAmelCase : Union[str, Any]="gelu" , __lowerCAmelCase : Union[str, Any]=0.0 , __lowerCAmelCase : str=0.0 , __lowerCAmelCase : Optional[int]=0.02 , __lowerCAmelCase : Union[str, Any]=1E-12 , __lowerCAmelCase : Union[str, Any]=2_2_4 , __lowerCAmelCase : Union[str, Any]=1_6 , __lowerCAmelCase : Union[str, Any]=3 , __lowerCAmelCase : List[Any]=False , __lowerCAmelCase : Optional[int]=False , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : str=0.1 , __lowerCAmelCase : Tuple=0.1 , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : Union[str, Any]=[3, 5, 7, 1_1] , __lowerCAmelCase : List[Any]=[1, 2, 3, 6] , __lowerCAmelCase : Any=True , __lowerCAmelCase : List[str]=0.4 , __lowerCAmelCase : int=2_5_6 , __lowerCAmelCase : Union[str, Any]=1 , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Union[str, Any]=2_5_5 , **__lowerCAmelCase : int , ):
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
_lowerCamelCase : List[str] = vocab_size
_lowerCamelCase : str = hidden_size
_lowerCamelCase : Union[str, Any] = num_hidden_layers
_lowerCamelCase : str = num_attention_heads
_lowerCamelCase : Dict = intermediate_size
_lowerCamelCase : Union[str, Any] = hidden_act
_lowerCamelCase : Any = hidden_dropout_prob
_lowerCamelCase : List[str] = attention_probs_dropout_prob
_lowerCamelCase : List[Any] = initializer_range
_lowerCamelCase : Optional[int] = layer_norm_eps
_lowerCamelCase : Any = image_size
_lowerCamelCase : Tuple = patch_size
_lowerCamelCase : Optional[Any] = num_channels
_lowerCamelCase : Any = use_mask_token
_lowerCamelCase : List[str] = use_absolute_position_embeddings
_lowerCamelCase : Tuple = use_relative_position_bias
_lowerCamelCase : Any = use_shared_relative_position_bias
_lowerCamelCase : List[Any] = layer_scale_init_value
_lowerCamelCase : str = drop_path_rate
_lowerCamelCase : Any = use_mean_pooling
# decode head attributes (semantic segmentation)
_lowerCamelCase : Any = out_indices
_lowerCamelCase : List[str] = pool_scales
# auxiliary head attributes (semantic segmentation)
_lowerCamelCase : int = use_auxiliary_head
_lowerCamelCase : Tuple = auxiliary_loss_weight
_lowerCamelCase : Tuple = auxiliary_channels
_lowerCamelCase : List[Any] = auxiliary_num_convs
_lowerCamelCase : List[str] = auxiliary_concat_input
_lowerCamelCase : str = semantic_loss_ignore_index
class __snake_case ( _lowercase):
snake_case__ : List[str] = version.parse("1.11")
@property
def SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def SCREAMING_SNAKE_CASE ( self : Dict ):
"""simple docstring"""
return 1E-4
| 175 |
"""simple docstring"""
def snake_case_ ( A_ : float ):
'''simple docstring'''
if edge <= 0 or not isinstance(A_, A_ ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def snake_case_ ( A_ : float ):
'''simple docstring'''
if edge <= 0 or not isinstance(A_, A_ ):
raise ValueError('''Length must be a positive.''' )
return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 175 | 1 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = ['''image_processor''', '''tokenizer''']
UpperCAmelCase : Optional[int] = '''AutoImageProcessor'''
UpperCAmelCase : Any = '''AutoTokenizer'''
def __init__( self : Optional[int] , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : Union[str, Any]=None , **_UpperCAmelCase : int ):
_A = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , _UpperCAmelCase , )
_A = kwargs.pop('feature_extractor' )
_A = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(_UpperCAmelCase , _UpperCAmelCase )
_A = self.image_processor
_A = False
def __call__( self : int , *_UpperCAmelCase : int , **_UpperCAmelCase : Any ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*_UpperCAmelCase , **_UpperCAmelCase )
_A = kwargs.pop('images' , _UpperCAmelCase )
_A = kwargs.pop('text' , _UpperCAmelCase )
if len(_UpperCAmelCase ) > 0:
_A = args[0]
_A = args[1:]
if images is None and text is None:
raise ValueError('You need to specify either an `images` or `text` input to process.' )
if images is not None:
_A = self.image_processor(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
if text is not None:
_A = self.tokenizer(_UpperCAmelCase , **_UpperCAmelCase )
if text is None:
return inputs
elif images is None:
return encodings
else:
_A = encodings['input_ids']
return inputs
def lowerCAmelCase_ ( self : Union[str, Any] , *_UpperCAmelCase : Any , **_UpperCAmelCase : int ):
return self.tokenizer.batch_decode(*_UpperCAmelCase , **_UpperCAmelCase )
def lowerCAmelCase_ ( self : List[Any] , *_UpperCAmelCase : int , **_UpperCAmelCase : Dict ):
return self.tokenizer.decode(*_UpperCAmelCase , **_UpperCAmelCase )
@contextmanager
def lowerCAmelCase_ ( self : int ):
warnings.warn(
'`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
'your images inputs, or in a separate call.' )
_A = True
_A = self.tokenizer
yield
_A = self.image_processor
_A = False
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict=False , _UpperCAmelCase : int=None ):
if added_vocab is None:
_A = self.tokenizer.get_added_vocab()
_A = {}
while tokens:
_A = re.search(r'<s_(.*?)>' , _UpperCAmelCase , re.IGNORECASE )
if start_token is None:
break
_A = start_token.group(1 )
_A = re.search(rF'''</s_{key}>''' , _UpperCAmelCase , re.IGNORECASE )
_A = start_token.group()
if end_token is None:
_A = tokens.replace(_UpperCAmelCase , '' )
else:
_A = end_token.group()
_A = re.escape(_UpperCAmelCase )
_A = re.escape(_UpperCAmelCase )
_A = re.search(F'''{start_token_escaped}(.*?){end_token_escaped}''' , _UpperCAmelCase , re.IGNORECASE )
if content is not None:
_A = content.group(1 ).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
_A = self.tokenajson(_UpperCAmelCase , is_inner_value=_UpperCAmelCase , added_vocab=_UpperCAmelCase )
if value:
if len(_UpperCAmelCase ) == 1:
_A = value[0]
_A = value
else: # leaf nodes
_A = []
for leaf in content.split(r'<sep/>' ):
_A = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
_A = leaf[1:-2] # for categorical special tokens
output[key].append(_UpperCAmelCase )
if len(output[key] ) == 1:
_A = output[key][0]
_A = tokens[tokens.find(_UpperCAmelCase ) + len(_UpperCAmelCase ) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:] , is_inner_value=_UpperCAmelCase , added_vocab=_UpperCAmelCase )
if len(_UpperCAmelCase ):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def lowerCAmelCase_ ( self : Optional[Any] ):
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , _UpperCAmelCase , )
return self.image_processor_class
@property
def lowerCAmelCase_ ( self : List[Any] ):
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , _UpperCAmelCase , )
return self.image_processor
| 315 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : List[str] ):
_A = []
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=_UpperCAmelCase , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_UpperCAmelCase )
_A = resnets
_A = attentions
if self.add_downsample:
_A = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Dict , _UpperCAmelCase : int , _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Tuple=True ):
_A = ()
for resnet, attn in zip(self.resnets , self.attentions ):
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
_A = attn(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
output_states += (hidden_states,)
if self.add_downsample:
_A = self.downsamplers_a(_UpperCAmelCase )
output_states += (hidden_states,)
return hidden_states, output_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : List[Any] ):
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=_UpperCAmelCase , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = resnets
if self.add_downsample:
_A = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : List[str] , _UpperCAmelCase : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : List[str]=True ):
_A = ()
for resnet in self.resnets:
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
output_states += (hidden_states,)
if self.add_downsample:
_A = self.downsamplers_a(_UpperCAmelCase )
output_states += (hidden_states,)
return hidden_states, output_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : Any ):
_A = []
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if (i == self.num_layers - 1) else self.out_channels
_A = self.prev_output_channel if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_UpperCAmelCase )
_A = resnets
_A = attentions
if self.add_upsample:
_A = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any]=True ):
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
_A = res_hidden_states_tuple[-1]
_A = res_hidden_states_tuple[:-1]
_A = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
_A = attn(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
if self.add_upsample:
_A = self.upsamplers_a(_UpperCAmelCase )
return hidden_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : Any ):
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if (i == self.num_layers - 1) else self.out_channels
_A = self.prev_output_channel if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = resnets
if self.add_upsample:
_A = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : int , _UpperCAmelCase : int , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[int]=True ):
for resnet in self.resnets:
# pop res hidden states
_A = res_hidden_states_tuple[-1]
_A = res_hidden_states_tuple[:-1]
_A = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
if self.add_upsample:
_A = self.upsamplers_a(_UpperCAmelCase )
return hidden_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : int = 1
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : Dict ):
# there is always at least one resnet
_A = [
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
_A = []
for _ in range(self.num_layers ):
_A = FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_UpperCAmelCase )
_A = FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = resnets
_A = attentions
def __call__( self : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[int]=True ):
_A = self.resnets[0](_UpperCAmelCase , _UpperCAmelCase )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
_A = attn(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
return hidden_states
| 315 | 1 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range(10 )]),
({"num_shards": 10, "max_num_jobs": 10}, [range(_a , i + 1 ) for i in range(10 )]),
({"num_shards": 1, "max_num_jobs": 10}, [range(1 )]),
({"num_shards": 10, "max_num_jobs": 3}, [range(0 , 4 ), range(4 , 7 ), range(7 , 10 )]),
({"num_shards": 3, "max_num_jobs": 10}, [range(0 , 1 ), range(1 , 2 ), range(2 , 3 )]),
] , )
def __a ( _UpperCamelCase: int , _UpperCamelCase: Dict ) -> List[str]:
"""simple docstring"""
_snake_case = _distribute_shards(**_a )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, max_num_jobs, expected" , [
({"foo": 0}, 10, [{"foo": 0}]),
({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
] , )
def __a ( _UpperCamelCase: List[str] , _UpperCamelCase: List[str] , _UpperCamelCase: str ) -> List[Any]:
"""simple docstring"""
_snake_case = _split_gen_kwargs(_a , _a )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, expected" , [
({"foo": 0}, 1),
({"shards": [0]}, 1),
({"shards": [0, 1, 2, 3]}, 4),
({"shards": [0, 1, 2, 3], "foo": 0}, 4),
({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
] , )
def __a ( _UpperCamelCase: Union[str, Any] , _UpperCamelCase: str ) -> List[Any]:
"""simple docstring"""
if expected is RuntimeError:
with pytest.raises(_a ):
_number_of_shards_in_gen_kwargs(_a )
else:
_snake_case = _number_of_shards_in_gen_kwargs(_a )
assert out == expected
| 365 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ : Union[str, Any] = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ : Optional[int] = ['''PLBartTokenizer''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ : int = [
'''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
UpperCamelCase_ : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 142 | 0 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : int = 1000 ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = -1
__UpperCAmelCase : Union[str, Any] = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
__UpperCAmelCase : Any = (n * n - 2 * a * n) // (2 * n - 2 * a)
__UpperCAmelCase : int = n - a - b
if c * c == (a * a + b * b):
__UpperCAmelCase : Dict = a * b * c
if candidate >= product:
__UpperCAmelCase : Any = candidate
return product
if __name__ == "__main__":
print(F'{solution() = }')
| 254 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = [
'''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TimesformerModel''',
'''TimesformerForVideoClassification''',
'''TimesformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
_UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 254 | 1 |
class lowercase :
'''simple docstring'''
def __init__( self ) -> None:
"""simple docstring"""
UpperCAmelCase = {} # Mapping from char to TrieNode
UpperCAmelCase = False
def snake_case_ ( self , _snake_case ) -> None:
"""simple docstring"""
for word in words:
self.insert(_snake_case )
def snake_case_ ( self , _snake_case ) -> None:
"""simple docstring"""
UpperCAmelCase = self
for char in word:
if char not in curr.nodes:
UpperCAmelCase = TrieNode()
UpperCAmelCase = curr.nodes[char]
UpperCAmelCase = True
def snake_case_ ( self , _snake_case ) -> bool:
"""simple docstring"""
UpperCAmelCase = self
for char in word:
if char not in curr.nodes:
return False
UpperCAmelCase = curr.nodes[char]
return curr.is_leaf
def snake_case_ ( self , _snake_case ) -> None:
"""simple docstring"""
def _delete(_snake_case , _snake_case , _snake_case ) -> bool:
if index == len(_snake_case ):
# If word does not exist
if not curr.is_leaf:
return False
UpperCAmelCase = False
return len(curr.nodes ) == 0
UpperCAmelCase = word[index]
UpperCAmelCase = curr.nodes.get(_snake_case )
# If char not in current trie node
if not char_node:
return False
# Flag to check if node can be deleted
UpperCAmelCase = _delete(_snake_case , _snake_case , index + 1 )
if delete_curr:
del curr.nodes[char]
return len(curr.nodes ) == 0
return delete_curr
_delete(self , _snake_case , 0 )
def _lowerCAmelCase ( A__: TrieNode , A__: str ):
'''simple docstring'''
if node.is_leaf:
print(A__ , end=''' ''' )
for key, value in node.nodes.items():
print_words(A__ , word + key )
def _lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase = '''banana bananas bandana band apple all beast'''.split()
UpperCAmelCase = TrieNode()
root.insert_many(A__ )
# print_words(root, "")
assert all(root.find(A__ ) for word in words )
assert root.find('''banana''' )
assert not root.find('''bandanas''' )
assert not root.find('''apps''' )
assert root.find('''apple''' )
assert root.find('''all''' )
root.delete('''all''' )
assert not root.find('''all''' )
root.delete('''banana''' )
assert not root.find('''banana''' )
assert root.find('''bananas''' )
return True
def _lowerCAmelCase ( A__: str , A__: bool ):
'''simple docstring'''
print(str(A__ ) , '''works!''' if passes else '''doesn\'t work :(''' )
def _lowerCAmelCase ( ):
'''simple docstring'''
assert test_trie()
def _lowerCAmelCase ( ):
'''simple docstring'''
print_results('''Testing trie functionality''' , test_trie() )
if __name__ == "__main__":
main()
| 152 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class lowercase ( TensorFormatter[Mapping, """torch.Tensor""", Mapping] ):
'''simple docstring'''
def __init__( self , _snake_case=None , **_snake_case ) -> int:
"""simple docstring"""
super().__init__(features=_snake_case )
UpperCAmelCase = torch_tensor_kwargs
import torch # noqa import torch at initialization
def snake_case_ ( self , _snake_case ) -> Union[str, Any]:
"""simple docstring"""
import torch
if isinstance(_snake_case , _snake_case ) and column:
if all(
isinstance(_snake_case , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(_snake_case )
return column
def snake_case_ ( self , _snake_case ) -> Optional[int]:
"""simple docstring"""
import torch
if isinstance(_snake_case , (str, bytes, type(_snake_case )) ):
return value
elif isinstance(_snake_case , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
UpperCAmelCase = {}
if isinstance(_snake_case , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
UpperCAmelCase = {'''dtype''': torch.intaa}
elif isinstance(_snake_case , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
UpperCAmelCase = {'''dtype''': torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(_snake_case , PIL.Image.Image ):
UpperCAmelCase = np.asarray(_snake_case )
return torch.tensor(_snake_case , **{**default_dtype, **self.torch_tensor_kwargs} )
def snake_case_ ( self , _snake_case ) -> Optional[Any]:
"""simple docstring"""
import torch
# support for torch, tf, jax etc.
if hasattr(_snake_case , '''__array__''' ) and not isinstance(_snake_case , torch.Tensor ):
UpperCAmelCase = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(_snake_case , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(_snake_case ) for substruct in data_struct] )
elif isinstance(_snake_case , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(_snake_case ) for substruct in data_struct] )
return self._tensorize(_snake_case )
def snake_case_ ( self , _snake_case ) -> List[Any]:
"""simple docstring"""
return map_nested(self._recursive_tensorize , _snake_case , map_list=_snake_case )
def snake_case_ ( self , _snake_case ) -> Mapping:
"""simple docstring"""
UpperCAmelCase = self.numpy_arrow_extractor().extract_row(_snake_case )
UpperCAmelCase = self.python_features_decoder.decode_row(_snake_case )
return self.recursive_tensorize(_snake_case )
def snake_case_ ( self , _snake_case ) -> "torch.Tensor":
"""simple docstring"""
UpperCAmelCase = self.numpy_arrow_extractor().extract_column(_snake_case )
UpperCAmelCase = self.python_features_decoder.decode_column(_snake_case , pa_table.column_names[0] )
UpperCAmelCase = self.recursive_tensorize(_snake_case )
UpperCAmelCase = self._consolidate(_snake_case )
return column
def snake_case_ ( self , _snake_case ) -> Mapping:
"""simple docstring"""
UpperCAmelCase = self.numpy_arrow_extractor().extract_batch(_snake_case )
UpperCAmelCase = self.python_features_decoder.decode_batch(_snake_case )
UpperCAmelCase = self.recursive_tensorize(_snake_case )
for column_name in batch:
UpperCAmelCase = self._consolidate(batch[column_name] )
return batch
| 152 | 1 |
import torch
from diffusers import StableDiffusionPipeline
SCREAMING_SNAKE_CASE : str = "path-to-your-trained-model"
SCREAMING_SNAKE_CASE : Optional[int] = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
SCREAMING_SNAKE_CASE : Any = "A photo of sks dog in a bucket"
SCREAMING_SNAKE_CASE : List[str] = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("dog-bucket.png")
| 21 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
snake_case_ : Any = logging.get_logger(__name__)
snake_case_ : Optional[int] = TypeVar("DatasetType", Dataset, IterableDataset)
def A (__A : List[DatasetType] , __A : Optional[List[float]] = None , __A : Optional[int] = None , __A : Optional[DatasetInfo] = None , __A : Optional[NamedSplit] = None , __A : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ) -> DatasetType:
"""simple docstring"""
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('''Unable to interleave an empty list of datasets.''' )
for i, dataset in enumerate(__A ):
if not isinstance(__A , (Dataset, IterableDataset) ):
if isinstance(__A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
'''is an empty dataset dictionary.''' )
raise ValueError(
F"""Dataset at position {i} has at least one split: {list(__A )}\n"""
F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__A ) )}']""" )
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__A ).__name__}.""" )
if i == 0:
UpperCAmelCase_ , UpperCAmelCase_ = (
(Dataset, IterableDataset) if isinstance(__A , __A ) else (IterableDataset, Dataset)
)
elif not isinstance(__A , __A ):
raise ValueError(
F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.""" )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy.""" )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
__A , __A , __A , info=__A , split=__A , stopping_strategy=__A )
else:
return _interleave_iterable_datasets(
__A , __A , __A , info=__A , split=__A , stopping_strategy=__A )
def A (__A : List[DatasetType] , __A : Optional[DatasetInfo] = None , __A : Optional[NamedSplit] = None , __A : int = 0 , ) -> DatasetType:
"""simple docstring"""
if not dsets:
raise ValueError('''Unable to concatenate an empty list of datasets.''' )
for i, dataset in enumerate(__A ):
if not isinstance(__A , (Dataset, IterableDataset) ):
if isinstance(__A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
'''is an empty dataset dictionary.''' )
raise ValueError(
F"""Dataset at position {i} has at least one split: {list(__A )}\n"""
F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__A ) )}']""" )
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__A ).__name__}.""" )
if i == 0:
UpperCAmelCase_ , UpperCAmelCase_ = (
(Dataset, IterableDataset) if isinstance(__A , __A ) else (IterableDataset, Dataset)
)
elif not isinstance(__A , __A ):
raise ValueError(
F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.""" )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(__A , info=__A , split=__A , axis=__A )
else:
return _concatenate_iterable_datasets(__A , info=__A , split=__A , axis=__A )
| 51 | 0 |
"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class __snake_case :
def __init__( self : str , __lowerCAmelCase : str ):
"""simple docstring"""
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
# modified it, it will not be accepted here again, since `auto` values would have been overridden
_lowerCamelCase : str = deepcopy(lowerCamelCase_ )
elif os.path.exists(lowerCamelCase_ ):
with io.open(lowerCamelCase_ , '''r''' , encoding='''utf-8''' ) as f:
_lowerCamelCase : str = json.load(lowerCamelCase_ )
else:
try:
_lowerCamelCase : List[Any] = baseaa.urlsafe_baadecode(lowerCamelCase_ ).decode('''utf-8''' )
_lowerCamelCase : List[Any] = json.loads(lowerCamelCase_ )
except (UnicodeDecodeError, AttributeError, ValueError):
raise ValueError(
f'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}''' )
_lowerCamelCase : List[Any] = config
self.set_stage_and_offload()
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : Any = self.get_value('''zero_optimization.stage''' , -1 )
# offload
_lowerCamelCase : int = False
if self.is_zeroa() or self.is_zeroa():
_lowerCamelCase : Any = set(['''cpu''', '''nvme'''] )
_lowerCamelCase : Union[str, Any] = set(
[
self.get_value('''zero_optimization.offload_optimizer.device''' ),
self.get_value('''zero_optimization.offload_param.device''' ),
] )
if len(offload_devices & offload_devices_valid ) > 0:
_lowerCamelCase : Optional[Any] = True
def SCREAMING_SNAKE_CASE ( self : str , __lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Dict = self.config
# find the config node of interest if it exists
_lowerCamelCase : str = ds_key_long.split('''.''' )
_lowerCamelCase : Dict = nodes.pop()
for node in nodes:
_lowerCamelCase : Optional[int] = config.get(lowerCamelCase_ )
if config is None:
return None, ds_key
return config, ds_key
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int=None ):
"""simple docstring"""
_lowerCamelCase , _lowerCamelCase : str = self.find_config_node(lowerCamelCase_ )
if config is None:
return default
return config.get(lowerCamelCase_ , lowerCamelCase_ )
def SCREAMING_SNAKE_CASE ( self : List[str] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[Any]=False ):
"""simple docstring"""
_lowerCamelCase : str = self.config
# find the config node of interest if it exists
_lowerCamelCase : str = ds_key_long.split('''.''' )
for node in nodes:
_lowerCamelCase : List[Any] = config
_lowerCamelCase : Union[str, Any] = config.get(lowerCamelCase_ )
if config is None:
if must_exist:
raise ValueError(f'''Can\'t find {ds_key_long} entry in the config: {self.config}''' )
else:
return
# if found remove it
if parent_config is not None:
parent_config.pop(lowerCamelCase_ )
def SCREAMING_SNAKE_CASE ( self : Any , __lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : int = self.get_value(lowerCamelCase_ )
return False if value is None else bool(lowerCamelCase_ )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , __lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = self.get_value(lowerCamelCase_ )
return False if value is None else not bool(lowerCamelCase_ )
def SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
return self._stage == 2
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
"""simple docstring"""
return self._stage == 3
def SCREAMING_SNAKE_CASE ( self : Dict ):
"""simple docstring"""
return self._offload
class __snake_case :
def __init__( self : Optional[int] , __lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = engine
def SCREAMING_SNAKE_CASE ( self : str , __lowerCAmelCase : int , **__lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
self.engine.backward(lowerCamelCase_ , **lowerCamelCase_ )
# Deepspeed's `engine.step` performs the following operations:
# - gradient accumulation check
# - gradient clipping
# - optimizer step
# - zero grad
# - checking overflow
# - lr_scheduler step (only if engine.lr_scheduler is not None)
self.engine.step()
# and this plugin overrides the above calls with no-ops when Accelerate runs under
# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
# training loop that works transparently under many training regimes.
class __snake_case ( _lowercase):
def __init__( self : Union[str, Any] , __lowerCAmelCase : List[Any] ):
"""simple docstring"""
super().__init__(lowerCamelCase_ , device_placement=lowerCamelCase_ , scaler=lowerCamelCase_ )
_lowerCamelCase : Optional[int] = hasattr(self.optimizer , '''overflow''' )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __lowerCAmelCase : Optional[Any]=None ):
"""simple docstring"""
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
def SCREAMING_SNAKE_CASE ( self : List[str] ):
"""simple docstring"""
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
@property
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
if self.__has_overflow__:
return self.optimizer.overflow
return False
class __snake_case ( _lowercase):
def __init__( self : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any ):
"""simple docstring"""
super().__init__(lowerCamelCase_ , lowerCamelCase_ )
def SCREAMING_SNAKE_CASE ( self : int ):
"""simple docstring"""
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
class __snake_case :
def __init__( self : Dict , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str]=0.0_01 , __lowerCAmelCase : Any=0 , **__lowerCAmelCase : Tuple ):
"""simple docstring"""
_lowerCamelCase : str = params
_lowerCamelCase : int = lr
_lowerCamelCase : int = weight_decay
_lowerCamelCase : Dict = kwargs
class __snake_case :
def __init__( self : List[str] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : Optional[Any]=0 , **__lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : List[str] = optimizer
_lowerCamelCase : str = total_num_steps
_lowerCamelCase : Tuple = warmup_num_steps
_lowerCamelCase : Union[str, Any] = kwargs
| 361 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
lowerCAmelCase__ = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class __snake_case ( datasets.BuilderConfig):
snake_case__ : Optional[datasets.Features] = None
def snake_case_ ( A_ : "pyspark.sql.DataFrame", A_ : List[int], ):
'''simple docstring'''
import pyspark
def generate_fn():
_lowerCamelCase : int = df.select('''*''', pyspark.sql.functions.spark_partition_id().alias('''part_id''' ) )
for partition_id in partition_order:
_lowerCamelCase : Any = df_with_partition_id.select('''*''' ).where(F'''part_id = {partition_id}''' ).drop('''part_id''' )
_lowerCamelCase : Optional[int] = partition_df.collect()
_lowerCamelCase : List[str] = 0
for row in rows:
yield F'''{partition_id}_{row_id}''', row.asDict()
row_id += 1
return generate_fn
class __snake_case ( _BaseExamplesIterable):
def __init__( self : Tuple , __lowerCAmelCase : "pyspark.sql.DataFrame" , __lowerCAmelCase : Optional[int]=None , ):
"""simple docstring"""
_lowerCamelCase : Dict = df
_lowerCamelCase : Union[str, Any] = partition_order or range(self.df.rdd.getNumPartitions() )
_lowerCamelCase : Dict = _generate_iterable_examples(self.df , self.partition_order )
def __iter__( self : List[Any] ):
"""simple docstring"""
yield from self.generate_examples_fn()
def SCREAMING_SNAKE_CASE ( self : Tuple , __lowerCAmelCase : np.random.Generator ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(__lowerCAmelCase )
return SparkExamplesIterable(self.df , partition_order=__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : List[Any] = self.split_shard_indices_by_worker(__lowerCAmelCase , __lowerCAmelCase )
return SparkExamplesIterable(self.df , partition_order=__lowerCAmelCase )
@property
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
"""simple docstring"""
return len(self.partition_order )
class __snake_case ( datasets.DatasetBuilder):
snake_case__ : List[Any] = SparkConfig
def __init__( self : Union[str, Any] , __lowerCAmelCase : "pyspark.sql.DataFrame" , __lowerCAmelCase : str = None , __lowerCAmelCase : str = None , **__lowerCAmelCase : int , ):
"""simple docstring"""
import pyspark
_lowerCamelCase : Optional[int] = pyspark.sql.SparkSession.builder.getOrCreate()
_lowerCamelCase : int = df
_lowerCamelCase : Any = working_dir
super().__init__(
cache_dir=__lowerCAmelCase , config_name=str(self.df.semanticHash() ) , **__lowerCAmelCase , )
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
def create_cache_and_write_probe(__lowerCAmelCase : Optional[int] ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir , exist_ok=__lowerCAmelCase )
_lowerCamelCase : Optional[int] = os.path.join(self._cache_dir , '''fs_test''' + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(__lowerCAmelCase , '''a''' )
return [probe_file]
if self._spark.conf.get('''spark.master''' , '''''' ).startswith('''local''' ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
_lowerCamelCase : Optional[Any] = (
self._spark.sparkContext.parallelize(range(1 ) , 1 ).mapPartitions(__lowerCAmelCase ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
'''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir''' )
def SCREAMING_SNAKE_CASE ( self : Any ):
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def SCREAMING_SNAKE_CASE ( self : int , __lowerCAmelCase : datasets.download.download_manager.DownloadManager ):
"""simple docstring"""
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def SCREAMING_SNAKE_CASE ( self : Tuple , __lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
import pyspark
def get_arrow_batch_size(__lowerCAmelCase : Dict ):
for batch in it:
yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]} )
_lowerCamelCase : Any = self.df.count()
_lowerCamelCase : Union[str, Any] = df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
_lowerCamelCase : List[Any] = (
self.df.limit(__lowerCAmelCase )
.repartition(1 )
.mapInArrow(__lowerCAmelCase , '''batch_bytes: long''' )
.agg(pyspark.sql.functions.sum('''batch_bytes''' ).alias('''sample_bytes''' ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
_lowerCamelCase : Dict = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
_lowerCamelCase : List[str] = min(__lowerCAmelCase , int(approx_total_size / max_shard_size ) )
_lowerCamelCase : Optional[int] = self.df.repartition(__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : int , ):
"""simple docstring"""
import pyspark
_lowerCamelCase : Optional[Any] = ParquetWriter if file_format == '''parquet''' else ArrowWriter
_lowerCamelCase : List[Any] = os.path.join(self._working_dir , os.path.basename(__lowerCAmelCase ) ) if self._working_dir else fpath
_lowerCamelCase : Dict = file_format == '''parquet'''
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
_lowerCamelCase : str = self.config.features
_lowerCamelCase : Dict = self._writer_batch_size
_lowerCamelCase : List[str] = self._fs.storage_options
def write_arrow(__lowerCAmelCase : List[str] ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
_lowerCamelCase : List[str] = pyspark.TaskContext().taskAttemptId()
_lowerCamelCase : Any = next(__lowerCAmelCase , __lowerCAmelCase )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
_lowerCamelCase : List[Any] = 0
_lowerCamelCase : Optional[int] = writer_class(
features=__lowerCAmelCase , path=working_fpath.replace('''SSSSS''' , f'''{shard_id:05d}''' ).replace('''TTTTT''' , f'''{task_id:05d}''' ) , writer_batch_size=__lowerCAmelCase , storage_options=__lowerCAmelCase , embed_local_files=__lowerCAmelCase , )
_lowerCamelCase : int = pa.Table.from_batches([first_batch] )
writer.write_table(__lowerCAmelCase )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
_lowerCamelCase , _lowerCamelCase : Any = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
shard_id += 1
_lowerCamelCase : Optional[int] = writer_class(
features=writer._features , path=working_fpath.replace('''SSSSS''' , f'''{shard_id:05d}''' ).replace('''TTTTT''' , f'''{task_id:05d}''' ) , writer_batch_size=__lowerCAmelCase , storage_options=__lowerCAmelCase , embed_local_files=__lowerCAmelCase , )
_lowerCamelCase : Optional[int] = pa.Table.from_batches([batch] )
writer.write_table(__lowerCAmelCase )
if writer._num_bytes > 0:
_lowerCamelCase , _lowerCamelCase : Optional[int] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(__lowerCAmelCase ) ):
_lowerCamelCase : Optional[Any] = os.path.join(os.path.dirname(__lowerCAmelCase ) , os.path.basename(__lowerCAmelCase ) )
shutil.move(__lowerCAmelCase , __lowerCAmelCase )
_lowerCamelCase : List[Any] = (
self.df.mapInArrow(__lowerCAmelCase , '''task_id: long, num_examples: long, num_bytes: long''' )
.groupBy('''task_id''' )
.agg(
pyspark.sql.functions.sum('''num_examples''' ).alias('''total_num_examples''' ) , pyspark.sql.functions.sum('''num_bytes''' ).alias('''total_num_bytes''' ) , pyspark.sql.functions.count('''num_bytes''' ).alias('''num_shards''' ) , pyspark.sql.functions.collect_list('''num_examples''' ).alias('''shard_lengths''' ) , )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def SCREAMING_SNAKE_CASE ( self : Tuple , __lowerCAmelCase : "datasets.SplitGenerator" , __lowerCAmelCase : str = "arrow" , __lowerCAmelCase : Optional[Union[str, int]] = None , __lowerCAmelCase : Optional[int] = None , **__lowerCAmelCase : Tuple , ):
"""simple docstring"""
self._validate_cache_dir()
_lowerCamelCase : str = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(__lowerCAmelCase )
_lowerCamelCase : str = not is_remote_filesystem(self._fs )
_lowerCamelCase : Tuple = os.path.join if is_local else posixpath.join
_lowerCamelCase : int = '''-TTTTT-SSSSS-of-NNNNN'''
_lowerCamelCase : Tuple = f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
_lowerCamelCase : List[Any] = path_join(self._output_dir , __lowerCAmelCase )
_lowerCamelCase : List[Any] = 0
_lowerCamelCase : Any = 0
_lowerCamelCase : str = 0
_lowerCamelCase : int = []
_lowerCamelCase : List[str] = []
for task_id, content in self._prepare_split_single(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
(
(
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) ,
) : str = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(__lowerCAmelCase )
_lowerCamelCase : int = total_num_examples
_lowerCamelCase : str = total_num_bytes
# should rename everything at the end
logger.debug(f'''Renaming {total_shards} shards.''' )
if total_shards > 1:
_lowerCamelCase : Optional[Any] = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
_lowerCamelCase : str = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
__lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : int , ):
rename(
__lowerCAmelCase , fpath.replace('''SSSSS''' , f'''{shard_id:05d}''' ).replace('''TTTTT''' , f'''{task_id:05d}''' ) , fpath.replace('''TTTTT-SSSSS''' , f'''{global_shard_id:05d}''' ).replace('''NNNNN''' , f'''{total_shards:05d}''' ) , )
_lowerCamelCase : Union[str, Any] = []
_lowerCamelCase : Any = 0
for i in range(len(__lowerCAmelCase ) ):
_lowerCamelCase , _lowerCamelCase : Dict = task_id_and_num_shards[i]
for shard_id in range(__lowerCAmelCase ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(__lowerCAmelCase , len(__lowerCAmelCase ) ).map(lambda __lowerCAmelCase : _rename_shard(*__lowerCAmelCase ) ).collect()
else:
# don't use any pattern
_lowerCamelCase : Any = 0
_lowerCamelCase : List[str] = task_id_and_num_shards[0][0]
self._rename(
fpath.replace('''SSSSS''' , f'''{shard_id:05d}''' ).replace('''TTTTT''' , f'''{task_id:05d}''' ) , fpath.replace(__lowerCAmelCase , '''''' ) , )
def SCREAMING_SNAKE_CASE ( self : Any , __lowerCAmelCase : "datasets.SplitGenerator" , ):
"""simple docstring"""
return SparkExamplesIterable(self.df )
| 175 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
__lowerCamelCase : Tuple = None
__lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
__lowerCamelCase : str = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
__lowerCamelCase : Optional[int] = {
'''vocab_file''': {
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
),
},
'''tokenizer_file''': {
'''google/bigbird-roberta-base''': (
'''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
),
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
),
},
}
__lowerCamelCase : List[str] = {
'''google/bigbird-roberta-base''': 40_96,
'''google/bigbird-roberta-large''': 40_96,
'''google/bigbird-base-trivia-itc''': 40_96,
}
__lowerCamelCase : Optional[Any] = '''▁'''
class a__ ( A__ ):
A = VOCAB_FILES_NAMES
A = PRETRAINED_VOCAB_FILES_MAP
A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A = BigBirdTokenizer
A = ['input_ids', 'attention_mask']
A = []
def __init__( self : Union[str, Any],_A : Any=None,_A : Any=None,_A : str="<unk>",_A : str="<s>",_A : int="</s>",_A : Union[str, Any]="<pad>",_A : Dict="[SEP]",_A : int="[MASK]",_A : int="[CLS]",**_A : Any,):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = AddedToken(_A,lstrip=_A,rstrip=_A ) if isinstance(_A,_A ) else bos_token
SCREAMING_SNAKE_CASE_ : int = AddedToken(_A,lstrip=_A,rstrip=_A ) if isinstance(_A,_A ) else eos_token
SCREAMING_SNAKE_CASE_ : Dict = AddedToken(_A,lstrip=_A,rstrip=_A ) if isinstance(_A,_A ) else unk_token
SCREAMING_SNAKE_CASE_ : str = AddedToken(_A,lstrip=_A,rstrip=_A ) if isinstance(_A,_A ) else pad_token
SCREAMING_SNAKE_CASE_ : int = AddedToken(_A,lstrip=_A,rstrip=_A ) if isinstance(_A,_A ) else cls_token
SCREAMING_SNAKE_CASE_ : List[str] = AddedToken(_A,lstrip=_A,rstrip=_A ) if isinstance(_A,_A ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE_ : List[Any] = AddedToken(_A,lstrip=_A,rstrip=_A ) if isinstance(_A,_A ) else mask_token
super().__init__(
_A,tokenizer_file=_A,bos_token=_A,eos_token=_A,unk_token=_A,sep_token=_A,pad_token=_A,cls_token=_A,mask_token=_A,**_A,)
SCREAMING_SNAKE_CASE_ : Optional[int] = vocab_file
SCREAMING_SNAKE_CASE_ : Union[str, Any] = False if not self.vocab_file else True
def __UpperCamelCase ( self : Union[str, Any],_A : List[int],_A : Optional[List[int]] = None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = [self.sep_token_id]
SCREAMING_SNAKE_CASE_ : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def __UpperCamelCase ( self : Union[str, Any],_A : List[int],_A : Optional[List[int]] = None,_A : bool = False ):
"""simple docstring"""
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model." )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_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 : List[Any],_A : List[int],_A : Optional[List[int]] = None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = [self.sep_token_id]
SCREAMING_SNAKE_CASE_ : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __UpperCamelCase ( self : str,_A : str,_A : Optional[str] = None ):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(_A ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
SCREAMING_SNAKE_CASE_ : List[str] = os.path.join(
_A,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_A ):
copyfile(self.vocab_file,_A )
return (out_vocab_file,)
| 18 | from functools import lru_cache
@lru_cache
def _snake_case ( lowerCAmelCase : int ):
"""simple docstring"""
if num < 0:
raise ValueError("Number should not be negative." )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 18 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__snake_case = {
'''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2Config'''],
'''processing_layoutlmv2''': ['''LayoutLMv2Processor'''],
'''tokenization_layoutlmv2''': ['''LayoutLMv2Tokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''LayoutLMv2TokenizerFast''']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''LayoutLMv2FeatureExtractor''']
__snake_case = ['''LayoutLMv2ImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LayoutLMv2ForQuestionAnswering''',
'''LayoutLMv2ForSequenceClassification''',
'''LayoutLMv2ForTokenClassification''',
'''LayoutLMv2Layer''',
'''LayoutLMv2Model''',
'''LayoutLMv2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaLayer,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 78 | import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __snake_case ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : List[Any] = KandinskyVaaControlnetPipeline
__lowerCamelCase : int = ["""image_embeds""", """negative_image_embeds""", """hint"""]
__lowerCamelCase : Optional[int] = ["""image_embeds""", """negative_image_embeds""", """hint"""]
__lowerCamelCase : Optional[Any] = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
__lowerCamelCase : Dict = False
@property
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
return 32
@property
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
return 32
@property
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
return self.time_input_dim
@property
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
return self.time_input_dim * 4
@property
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
return 100
@property
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase : Any ={
'''in_channels''': 8,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image_hint''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
UpperCAmelCase : List[Any] =UNetaDConditionModel(**snake_case__ )
return model
@property
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase : Any =VQModel(**self.dummy_movq_kwargs )
return model
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[str] =self.dummy_unet
UpperCAmelCase : Tuple =self.dummy_movq
UpperCAmelCase : Union[str, Any] =DDIMScheduler(
num_train_timesteps=1000 , beta_schedule='''linear''' , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=snake_case__ , set_alpha_to_one=snake_case__ , steps_offset=1 , prediction_type='''epsilon''' , thresholding=snake_case__ , )
UpperCAmelCase : Tuple ={
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def UpperCAmelCase__ ( self , snake_case__ , snake_case__=0 ) -> Any:
'''simple docstring'''
UpperCAmelCase : str =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(snake_case__ ) ).to(snake_case__ )
UpperCAmelCase : Tuple =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
snake_case__ )
# create hint
UpperCAmelCase : Tuple =floats_tensor((1, 3, 64, 64) , rng=random.Random(snake_case__ ) ).to(snake_case__ )
if str(snake_case__ ).startswith('''mps''' ):
UpperCAmelCase : Optional[int] =torch.manual_seed(snake_case__ )
else:
UpperCAmelCase : int =torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
UpperCAmelCase : List[str] ={
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''hint''': hint,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''guidance_scale''': 4.0,
'''num_inference_steps''': 2,
'''output_type''': '''np''',
}
return inputs
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] ='''cpu'''
UpperCAmelCase : List[Any] =self.get_dummy_components()
UpperCAmelCase : Tuple =self.pipeline_class(**snake_case__ )
UpperCAmelCase : Tuple =pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : Optional[int] =pipe(**self.get_dummy_inputs(snake_case__ ) )
UpperCAmelCase : str =output.images
UpperCAmelCase : List[str] =pipe(
**self.get_dummy_inputs(snake_case__ ) , return_dict=snake_case__ , )[0]
UpperCAmelCase : Union[str, Any] =image[0, -3:, -3:, -1]
UpperCAmelCase : List[str] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase : Union[str, Any] =np.array(
[0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy''' )
UpperCAmelCase : Tuple =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/hint_image_cat.png''' )
UpperCAmelCase : int =torch.from_numpy(np.array(snake_case__ ) ).float() / 255.0
UpperCAmelCase : List[str] =hint.permute(2 , 0 , 1 ).unsqueeze(0 )
UpperCAmelCase : Dict =KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(snake_case__ )
UpperCAmelCase : int =KandinskyVaaControlnetPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-controlnet-depth''' , torch_dtype=torch.floataa )
UpperCAmelCase : str =pipeline.to(snake_case__ )
pipeline.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : int ='''A robot, 4k photo'''
UpperCAmelCase : int =torch.Generator(device='''cuda''' ).manual_seed(0 )
UpperCAmelCase , UpperCAmelCase : List[str] =pipe_prior(
snake_case__ , generator=snake_case__ , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
UpperCAmelCase : List[str] =torch.Generator(device='''cuda''' ).manual_seed(0 )
UpperCAmelCase : Dict =pipeline(
image_embeds=snake_case__ , negative_image_embeds=snake_case__ , hint=snake_case__ , generator=snake_case__ , num_inference_steps=100 , output_type='''np''' , )
UpperCAmelCase : List[Any] =output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(snake_case__ , snake_case__ )
| 78 | 1 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
lowerCamelCase : Optional[Any] = 'src/transformers'
# Matches is_xxx_available()
lowerCamelCase : Union[str, Any] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
lowerCamelCase : int = re.compile(R'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowerCamelCase : int = re.compile(R'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
lowerCamelCase : int = re.compile(R'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
lowerCamelCase : Optional[Any] = re.compile(R'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowerCamelCase : Optional[Any] = re.compile(R'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma: "MyModel",
lowerCamelCase : Any = re.compile(R'^\s+"([^"]+)",')
# Catches a line with objects between brackets only: ["foo", "bar"],
lowerCamelCase : List[Any] = re.compile(R'^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
lowerCamelCase : Union[str, Any] = re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
lowerCamelCase : Union[str, Any] = re.compile(R'^\s*try:')
# Catches a line with else:
lowerCamelCase : Tuple = re.compile(R'^\s*else:')
def _SCREAMING_SNAKE_CASE (A ) -> Union[str, Any]:
"""simple docstring"""
if _re_test_backend.search(A ) is None:
return None
lowercase__ = [b[0] for b in _re_backend.findall(A )]
backends.sort()
return "_and_".join(A )
def _SCREAMING_SNAKE_CASE (A ) -> Any:
"""simple docstring"""
with open(A , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowercase__ = f.readlines()
lowercase__ = 0
while line_index < len(A ) and not lines[line_index].startswith('''_import_structure = {''' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(A ):
return None
# First grab the objects without a specific backend in _import_structure
lowercase__ = []
while not lines[line_index].startswith('''if TYPE_CHECKING''' ) and find_backend(lines[line_index] ) is None:
lowercase__ = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(A ):
lowercase__ = _re_one_line_import_struct.search(A ).groups()[0]
lowercase__ = re.findall(R'''\[([^\]]+)\]''' , A )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(''', ''' )] )
line_index += 1
continue
lowercase__ = _re_import_struct_key_value.search(A )
if single_line_import_search is not None:
lowercase__ = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', ''' ) if len(A ) > 0]
objects.extend(A )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
line_index += 1
lowercase__ = {'''none''': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('''if TYPE_CHECKING''' ):
# If the line is an if not is_backend_available, we grab all objects associated.
lowercase__ = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase__ = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase__ = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 4 ):
lowercase__ = lines[line_index]
if _re_import_struct_add_one.search(A ) is not None:
objects.append(_re_import_struct_add_one.search(A ).groups()[0] )
elif _re_import_struct_add_many.search(A ) is not None:
lowercase__ = _re_import_struct_add_many.search(A ).groups()[0].split(''', ''' )
lowercase__ = [obj[1:-1] for obj in imports if len(A ) > 0]
objects.extend(A )
elif _re_between_brackets.search(A ) is not None:
lowercase__ = _re_between_brackets.search(A ).groups()[0].split(''', ''' )
lowercase__ = [obj[1:-1] for obj in imports if len(A ) > 0]
objects.extend(A )
elif _re_quote_object.search(A ) is not None:
objects.append(_re_quote_object.search(A ).groups()[0] )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
elif line.startswith(''' ''' * 12 + '''"''' ):
objects.append(line[13:-3] )
line_index += 1
lowercase__ = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
lowercase__ = []
while (
line_index < len(A )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('''else''' )
):
lowercase__ = lines[line_index]
lowercase__ = _re_import.search(A )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 8 ):
objects.append(line[8:-2] )
line_index += 1
lowercase__ = {'''none''': objects}
# Let's continue with backend-specific objects
while line_index < len(A ):
# If the line is an if is_backend_available, we grab all objects associated.
lowercase__ = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase__ = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase__ = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 8 ):
lowercase__ = lines[line_index]
lowercase__ = _re_import.search(A )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 12 ):
objects.append(line[12:-2] )
line_index += 1
lowercase__ = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def _SCREAMING_SNAKE_CASE (A , A ) -> List[str]:
"""simple docstring"""
def find_duplicates(A ):
return [k for k, v in collections.Counter(A ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
lowercase__ = []
for key in import_dict_objects.keys():
lowercase__ = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(f"Duplicate _import_structure definitions for: {duplicate_imports}" )
lowercase__ = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(f"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}" )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
lowercase__ = '''base imports''' if key == '''none''' else f"{key} backend"
errors.append(f"Differences for {name}:" )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(f" {a} in TYPE_HINT but not in _import_structure." )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(f" {a} in _import_structure but not in TYPE_HINT." )
return errors
def _SCREAMING_SNAKE_CASE () -> int:
"""simple docstring"""
lowercase__ = []
for root, _, files in os.walk(A ):
if "__init__.py" in files:
lowercase__ = os.path.join(A , '''__init__.py''' )
lowercase__ = parse_init(A )
if objects is not None:
lowercase__ = analyze_results(*A )
if len(A ) > 0:
lowercase__ = f"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
failures.append('''\n'''.join(A ) )
if len(A ) > 0:
raise ValueError('''\n\n'''.join(A ) )
def _SCREAMING_SNAKE_CASE () -> Dict:
"""simple docstring"""
lowercase__ = []
for path, directories, files in os.walk(A ):
for folder in directories:
# Ignore private modules
if folder.startswith('''_''' ):
directories.remove(A )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(A ) / folder).glob('''*.py''' ) ) ) == 0:
continue
lowercase__ = str((Path(A ) / folder).relative_to(A ) )
lowercase__ = short_path.replace(os.path.sep , '''.''' )
submodules.append(A )
for fname in files:
if fname == "__init__.py":
continue
lowercase__ = str((Path(A ) / fname).relative_to(A ) )
lowercase__ = short_path.replace('''.py''' , '''''' ).replace(os.path.sep , '''.''' )
if len(submodule.split('''.''' ) ) == 1:
submodules.append(A )
return submodules
lowerCamelCase : List[Any] = [
'convert_pytorch_checkpoint_to_tf2',
'modeling_flax_pytorch_utils',
'models.esm.openfold_utils',
]
def _SCREAMING_SNAKE_CASE () -> str:
"""simple docstring"""
from transformers.utils import direct_transformers_import
lowercase__ = direct_transformers_import(A )
lowercase__ = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(A , '''__init__.py''' ) , '''r''' ) as f:
lowercase__ = f.read()
import_structure_keys.update(set(re.findall(R'''import_structure\[\"([^\"]*)\"\]''' , A ) ) )
lowercase__ = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(A ) > 0:
lowercase__ = '''\n'''.join(f"- {module}" for module in module_not_registered )
raise ValueError(
'''The following submodules are not properly registed in the main init of Transformers:\n'''
f"{list_of_modules}\n"
'''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.''' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 2 |
"""simple docstring"""
# Lint as: python3
import itertools
import os
import re
_UpperCAmelCase = re.compile(r"""([A-Z]+)([A-Z][a-z])""")
_UpperCAmelCase = re.compile(r"""([a-z\d])([A-Z])""")
_UpperCAmelCase = re.compile(r"""(?<!_)_(?!_)""")
_UpperCAmelCase = re.compile(r"""(_{2,})""")
_UpperCAmelCase = r"""^\w+(\.\w+)*$"""
_UpperCAmelCase = r"""<>:/\|?*"""
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Optional[int] =_uppercase_uppercase_re.sub(R"""\1_\2""" , lowercase )
SCREAMING_SNAKE_CASE_: str =_lowercase_uppercase_re.sub(R"""\1_\2""" , lowercase )
return name.lower()
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Optional[int] =_single_underscore_re.split(lowercase )
SCREAMING_SNAKE_CASE_: Any =[_multiple_underscores_re.split(lowercase ) for n in name]
return "".join(n.capitalize() for n in itertools.chain.from_iterable(lowercase ) if n != """""" )
def __magic_name__ ( lowercase ):
if os.path.basename(lowercase ) != name:
raise ValueError(f'''Should be a dataset name, not a path: {name}''' )
return camelcase_to_snakecase(lowercase )
def __magic_name__ ( lowercase , lowercase ):
if os.path.basename(lowercase ) != name:
raise ValueError(f'''Should be a dataset name, not a path: {name}''' )
if not re.match(_split_re , lowercase ):
raise ValueError(f'''Split name should match \'{_split_re}\'\' but got \'{split}\'.''' )
return f'''{filename_prefix_for_name(lowercase )}-{split}'''
def __magic_name__ ( lowercase , lowercase , lowercase , lowercase=None ):
SCREAMING_SNAKE_CASE_: List[Any] =filename_prefix_for_split(lowercase , lowercase )
if filetype_suffix:
prefix += f'''.{filetype_suffix}'''
SCREAMING_SNAKE_CASE_: Dict =os.path.join(lowercase , lowercase )
return f'''{filepath}*'''
def __magic_name__ ( lowercase , lowercase , lowercase , lowercase=None , lowercase=None ):
SCREAMING_SNAKE_CASE_: List[Any] =filename_prefix_for_split(lowercase , lowercase )
SCREAMING_SNAKE_CASE_: int =os.path.join(lowercase , lowercase )
if shard_lengths:
SCREAMING_SNAKE_CASE_: Any =len(lowercase )
SCREAMING_SNAKE_CASE_: Optional[Any] =[f'''{prefix}-{shard_id:05d}-of-{num_shards:05d}''' for shard_id in range(lowercase )]
if filetype_suffix:
SCREAMING_SNAKE_CASE_: Optional[int] =[filename + f'''.{filetype_suffix}''' for filename in filenames]
return filenames
else:
SCREAMING_SNAKE_CASE_: List[Any] =prefix
if filetype_suffix:
filename += f'''.{filetype_suffix}'''
return [filename]
| 173 | 0 |
"""simple docstring"""
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 UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
lowerCAmelCase_ : str = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , 'hidden_sizes' ) )
self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , 'num_attention_heads' ) )
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Optional[Any]=1_3 , SCREAMING_SNAKE_CASE_ : int=6_4 , SCREAMING_SNAKE_CASE_ : Tuple=3 , SCREAMING_SNAKE_CASE_ : Tuple=3 , SCREAMING_SNAKE_CASE_ : Any=2 , SCREAMING_SNAKE_CASE_ : Tuple=1 , SCREAMING_SNAKE_CASE_ : Tuple=1_6 , SCREAMING_SNAKE_CASE_ : Optional[int]=[1_2_8, 2_5_6, 3_8_4] , SCREAMING_SNAKE_CASE_ : Union[str, Any]=[4, 6, 8] , SCREAMING_SNAKE_CASE_ : List[Any]=[2, 3, 4] , SCREAMING_SNAKE_CASE_ : Any=[1_6, 1_6, 1_6] , SCREAMING_SNAKE_CASE_ : int=0 , SCREAMING_SNAKE_CASE_ : Any=[2, 2, 2] , SCREAMING_SNAKE_CASE_ : str=[2, 2, 2] , SCREAMING_SNAKE_CASE_ : Dict=0.02 , SCREAMING_SNAKE_CASE_ : List[Any]=True , SCREAMING_SNAKE_CASE_ : Union[str, Any]=True , SCREAMING_SNAKE_CASE_ : Tuple=2 , ):
lowerCAmelCase_ : Optional[Any] = parent
lowerCAmelCase_ : Tuple = batch_size
lowerCAmelCase_ : str = image_size
lowerCAmelCase_ : str = num_channels
lowerCAmelCase_ : Dict = kernel_size
lowerCAmelCase_ : Tuple = stride
lowerCAmelCase_ : Union[str, Any] = padding
lowerCAmelCase_ : Union[str, Any] = hidden_sizes
lowerCAmelCase_ : Any = num_attention_heads
lowerCAmelCase_ : Optional[int] = depths
lowerCAmelCase_ : Optional[int] = key_dim
lowerCAmelCase_ : Dict = drop_path_rate
lowerCAmelCase_ : List[Any] = patch_size
lowerCAmelCase_ : int = attention_ratio
lowerCAmelCase_ : Optional[Any] = mlp_ratio
lowerCAmelCase_ : Union[str, Any] = initializer_range
lowerCAmelCase_ : 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_ : List[Any] = is_training
lowerCAmelCase_ : List[Any] = use_labels
lowerCAmelCase_ : str = num_labels
lowerCAmelCase_ : str = initializer_range
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
lowerCAmelCase_ : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_ : Union[str, Any] = None
if self.use_labels:
lowerCAmelCase_ : Dict = ids_tensor([self.batch_size] , self.num_labels )
lowerCAmelCase_ : Optional[Any] = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
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 SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
lowerCAmelCase_ : Any = LevitModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase_ : Optional[Any] = model(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : str = (self.image_size, self.image_size)
lowerCAmelCase_ ,lowerCAmelCase_ : Tuple = image_size[0], image_size[1]
for _ in range(4 ):
lowerCAmelCase_ : str = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
lowerCAmelCase_ : Optional[int] = 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 SCREAMING_SNAKE_CASE__ ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : int ):
lowerCAmelCase_ : List[str] = self.num_labels
lowerCAmelCase_ : Optional[Any] = LevitForImageClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase_ : Tuple = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
lowerCAmelCase_ : List[Any] = self.prepare_config_and_inputs()
lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ : Any = config_and_inputs
lowerCAmelCase_ : Dict = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCamelCase__ ( lowercase_, lowercase_, unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (
(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
if is_torch_available()
else ()
)
_SCREAMING_SNAKE_CASE = (
{
"""feature-extraction""": LevitModel,
"""image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
lowerCAmelCase_ : int = LevitModelTester(self )
lowerCAmelCase_ : List[str] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , has_text_modality=SCREAMING_SNAKE_CASE_ , hidden_size=3_7 )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
return
@unittest.skip(reason='Levit does not use inputs_embeds' )
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
pass
@unittest.skip(reason='Levit does not support input and output embeddings' )
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
pass
@unittest.skip(reason='Levit does not output attentions' )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
pass
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
lowerCAmelCase_ ,lowerCAmelCase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : Dict = model_class(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ : Any = [*signature.parameters.keys()]
lowerCAmelCase_ : Optional[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
def check_hidden_states_output(SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
lowerCAmelCase_ : int = model_class(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
with torch.no_grad():
lowerCAmelCase_ : str = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
lowerCAmelCase_ : List[str] = outputs.hidden_states
lowerCAmelCase_ : Dict = len(self.model_tester.depths ) + 1
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Optional[Any] = (self.model_tester.image_size, self.model_tester.image_size)
lowerCAmelCase_ ,lowerCAmelCase_ : List[str] = image_size[0], image_size[1]
for _ in range(4 ):
lowerCAmelCase_ : int = floor(
(
(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
lowerCAmelCase_ : str = 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_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : str = True
check_hidden_states_output(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase_ : Dict = True
check_hidden_states_output(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
pass
def SCREAMING_SNAKE_CASE__ ( self : str , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : List[str]=False ):
lowerCAmelCase_ : Tuple = super()._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
if return_labels:
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
lowerCAmelCase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
lowerCAmelCase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
if not self.model_tester.is_training:
return
lowerCAmelCase_ ,lowerCAmelCase_ : Any = 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(SCREAMING_SNAKE_CASE_ )
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
lowerCAmelCase_ : Dict = model_class(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.train()
lowerCAmelCase_ : List[str] = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : str = model(**SCREAMING_SNAKE_CASE_ ).loss
loss.backward()
def SCREAMING_SNAKE_CASE__ ( self : str ):
lowerCAmelCase_ ,lowerCAmelCase_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
lowerCAmelCase_ : Optional[int] = False
lowerCAmelCase_ : Dict = True
for model_class in self.all_model_classes:
if model_class in get_values(SCREAMING_SNAKE_CASE_ ) or not model_class.supports_gradient_checkpointing:
continue
# LevitForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
continue
lowerCAmelCase_ : Dict = model_class(SCREAMING_SNAKE_CASE_ )
model.gradient_checkpointing_enable()
model.to(SCREAMING_SNAKE_CASE_ )
model.train()
lowerCAmelCase_ : Optional[int] = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Union[str, Any] = model(**SCREAMING_SNAKE_CASE_ ).loss
loss.backward()
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
lowerCAmelCase_ ,lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase_ : Dict = [
{'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(SCREAMING_SNAKE_CASE_ ),
]
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[int] = problem_type['num_labels']
lowerCAmelCase_ : str = model_class(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.train()
lowerCAmelCase_ : List[Any] = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
if problem_type["num_labels"] > 1:
lowerCAmelCase_ : List[Any] = 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=SCREAMING_SNAKE_CASE_ ) as warning_list:
lowerCAmelCase_ : Tuple = model(**SCREAMING_SNAKE_CASE_ ).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 SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : Any = LevitModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase_ : Dict = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : Any ):
return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def SCREAMING_SNAKE_CASE__ ( self : str ):
lowerCAmelCase_ : Union[str, Any] = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : str = self.default_image_processor
lowerCAmelCase_ : List[Any] = prepare_img()
lowerCAmelCase_ : List[Any] = image_processor(images=SCREAMING_SNAKE_CASE_ , return_tensors='pt' ).to(SCREAMING_SNAKE_CASE_ )
# forward pass
with torch.no_grad():
lowerCAmelCase_ : Optional[Any] = model(**SCREAMING_SNAKE_CASE_ )
# verify the logits
lowerCAmelCase_ : Optional[int] = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : str = torch.tensor([1.04_48, -0.37_45, -1.83_17] ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
| 289 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : List[Any] = logging.get_logger(__name__)
lowercase__ : List[str] = {
"""s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """open-llama"""
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : Tuple=1_0_0_0_0_0 , SCREAMING_SNAKE_CASE_ : Optional[int]=4_0_9_6 , SCREAMING_SNAKE_CASE_ : List[Any]=1_1_0_0_8 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=3_2 , SCREAMING_SNAKE_CASE_ : Optional[Any]=3_2 , SCREAMING_SNAKE_CASE_ : str="silu" , SCREAMING_SNAKE_CASE_ : Optional[Any]=2_0_4_8 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=0.02 , SCREAMING_SNAKE_CASE_ : int=1E-6 , SCREAMING_SNAKE_CASE_ : List[str]=True , SCREAMING_SNAKE_CASE_ : Dict=0 , SCREAMING_SNAKE_CASE_ : Dict=1 , SCREAMING_SNAKE_CASE_ : Optional[Any]=2 , SCREAMING_SNAKE_CASE_ : str=False , SCREAMING_SNAKE_CASE_ : Optional[int]=True , SCREAMING_SNAKE_CASE_ : List[Any]=0.1 , SCREAMING_SNAKE_CASE_ : Optional[Any]=0.1 , SCREAMING_SNAKE_CASE_ : Dict=True , SCREAMING_SNAKE_CASE_ : Any=True , SCREAMING_SNAKE_CASE_ : Tuple=None , **SCREAMING_SNAKE_CASE_ : Optional[int] , ):
lowerCAmelCase_ : Union[str, Any] = vocab_size
lowerCAmelCase_ : int = max_position_embeddings
lowerCAmelCase_ : Union[str, Any] = hidden_size
lowerCAmelCase_ : Optional[Any] = intermediate_size
lowerCAmelCase_ : List[Any] = num_hidden_layers
lowerCAmelCase_ : Optional[int] = num_attention_heads
lowerCAmelCase_ : List[Any] = hidden_act
lowerCAmelCase_ : List[Any] = initializer_range
lowerCAmelCase_ : List[str] = rms_norm_eps
lowerCAmelCase_ : List[Any] = use_cache
lowerCAmelCase_ : Optional[int] = kwargs.pop(
'use_memorry_efficient_attention' , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : List[Any] = hidden_dropout_prob
lowerCAmelCase_ : List[str] = attention_dropout_prob
lowerCAmelCase_ : Tuple = use_stable_embedding
lowerCAmelCase_ : Optional[Any] = shared_input_output_embedding
lowerCAmelCase_ : Optional[Any] = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , tie_word_embeddings=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , SCREAMING_SNAKE_CASE_ ) or len(self.rope_scaling ) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
F"got {self.rope_scaling}" )
lowerCAmelCase_ : int = self.rope_scaling.get('type' , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Union[str, Any] = self.rope_scaling.get('factor' , SCREAMING_SNAKE_CASE_ )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" )
if rope_scaling_factor is None or not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 289 | 1 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, 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 MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class UpperCAmelCase :
def __init__( self : Optional[int] , __snake_case : int , __snake_case : Optional[Any]=2 , __snake_case : int=True , __snake_case : str=False , __snake_case : List[str]=10 , __snake_case : Union[str, Any]=3 , __snake_case : List[Any]=32 * 4 , __snake_case : str=32 * 6 , __snake_case : int=4 , __snake_case : str=32 , ) -> str:
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = is_training
_lowerCAmelCase = use_auxiliary_loss
_lowerCAmelCase = num_queries
_lowerCAmelCase = num_channels
_lowerCAmelCase = min_size
_lowerCAmelCase = max_size
_lowerCAmelCase = num_labels
_lowerCAmelCase = mask_feature_size
def lowercase__ ( self : Optional[int] ) -> Optional[int]:
_lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__snake_case )
_lowerCAmelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__snake_case )
_lowerCAmelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__snake_case ) > 0.5
).float()
_lowerCAmelCase = (torch.rand((self.batch_size, self.num_labels) , device=__snake_case ) > 0.5).long()
_lowerCAmelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def lowercase__ ( self : Any ) -> Union[str, Any]:
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_28 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def lowercase__ ( self : Tuple ) -> Tuple:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.prepare_config_and_inputs()
_lowerCAmelCase = {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def lowercase__ ( self : List[Any] , __snake_case : str , __snake_case : Optional[int] ) -> List[Any]:
_lowerCAmelCase = output.encoder_hidden_states
_lowerCAmelCase = output.pixel_decoder_hidden_states
_lowerCAmelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__snake_case ) , config.decoder_config.decoder_layers )
def lowercase__ ( self : str , __snake_case : Any , __snake_case : Union[str, Any] , __snake_case : Optional[int] , __snake_case : Dict=False ) -> Dict:
with torch.no_grad():
_lowerCAmelCase = MaskFormerModel(config=__snake_case )
model.to(__snake_case )
model.eval()
_lowerCAmelCase = model(pixel_values=__snake_case , pixel_mask=__snake_case )
_lowerCAmelCase = model(__snake_case , output_hidden_states=__snake_case )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# 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(__snake_case , __snake_case )
def lowercase__ ( self : Union[str, Any] , __snake_case : int , __snake_case : int , __snake_case : Dict , __snake_case : Dict , __snake_case : str ) -> str:
_lowerCAmelCase = MaskFormerForInstanceSegmentation(config=__snake_case )
model.to(__snake_case )
model.eval()
def comm_check_on_output(__snake_case : List[str] ):
# 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():
_lowerCAmelCase = model(pixel_values=__snake_case , pixel_mask=__snake_case )
_lowerCAmelCase = model(__snake_case )
comm_check_on_output(__snake_case )
_lowerCAmelCase = model(
pixel_values=__snake_case , pixel_mask=__snake_case , mask_labels=__snake_case , class_labels=__snake_case )
comm_check_on_output(__snake_case )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class UpperCAmelCase ( snake_case_ , snake_case_ , unittest.TestCase ):
_lowercase: Any = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
_lowercase: int = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
_lowercase: Optional[int] = False
_lowercase: Union[str, Any] = False
_lowercase: Dict = False
_lowercase: Union[str, Any] = False
def lowercase__ ( self : Tuple ) -> List[Any]:
_lowerCAmelCase = MaskFormerModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case )
def lowercase__ ( self : Dict ) -> Dict:
self.config_tester.run_common_tests()
def lowercase__ ( self : Optional[Any] ) -> str:
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case )
def lowercase__ ( self : str ) -> Optional[int]:
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__snake_case )
@unittest.skip(reason="""MaskFormer does not use inputs_embeds""" )
def lowercase__ ( self : List[Any] ) -> List[str]:
pass
@unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method""" )
def lowercase__ ( self : Optional[Any] ) -> Optional[int]:
pass
@unittest.skip(reason="""MaskFormer is not a generative model""" )
def lowercase__ ( self : Union[str, Any] ) -> List[str]:
pass
@unittest.skip(reason="""MaskFormer does not use token embeddings""" )
def lowercase__ ( self : List[str] ) -> Any:
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def lowercase__ ( self : Optional[int] ) -> Optional[int]:
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def lowercase__ ( self : Optional[Any] ) -> Dict:
pass
def lowercase__ ( self : Tuple ) -> Union[str, Any]:
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(__snake_case )
_lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCAmelCase = [*signature.parameters.keys()]
_lowerCAmelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __snake_case )
@slow
def lowercase__ ( self : Optional[Any] ) -> str:
for model_name in ["facebook/maskformer-swin-small-coco"]:
_lowerCAmelCase = MaskFormerModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
def lowercase__ ( self : str ) -> int:
_lowerCAmelCase = (self.model_tester.min_size,) * 2
_lowerCAmelCase = {
"""pixel_values""": torch.randn((2, 3, *size) , device=__snake_case ),
"""mask_labels""": torch.randn((2, 10, *size) , device=__snake_case ),
"""class_labels""": torch.zeros(2 , 10 , device=__snake_case ).long(),
}
_lowerCAmelCase = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__snake_case )
_lowerCAmelCase = model(**__snake_case )
self.assertTrue(outputs.loss is not None )
def lowercase__ ( self : Optional[Any] ) -> List[Any]:
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case )
def lowercase__ ( self : str ) -> Optional[int]:
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(__snake_case ).to(__snake_case )
_lowerCAmelCase = model(**__snake_case , output_attentions=__snake_case )
self.assertTrue(outputs.attentions is not None )
def lowercase__ ( self : Tuple ) -> str:
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_lowerCAmelCase = self.all_model_classes[1]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
_lowerCAmelCase = model_class(__snake_case )
model.to(__snake_case )
model.train()
_lowerCAmelCase = model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case ).loss
loss.backward()
def lowercase__ ( self : Dict ) -> int:
# only MaskFormerForInstanceSegmentation has the loss
_lowerCAmelCase = self.all_model_classes[1]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
_lowerCAmelCase = True
_lowerCAmelCase = True
_lowerCAmelCase = model_class(__snake_case )
model.to(__snake_case )
model.train()
_lowerCAmelCase = model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case )
_lowerCAmelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_lowerCAmelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_lowerCAmelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_lowerCAmelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__snake_case )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
A__ : int =1e-4
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class UpperCAmelCase ( unittest.TestCase ):
@cached_property
def lowercase__ ( self : Dict ) -> Dict:
return (
MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco""" )
if is_vision_available()
else None
)
def lowercase__ ( self : str ) -> Union[str, Any]:
_lowerCAmelCase = MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco""" ).to(__snake_case )
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case )
_lowerCAmelCase = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__snake_case , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_lowerCAmelCase = model(**__snake_case )
_lowerCAmelCase = torch.tensor(
[[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]] ).to(__snake_case )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
_lowerCAmelCase = torch.tensor(
[[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]] ).to(__snake_case )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
_lowerCAmelCase = torch.tensor(
[[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]] ).to(__snake_case )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __snake_case , atol=__snake_case ) )
def lowercase__ ( self : Any ) -> Tuple:
_lowerCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" )
.to(__snake_case )
.eval()
)
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case )
_lowerCAmelCase = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__snake_case , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_lowerCAmelCase = model(**__snake_case )
# masks_queries_logits
_lowerCAmelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_lowerCAmelCase = [
[-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
[-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
[-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
]
_lowerCAmelCase = torch.tensor(__snake_case ).to(__snake_case )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
# class_queries_logits
_lowerCAmelCase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_lowerCAmelCase = torch.tensor(
[
[1.6_5_1_2E0_0, -5.2_5_7_2E0_0, -3.3_5_1_9E0_0],
[3.6_1_6_9E-0_2, -5.9_0_2_5E0_0, -2.9_3_1_3E0_0],
[1.0_7_6_6E-0_4, -7.7_6_3_0E0_0, -5.1_2_6_3E0_0],
] ).to(__snake_case )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __snake_case , atol=__snake_case ) )
def lowercase__ ( self : Tuple ) -> Optional[Any]:
_lowerCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff""" )
.to(__snake_case )
.eval()
)
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case )
_lowerCAmelCase = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__snake_case , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_lowerCAmelCase = model(**__snake_case )
# masks_queries_logits
_lowerCAmelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_lowerCAmelCase = [[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
_lowerCAmelCase = torch.tensor(__snake_case ).to(__snake_case )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __snake_case , atol=__snake_case ) )
# class_queries_logits
_lowerCAmelCase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_lowerCAmelCase = torch.tensor(
[[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]] ).to(__snake_case )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __snake_case , atol=__snake_case ) )
def lowercase__ ( self : Optional[int] ) -> Union[str, Any]:
_lowerCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" )
.to(__snake_case )
.eval()
)
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors="""pt""" , )
_lowerCAmelCase = inputs["""pixel_values"""].to(__snake_case )
_lowerCAmelCase = [el.to(__snake_case ) for el in inputs["""mask_labels"""]]
_lowerCAmelCase = [el.to(__snake_case ) for el in inputs["""class_labels"""]]
with torch.no_grad():
_lowerCAmelCase = model(**__snake_case )
self.assertTrue(outputs.loss is not None )
| 70 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class A ( nn.Module ):
__magic_name__ = 42
__magic_name__ = 42
__magic_name__ = 0.0
__magic_name__ = 1
__magic_name__ = 1
__magic_name__ = True
__magic_name__ = False
__magic_name__ = False
__magic_name__ = False
__magic_name__ = jnp.floataa
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
A : Union[str, Any] = []
A : Union[str, Any] = []
for i in range(self.num_layers ):
A : Any = self.in_channels if i == 0 else self.out_channels
A : Optional[Any] = FlaxResnetBlockaD(
in_channels=SCREAMING_SNAKE_CASE , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(SCREAMING_SNAKE_CASE )
A : Optional[int] = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(SCREAMING_SNAKE_CASE )
A : Union[str, Any] = resnets
A : Union[str, Any] = attentions
if self.add_downsample:
A : int = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=True ) -> Union[str, Any]:
"""simple docstring"""
A : Optional[Any] = ()
for resnet, attn in zip(self.resnets , self.attentions ):
A : int = resnet(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , deterministic=SCREAMING_SNAKE_CASE )
A : Dict = attn(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , deterministic=SCREAMING_SNAKE_CASE )
output_states += (hidden_states,)
if self.add_downsample:
A : Optional[Any] = self.downsamplers_a(SCREAMING_SNAKE_CASE )
output_states += (hidden_states,)
return hidden_states, output_states
class A ( nn.Module ):
__magic_name__ = 42
__magic_name__ = 42
__magic_name__ = 0.0
__magic_name__ = 1
__magic_name__ = True
__magic_name__ = jnp.floataa
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
A : Optional[Any] = []
for i in range(self.num_layers ):
A : Optional[Any] = self.in_channels if i == 0 else self.out_channels
A : List[str] = FlaxResnetBlockaD(
in_channels=SCREAMING_SNAKE_CASE , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(SCREAMING_SNAKE_CASE )
A : Dict = resnets
if self.add_downsample:
A : Dict = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=True ) -> Optional[Any]:
"""simple docstring"""
A : str = ()
for resnet in self.resnets:
A : Optional[int] = resnet(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , deterministic=SCREAMING_SNAKE_CASE )
output_states += (hidden_states,)
if self.add_downsample:
A : Optional[int] = self.downsamplers_a(SCREAMING_SNAKE_CASE )
output_states += (hidden_states,)
return hidden_states, output_states
class A ( nn.Module ):
__magic_name__ = 42
__magic_name__ = 42
__magic_name__ = 42
__magic_name__ = 0.0
__magic_name__ = 1
__magic_name__ = 1
__magic_name__ = True
__magic_name__ = False
__magic_name__ = False
__magic_name__ = False
__magic_name__ = jnp.floataa
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
A : Optional[Any] = []
A : Optional[int] = []
for i in range(self.num_layers ):
A : str = self.in_channels if (i == self.num_layers - 1) else self.out_channels
A : Dict = self.prev_output_channel if i == 0 else self.out_channels
A : List[str] = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(SCREAMING_SNAKE_CASE )
A : int = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(SCREAMING_SNAKE_CASE )
A : Dict = resnets
A : Optional[Any] = attentions
if self.add_upsample:
A : Optional[int] = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=True ) -> Optional[int]:
"""simple docstring"""
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
A : List[str] = res_hidden_states_tuple[-1]
A : int = res_hidden_states_tuple[:-1]
A : List[str] = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
A : Union[str, Any] = resnet(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , deterministic=SCREAMING_SNAKE_CASE )
A : Tuple = attn(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , deterministic=SCREAMING_SNAKE_CASE )
if self.add_upsample:
A : Dict = self.upsamplers_a(SCREAMING_SNAKE_CASE )
return hidden_states
class A ( nn.Module ):
__magic_name__ = 42
__magic_name__ = 42
__magic_name__ = 42
__magic_name__ = 0.0
__magic_name__ = 1
__magic_name__ = True
__magic_name__ = jnp.floataa
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
A : int = []
for i in range(self.num_layers ):
A : List[Any] = self.in_channels if (i == self.num_layers - 1) else self.out_channels
A : List[str] = self.prev_output_channel if i == 0 else self.out_channels
A : str = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(SCREAMING_SNAKE_CASE )
A : List[Any] = resnets
if self.add_upsample:
A : Optional[Any] = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=True ) -> Tuple:
"""simple docstring"""
for resnet in self.resnets:
# pop res hidden states
A : Optional[int] = res_hidden_states_tuple[-1]
A : Optional[Any] = res_hidden_states_tuple[:-1]
A : List[Any] = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
A : Optional[Any] = resnet(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , deterministic=SCREAMING_SNAKE_CASE )
if self.add_upsample:
A : List[str] = self.upsamplers_a(SCREAMING_SNAKE_CASE )
return hidden_states
class A ( nn.Module ):
__magic_name__ = 42
__magic_name__ = 0.0
__magic_name__ = 1
__magic_name__ = 1
__magic_name__ = False
__magic_name__ = False
__magic_name__ = jnp.floataa
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A : str = [
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
A : List[Any] = []
for _ in range(self.num_layers ):
A : int = FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(SCREAMING_SNAKE_CASE )
A : Union[str, Any] = FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(SCREAMING_SNAKE_CASE )
A : List[str] = resnets
A : List[str] = attentions
def __call__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=True ) -> Dict:
"""simple docstring"""
A : Optional[Any] = self.resnets[0](SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
A : Optional[int] = attn(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , deterministic=SCREAMING_SNAKE_CASE )
A : Union[str, Any] = resnet(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , deterministic=SCREAMING_SNAKE_CASE )
return hidden_states
| 3 | 0 |
"""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 _a :
"""simple docstring"""
def __init__( self : List[str] , __UpperCamelCase : Any , __UpperCamelCase : Optional[Any]=9_9 , __UpperCamelCase : List[str]=1_3 , __UpperCamelCase : Optional[int]=7 , __UpperCamelCase : Any=9 , __UpperCamelCase : List[Any]=True , __UpperCamelCase : Any=True , __UpperCamelCase : str=False , __UpperCamelCase : Optional[Any]=3_2 , __UpperCamelCase : List[str]=5 , __UpperCamelCase : List[Any]=4 , __UpperCamelCase : Any=3_7 , __UpperCamelCase : str=8 , __UpperCamelCase : int=0.1 , __UpperCamelCase : Tuple=0.0_0_2 , __UpperCamelCase : Dict=1 , __UpperCamelCase : List[str]=0 , __UpperCamelCase : Union[str, Any]=0 , __UpperCamelCase : Optional[int]=None , __UpperCamelCase : Dict=None , )->int:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = encoder_seq_length
_UpperCAmelCase = decoder_seq_length
# For common tests
_UpperCAmelCase = self.decoder_seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_attention_mask
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = d_ff
_UpperCAmelCase = relative_attention_num_buckets
_UpperCAmelCase = dropout_rate
_UpperCAmelCase = initializer_factor
_UpperCAmelCase = eos_token_id
_UpperCAmelCase = pad_token_id
_UpperCAmelCase = decoder_start_token_id
_UpperCAmelCase = None
_UpperCAmelCase = decoder_layers
def lowercase__ ( self : Optional[int] )->Union[str, Any]:
return TaConfig.from_pretrained('''google/umt5-base''' )
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : Any , __UpperCamelCase : Tuple , __UpperCamelCase : int , __UpperCamelCase : Union[str, Any]=None , __UpperCamelCase : str=None , __UpperCamelCase : List[str]=None , __UpperCamelCase : List[Any]=None , __UpperCamelCase : Optional[Any]=None , )->Optional[int]:
if attention_mask is None:
_UpperCAmelCase = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
_UpperCAmelCase = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
_UpperCAmelCase = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=__UpperCamelCase )
if decoder_head_mask is None:
_UpperCAmelCase = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=__UpperCamelCase )
if cross_attn_head_mask is None:
_UpperCAmelCase = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=__UpperCamelCase )
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 lowercase__ ( self : Dict )->Optional[Any]:
_UpperCAmelCase = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
_UpperCAmelCase = 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
_UpperCAmelCase = input_ids.clamp(self.pad_token_id + 1 )
_UpperCAmelCase = decoder_input_ids.clamp(self.pad_token_id + 1 )
_UpperCAmelCase = self.get_config()
_UpperCAmelCase = config.num_attention_heads
_UpperCAmelCase = self.prepare_inputs_dict(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return config, input_dict
def lowercase__ ( self : Any )->Optional[int]:
_UpperCAmelCase , _UpperCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def lowercase__ ( self : Dict )->List[str]:
return TaConfig(
vocab_size=1_6_6 , 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 lowercase__ ( self : Tuple )->Optional[Any]:
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 lowercase__ ( self : Dict , __UpperCamelCase : Dict , __UpperCamelCase : int , __UpperCamelCase : Optional[Any] , __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Union[str, Any] , )->int:
_UpperCAmelCase = UMTaModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(
input_ids=__UpperCamelCase , decoder_input_ids=__UpperCamelCase , attention_mask=__UpperCamelCase , decoder_attention_mask=__UpperCamelCase , )
_UpperCAmelCase = model(input_ids=__UpperCamelCase , decoder_input_ids=__UpperCamelCase )
_UpperCAmelCase = result.last_hidden_state
_UpperCAmelCase = result.past_key_values
_UpperCAmelCase = 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(__UpperCamelCase ) , 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 lowercase__ ( self : str , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Union[str, Any] , )->List[str]:
_UpperCAmelCase = UMTaModel(config=__UpperCamelCase ).get_decoder().to(__UpperCamelCase ).eval()
# first forward pass
_UpperCAmelCase = model(__UpperCamelCase , use_cache=__UpperCamelCase )
_UpperCAmelCase = model(__UpperCamelCase )
_UpperCAmelCase = model(__UpperCamelCase , use_cache=__UpperCamelCase )
self.parent.assertTrue(len(__UpperCamelCase ) == len(__UpperCamelCase ) )
self.parent.assertTrue(len(__UpperCamelCase ) == len(__UpperCamelCase ) + 1 )
_UpperCAmelCase , _UpperCAmelCase = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_UpperCAmelCase = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
_UpperCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase = model(__UpperCamelCase )['''last_hidden_state''']
_UpperCAmelCase = model(__UpperCamelCase , past_key_values=__UpperCamelCase )['''last_hidden_state''']
# select random slice
_UpperCAmelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase = output_from_no_past[:, -1, random_slice_idx].detach()
_UpperCAmelCase = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : List[Any] , __UpperCamelCase : Any , )->List[Any]:
_UpperCAmelCase = UMTaModel(config=__UpperCamelCase ).to(__UpperCamelCase ).half().eval()
_UpperCAmelCase = model(**__UpperCamelCase )['''last_hidden_state''']
self.parent.assertFalse(torch.isnan(__UpperCamelCase ).any().item() )
@require_torch
class _a ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
UpperCamelCase__ = (UMTaForConditionalGeneration,) if is_torch_available() else ()
UpperCamelCase__ = (
{
"""conversational""": UMTaForConditionalGeneration,
"""feature-extraction""": UMTaModel,
"""summarization""": UMTaForConditionalGeneration,
"""text2text-generation""": UMTaForConditionalGeneration,
"""translation""": UMTaForConditionalGeneration,
"""question-answering""": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
UpperCamelCase__ = True
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = True
# The small UMT5 model needs higher percentages for CPU/MP tests
UpperCamelCase__ = [0.8, 0.9]
def lowercase__ ( self : Optional[Any] )->Tuple:
_UpperCAmelCase = UMTaModelTester(self )
@unittest.skip('''Test has a segmentation fault on torch 1.8.0''' )
def lowercase__ ( self : Tuple )->Union[str, Any]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = UMTaModel(config_and_inputs[0] ).to(__UpperCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
__UpperCamelCase , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , F'{tmpdirname}/t5_test.onnx' , export_params=__UpperCamelCase , opset_version=9 , input_names=['''input_ids''', '''decoder_input_ids'''] , )
@unittest.skipIf(torch_device == '''cpu''' , '''Cant do half precision''' )
def lowercase__ ( self : Optional[int] )->Dict:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*__UpperCamelCase )
def lowercase__ ( self : Optional[int] )->Tuple:
_UpperCAmelCase = ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = config_and_inputs[0]
_UpperCAmelCase = UMTaForConditionalGeneration(__UpperCamelCase ).eval()
model.to(__UpperCamelCase )
_UpperCAmelCase = {
'''head_mask''': torch.zeros(config.num_layers , config.num_heads , device=__UpperCamelCase ),
'''decoder_head_mask''': torch.zeros(config.num_decoder_layers , config.num_heads , device=__UpperCamelCase ),
'''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers , config.num_heads , device=__UpperCamelCase ),
}
for attn_name, (name, mask) in zip(__UpperCamelCase , head_masking.items() ):
_UpperCAmelCase = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
_UpperCAmelCase = torch.ones(
config.num_decoder_layers , config.num_heads , device=__UpperCamelCase )
_UpperCAmelCase = model.generate(
config_and_inputs[1]['''input_ids'''] , num_beams=1 , max_length=3 , output_attentions=__UpperCamelCase , return_dict_in_generate=__UpperCamelCase , **__UpperCamelCase , )
# We check the state of decoder_attentions and cross_attentions just from the last step
_UpperCAmelCase = 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 lowercase__ ( self : str )->List[Any]:
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class _a ( unittest.TestCase):
"""simple docstring"""
@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 lowercase__ ( self : List[Any] )->Optional[Any]:
_UpperCAmelCase = UMTaForConditionalGeneration.from_pretrained('''google/umt5-small''' , return_dict=__UpperCamelCase ).to(__UpperCamelCase )
_UpperCAmelCase = AutoTokenizer.from_pretrained('''google/umt5-small''' , use_fast=__UpperCamelCase , legacy=__UpperCamelCase )
_UpperCAmelCase = [
'''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>.''',
]
_UpperCAmelCase = tokenizer(__UpperCamelCase , return_tensors='''pt''' , padding=__UpperCamelCase ).input_ids
# fmt: off
_UpperCAmelCase = torch.tensor(
[
[ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
] )
# fmt: on
torch.testing.assert_allclose(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = model.generate(input_ids.to(__UpperCamelCase ) )
_UpperCAmelCase = [
'''<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>''',
]
_UpperCAmelCase = tokenizer.batch_decode(__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
| 326 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__A : Union[str, Any] = "src/transformers"
# This is to make sure the transformers module imported is the one in the repo.
__A : Tuple = importlib.util.spec_from_file_location(
"transformers",
os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
__A : List[str] = spec.loader.load_module()
__A : Any = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
__A : Optional[int] = re.compile("\[(.+?)\]\((https://huggingface\.co/.+?)\)")
__A : List[str] = {
"CLIPConfigMixin",
"DecisionTransformerConfigMixin",
"EncoderDecoderConfigMixin",
"RagConfigMixin",
"SpeechEncoderDecoderConfigMixin",
"VisionEncoderDecoderConfigMixin",
"VisionTextDualEncoderConfigMixin",
}
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = []
for config_class in list(CONFIG_MAPPING.values() ):
_UpperCAmelCase = False
# source code of `config_class`
_UpperCAmelCase = inspect.getsource(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = _re_checkpoint.findall(_SCREAMING_SNAKE_CASE )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
_UpperCAmelCase , _UpperCAmelCase = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
_UpperCAmelCase = f'https://huggingface.co/{ckpt_name}'
if ckpt_link == ckpt_link_from_name:
_UpperCAmelCase = True
break
_UpperCAmelCase = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(_SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) > 0:
_UpperCAmelCase = '''\n'''.join(sorted(_SCREAMING_SNAKE_CASE ) )
raise ValueError(f'The following configurations don\'t contain any valid checkpoint:\n{message}' )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 326 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_a = '\\n\n'
_a = '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'
_a = '\nArgs:\n model_id (str): model used for calculating Perplexity\n NOTE: Perplexity can only be calculated for causal language models.\n This includes models such as gpt2, causal variations of bert,\n causal versions of t5, and more (the full list can be found\n in the AutoModelForCausalLM documentation here:\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n input_texts (list of str): input text, each separate text snippet\n is one list entry.\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\n add_start_token (bool): whether to add the start token to the texts,\n so the perplexity can include the probability of the first word. Defaults to True.\n device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n perplexity: dictionary containing the perplexity scores for the texts\n in the input list, as well as the mean perplexity. If one of the input texts is\n longer than the max input length of the model, then it is truncated to the\n max length for the perplexity computation.\nExamples:\n Example 1:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... add_start_token=False,\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 78.22\n >>> print(round(results["perplexities"][0], 2))\n 11.11\n\n Example 2:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = datasets.load_dataset("wikitext",\n ... "wikitext-2-raw-v1",\n ... split="test")["text"][:50] # doctest:+ELLIPSIS\n [...]\n >>> input_texts = [s for s in input_texts if s!=\'\']\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 60.35\n >>> print(round(results["perplexities"][0], 2))\n 81.12\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def _lowercase ( self : Optional[Any] ):
return datasets.MetricInfo(
description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
{
"input_texts": datasets.Value("string" ),
} ), reference_urls=["https://huggingface.co/docs/transformers/perplexity"], )
def _lowercase ( self : Optional[Any], UpperCAmelCase__ : int, UpperCAmelCase__ : Any, UpperCAmelCase__ : int = 1_6, UpperCAmelCase__ : bool = True, UpperCAmelCase__ : Dict=None ):
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
__lowercase = "cuda"
else:
__lowercase = "cuda" if torch.cuda.is_available() else "cpu"
__lowercase = AutoModelForCausalLM.from_pretrained(UpperCAmelCase__ )
__lowercase = model.to(UpperCAmelCase__ )
__lowercase = AutoTokenizer.from_pretrained(UpperCAmelCase__ )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
__lowercase = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(UpperCAmelCase__ ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({"pad_token": existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
__lowercase = model.config.max_length - 1
else:
__lowercase = model.config.max_length
__lowercase = tokenizer(
UpperCAmelCase__, add_special_tokens=UpperCAmelCase__, padding=UpperCAmelCase__, truncation=UpperCAmelCase__, max_length=UpperCAmelCase__, return_tensors="pt", return_attention_mask=UpperCAmelCase__, ).to(UpperCAmelCase__ )
__lowercase = encodings["input_ids"]
__lowercase = encodings["attention_mask"]
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ), 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ), 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
__lowercase = []
__lowercase = CrossEntropyLoss(reduction="none" )
for start_index in logging.tqdm(range(0, len(UpperCAmelCase__ ), UpperCAmelCase__ ) ):
__lowercase = min(start_index + batch_size, len(UpperCAmelCase__ ) )
__lowercase = encoded_texts[start_index:end_index]
__lowercase = attn_masks[start_index:end_index]
if add_start_token:
__lowercase = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(UpperCAmelCase__ )
__lowercase = torch.cat([bos_tokens_tensor, encoded_batch], dim=1 )
__lowercase = torch.cat(
[torch.ones(bos_tokens_tensor.size(), dtype=torch.intaa ).to(UpperCAmelCase__ ), attn_mask], dim=1 )
__lowercase = encoded_batch
with torch.no_grad():
__lowercase = model(UpperCAmelCase__, attention_mask=UpperCAmelCase__ ).logits
__lowercase = out_logits[..., :-1, :].contiguous()
__lowercase = labels[..., 1:].contiguous()
__lowercase = attn_mask[..., 1:].contiguous()
__lowercase = torch.expa(
(loss_fct(shift_logits.transpose(1, 2 ), UpperCAmelCase__ ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(UpperCAmelCase__ )}
| 17 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
_lowerCAmelCase = [
# (stable-diffusion, HF Diffusers)
('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
('''input_blocks.0.0.weight''', '''conv_in.weight'''),
('''input_blocks.0.0.bias''', '''conv_in.bias'''),
('''out.0.weight''', '''conv_norm_out.weight'''),
('''out.0.bias''', '''conv_norm_out.bias'''),
('''out.2.weight''', '''conv_out.weight'''),
('''out.2.bias''', '''conv_out.bias'''),
]
_lowerCAmelCase = [
# (stable-diffusion, HF Diffusers)
('''in_layers.0''', '''norm1'''),
('''in_layers.2''', '''conv1'''),
('''out_layers.0''', '''norm2'''),
('''out_layers.3''', '''conv2'''),
('''emb_layers.1''', '''time_emb_proj'''),
('''skip_connection''', '''conv_shortcut'''),
]
_lowerCAmelCase = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
# loop over downblocks/upblocks
for j in range(2):
# loop over resnets/attentions for downblocks
_lowerCAmelCase = F"""down_blocks.{i}.resnets.{j}."""
_lowerCAmelCase = F"""input_blocks.{3*i + j + 1}.0."""
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
_lowerCAmelCase = F"""down_blocks.{i}.attentions.{j}."""
_lowerCAmelCase = F"""input_blocks.{3*i + j + 1}.1."""
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
_lowerCAmelCase = F"""up_blocks.{i}.resnets.{j}."""
_lowerCAmelCase = F"""output_blocks.{3*i + j}.0."""
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
_lowerCAmelCase = F"""up_blocks.{i}.attentions.{j}."""
_lowerCAmelCase = F"""output_blocks.{3*i + j}.1."""
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
_lowerCAmelCase = F"""down_blocks.{i}.downsamplers.0.conv."""
_lowerCAmelCase = F"""input_blocks.{3*(i+1)}.0.op."""
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
_lowerCAmelCase = F"""up_blocks.{i}.upsamplers.0."""
_lowerCAmelCase = F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
_lowerCAmelCase = '''mid_block.attentions.0.'''
_lowerCAmelCase = '''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
for j in range(2):
_lowerCAmelCase = F"""mid_block.resnets.{j}."""
_lowerCAmelCase = F"""middle_block.{2*j}."""
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : Any = {k: k for k in unet_state_dict.keys()}
for sd_name, hf_name in unet_conversion_map:
lowerCAmelCase__ : Optional[int] = sd_name
for k, v in mapping.items():
if "resnets" in k:
for sd_part, hf_part in unet_conversion_map_resnet:
lowerCAmelCase__ : Any = v.replace(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : List[Any] = v
for k, v in mapping.items():
for sd_part, hf_part in unet_conversion_map_layer:
lowerCAmelCase__ : List[Any] = v.replace(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : Optional[Any] = v
lowerCAmelCase__ : Tuple = {v: unet_state_dict[k] for k, v in mapping.items()}
return new_state_dict
# ================#
# VAE Conversion #
# ================#
_lowerCAmelCase = [
# (stable-diffusion, HF Diffusers)
('''nin_shortcut''', '''conv_shortcut'''),
('''norm_out''', '''conv_norm_out'''),
('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]
for i in range(4):
# down_blocks have two resnets
for j in range(2):
_lowerCAmelCase = F"""encoder.down_blocks.{i}.resnets.{j}."""
_lowerCAmelCase = F"""encoder.down.{i}.block.{j}."""
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
if i < 3:
_lowerCAmelCase = F"""down_blocks.{i}.downsamplers.0."""
_lowerCAmelCase = F"""down.{i}.downsample."""
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
_lowerCAmelCase = F"""up_blocks.{i}.upsamplers.0."""
_lowerCAmelCase = F"""up.{3-i}.upsample."""
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
_lowerCAmelCase = F"""decoder.up_blocks.{i}.resnets.{j}."""
_lowerCAmelCase = F"""decoder.up.{3-i}.block.{j}."""
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
_lowerCAmelCase = F"""mid_block.resnets.{i}."""
_lowerCAmelCase = F"""mid.block_{i+1}."""
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
_lowerCAmelCase = [
# (stable-diffusion, HF Diffusers)
('''norm.''', '''group_norm.'''),
('''q.''', '''query.'''),
('''k.''', '''key.'''),
('''v.''', '''value.'''),
('''proj_out.''', '''proj_attn.'''),
]
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
return w.reshape(*w.shape , 1 , 1 )
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = {k: k for k in vae_state_dict.keys()}
for k, v in mapping.items():
for sd_part, hf_part in vae_conversion_map:
lowerCAmelCase__ : str = v.replace(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : Optional[Any] = v
for k, v in mapping.items():
if "attentions" in k:
for sd_part, hf_part in vae_conversion_map_attn:
lowerCAmelCase__ : Dict = v.replace(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : List[Any] = v
lowerCAmelCase__ : Union[str, Any] = {v: vae_state_dict[k] for k, v in mapping.items()}
lowerCAmelCase__ : Tuple = ["""q""", """k""", """v""", """proj_out"""]
for k, v in new_state_dict.items():
for weight_name in weights_to_convert:
if f"""mid.attn_1.{weight_name}.weight""" in k:
print(f"""Reshaping {k} for SD format""" )
lowerCAmelCase__ : Optional[int] = reshape_weight_for_sd(UpperCamelCase )
return new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
_lowerCAmelCase = [
# (stable-diffusion, HF Diffusers)
('''resblocks.''', '''text_model.encoder.layers.'''),
('''ln_1''', '''layer_norm1'''),
('''ln_2''', '''layer_norm2'''),
('''.c_fc.''', '''.fc1.'''),
('''.c_proj.''', '''.fc2.'''),
('''.attn''', '''.self_attn'''),
('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
_lowerCAmelCase = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_lowerCAmelCase = re.compile('''|'''.join(protected.keys()))
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
_lowerCAmelCase = {'''q''': 0, '''k''': 1, '''v''': 2}
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = {}
lowerCAmelCase__ : int = {}
lowerCAmelCase__ : List[Any] = {}
for k, v in text_enc_dict.items():
if (
k.endswith(""".self_attn.q_proj.weight""" )
or k.endswith(""".self_attn.k_proj.weight""" )
or k.endswith(""".self_attn.v_proj.weight""" )
):
lowerCAmelCase__ : Optional[int] = k[: -len(""".q_proj.weight""" )]
lowerCAmelCase__ : Tuple = k[-len("""q_proj.weight""" )]
if k_pre not in capture_qkv_weight:
lowerCAmelCase__ : List[Any] = [None, None, None]
lowerCAmelCase__ : Dict = v
continue
if (
k.endswith(""".self_attn.q_proj.bias""" )
or k.endswith(""".self_attn.k_proj.bias""" )
or k.endswith(""".self_attn.v_proj.bias""" )
):
lowerCAmelCase__ : str = k[: -len(""".q_proj.bias""" )]
lowerCAmelCase__ : List[str] = k[-len("""q_proj.bias""" )]
if k_pre not in capture_qkv_bias:
lowerCAmelCase__ : Union[str, Any] = [None, None, None]
lowerCAmelCase__ : Any = v
continue
lowerCAmelCase__ : Dict = textenc_pattern.sub(lambda UpperCamelCase : protected[re.escape(m.group(0 ) )] , UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = v
for k_pre, tensors in capture_qkv_weight.items():
if None in tensors:
raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing""" )
lowerCAmelCase__ : Any = textenc_pattern.sub(lambda UpperCamelCase : protected[re.escape(m.group(0 ) )] , UpperCamelCase )
lowerCAmelCase__ : Tuple = torch.cat(UpperCamelCase )
for k_pre, tensors in capture_qkv_bias.items():
if None in tensors:
raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing""" )
lowerCAmelCase__ : str = textenc_pattern.sub(lambda UpperCamelCase : protected[re.escape(m.group(0 ) )] , UpperCamelCase )
lowerCAmelCase__ : List[Any] = torch.cat(UpperCamelCase )
return new_state_dict
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
return text_enc_dict
if __name__ == "__main__":
_lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
parser.add_argument(
'''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
)
_lowerCAmelCase = parser.parse_args()
assert args.model_path is not None, "Must provide a model path!"
assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
# Path for safetensors
_lowerCAmelCase = osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
_lowerCAmelCase = osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
_lowerCAmelCase = osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')
# Load models from safetensors if it exists, if it doesn't pytorch
if osp.exists(unet_path):
_lowerCAmelCase = load_file(unet_path, device='''cpu''')
else:
_lowerCAmelCase = osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
_lowerCAmelCase = torch.load(unet_path, map_location='''cpu''')
if osp.exists(vae_path):
_lowerCAmelCase = load_file(vae_path, device='''cpu''')
else:
_lowerCAmelCase = osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
_lowerCAmelCase = torch.load(vae_path, map_location='''cpu''')
if osp.exists(text_enc_path):
_lowerCAmelCase = load_file(text_enc_path, device='''cpu''')
else:
_lowerCAmelCase = osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
_lowerCAmelCase = torch.load(text_enc_path, map_location='''cpu''')
# Convert the UNet model
_lowerCAmelCase = convert_unet_state_dict(unet_state_dict)
_lowerCAmelCase = {'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
_lowerCAmelCase = convert_vae_state_dict(vae_state_dict)
_lowerCAmelCase = {'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
_lowerCAmelCase = '''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict
if is_vaa_model:
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
_lowerCAmelCase = {'''transformer.''' + k: v for k, v in text_enc_dict.items()}
_lowerCAmelCase = convert_text_enc_state_dict_vaa(text_enc_dict)
_lowerCAmelCase = {'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
else:
_lowerCAmelCase = convert_text_enc_state_dict(text_enc_dict)
_lowerCAmelCase = {'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
_lowerCAmelCase = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
if args.half:
_lowerCAmelCase = {k: v.half() for k, v in state_dict.items()}
if args.use_safetensors:
save_file(state_dict, args.checkpoint_path)
else:
_lowerCAmelCase = {'''state_dict''': state_dict}
torch.save(state_dict, args.checkpoint_path)
| 37 | 0 |
"""simple docstring"""
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
snake_case_ = {
"""tiny.en""": """https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt""",
"""tiny""": """https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt""",
"""base.en""": """https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt""",
"""base""": """https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt""",
"""small.en""": """https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt""",
"""small""": """https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt""",
"""medium.en""": """https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt""",
"""medium""": """https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt""",
"""large""": """https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt""",
"""large-v2""": """https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt""",
}
def _lowerCAmelCase ( lowercase_ ):
UpperCAmelCase = ['layers', 'blocks']
for k in ignore_keys:
state_dict.pop(lowercase_ , lowercase_ )
snake_case_ = {
"""blocks""": """layers""",
"""mlp.0""": """fc1""",
"""mlp.2""": """fc2""",
"""mlp_ln""": """final_layer_norm""",
""".attn.query""": """.self_attn.q_proj""",
""".attn.key""": """.self_attn.k_proj""",
""".attn.value""": """.self_attn.v_proj""",
""".attn_ln""": """.self_attn_layer_norm""",
""".attn.out""": """.self_attn.out_proj""",
""".cross_attn.query""": """.encoder_attn.q_proj""",
""".cross_attn.key""": """.encoder_attn.k_proj""",
""".cross_attn.value""": """.encoder_attn.v_proj""",
""".cross_attn_ln""": """.encoder_attn_layer_norm""",
""".cross_attn.out""": """.encoder_attn.out_proj""",
"""decoder.ln.""": """decoder.layer_norm.""",
"""encoder.ln.""": """encoder.layer_norm.""",
"""token_embedding""": """embed_tokens""",
"""encoder.positional_embedding""": """encoder.embed_positions.weight""",
"""decoder.positional_embedding""": """decoder.embed_positions.weight""",
"""ln_post""": """layer_norm""",
}
def _lowerCAmelCase ( lowercase_ ):
UpperCAmelCase = list(s_dict.keys() )
for key in keys:
UpperCAmelCase = key
for k, v in WHISPER_MAPPING.items():
if k in key:
UpperCAmelCase = new_key.replace(lowercase_ , lowercase_ )
print(F"""{key} -> {new_key}""" )
UpperCAmelCase = s_dict.pop(lowercase_ )
return s_dict
def _lowerCAmelCase ( lowercase_ ):
UpperCAmelCase , UpperCAmelCase = emb.weight.shape
UpperCAmelCase = nn.Linear(lowercase_ , lowercase_ , bias=lowercase_ )
UpperCAmelCase = emb.weight.data
return lin_layer
def _lowerCAmelCase ( lowercase_ , lowercase_ ):
os.makedirs(lowercase_ , exist_ok=lowercase_ )
UpperCAmelCase = os.path.basename(lowercase_ )
UpperCAmelCase = url.split('/' )[-2]
UpperCAmelCase = os.path.join(lowercase_ , lowercase_ )
if os.path.exists(lowercase_ ) and not os.path.isfile(lowercase_ ):
raise RuntimeError(F"""{download_target} exists and is not a regular file""" )
if os.path.isfile(lowercase_ ):
UpperCAmelCase = open(lowercase_ , 'rb' ).read()
if hashlib.shaaaa(lowercase_ ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(F"""{download_target} exists, but the SHA256 checksum does not match; re-downloading the file""" )
with urllib.request.urlopen(lowercase_ ) as source, open(lowercase_ , 'wb' ) as output:
with tqdm(
total=int(source.info().get('Content-Length' ) ) , ncols=80 , unit='iB' , unit_scale=lowercase_ , unit_divisor=1024 ) as loop:
while True:
UpperCAmelCase = source.read(8192 )
if not buffer:
break
output.write(lowercase_ )
loop.update(len(lowercase_ ) )
UpperCAmelCase = open(lowercase_ , 'rb' ).read()
if hashlib.shaaaa(lowercase_ ).hexdigest() != expected_shaaaa:
raise RuntimeError(
'Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.' )
return model_bytes
def _lowerCAmelCase ( lowercase_ , lowercase_ ):
if ".pt" not in checkpoint_path:
UpperCAmelCase = _download(_MODELS[checkpoint_path] )
else:
UpperCAmelCase = torch.load(lowercase_ , map_location='cpu' )
UpperCAmelCase = original_checkpoint['dims']
UpperCAmelCase = original_checkpoint['model_state_dict']
UpperCAmelCase = state_dict['decoder.token_embedding.weight']
remove_ignore_keys_(lowercase_ )
rename_keys(lowercase_ )
UpperCAmelCase = True
UpperCAmelCase = state_dict['decoder.layers.0.fc1.weight'].shape[0]
UpperCAmelCase = WhisperConfig(
vocab_size=dimensions['n_vocab'] , encoder_ffn_dim=lowercase_ , decoder_ffn_dim=lowercase_ , num_mel_bins=dimensions['n_mels'] , d_model=dimensions['n_audio_state'] , max_target_positions=dimensions['n_text_ctx'] , encoder_layers=dimensions['n_audio_layer'] , encoder_attention_heads=dimensions['n_audio_head'] , decoder_layers=dimensions['n_text_layer'] , decoder_attention_heads=dimensions['n_text_state'] , max_source_positions=dimensions['n_audio_ctx'] , )
UpperCAmelCase = WhisperForConditionalGeneration(lowercase_ )
UpperCAmelCase , UpperCAmelCase = model.model.load_state_dict(lowercase_ , strict=lowercase_ )
if len(lowercase_ ) > 0 and not set(lowercase_ ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'
F""" but all the following weights are missing {missing}""" )
if tie_embeds:
UpperCAmelCase = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
UpperCAmelCase = proj_out_weights
model.save_pretrained(lowercase_ )
if __name__ == "__main__":
snake_case_ = argparse.ArgumentParser()
# # Required parameters
parser.add_argument("""--checkpoint_path""", type=str, help="""Patht to the downloaded checkpoints""")
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
snake_case_ = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 181 |
"""simple docstring"""
# Copyright 2022 The HuggingFace Team and The OpenBMB 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
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
snake_case_ = {
"""configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
"""tokenization_cpmant""": ["""CpmAntTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
"""CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CpmAntForCausalLM""",
"""CpmAntModel""",
"""CpmAntPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
from .tokenization_cpmant import CpmAntTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_cpmant import (
CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
CpmAntForCausalLM,
CpmAntModel,
CpmAntPreTrainedModel,
)
else:
import sys
snake_case_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 181 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ = {
'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
'tokenization_xlm': ['XLMTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLMForMultipleChoice',
'XLMForQuestionAnswering',
'XLMForQuestionAnsweringSimple',
'XLMForSequenceClassification',
'XLMForTokenClassification',
'XLMModel',
'XLMPreTrainedModel',
'XLMWithLMHeadModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFXLMForMultipleChoice',
'TFXLMForQuestionAnsweringSimple',
'TFXLMForSequenceClassification',
'TFXLMForTokenClassification',
'TFXLMMainLayer',
'TFXLMModel',
'TFXLMPreTrainedModel',
'TFXLMWithLMHeadModel',
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 175 | from torch import nn
class _lowercase ( nn.Module ):
def __init__( self : Any , snake_case : Dict , snake_case : Union[str, Any] ) -> Dict:
"""simple docstring"""
super().__init__()
UpperCamelCase_ : List[Any] = class_size
UpperCamelCase_ : List[Any] = embed_size
# self.mlp1 = nn.Linear(embed_size, embed_size)
# self.mlp2 = (nn.Linear(embed_size, class_size))
UpperCamelCase_ : int = nn.Linear(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , snake_case : Any ) -> str:
"""simple docstring"""
UpperCamelCase_ : Dict = self.mlp(snake_case )
return logits
| 175 | 1 |
'''simple docstring'''
def __lowerCamelCase ( A__ , A__ ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = ''
for i in table:
res += inp[i - 1]
return res
def __lowerCamelCase ( A__ ) -> Dict:
"""simple docstring"""
return data[1:] + data[0]
def __lowerCamelCase ( A__ , A__ ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = ''
for i in range(len(A__ ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def __lowerCamelCase ( A__ , A__ ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = int('0b' + data[0] + data[-1] , 2 )
UpperCamelCase = int('0b' + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def __lowerCamelCase ( A__ , A__ , A__ , A__ , A__ ) -> List[str]:
"""simple docstring"""
UpperCamelCase = message[:4]
UpperCamelCase = message[4:]
UpperCamelCase = apply_table(A__ , A__ )
UpperCamelCase = xor(A__ , A__ )
UpperCamelCase = apply_sbox(A__ , temp[:4] ) # noqa: E741
UpperCamelCase = apply_sbox(A__ , temp[4:] )
UpperCamelCase = '0' * (2 - len(A__ )) + l # noqa: E741
UpperCamelCase = '0' * (2 - len(A__ )) + r
UpperCamelCase = apply_table(l + r , A__ )
UpperCamelCase = xor(A__ , A__ )
return temp + right
if __name__ == "__main__":
_lowerCamelCase : str = input("Enter 10 bit key: ")
_lowerCamelCase : Optional[Any] = input("Enter 8 bit message: ")
_lowerCamelCase : Tuple = [6, 3, 7, 4, 8, 5, 10, 9]
_lowerCamelCase : Union[str, Any] = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
_lowerCamelCase : Union[str, Any] = [2, 4, 3, 1]
_lowerCamelCase : int = [2, 6, 3, 1, 4, 8, 5, 7]
_lowerCamelCase : Tuple = [4, 1, 3, 5, 7, 2, 8, 6]
_lowerCamelCase : Any = [4, 1, 2, 3, 2, 3, 4, 1]
_lowerCamelCase : Tuple = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
_lowerCamelCase : Any = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
_lowerCamelCase : str = apply_table(key, paa_table)
_lowerCamelCase : str = temp[:5]
_lowerCamelCase : Any = temp[5:]
_lowerCamelCase : Dict = left_shift(left)
_lowerCamelCase : int = left_shift(right)
_lowerCamelCase : Optional[int] = apply_table(left + right, pa_table)
_lowerCamelCase : Optional[int] = left_shift(left)
_lowerCamelCase : Union[str, Any] = left_shift(right)
_lowerCamelCase : Tuple = left_shift(left)
_lowerCamelCase : Optional[int] = left_shift(right)
_lowerCamelCase : Optional[int] = apply_table(left + right, pa_table)
# encryption
_lowerCamelCase : Dict = apply_table(message, IP)
_lowerCamelCase : Optional[int] = function(expansion, sa, sa, keya, temp)
_lowerCamelCase : Any = temp[4:] + temp[:4]
_lowerCamelCase : List[Any] = function(expansion, sa, sa, keya, temp)
_lowerCamelCase : Tuple = apply_table(temp, IP_inv)
print("Cipher text is:", CT)
# decryption
_lowerCamelCase : List[str] = apply_table(CT, IP)
_lowerCamelCase : Union[str, Any] = function(expansion, sa, sa, keya, temp)
_lowerCamelCase : Tuple = temp[4:] + temp[:4]
_lowerCamelCase : Any = function(expansion, sa, sa, keya, temp)
_lowerCamelCase : Optional[int] = apply_table(temp, IP_inv)
print("Plain text after decypting is:", PT)
| 249 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = MODEL_FOR_CAUSAL_LM_MAPPING
_SCREAMING_SNAKE_CASE = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def A ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = pipeline(task='text-generation' , model='sshleifer/tiny-ctrl' , framework='pt' )
# Using `do_sample=False` to force deterministic output
UpperCamelCase = text_generator('This is a test' , do_sample=UpperCamelCase__ )
self.assertEqual(
UpperCamelCase__ , [
{
'generated_text': (
'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
' oscope. FiliFili@@'
)
}
] , )
UpperCamelCase = text_generator(['This is a test', 'This is a second test'] )
self.assertEqual(
UpperCamelCase__ , [
[
{
'generated_text': (
'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
' oscope. FiliFili@@'
)
}
],
[
{
'generated_text': (
'This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'
' oscope. oscope. FiliFili@@'
)
}
],
] , )
UpperCamelCase = text_generator('This is a test' , do_sample=UpperCamelCase__ , num_return_sequences=2 , return_tensors=UpperCamelCase__ )
self.assertEqual(
UpperCamelCase__ , [
{'generated_token_ids': ANY(UpperCamelCase__ )},
{'generated_token_ids': ANY(UpperCamelCase__ )},
] , )
UpperCamelCase = text_generator.model.config.eos_token_id
UpperCamelCase = '<pad>'
UpperCamelCase = text_generator(
['This is a test', 'This is a second test'] , do_sample=UpperCamelCase__ , num_return_sequences=2 , batch_size=2 , return_tensors=UpperCamelCase__ , )
self.assertEqual(
UpperCamelCase__ , [
[
{'generated_token_ids': ANY(UpperCamelCase__ )},
{'generated_token_ids': ANY(UpperCamelCase__ )},
],
[
{'generated_token_ids': ANY(UpperCamelCase__ )},
{'generated_token_ids': ANY(UpperCamelCase__ )},
],
] , )
@require_tf
def A ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = pipeline(task='text-generation' , model='sshleifer/tiny-ctrl' , framework='tf' )
# Using `do_sample=False` to force deterministic output
UpperCamelCase = text_generator('This is a test' , do_sample=UpperCamelCase__ )
self.assertEqual(
UpperCamelCase__ , [
{
'generated_text': (
'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
' please,'
)
}
] , )
UpperCamelCase = text_generator(['This is a test', 'This is a second test'] , do_sample=UpperCamelCase__ )
self.assertEqual(
UpperCamelCase__ , [
[
{
'generated_text': (
'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
' please,'
)
}
],
[
{
'generated_text': (
'This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'
' Cannes 閲閲Cannes Cannes Cannes 攵 please,'
)
}
],
] , )
def A ( self : List[str] , UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[Any] ):
"""simple docstring"""
UpperCamelCase = TextGenerationPipeline(model=UpperCamelCase__ , tokenizer=UpperCamelCase__ )
return text_generator, ["This is a test", "Another test"]
def A ( self : int ):
"""simple docstring"""
UpperCamelCase = 'Hello I believe in'
UpperCamelCase = pipeline('text-generation' , model='hf-internal-testing/tiny-random-gpt2' )
UpperCamelCase = text_generator(UpperCamelCase__ )
self.assertEqual(
UpperCamelCase__ , [{'generated_text': 'Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'}] , )
UpperCamelCase = text_generator(UpperCamelCase__ , stop_sequence=' fe' )
self.assertEqual(UpperCamelCase__ , [{'generated_text': 'Hello I believe in fe'}] )
def A ( self : List[Any] , UpperCamelCase__ : int , UpperCamelCase__ : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = text_generator.model
UpperCamelCase = text_generator.tokenizer
UpperCamelCase = text_generator('This is a test' )
self.assertEqual(UpperCamelCase__ , [{'generated_text': ANY(UpperCamelCase__ )}] )
self.assertTrue(outputs[0]['generated_text'].startswith('This is a test' ) )
UpperCamelCase = text_generator('This is a test' , return_full_text=UpperCamelCase__ )
self.assertEqual(UpperCamelCase__ , [{'generated_text': ANY(UpperCamelCase__ )}] )
self.assertNotIn('This is a test' , outputs[0]['generated_text'] )
UpperCamelCase = pipeline(task='text-generation' , model=UpperCamelCase__ , tokenizer=UpperCamelCase__ , return_full_text=UpperCamelCase__ )
UpperCamelCase = text_generator('This is a test' )
self.assertEqual(UpperCamelCase__ , [{'generated_text': ANY(UpperCamelCase__ )}] )
self.assertNotIn('This is a test' , outputs[0]['generated_text'] )
UpperCamelCase = text_generator('This is a test' , return_full_text=UpperCamelCase__ )
self.assertEqual(UpperCamelCase__ , [{'generated_text': ANY(UpperCamelCase__ )}] )
self.assertTrue(outputs[0]['generated_text'].startswith('This is a test' ) )
UpperCamelCase = text_generator(['This is great !', 'Something else'] , num_return_sequences=2 , do_sample=UpperCamelCase__ )
self.assertEqual(
UpperCamelCase__ , [
[{'generated_text': ANY(UpperCamelCase__ )}, {'generated_text': ANY(UpperCamelCase__ )}],
[{'generated_text': ANY(UpperCamelCase__ )}, {'generated_text': ANY(UpperCamelCase__ )}],
] , )
if text_generator.tokenizer.pad_token is not None:
UpperCamelCase = text_generator(
['This is great !', 'Something else'] , num_return_sequences=2 , batch_size=2 , do_sample=UpperCamelCase__ )
self.assertEqual(
UpperCamelCase__ , [
[{'generated_text': ANY(UpperCamelCase__ )}, {'generated_text': ANY(UpperCamelCase__ )}],
[{'generated_text': ANY(UpperCamelCase__ )}, {'generated_text': ANY(UpperCamelCase__ )}],
] , )
with self.assertRaises(UpperCamelCase__ ):
UpperCamelCase = text_generator('test' , return_full_text=UpperCamelCase__ , return_text=UpperCamelCase__ )
with self.assertRaises(UpperCamelCase__ ):
UpperCamelCase = text_generator('test' , return_full_text=UpperCamelCase__ , return_tensors=UpperCamelCase__ )
with self.assertRaises(UpperCamelCase__ ):
UpperCamelCase = text_generator('test' , return_text=UpperCamelCase__ , return_tensors=UpperCamelCase__ )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
UpperCamelCase = text_generator('' )
self.assertEqual(UpperCamelCase__ , [{'generated_text': ANY(UpperCamelCase__ )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
UpperCamelCase = text_generator('' )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
UpperCamelCase = ['RwkvForCausalLM', 'XGLMForCausalLM', 'GPTNeoXForCausalLM']
if (
tokenizer.model_max_length < 1_0_0_0_0
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator('This is a test' * 5_0_0 , max_new_tokens=2_0 )
UpperCamelCase = text_generator('This is a test' * 5_0_0 , handle_long_generation='hole' , max_new_tokens=2_0 )
# Hole strategy cannot work
with self.assertRaises(UpperCamelCase__ ):
text_generator(
'This is a test' * 5_0_0 , handle_long_generation='hole' , max_new_tokens=tokenizer.model_max_length + 1_0 , )
@require_torch
@require_accelerate
@require_torch_gpu
def A ( self : int ):
"""simple docstring"""
import torch
# Classic `model_kwargs`
UpperCamelCase = pipeline(
model='hf-internal-testing/tiny-random-bloom' , model_kwargs={'device_map': 'auto', 'torch_dtype': torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
UpperCamelCase = pipe('This is a test' )
self.assertEqual(
UpperCamelCase__ , [
{
'generated_text': (
'This is a test test test test test test test test test test test test test test test test'
' test'
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
UpperCamelCase = pipeline(model='hf-internal-testing/tiny-random-bloom' , device_map='auto' , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
UpperCamelCase = pipe('This is a test' )
self.assertEqual(
UpperCamelCase__ , [
{
'generated_text': (
'This is a test test test test test test test test test test test test test test test test'
' test'
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
UpperCamelCase = pipeline(model='hf-internal-testing/tiny-random-bloom' , device_map='auto' )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
UpperCamelCase = pipe('This is a test' )
self.assertEqual(
UpperCamelCase__ , [
{
'generated_text': (
'This is a test test test test test test test test test test test test test test test test'
' test'
)
}
] , )
@require_torch
@require_torch_gpu
def A ( self : int ):
"""simple docstring"""
import torch
UpperCamelCase = pipeline(model='hf-internal-testing/tiny-random-bloom' , device=0 , torch_dtype=torch.floataa )
pipe('This is a test' )
@require_torch
@require_accelerate
@require_torch_gpu
def A ( self : Any ):
"""simple docstring"""
import torch
UpperCamelCase = pipeline(model='hf-internal-testing/tiny-random-bloom' , device_map='auto' , torch_dtype=torch.floataa )
pipe('This is a test' , do_sample=UpperCamelCase__ , top_p=0.5 )
def A ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = 'Hello world'
UpperCamelCase = pipeline('text-generation' , model='hf-internal-testing/tiny-random-gpt2' )
if text_generator.model.framework == "tf":
UpperCamelCase = logging.get_logger('transformers.generation.tf_utils' )
else:
UpperCamelCase = logging.get_logger('transformers.generation.utils' )
UpperCamelCase = 'Both `max_new_tokens`' # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(UpperCamelCase__ ) as cl:
UpperCamelCase = text_generator(UpperCamelCase__ , max_length=1_0 , max_new_tokens=1 )
self.assertIn(UpperCamelCase__ , cl.out )
# The user only sets one -> no warning
with CaptureLogger(UpperCamelCase__ ) as cl:
UpperCamelCase = text_generator(UpperCamelCase__ , max_new_tokens=1 )
self.assertNotIn(UpperCamelCase__ , cl.out )
with CaptureLogger(UpperCamelCase__ ) as cl:
UpperCamelCase = text_generator(UpperCamelCase__ , max_length=1_0 )
self.assertNotIn(UpperCamelCase__ , cl.out )
| 249 | 1 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
)
from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class a ( _lowerCamelCase ):
def __init__( self : Tuple , lowercase_ : Union[str, Any] , lowercase_ : Dict=13 , lowercase_ : Tuple=7 , lowercase_ : List[Any]=True , lowercase_ : Any=True , lowercase_ : Union[str, Any]=True , lowercase_ : Tuple=True , lowercase_ : List[Any]=99 , lowercase_ : int=32 , lowercase_ : List[str]=5 , lowercase_ : str=4 , lowercase_ : Optional[Any]=37 , lowercase_ : List[str]="gelu" , lowercase_ : List[str]=0.1 , lowercase_ : int=0.1 , lowercase_ : Tuple=512 , lowercase_ : List[Any]=16 , lowercase_ : Optional[int]=2 , lowercase_ : Optional[int]=0.02 , lowercase_ : List[Any]=False , lowercase_ : Dict=True , lowercase_ : List[str]="None" , lowercase_ : List[str]=3 , lowercase_ : Optional[Any]=4 , lowercase_ : Tuple=None , ):
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_mask
snake_case_ = use_token_type_ids
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = num_labels
snake_case_ = num_choices
snake_case_ = relative_attention
snake_case_ = position_biased_input
snake_case_ = pos_att_type
snake_case_ = scope
def A_ ( self : Any ):
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = None
if self.use_input_mask:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
snake_case_ = None
if self.use_token_type_ids:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ = None
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A_ ( self : Dict ):
return DebertaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def A_ ( self : List[str] ):
snake_case_ = self.get_config()
snake_case_ = 300
return config
def A_ ( self : Tuple , lowercase_ : List[str] ):
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def A_ ( self : Tuple , lowercase_ : List[Any] , lowercase_ : Tuple , lowercase_ : List[str] , lowercase_ : Any , lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Optional[Any] ):
snake_case_ = DebertaModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ )[0]
snake_case_ = model(lowercase_ , token_type_ids=lowercase_ )[0]
snake_case_ = model(lowercase_ )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def A_ ( self : Any , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : List[Any] , lowercase_ : Any ):
snake_case_ = DebertaForMaskedLM(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A_ ( self : Optional[int] , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Optional[Any] ):
snake_case_ = self.num_labels
snake_case_ = DebertaForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(lowercase_ )
def A_ ( self : List[Any] , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : Dict , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : List[str] ):
snake_case_ = self.num_labels
snake_case_ = DebertaForTokenClassification(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A_ ( self : List[str] , lowercase_ : List[str] , lowercase_ : int , lowercase_ : Optional[int] , lowercase_ : Tuple , lowercase_ : Dict , lowercase_ : Optional[int] , lowercase_ : Optional[Any] ):
snake_case_ = DebertaForQuestionAnswering(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(
lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , start_positions=lowercase_ , end_positions=lowercase_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A_ ( self : Tuple ):
snake_case_ = self.prepare_config_and_inputs()
(
(
snake_case_
) ,(
snake_case_
) ,(
snake_case_
) ,(
snake_case_
) ,(
snake_case_
) ,(
snake_case_
) ,(
snake_case_
) ,
) = config_and_inputs
snake_case_ = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
DebertaModel,
DebertaForMaskedLM,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
snake_case_ = (
{
"feature-extraction": DebertaModel,
"fill-mask": DebertaForMaskedLM,
"question-answering": DebertaForQuestionAnswering,
"text-classification": DebertaForSequenceClassification,
"token-classification": DebertaForTokenClassification,
"zero-shot": DebertaForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case_ = True
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def A_ ( self : Optional[Any] ):
snake_case_ = DebertaModelTester(self )
snake_case_ = ConfigTester(self , config_class=lowercase_ , hidden_size=37 )
def A_ ( self : List[Any] ):
self.config_tester.run_common_tests()
def A_ ( self : int ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*lowercase_ )
def A_ ( self : Tuple ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*lowercase_ )
def A_ ( self : int ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*lowercase_ )
def A_ ( self : int ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*lowercase_ )
def A_ ( self : Union[str, Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*lowercase_ )
@slow
def A_ ( self : List[str] ):
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = DebertaModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@require_torch
@require_sentencepiece
@require_tokenizers
class a ( unittest.TestCase ):
@unittest.skip(reason='''Model not available yet''' )
def A_ ( self : Optional[int] ):
pass
@slow
def A_ ( self : str ):
snake_case_ = DebertaModel.from_pretrained('''microsoft/deberta-base''' )
snake_case_ = torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] )
snake_case_ = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
snake_case_ = model(lowercase_ , attention_mask=lowercase_ )[0]
# compare the actual values for a slice.
snake_case_ = torch.tensor(
[[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , lowercase_ , atol=1e-4 ) , F"{output[:, 1:4, 1:4]}" )
| 56 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
_A : Dict = get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
_A : int = 25_00_04
_A : str = 25_00_20
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ,unittest.TestCase ):
_UpperCAmelCase : Optional[Any] = MBartTokenizer
_UpperCAmelCase : List[Any] = MBartTokenizerFast
_UpperCAmelCase : Optional[Any] = True
_UpperCAmelCase : Optional[int] = True
def __lowerCamelCase ( self : Union[str, Any] ) ->Dict:
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase__ : Optional[Any] = MBartTokenizer(A , keep_accents=A )
tokenizer.save_pretrained(self.tmpdirname )
def __lowerCamelCase ( self : Tuple ) ->List[str]:
lowerCamelCase__ : str = MBartTokenizer(A , keep_accents=A )
lowerCamelCase__ : Optional[int] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(A , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(A ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
lowerCamelCase__ : Dict = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
A , [
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__ : Optional[Any] = tokenizer.convert_tokens_to_ids(A )
self.assertListEqual(
A , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
lowerCamelCase__ : List[str] = tokenizer.convert_ids_to_tokens(A )
self.assertListEqual(
A , [
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 __lowerCamelCase ( self : List[Any] ) ->List[str]:
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
lowerCamelCase__ : Any = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
lowerCamelCase__ : List[Any] = self.rust_tokenizer_class.from_pretrained(A , **A )
lowerCamelCase__ : Any = self.tokenizer_class.from_pretrained(A , **A )
lowerCamelCase__ : List[str] = tempfile.mkdtemp()
lowerCamelCase__ : Union[str, Any] = tokenizer_r.save_pretrained(A )
lowerCamelCase__ : List[Any] = tokenizer_p.save_pretrained(A )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
lowerCamelCase__ : Any = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(A , A )
# Checks everything loads correctly in the same way
lowerCamelCase__ : Optional[int] = tokenizer_r.from_pretrained(A )
lowerCamelCase__ : Optional[Any] = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(A )
# Save tokenizer rust, legacy_format=True
lowerCamelCase__ : List[Any] = tempfile.mkdtemp()
lowerCamelCase__ : Union[str, Any] = tokenizer_r.save_pretrained(A , legacy_format=A )
lowerCamelCase__ : str = tokenizer_p.save_pretrained(A )
# Checks it save with the same files
self.assertSequenceEqual(A , A )
# Checks everything loads correctly in the same way
lowerCamelCase__ : Optional[Any] = tokenizer_r.from_pretrained(A )
lowerCamelCase__ : List[Any] = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
# Save tokenizer rust, legacy_format=False
lowerCamelCase__ : Optional[Any] = tempfile.mkdtemp()
lowerCamelCase__ : Optional[Any] = tokenizer_r.save_pretrained(A , legacy_format=A )
lowerCamelCase__ : List[str] = tokenizer_p.save_pretrained(A )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
lowerCamelCase__ : Any = tokenizer_r.from_pretrained(A )
lowerCamelCase__ : Optional[Any] = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
@require_torch
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
_UpperCAmelCase : Any = "facebook/mbart-large-en-ro"
_UpperCAmelCase : Optional[int] = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
]
_UpperCAmelCase : Optional[Any] = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
_UpperCAmelCase : Tuple = [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]
@classmethod
def __lowerCamelCase ( cls : Optional[Any] ) ->Dict:
lowerCamelCase__ : MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en_XX''' , tgt_lang='''ro_RO''' )
lowerCamelCase__ : int = 1
return cls
def __lowerCamelCase ( self : int ) ->Optional[Any]:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR'''] , 2_5_0_0_0_1 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN'''] , 2_5_0_0_0_4 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO'''] , 2_5_0_0_2_0 )
def __lowerCamelCase ( self : str ) ->Any:
lowerCamelCase__ : str = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , A )
def __lowerCamelCase ( self : Tuple ) ->Tuple:
self.assertIn(A , self.tokenizer.all_special_ids )
lowerCamelCase__ : List[str] = [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
lowerCamelCase__ : str = self.tokenizer.decode(A , skip_special_tokens=A )
lowerCamelCase__ : str = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=A )
self.assertEqual(A , A )
self.assertNotIn(self.tokenizer.eos_token , A )
def __lowerCamelCase ( self : Optional[Any] ) ->int:
lowerCamelCase__ : List[str] = ['''this is gunna be a long sentence ''' * 2_0]
assert isinstance(src_text[0] , A )
lowerCamelCase__ : str = 1_0
lowerCamelCase__ : Dict = self.tokenizer(A , max_length=A , truncation=A ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , A )
self.assertEqual(len(A ) , A )
def __lowerCamelCase ( self : List[str] ) ->str:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''] ) , [2_5_0_0_2_6, 2_5_0_0_0_1] )
def __lowerCamelCase ( self : List[Any] ) ->List[Any]:
lowerCamelCase__ : List[str] = tempfile.mkdtemp()
lowerCamelCase__ : Tuple = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(A )
lowerCamelCase__ : List[Any] = MBartTokenizer.from_pretrained(A )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , A )
@require_torch
def __lowerCamelCase ( self : Union[str, Any] ) ->Any:
lowerCamelCase__ : int = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=A , return_tensors='''pt''' )
lowerCamelCase__ : str = shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def __lowerCamelCase ( self : Any ) ->List[str]:
lowerCamelCase__ : str = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=A , truncation=A , max_length=len(self.expected_src_tokens ) , return_tensors='''pt''' , )
lowerCamelCase__ : Optional[Any] = shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
self.assertIsInstance(A , A )
self.assertEqual((2, 1_4) , batch.input_ids.shape )
self.assertEqual((2, 1_4) , batch.attention_mask.shape )
lowerCamelCase__ : Dict = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , A )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def __lowerCamelCase ( self : Any ) ->List[str]:
lowerCamelCase__ : str = self.tokenizer(self.src_text , padding=A , truncation=A , max_length=3 , return_tensors='''pt''' )
lowerCamelCase__ : Any = self.tokenizer(
text_target=self.tgt_text , padding=A , truncation=A , max_length=1_0 , return_tensors='''pt''' )
lowerCamelCase__ : str = targets['''input_ids''']
lowerCamelCase__ : int = shift_tokens_right(A , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 1_0 )
@require_torch
def __lowerCamelCase ( self : Optional[Any] ) ->Optional[Any]:
lowerCamelCase__ : Dict = self.tokenizer._build_translation_inputs(
'''A test''' , return_tensors='''pt''' , src_lang='''en_XX''' , tgt_lang='''ar_AR''' )
self.assertEqual(
nested_simplify(A ) , {
# A, test, EOS, en_XX
'''input_ids''': [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 2_5_0_0_0_1,
} , )
| 142 | 0 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__snake_case : Optional[Any] = logging.get_logger(__name__)
def _UpperCamelCase ( UpperCamelCase_ : str , UpperCamelCase_ : int , UpperCamelCase_ : int ) -> Any:
"""simple docstring"""
lowerCAmelCase__ = WavaVecaForSequenceClassification.from_pretrained(UpperCamelCase_ , config=UpperCamelCase_ )
lowerCAmelCase__ = downstream_dict['projector.weight']
lowerCAmelCase__ = downstream_dict['projector.bias']
lowerCAmelCase__ = downstream_dict['model.post_net.linear.weight']
lowerCAmelCase__ = downstream_dict['model.post_net.linear.bias']
return model
def _UpperCamelCase ( UpperCamelCase_ : Tuple , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : int ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ = WavaVecaForAudioFrameClassification.from_pretrained(UpperCamelCase_ , config=UpperCamelCase_ )
lowerCAmelCase__ = downstream_dict['model.linear.weight']
lowerCAmelCase__ = downstream_dict['model.linear.bias']
return model
def _UpperCamelCase ( UpperCamelCase_ : Optional[int] , UpperCamelCase_ : int , UpperCamelCase_ : Optional[int] ) -> Any:
"""simple docstring"""
lowerCAmelCase__ = WavaVecaForXVector.from_pretrained(UpperCamelCase_ , config=UpperCamelCase_ )
lowerCAmelCase__ = downstream_dict['connector.weight']
lowerCAmelCase__ = downstream_dict['connector.bias']
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
lowerCAmelCase__ = downstream_dict[
F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
]
lowerCAmelCase__ = downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]
lowerCAmelCase__ = downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
lowerCAmelCase__ = downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
lowerCAmelCase__ = downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
lowerCAmelCase__ = downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
lowerCAmelCase__ = downstream_dict['objective.W']
return model
@torch.no_grad()
def _UpperCamelCase ( UpperCamelCase_ : List[Any] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase__ = torch.load(UpperCamelCase_ , map_location='cpu' )
lowerCAmelCase__ = checkpoint['Downstream']
lowerCAmelCase__ = WavaVecaConfig.from_pretrained(UpperCamelCase_ )
lowerCAmelCase__ = WavaVecaFeatureExtractor.from_pretrained(
UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , do_normalize=UpperCamelCase_ )
lowerCAmelCase__ = hf_config.architectures[0]
if arch.endswith('ForSequenceClassification' ):
lowerCAmelCase__ = convert_classification(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
elif arch.endswith('ForAudioFrameClassification' ):
lowerCAmelCase__ = convert_diarization(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
elif arch.endswith('ForXVector' ):
lowerCAmelCase__ = convert_xvector(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
else:
raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}" )
if hf_config.use_weighted_layer_sum:
lowerCAmelCase__ = checkpoint['Featurizer']['weights']
hf_feature_extractor.save_pretrained(UpperCamelCase_ )
hf_model.save_pretrained(UpperCamelCase_ )
if __name__ == "__main__":
__snake_case : List[str] = argparse.ArgumentParser()
parser.add_argument(
"""--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
)
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
__snake_case : List[Any] = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 366 |
def _UpperCamelCase ( UpperCamelCase_ : str ) -> str:
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 122 | 0 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.txt'}
a_ = {
'vocab_file': {
'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
},
}
a_ = {
'openbmb/cpm-ant-10b': 1_0_2_4,
}
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =collections.OrderedDict()
with open(UpperCamelCase__, '''r''', encoding='''utf-8''' ) as reader:
SCREAMING_SNAKE_CASE__ : Optional[int] =reader.readlines()
for index, token in enumerate(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Dict =token.rstrip('''\n''' )
SCREAMING_SNAKE_CASE__ : Optional[int] =index
return vocab
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : Dict , __lowercase : List[Any] , __lowercase : Union[str, Any]="<unk>" , __lowercase : int=2_00 ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Dict =vocab
SCREAMING_SNAKE_CASE__ : Union[str, Any] =unk_token
SCREAMING_SNAKE_CASE__ : str =max_input_chars_per_word
def __magic_name__ ( self : Any , __lowercase : List[str] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Any =list(__lowercase )
if len(__lowercase ) > self.max_input_chars_per_word:
return [self.unk_token]
SCREAMING_SNAKE_CASE__ : Dict =0
SCREAMING_SNAKE_CASE__ : Any =[]
while start < len(__lowercase ):
SCREAMING_SNAKE_CASE__ : Optional[int] =len(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =None
while start < end:
SCREAMING_SNAKE_CASE__ : Optional[Any] =''''''.join(chars[start:end] )
if substr in self.vocab:
SCREAMING_SNAKE_CASE__ : Optional[int] =substr
break
end -= 1
if cur_substr is None:
sub_tokens.append(self.unk_token )
start += 1
else:
sub_tokens.append(__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =end
return sub_tokens
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ["""input_ids""", """attention_mask"""]
snake_case_ = False
def __init__( self : Union[str, Any] , __lowercase : Any , __lowercase : Any="<d>" , __lowercase : List[Any]="</d>" , __lowercase : List[Any]="<s>" , __lowercase : Optional[int]="</s>" , __lowercase : Tuple="<pad>" , __lowercase : Dict="<unk>" , __lowercase : Tuple="</n>" , __lowercase : Tuple="</_>" , __lowercase : List[Any]="left" , **__lowercase : Tuple , ) -> str:
requires_backends(self , ['''jieba'''] )
super().__init__(
bod_token=__lowercase , eod_token=__lowercase , bos_token=__lowercase , eos_token=__lowercase , pad_token=__lowercase , unk_token=__lowercase , line_token=__lowercase , space_token=__lowercase , padding_side=__lowercase , **__lowercase , )
SCREAMING_SNAKE_CASE__ : Any =bod_token
SCREAMING_SNAKE_CASE__ : List[str] =eod_token
SCREAMING_SNAKE_CASE__ : Optional[Any] =load_vocab(__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.encoder[space_token]
SCREAMING_SNAKE_CASE__ : Optional[int] =self.encoder[line_token]
del self.encoder[space_token]
del self.encoder[line_token]
SCREAMING_SNAKE_CASE__ : str =collections.OrderedDict(sorted(self.encoder.items() , key=lambda __lowercase : x[1] ) )
SCREAMING_SNAKE_CASE__ : Any ={v: k for k, v in self.encoder.items()}
SCREAMING_SNAKE_CASE__ : str =WordpieceTokenizer(vocab=self.encoder , unk_token=self.unk_token )
@property
def __magic_name__ ( self : List[str] ) -> List[str]:
return self.encoder[self.bod_token]
@property
def __magic_name__ ( self : str ) -> Dict:
return self.encoder[self.eod_token]
@property
def __magic_name__ ( self : Any ) -> str:
return self.encoder["\n"]
@property
def __magic_name__ ( self : Tuple ) -> int:
return len(self.encoder )
def __magic_name__ ( self : Any ) -> str:
return dict(self.encoder , **self.added_tokens_encoder )
def __magic_name__ ( self : Optional[int] , __lowercase : Optional[Any] ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[int] =[]
for x in jieba.cut(__lowercase , cut_all=__lowercase ):
output_tokens.extend(self.wordpiece_tokenizer.tokenize(__lowercase ) )
return output_tokens
def __magic_name__ ( self : Dict , __lowercase : Dict , **__lowercase : Union[str, Any] ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Any =[i for i in token_ids if i >= 0]
SCREAMING_SNAKE_CASE__ : Tuple =[
x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
]
return super()._decode(__lowercase , **__lowercase )
def __magic_name__ ( self : List[str] , __lowercase : Dict ) -> List[Any]:
return token in self.encoder
def __magic_name__ ( self : Any , __lowercase : List[str] ) -> str:
return "".join(__lowercase )
def __magic_name__ ( self : str , __lowercase : str ) -> Optional[Any]:
return self.encoder.get(__lowercase , self.encoder.get(self.unk_token ) )
def __magic_name__ ( self : List[str] , __lowercase : Optional[int] ) -> Any:
return self.decoder.get(__lowercase , self.unk_token )
def __magic_name__ ( self : Optional[int] , __lowercase : str , __lowercase : Optional[str] = None ) -> Tuple[str]:
if os.path.isdir(__lowercase ):
SCREAMING_SNAKE_CASE__ : int =os.path.join(
__lowercase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
else:
SCREAMING_SNAKE_CASE__ : Dict =(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
SCREAMING_SNAKE_CASE__ : List[str] =0
if " " in self.encoder:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.encoder[''' ''']
del self.encoder[" "]
if "\n" in self.encoder:
SCREAMING_SNAKE_CASE__ : Any =self.encoder['''\n''']
del self.encoder["\n"]
SCREAMING_SNAKE_CASE__ : Dict =collections.OrderedDict(sorted(self.encoder.items() , key=lambda __lowercase : x[1] ) )
with open(__lowercase , '''w''' , encoding='''utf-8''' ) as writer:
for token, token_index in self.encoder.items():
if index != token_index:
logger.warning(
F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
''' Please check that the vocabulary is not corrupted!''' )
SCREAMING_SNAKE_CASE__ : str =token_index
writer.write(token + '''\n''' )
index += 1
return (vocab_file,)
def __magic_name__ ( self : Union[str, Any] , __lowercase : List[int] , __lowercase : List[int] = None ) -> List[int]:
if token_ids_a is None:
return [self.bos_token_id] + token_ids_a
return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a
def __magic_name__ ( self : Optional[int] , __lowercase : List[int] , __lowercase : Optional[List[int]] = None , __lowercase : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowercase , token_ids_a=__lowercase , already_has_special_tokens=__lowercase )
if token_ids_a is not None:
return [1] + ([0] * len(__lowercase )) + [1] + ([0] * len(__lowercase ))
return [1] + ([0] * len(__lowercase )) | 152 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def _a( UpperCamelCase__ : int, UpperCamelCase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =old_name
if "patch_embed" in old_name:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int =old_name.split('''.''' )
if layer == "0":
SCREAMING_SNAKE_CASE__ : int =old_name.replace('''0''', '''convolution1''' )
elif layer == "1":
SCREAMING_SNAKE_CASE__ : Tuple =old_name.replace('''1''', '''batchnorm_before''' )
elif layer == "3":
SCREAMING_SNAKE_CASE__ : List[Any] =old_name.replace('''3''', '''convolution2''' )
else:
SCREAMING_SNAKE_CASE__ : Dict =old_name.replace('''4''', '''batchnorm_after''' )
if "network" in old_name and re.search(R'''\d\.\d''', UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Tuple =R'''\b\d{2}\b'''
if bool(re.search(UpperCamelCase__, UpperCamelCase__ ) ):
SCREAMING_SNAKE_CASE__ : int =re.search(R'''\d\.\d\d.''', UpperCamelCase__ ).group()
else:
SCREAMING_SNAKE_CASE__ : Tuple =re.search(R'''\d\.\d.''', UpperCamelCase__ ).group()
if int(match[0] ) < 6:
SCREAMING_SNAKE_CASE__ : List[str] =old_name.replace(UpperCamelCase__, '''''' )
SCREAMING_SNAKE_CASE__ : Any =trimmed_name.replace('''network''', match[0] + '''.meta4D_layers.blocks.''' + match[2:-1] )
SCREAMING_SNAKE_CASE__ : Any ='''intermediate_stages.''' + trimmed_name
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =old_name.replace(UpperCamelCase__, '''''' )
if int(match[2] ) < num_meta4D_last_stage:
SCREAMING_SNAKE_CASE__ : str =trimmed_name.replace('''network''', '''meta4D_layers.blocks.''' + match[2] )
else:
SCREAMING_SNAKE_CASE__ : int =str(int(match[2] ) - num_meta4D_last_stage )
SCREAMING_SNAKE_CASE__ : Any =trimmed_name.replace('''network''', '''meta3D_layers.blocks.''' + layer_index )
if "norm1" in old_name:
SCREAMING_SNAKE_CASE__ : Optional[int] =trimmed_name.replace('''norm1''', '''layernorm1''' )
elif "norm2" in old_name:
SCREAMING_SNAKE_CASE__ : List[Any] =trimmed_name.replace('''norm2''', '''layernorm2''' )
elif "fc1" in old_name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =trimmed_name.replace('''fc1''', '''linear_in''' )
elif "fc2" in old_name:
SCREAMING_SNAKE_CASE__ : str =trimmed_name.replace('''fc2''', '''linear_out''' )
SCREAMING_SNAKE_CASE__ : Any ='''last_stage.''' + trimmed_name
elif "network" in old_name and re.search(R'''.\d.''', UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : int =old_name.replace('''network''', '''intermediate_stages''' )
if "fc" in new_name:
SCREAMING_SNAKE_CASE__ : str =new_name.replace('''fc''', '''convolution''' )
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
SCREAMING_SNAKE_CASE__ : Tuple =new_name.replace('''norm1''', '''batchnorm_before''' )
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
SCREAMING_SNAKE_CASE__ : List[str] =new_name.replace('''norm2''', '''batchnorm_after''' )
if "proj" in new_name:
SCREAMING_SNAKE_CASE__ : Optional[int] =new_name.replace('''proj''', '''projection''' )
if "dist_head" in new_name:
SCREAMING_SNAKE_CASE__ : Optional[Any] =new_name.replace('''dist_head''', '''distillation_classifier''' )
elif "head" in new_name:
SCREAMING_SNAKE_CASE__ : Tuple =new_name.replace('''head''', '''classifier''' )
elif "patch_embed" in new_name:
SCREAMING_SNAKE_CASE__ : Optional[int] ='''efficientformer.''' + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
SCREAMING_SNAKE_CASE__ : Any =new_name.replace('''norm''', '''layernorm''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] ='''efficientformer.''' + new_name
else:
SCREAMING_SNAKE_CASE__ : str ='''efficientformer.encoder.''' + new_name
return new_name
def _a( UpperCamelCase__ : int, UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
for key in checkpoint.copy().keys():
SCREAMING_SNAKE_CASE__ : List[str] =checkpoint.pop(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : str =val
return checkpoint
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
SCREAMING_SNAKE_CASE__ : List[str] =Image.open(requests.get(UpperCamelCase__, stream=UpperCamelCase__ ).raw )
return image
def _a( UpperCamelCase__ : Path, UpperCamelCase__ : Path, UpperCamelCase__ : Path, UpperCamelCase__ : bool ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict =torch.load(UpperCamelCase__, map_location='''cpu''' )['''model''']
SCREAMING_SNAKE_CASE__ : Optional[int] =EfficientFormerConfig.from_json_file(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[Any] =EfficientFormerForImageClassificationWithTeacher(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : str ='''_'''.join(checkpoint_path.split('''/''' )[-1].split('''.''' )[0].split('''_''' )[:-1] )
SCREAMING_SNAKE_CASE__ : Tuple =config.depths[-1] - config.num_metaad_blocks + 1
SCREAMING_SNAKE_CASE__ : Tuple =convert_torch_checkpoint(UpperCamelCase__, UpperCamelCase__ )
model.load_state_dict(UpperCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE__ : Any ={
'''bilinear''': PILImageResampling.BILINEAR,
'''bicubic''': PILImageResampling.BICUBIC,
'''nearest''': PILImageResampling.NEAREST,
}
# prepare image
SCREAMING_SNAKE_CASE__ : Any =prepare_img()
SCREAMING_SNAKE_CASE__ : List[str] =2_5_6
SCREAMING_SNAKE_CASE__ : Optional[int] =2_2_4
SCREAMING_SNAKE_CASE__ : List[Any] =EfficientFormerImageProcessor(
size={'''shortest_edge''': image_size}, crop_size={'''height''': crop_size, '''width''': crop_size}, resample=pillow_resamplings['''bicubic'''], )
SCREAMING_SNAKE_CASE__ : str =processor(images=UpperCamelCase__, return_tensors='''pt''' ).pixel_values
# original processing pipeline
SCREAMING_SNAKE_CASE__ : List[Any] =Compose(
[
Resize(UpperCamelCase__, interpolation=pillow_resamplings['''bicubic'''] ),
CenterCrop(UpperCamelCase__ ),
ToTensor(),
Normalize(UpperCamelCase__, UpperCamelCase__ ),
] )
SCREAMING_SNAKE_CASE__ : List[str] =image_transforms(UpperCamelCase__ ).unsqueeze(0 )
assert torch.allclose(UpperCamelCase__, UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : int =model(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Tuple =outputs.logits
SCREAMING_SNAKE_CASE__ : Dict =(1, 1_0_0_0)
if "l1" in model_name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =torch.Tensor(
[-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8] )
assert torch.allclose(logits[0, :1_0], UpperCamelCase__, atol=1e-3 )
assert logits.shape == expected_shape
elif "l3" in model_name:
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.Tensor(
[-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7] )
assert torch.allclose(logits[0, :1_0], UpperCamelCase__, atol=1e-3 )
assert logits.shape == expected_shape
elif "l7" in model_name:
SCREAMING_SNAKE_CASE__ : Optional[Any] =torch.Tensor(
[-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8] )
assert logits.shape == expected_shape
else:
raise ValueError(
f"Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7" )
# Save Checkpoints
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
model.save_pretrained(UpperCamelCase__ )
print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}" )
processor.save_pretrained(UpperCamelCase__ )
print(f"Processor successfuly saved at {pytorch_dump_path}" )
if push_to_hub:
print('''Pushing model to the hub...''' )
model.push_to_hub(
repo_id=f"Bearnardd/{pytorch_dump_path}", commit_message='''Add model''', use_temp_dir=UpperCamelCase__, )
processor.push_to_hub(
repo_id=f"Bearnardd/{pytorch_dump_path}", commit_message='''Add image processor''', use_temp_dir=UpperCamelCase__, )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--pytorch_model_path',
default=None,
type=str,
required=True,
help='Path to EfficientFormer pytorch checkpoint.',
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The json file for EfficientFormer model config.',
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
parser.add_argument(
'--no-push_to_hub',
dest='push_to_hub',
action='store_false',
help='Do not push model and image processor to the hub',
)
parser.set_defaults(push_to_hub=True)
a_ = parser.parse_args()
convert_efficientformer_checkpoint(
checkpoint_path=args.pytorch_model_path,
efficientformer_config_file=args.config_file,
pytorch_dump_path=args.pytorch_dump_path,
push_to_hub=args.push_to_hub,
) | 152 | 1 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmelCase : Tuple = """src/transformers"""
# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase : Optional[Any] = direct_transformers_import(PATH_TO_TRANSFORMERS)
UpperCAmelCase : List[Any] = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
UpperCAmelCase : Optional[int] = re.compile(r"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")
UpperCAmelCase : Dict = {
"""DecisionTransformerConfig""",
"""EncoderDecoderConfig""",
"""MusicgenConfig""",
"""RagConfig""",
"""SpeechEncoderDecoderConfig""",
"""TimmBackboneConfig""",
"""VisionEncoderDecoderConfig""",
"""VisionTextDualEncoderConfig""",
"""LlamaConfig""",
}
def _A ( SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
a__ : Any =None
# source code of `config_class`
a__ : Any =inspect.getsource(SCREAMING_SNAKE_CASE )
a__ : List[str] =_re_checkpoint.findall(SCREAMING_SNAKE_CASE )
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
for ckpt_name, ckpt_link in checkpoints:
# allow the link to end with `/`
if ckpt_link.endswith("/" ):
a__ : List[Any] =ckpt_link[:-1]
# verify the checkpoint name corresponds to the checkpoint link
a__ : Optional[Any] =f'''https://huggingface.co/{ckpt_name}'''
if ckpt_link == ckpt_link_from_name:
a__ : Union[str, Any] =ckpt_name
break
return checkpoint
def _A ( ):
"""simple docstring"""
a__ : Union[str, Any] =[]
for config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in config_class.__module__:
continue
a__ : Optional[Any] =get_checkpoint_from_config_class(SCREAMING_SNAKE_CASE )
a__ : Union[str, Any] =config_class.__name__
if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) > 0:
a__ : int ="\n".join(sorted(SCREAMING_SNAKE_CASE ) )
raise ValueError(f'''The following configurations don\'t contain any valid checkpoint:\n{message}''' )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 148 |
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
UpperCAmelCase : Union[str, Any] = 1.054571817E-34 # unit of ℏ : J * s
UpperCAmelCase : Union[str, Any] = 3E8 # unit of c : m * s^-1
def _A ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
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:
a__ : Tuple =(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
240 * (distance) ** 4
)
return {"force": force}
elif area == 0:
a__ : Any =(240 * force * (distance) ** 4) / (
REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
)
return {"area": area}
elif distance == 0:
a__ : List[str] =(
(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()
| 148 | 1 |
"""simple docstring"""
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
_lowercase = TypeVar('''T''')
def _snake_case ( snake_case__ : int ):
return (position - 1) // 2
def _snake_case ( snake_case__ : int ):
return (2 * position) + 1
def _snake_case ( snake_case__ : int ):
return (2 * position) + 2
class lowerCAmelCase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Optional[Any] ) -> None:
A = []
A = {}
A = 0
def __len__( self : Optional[int] ) -> int:
return self.elements
def __repr__( self : Optional[Any] ) -> str:
return str(self.heap )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> bool:
# Check if the priority queue is empty
return self.elements == 0
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ,A_ : T ,A_ : int ) -> None:
# Add an element with given priority to the queue
self.heap.append((elem, weight) )
A = self.elements
self.elements += 1
self._bubble_up(A_ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> T:
# Remove and return the element with lowest weight (highest priority)
if self.elements > 1:
self._swap_nodes(0 ,self.elements - 1 )
A , A = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
A , A = self.heap[0]
self._bubble_down(A_ )
return elem
def _SCREAMING_SNAKE_CASE ( self : Dict ,A_ : T ,A_ : int ) -> None:
# Update the weight of the given key
A = self.position_map[elem]
A = (elem, weight)
if position > 0:
A = get_parent_position(A_ )
A , A = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(A_ )
else:
self._bubble_down(A_ )
else:
self._bubble_down(A_ )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ,A_ : T ) -> None:
# Place a node at the proper position (upward movement) [to be used internally
# only]
A = self.position_map[elem]
if curr_pos == 0:
return None
A = get_parent_position(A_ )
A , A = self.heap[curr_pos]
A , A = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(A_ ,A_ )
return self._bubble_up(A_ )
return None
def _SCREAMING_SNAKE_CASE ( self : List[Any] ,A_ : T ) -> None:
# Place a node at the proper position (downward movement) [to be used
# internally only]
A = self.position_map[elem]
A , A = self.heap[curr_pos]
A = get_child_left_position(A_ )
A = get_child_right_position(A_ )
if child_left_position < self.elements and child_right_position < self.elements:
A , A = self.heap[child_left_position]
A , A = self.heap[child_right_position]
if child_right_weight < child_left_weight and child_right_weight < weight:
self._swap_nodes(A_ ,A_ )
return self._bubble_down(A_ )
if child_left_position < self.elements:
A , A = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(A_ ,A_ )
return self._bubble_down(A_ )
else:
return None
if child_right_position < self.elements:
A , A = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(A_ ,A_ )
return self._bubble_down(A_ )
return None
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ,A_ : int ,A_ : int ) -> None:
# Swap the nodes at the given positions
A = self.heap[nodea_pos][0]
A = self.heap[nodea_pos][0]
A , A = (
self.heap[nodea_pos],
self.heap[nodea_pos],
)
A = nodea_pos
A = nodea_pos
class lowerCAmelCase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Union[str, Any] ) -> None:
A = {}
A = 0
def __repr__( self : Tuple ) -> str:
return str(self.connections )
def __len__( self : str ) -> int:
return self.nodes
def _SCREAMING_SNAKE_CASE ( self : List[str] ,A_ : T ) -> None:
# Add a node in the graph if it is not in the graph
if node not in self.connections:
A = {}
self.nodes += 1
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,A_ : T ,A_ : T ,A_ : int ) -> None:
# Add an edge between 2 nodes in the graph
self.add_node(A_ )
self.add_node(A_ )
A = weight
A = weight
def _snake_case ( snake_case__ : GraphUndirectedWeighted[T] , ):
A = {node: maxsize for node in graph.connections}
A = {node: None for node in graph.connections}
A = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(snake_case__ , snake_case__ )
if priority_queue.is_empty():
return dist, parent
# initialization
A = priority_queue.extract_min()
A = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
A = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(snake_case__ , dist[neighbour] )
A = node
# running prim's algorithm
while not priority_queue.is_empty():
A = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
A = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(snake_case__ , dist[neighbour] )
A = node
return dist, parent | 74 | import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class _lowercase ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self : int ) -> str:
"""simple docstring"""
super().tearDown()
gc.collect()
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
UpperCamelCase_, UpperCamelCase_ : str = FlaxStableDiffusionPipeline.from_pretrained(
'stabilityai/stable-diffusion-2' , revision='bf16' , dtype=jnp.bfloataa , )
UpperCamelCase_ : str = 'A painting of a squirrel eating a burger'
UpperCamelCase_ : Any = jax.device_count()
UpperCamelCase_ : List[str] = num_samples * [prompt]
UpperCamelCase_ : List[Any] = sd_pipe.prepare_inputs(snake_case )
UpperCamelCase_ : Dict = replicate(snake_case )
UpperCamelCase_ : Optional[Any] = shard(snake_case )
UpperCamelCase_ : Dict = jax.random.PRNGKey(0 )
UpperCamelCase_ : Tuple = jax.random.split(snake_case , jax.device_count() )
UpperCamelCase_ : Optional[Any] = sd_pipe(snake_case , snake_case , snake_case , num_inference_steps=2_5 , jit=snake_case )[0]
assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)
UpperCamelCase_ : int = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
UpperCamelCase_ : Tuple = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
UpperCamelCase_ : str = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase_ : List[str] = jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512] )
print(f"output_slice: {output_slice}" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ : Tuple = 'stabilityai/stable-diffusion-2'
UpperCamelCase_, UpperCamelCase_ : Tuple = FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case , subfolder='scheduler' )
UpperCamelCase_, UpperCamelCase_ : Union[str, Any] = FlaxStableDiffusionPipeline.from_pretrained(
snake_case , scheduler=snake_case , revision='bf16' , dtype=jnp.bfloataa , )
UpperCamelCase_ : Optional[int] = scheduler_params
UpperCamelCase_ : Optional[Any] = 'A painting of a squirrel eating a burger'
UpperCamelCase_ : Union[str, Any] = jax.device_count()
UpperCamelCase_ : Union[str, Any] = num_samples * [prompt]
UpperCamelCase_ : Tuple = sd_pipe.prepare_inputs(snake_case )
UpperCamelCase_ : List[Any] = replicate(snake_case )
UpperCamelCase_ : Optional[Any] = shard(snake_case )
UpperCamelCase_ : Tuple = jax.random.PRNGKey(0 )
UpperCamelCase_ : str = jax.random.split(snake_case , jax.device_count() )
UpperCamelCase_ : str = sd_pipe(snake_case , snake_case , snake_case , num_inference_steps=2_5 , jit=snake_case )[0]
assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)
UpperCamelCase_ : str = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
UpperCamelCase_ : int = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
UpperCamelCase_ : int = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase_ : Union[str, Any] = jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297] )
print(f"output_slice: {output_slice}" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 175 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class lowercase__ :
def __init__( self : Dict , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Dict=13 , UpperCAmelCase_ : Optional[Any]=7 , UpperCAmelCase_ : Any=True , UpperCAmelCase_ : List[str]=True , UpperCAmelCase_ : List[str]=True , UpperCAmelCase_ : int=99 , UpperCAmelCase_ : Tuple=32 , UpperCAmelCase_ : int=5 , UpperCAmelCase_ : Tuple=4 , UpperCAmelCase_ : Tuple=37 , UpperCAmelCase_ : Optional[Any]="gelu" , UpperCAmelCase_ : Tuple=0.1 , UpperCAmelCase_ : Tuple=0.1 , UpperCAmelCase_ : Optional[Any]=512 , UpperCAmelCase_ : List[str]=16 , UpperCAmelCase_ : Any=2 , UpperCAmelCase_ : Dict=0.02 , UpperCAmelCase_ : Optional[int]=3 , UpperCAmelCase_ : Any=4 , UpperCAmelCase_ : Tuple=None , ):
SCREAMING_SNAKE_CASE__ = parent
SCREAMING_SNAKE_CASE__ = batch_size
SCREAMING_SNAKE_CASE__ = seq_length
SCREAMING_SNAKE_CASE__ = is_training
SCREAMING_SNAKE_CASE__ = use_token_type_ids
SCREAMING_SNAKE_CASE__ = use_labels
SCREAMING_SNAKE_CASE__ = vocab_size
SCREAMING_SNAKE_CASE__ = hidden_size
SCREAMING_SNAKE_CASE__ = num_hidden_layers
SCREAMING_SNAKE_CASE__ = num_attention_heads
SCREAMING_SNAKE_CASE__ = intermediate_size
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ = max_position_embeddings
SCREAMING_SNAKE_CASE__ = type_vocab_size
SCREAMING_SNAKE_CASE__ = type_sequence_label_size
SCREAMING_SNAKE_CASE__ = initializer_range
SCREAMING_SNAKE_CASE__ = num_labels
SCREAMING_SNAKE_CASE__ = num_choices
SCREAMING_SNAKE_CASE__ = scope
SCREAMING_SNAKE_CASE__ = self.vocab_size - 1
def A_ ( self : List[Any] ):
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
SCREAMING_SNAKE_CASE__ = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def A_ ( self : Union[str, Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : List[str] , *UpperCAmelCase_ : List[str] ):
SCREAMING_SNAKE_CASE__ = OpenAIGPTModel(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
SCREAMING_SNAKE_CASE__ = model(UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ , head_mask=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = model(UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = model(UpperCAmelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A_ ( self : Union[str, Any] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : Any , UpperCAmelCase_ : Tuple , *UpperCAmelCase_ : List[Any] ):
SCREAMING_SNAKE_CASE__ = OpenAIGPTLMHeadModel(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
SCREAMING_SNAKE_CASE__ = model(UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ , labels=UpperCAmelCase_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A_ ( self : str , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Union[str, Any] , *UpperCAmelCase_ : Union[str, Any] ):
SCREAMING_SNAKE_CASE__ = OpenAIGPTDoubleHeadsModel(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
SCREAMING_SNAKE_CASE__ = model(UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ , labels=UpperCAmelCase_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A_ ( self : int , UpperCAmelCase_ : Dict , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Any , *UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE__ = self.num_labels
SCREAMING_SNAKE_CASE__ = OpenAIGPTForSequenceClassification(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ = model(UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ , labels=UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A_ ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE__ = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) = config_and_inputs
SCREAMING_SNAKE_CASE__ = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_torch
class lowercase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
A__ : Union[str, Any] =(
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
A__ : Any =(
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
A__ : Dict =(
{
"""feature-extraction""": OpenAIGPTModel,
"""text-classification""": OpenAIGPTForSequenceClassification,
"""text-generation""": OpenAIGPTLMHeadModel,
"""zero-shot""": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def A_ ( self : str , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : List[Any] ):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def A_ ( self : List[str] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Dict=False ):
SCREAMING_SNAKE_CASE__ = super()._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_ , return_labels=UpperCAmelCase_ )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
SCREAMING_SNAKE_CASE__ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE__ = inputs_dict['labels']
SCREAMING_SNAKE_CASE__ = inputs_dict['labels']
SCREAMING_SNAKE_CASE__ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE__ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase_ )
return inputs_dict
def A_ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE__ = OpenAIGPTModelTester(self )
SCREAMING_SNAKE_CASE__ = ConfigTester(self , config_class=UpperCAmelCase_ , n_embd=37 )
def A_ ( self : Optional[int] ):
self.config_tester.run_common_tests()
def A_ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*UpperCAmelCase_ )
def A_ ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*UpperCAmelCase_ )
def A_ ( self : List[str] ):
SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*UpperCAmelCase_ )
def A_ ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*UpperCAmelCase_ )
@slow
def A_ ( self : Optional[int] ):
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ = OpenAIGPTModel.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
@require_torch
class lowercase__ ( unittest.TestCase ):
@slow
def A_ ( self : List[Any] ):
SCREAMING_SNAKE_CASE__ = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt' )
model.to(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=UpperCAmelCase_ ) # the president is
SCREAMING_SNAKE_CASE__ = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
40477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
SCREAMING_SNAKE_CASE__ = model.generate(UpperCAmelCase_ , do_sample=UpperCAmelCase_ )
self.assertListEqual(output_ids[0].tolist() , UpperCAmelCase_ )
| 169 |
from __future__ import annotations
from statistics import mean
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> list[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = [0] * no_of_processes
SCREAMING_SNAKE_CASE__ = [0] * no_of_processes
# Initialize remaining_time to waiting_time.
for i in range(UpperCamelCase_ ):
SCREAMING_SNAKE_CASE__ = burst_time[i]
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 0
# When processes are not completed,
# A process whose arrival time has passed \
# and has remaining execution time is put into the ready_process.
# The shortest process in the ready_process, target_process is executed.
while completed != no_of_processes:
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = -1
for i in range(UpperCamelCase_ ):
if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
ready_process.append(UpperCamelCase_ )
if len(UpperCamelCase_ ) > 0:
SCREAMING_SNAKE_CASE__ = ready_process[0]
for i in ready_process:
if remaining_time[i] < remaining_time[target_process]:
SCREAMING_SNAKE_CASE__ = i
total_time += burst_time[target_process]
completed += 1
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = (
total_time - arrival_time[target_process] - burst_time[target_process]
)
else:
total_time += 1
return waiting_time
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> list[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = [0] * no_of_processes
for i in range(UpperCamelCase_ ):
SCREAMING_SNAKE_CASE__ = burst_time[i] + waiting_time[i]
return turn_around_time
if __name__ == "__main__":
print("""[TEST CASE 01]""")
__snake_case = 4
__snake_case = [2, 5, 3, 7]
__snake_case = [0, 0, 0, 0]
__snake_case = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
__snake_case = calculate_turnaroundtime(
burst_time, no_of_processes, waiting_time
)
# Printing the Result
print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""")
for i, process_id in enumerate(list(range(1, 5))):
print(
F"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
F"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
)
print(F"""\nAverage waiting time = {mean(waiting_time):.5f}""")
print(F"""Average turnaround time = {mean(turn_around_time):.5f}""")
| 169 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
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,
)
snake_case_ = logging.get_logger(__name__) # pylint: disable=invalid-name
snake_case_ = """
Examples:
```py
>>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
>>> from diffusers.utils import load_image
>>> import torch
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
... )
>>> pipe_prior.to(\"cuda\")
>>> prompt = \"A red cartoon frog, 4k\"
>>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)
>>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
... \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
... )
>>> pipe.to(\"cuda\")
>>> init_image = load_image(
... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
... \"/kandinsky/frog.png\"
... )
>>> image = pipe(
... image=init_image,
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=100,
... strength=0.2,
... ).images
>>> image[0].save(\"red_frog.png\")
```
"""
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_=8 ):
UpperCAmelCase = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
UpperCAmelCase = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
def _lowerCAmelCase ( lowercase_ , lowercase_=512 , lowercase_=512 ):
UpperCAmelCase = pil_image.resize((w, h) , resample=Image.BICUBIC , reducing_gap=1 )
UpperCAmelCase = np.array(pil_image.convert('RGB' ) )
UpperCAmelCase = arr.astype(np.floataa ) / 1_2_7.5 - 1
UpperCAmelCase = np.transpose(lowercase_ , [2, 0, 1] )
UpperCAmelCase = torch.from_numpy(lowercase_ ).unsqueeze(0 )
return image
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self :Dict , lowercase_ :UNetaDConditionModel , lowercase_ :DDPMScheduler , lowercase_ :VQModel , ) -> List[str]:
super().__init__()
self.register_modules(
unet=lowercase_ , scheduler=lowercase_ , movq=lowercase_ , )
UpperCAmelCase = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :Optional[Any] , lowercase_ :Tuple , lowercase_ :Any ) -> Optional[int]:
# get the original timestep using init_timestep
UpperCAmelCase = min(int(num_inference_steps * strength ) , lowercase_ )
UpperCAmelCase = max(num_inference_steps - init_timestep , 0 )
UpperCAmelCase = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def UpperCAmelCase__ ( self :List[Any] , lowercase_ :Dict , lowercase_ :str , lowercase_ :Optional[Any] , lowercase_ :Union[str, Any] , lowercase_ :List[Any] , lowercase_ :Optional[Any] , lowercase_ :Any=None ) -> Any:
if not isinstance(lowercase_ , (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(lowercase_ )}""" )
UpperCAmelCase = image.to(device=lowercase_ , dtype=lowercase_ )
UpperCAmelCase = batch_size * num_images_per_prompt
if image.shape[1] == 4:
UpperCAmelCase = image
else:
if isinstance(lowercase_ , lowercase_ ) and len(lowercase_ ) != batch_size:
raise ValueError(
f"""You have passed a list of generators of length {len(lowercase_ )}, but requested an effective batch"""
f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )
elif isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = [
self.movq.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(lowercase_ )
]
UpperCAmelCase = torch.cat(lowercase_ , dim=0 )
else:
UpperCAmelCase = self.movq.encode(lowercase_ ).latent_dist.sample(lowercase_ )
UpperCAmelCase = self.movq.config.scaling_factor * init_latents
UpperCAmelCase = torch.cat([init_latents] , dim=0 )
UpperCAmelCase = init_latents.shape
UpperCAmelCase = randn_tensor(lowercase_ , generator=lowercase_ , device=lowercase_ , dtype=lowercase_ )
# get latents
UpperCAmelCase = self.scheduler.add_noise(lowercase_ , lowercase_ , lowercase_ )
UpperCAmelCase = init_latents
return latents
def UpperCAmelCase__ ( self :int , lowercase_ :int=0 ) -> List[str]:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
UpperCAmelCase = torch.device(f"""cuda:{gpu_id}""" )
UpperCAmelCase = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowercase_ , lowercase_ )
def UpperCAmelCase__ ( self :Union[str, Any] , lowercase_ :str=0 ) -> Dict:
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.' )
UpperCAmelCase = torch.device(f"""cuda:{gpu_id}""" )
if self.device.type != "cpu":
self.to('cpu' , silence_dtype_warnings=lowercase_ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
UpperCAmelCase = None
for cpu_offloaded_model in [self.unet, self.movq]:
UpperCAmelCase , UpperCAmelCase = cpu_offload_with_hook(lowercase_ , lowercase_ , prev_module_hook=lowercase_ )
# We'll offload the last model manually.
UpperCAmelCase = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def UpperCAmelCase__ ( self :List[Any] ) -> Dict:
if not hasattr(self.unet , '_hf_hook' ):
return self.device
for module in self.unet.modules():
if (
hasattr(lowercase_ , '_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(lowercase_ )
def __call__( self :str , lowercase_ :Union[torch.FloatTensor, List[torch.FloatTensor]] , lowercase_ :Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] , lowercase_ :Union[torch.FloatTensor, List[torch.FloatTensor]] , lowercase_ :int = 5_12 , lowercase_ :int = 5_12 , lowercase_ :int = 1_00 , lowercase_ :float = 4.0 , lowercase_ :float = 0.3 , lowercase_ :int = 1 , lowercase_ :Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase_ :Optional[str] = "pil" , lowercase_ :bool = True , ) -> List[str]:
UpperCAmelCase = self._execution_device
UpperCAmelCase = guidance_scale > 1.0
if isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = torch.cat(lowercase_ , dim=0 )
UpperCAmelCase = image_embeds.shape[0]
if isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = torch.cat(lowercase_ , dim=0 )
if do_classifier_free_guidance:
UpperCAmelCase = image_embeds.repeat_interleave(lowercase_ , dim=0 )
UpperCAmelCase = negative_image_embeds.repeat_interleave(lowercase_ , dim=0 )
UpperCAmelCase = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=lowercase_ )
if not isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = [image]
if not all(isinstance(lowercase_ , (PIL.Image.Image, torch.Tensor) ) for i in image ):
raise ValueError(
f"""Input is in incorrect format: {[type(lowercase_ ) for i in image]}. Currently, we only support PIL image and pytorch tensor""" )
UpperCAmelCase = torch.cat([prepare_image(lowercase_ , lowercase_ , lowercase_ ) for i in image] , dim=0 )
UpperCAmelCase = image.to(dtype=image_embeds.dtype , device=lowercase_ )
UpperCAmelCase = self.movq.encode(lowercase_ )['latents']
UpperCAmelCase = latents.repeat_interleave(lowercase_ , dim=0 )
self.scheduler.set_timesteps(lowercase_ , device=lowercase_ )
UpperCAmelCase , UpperCAmelCase = self.get_timesteps(lowercase_ , lowercase_ , lowercase_ )
UpperCAmelCase = timesteps[:1].repeat(batch_size * num_images_per_prompt )
UpperCAmelCase , UpperCAmelCase = downscale_height_and_width(lowercase_ , lowercase_ , self.movq_scale_factor )
UpperCAmelCase = self.prepare_latents(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , image_embeds.dtype , lowercase_ , lowercase_ )
for i, t in enumerate(self.progress_bar(lowercase_ ) ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase = {'image_embeds': image_embeds}
UpperCAmelCase = self.unet(
sample=lowercase_ , timestep=lowercase_ , encoder_hidden_states=lowercase_ , added_cond_kwargs=lowercase_ , return_dict=lowercase_ , )[0]
if do_classifier_free_guidance:
UpperCAmelCase , UpperCAmelCase = noise_pred.split(latents.shape[1] , dim=1 )
UpperCAmelCase , UpperCAmelCase = noise_pred.chunk(2 )
UpperCAmelCase , UpperCAmelCase = variance_pred.chunk(2 )
UpperCAmelCase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
UpperCAmelCase = 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"]
):
UpperCAmelCase , UpperCAmelCase = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase = self.scheduler.step(
lowercase_ , lowercase_ , lowercase_ , generator=lowercase_ , )[0]
# post-processing
UpperCAmelCase = self.movq.decode(lowercase_ , force_not_quantize=lowercase_ )['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"]:
UpperCAmelCase = image * 0.5 + 0.5
UpperCAmelCase = image.clamp(0 , 1 )
UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
UpperCAmelCase = self.numpy_to_pil(lowercase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowercase_ )
| 78 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self :Dict , lowercase_ :str = "▁" , lowercase_ :bool = True , lowercase_ :Union[str, AddedToken] = "<unk>" , lowercase_ :Union[str, AddedToken] = "</s>" , lowercase_ :Union[str, AddedToken] = "<pad>" , ) -> str:
UpperCAmelCase = {
'pad': {'id': 0, 'token': pad_token},
'eos': {'id': 1, 'token': eos_token},
'unk': {'id': 2, 'token': unk_token},
}
UpperCAmelCase = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
UpperCAmelCase = token_dict['token']
UpperCAmelCase = Tokenizer(Unigram() )
UpperCAmelCase = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(' {2,}' ) , ' ' ),
normalizers.Lowercase(),
] )
UpperCAmelCase = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=lowercase_ , add_prefix_space=lowercase_ ),
pre_tokenizers.Digits(individual_digits=lowercase_ ),
pre_tokenizers.Punctuation(),
] )
UpperCAmelCase = decoders.Metaspace(replacement=lowercase_ , add_prefix_space=lowercase_ )
UpperCAmelCase = TemplateProcessing(
single=f"""$A {self.special_tokens['eos']['token']}""" , special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])] , )
UpperCAmelCase = {
'model': 'SentencePieceUnigram',
'replacement': replacement,
'add_prefix_space': add_prefix_space,
}
super().__init__(lowercase_ , lowercase_ )
def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :Union[str, List[str]] , lowercase_ :int = 80_00 , lowercase_ :bool = True , ) -> Union[str, Any]:
UpperCAmelCase = trainers.UnigramTrainer(
vocab_size=lowercase_ , special_tokens=self.special_tokens_list , show_progress=lowercase_ , )
if isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = [files]
self._tokenizer.train(lowercase_ , trainer=lowercase_ )
self.add_unk_id()
def UpperCAmelCase__ ( self :str , lowercase_ :Union[Iterator[str], Iterator[Iterator[str]]] , lowercase_ :int = 80_00 , lowercase_ :bool = True , ) -> Tuple:
UpperCAmelCase = trainers.UnigramTrainer(
vocab_size=lowercase_ , special_tokens=self.special_tokens_list , show_progress=lowercase_ , )
self._tokenizer.train_from_iterator(lowercase_ , trainer=lowercase_ )
self.add_unk_id()
def UpperCAmelCase__ ( self :Union[str, Any] ) -> int:
UpperCAmelCase = json.loads(self._tokenizer.to_str() )
UpperCAmelCase = self.special_tokens['unk']['id']
UpperCAmelCase = Tokenizer.from_str(json.dumps(lowercase_ ) )
| 78 | 1 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def A__ ( UpperCAmelCase_ , UpperCAmelCase_=0.999 , UpperCAmelCase_="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(UpperCAmelCase_ ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(UpperCAmelCase_ ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}' )
_UpperCamelCase : Optional[int] = []
for i in range(UpperCAmelCase_ ):
_UpperCamelCase : Dict = i / num_diffusion_timesteps
_UpperCamelCase : Tuple = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(UpperCAmelCase_ ) / alpha_bar_fn(UpperCAmelCase_ ) , UpperCAmelCase_ ) )
return torch.tensor(UpperCAmelCase_ , dtype=torch.floataa )
class lowercase__ ( lowercase , lowercase ):
lowercase__ = [e.name for e in KarrasDiffusionSchedulers]
lowercase__ = 2
@register_to_config
def __init__( self : Union[str, Any] ,lowerCamelCase__ : int = 1000 ,lowerCamelCase__ : float = 0.0_0_0_8_5 ,lowerCamelCase__ : float = 0.0_1_2 ,lowerCamelCase__ : str = "linear" ,lowerCamelCase__ : Optional[Union[np.ndarray, List[float]]] = None ,lowerCamelCase__ : str = "epsilon" ,lowerCamelCase__ : Optional[bool] = False ,lowerCamelCase__ : Optional[bool] = False ,lowerCamelCase__ : float = 1.0 ,lowerCamelCase__ : str = "linspace" ,lowerCamelCase__ : int = 0 ,):
'''simple docstring'''
if trained_betas is not None:
_UpperCamelCase : Dict = torch.tensor(lowerCamelCase__ ,dtype=torch.floataa )
elif beta_schedule == "linear":
_UpperCamelCase : List[Any] = torch.linspace(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_UpperCamelCase : Union[str, Any] = (
torch.linspace(beta_start**0.5 ,beta_end**0.5 ,lowerCamelCase__ ,dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_UpperCamelCase : List[str] = betas_for_alpha_bar(lowerCamelCase__ ,alpha_transform_type='cosine' )
elif beta_schedule == "exp":
_UpperCamelCase : Union[str, Any] = betas_for_alpha_bar(lowerCamelCase__ ,alpha_transform_type='exp' )
else:
raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}' )
_UpperCamelCase : Dict = 1.0 - self.betas
_UpperCamelCase : List[Any] = torch.cumprod(self.alphas ,dim=0 )
# set all values
self.set_timesteps(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
_UpperCamelCase : Any = use_karras_sigmas
def UpperCamelCase_ ( self : Any ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : List[Any]=None ):
'''simple docstring'''
if schedule_timesteps is None:
_UpperCamelCase : Union[str, Any] = self.timesteps
_UpperCamelCase : str = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
_UpperCamelCase : int = 1 if len(lowerCamelCase__ ) > 1 else 0
else:
_UpperCamelCase : List[str] = timestep.cpu().item() if torch.is_tensor(lowerCamelCase__ ) else timestep
_UpperCamelCase : Optional[Any] = self._index_counter[timestep_int]
return indices[pos].item()
@property
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def UpperCamelCase_ ( self : Optional[int] ,lowerCamelCase__ : torch.FloatTensor ,lowerCamelCase__ : Union[float, torch.FloatTensor] ,):
'''simple docstring'''
_UpperCamelCase : List[str] = self.index_for_timestep(lowerCamelCase__ )
_UpperCamelCase : Optional[Any] = self.sigmas[step_index]
_UpperCamelCase : List[str] = sample / ((sigma**2 + 1) ** 0.5)
return sample
def UpperCamelCase_ ( self : str ,lowerCamelCase__ : int ,lowerCamelCase__ : Union[str, torch.device] = None ,lowerCamelCase__ : Optional[int] = None ,):
'''simple docstring'''
_UpperCamelCase : Tuple = num_inference_steps
_UpperCamelCase : List[str] = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
_UpperCamelCase : Any = np.linspace(0 ,num_train_timesteps - 1 ,lowerCamelCase__ ,dtype=lowerCamelCase__ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
_UpperCamelCase : List[Any] = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_UpperCamelCase : Dict = (np.arange(0 ,lowerCamelCase__ ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
_UpperCamelCase : List[Any] = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_UpperCamelCase : Optional[Any] = (np.arange(lowerCamelCase__ ,0 ,-step_ratio )).round().copy().astype(lowerCamelCase__ )
timesteps -= 1
else:
raise ValueError(
F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.' )
_UpperCamelCase : int = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
_UpperCamelCase : Any = np.log(lowerCamelCase__ )
_UpperCamelCase : Dict = np.interp(lowerCamelCase__ ,np.arange(0 ,len(lowerCamelCase__ ) ) ,lowerCamelCase__ )
if self.config.use_karras_sigmas:
_UpperCamelCase : Any = self._convert_to_karras(in_sigmas=lowerCamelCase__ ,num_inference_steps=self.num_inference_steps )
_UpperCamelCase : str = np.array([self._sigma_to_t(lowerCamelCase__ ,lowerCamelCase__ ) for sigma in sigmas] )
_UpperCamelCase : List[str] = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
_UpperCamelCase : Tuple = torch.from_numpy(lowerCamelCase__ ).to(device=lowerCamelCase__ )
_UpperCamelCase : Optional[Any] = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )
_UpperCamelCase : str = torch.from_numpy(lowerCamelCase__ )
_UpperCamelCase : Any = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )
if str(lowerCamelCase__ ).startswith('mps' ):
# mps does not support float64
_UpperCamelCase : Any = timesteps.to(lowerCamelCase__ ,dtype=torch.floataa )
else:
_UpperCamelCase : List[Any] = timesteps.to(device=lowerCamelCase__ )
# empty dt and derivative
_UpperCamelCase : Optional[int] = None
_UpperCamelCase : Union[str, Any] = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
_UpperCamelCase : str = defaultdict(lowerCamelCase__ )
def UpperCamelCase_ ( self : Any ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Any ):
'''simple docstring'''
_UpperCamelCase : Tuple = np.log(lowerCamelCase__ )
# get distribution
_UpperCamelCase : str = log_sigma - log_sigmas[:, np.newaxis]
# get sigmas range
_UpperCamelCase : Optional[int] = np.cumsum((dists >= 0) ,axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
_UpperCamelCase : Optional[int] = low_idx + 1
_UpperCamelCase : str = log_sigmas[low_idx]
_UpperCamelCase : Any = log_sigmas[high_idx]
# interpolate sigmas
_UpperCamelCase : List[str] = (low - log_sigma) / (low - high)
_UpperCamelCase : List[Any] = np.clip(lowerCamelCase__ ,0 ,1 )
# transform interpolation to time range
_UpperCamelCase : Optional[Any] = (1 - w) * low_idx + w * high_idx
_UpperCamelCase : str = t.reshape(sigma.shape )
return t
def UpperCamelCase_ ( self : Optional[int] ,lowerCamelCase__ : torch.FloatTensor ,lowerCamelCase__ : Tuple ):
'''simple docstring'''
_UpperCamelCase : float = in_sigmas[-1].item()
_UpperCamelCase : float = in_sigmas[0].item()
_UpperCamelCase : Dict = 7.0 # 7.0 is the value used in the paper
_UpperCamelCase : Optional[int] = np.linspace(0 ,1 ,lowerCamelCase__ )
_UpperCamelCase : Tuple = sigma_min ** (1 / rho)
_UpperCamelCase : str = sigma_max ** (1 / rho)
_UpperCamelCase : int = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return sigmas
@property
def UpperCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
return self.dt is None
def UpperCamelCase_ ( self : Union[str, Any] ,lowerCamelCase__ : Union[torch.FloatTensor, np.ndarray] ,lowerCamelCase__ : Union[float, torch.FloatTensor] ,lowerCamelCase__ : Union[torch.FloatTensor, np.ndarray] ,lowerCamelCase__ : bool = True ,):
'''simple docstring'''
_UpperCamelCase : Tuple = self.index_for_timestep(lowerCamelCase__ )
# advance index counter by 1
_UpperCamelCase : Union[str, Any] = timestep.cpu().item() if torch.is_tensor(lowerCamelCase__ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
_UpperCamelCase : Dict = self.sigmas[step_index]
_UpperCamelCase : Optional[Any] = self.sigmas[step_index + 1]
else:
# 2nd order / Heun's method
_UpperCamelCase : Optional[Any] = self.sigmas[step_index - 1]
_UpperCamelCase : Tuple = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
_UpperCamelCase : str = 0
_UpperCamelCase : Dict = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
_UpperCamelCase : Tuple = sigma_hat if self.state_in_first_order else sigma_next
_UpperCamelCase : Optional[int] = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
_UpperCamelCase : List[str] = sigma_hat if self.state_in_first_order else sigma_next
_UpperCamelCase : List[str] = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
_UpperCamelCase : Optional[int] = model_output
else:
raise ValueError(
F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`' )
if self.config.clip_sample:
_UpperCamelCase : Optional[int] = pred_original_sample.clamp(
-self.config.clip_sample_range ,self.config.clip_sample_range )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
_UpperCamelCase : Optional[Any] = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
_UpperCamelCase : str = sigma_next - sigma_hat
# store for 2nd order step
_UpperCamelCase : List[Any] = derivative
_UpperCamelCase : Optional[Any] = dt
_UpperCamelCase : Dict = sample
else:
# 2. 2nd order / Heun's method
_UpperCamelCase : Union[str, Any] = (sample - pred_original_sample) / sigma_next
_UpperCamelCase : Optional[int] = (self.prev_derivative + derivative) / 2
# 3. take prev timestep & sample
_UpperCamelCase : Union[str, Any] = self.dt
_UpperCamelCase : Optional[Any] = self.sample
# free dt and derivative
# Note, this puts the scheduler in "first order mode"
_UpperCamelCase : int = None
_UpperCamelCase : Dict = None
_UpperCamelCase : str = None
_UpperCamelCase : Any = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=lowerCamelCase__ )
def UpperCamelCase_ ( self : List[str] ,lowerCamelCase__ : torch.FloatTensor ,lowerCamelCase__ : torch.FloatTensor ,lowerCamelCase__ : torch.FloatTensor ,):
'''simple docstring'''
_UpperCamelCase : Any = self.sigmas.to(device=original_samples.device ,dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase__ ):
# mps does not support float64
_UpperCamelCase : Optional[Any] = self.timesteps.to(original_samples.device ,dtype=torch.floataa )
_UpperCamelCase : Optional[Any] = timesteps.to(original_samples.device ,dtype=torch.floataa )
else:
_UpperCamelCase : List[str] = self.timesteps.to(original_samples.device )
_UpperCamelCase : int = timesteps.to(original_samples.device )
_UpperCamelCase : int = [self.index_for_timestep(lowerCamelCase__ ,lowerCamelCase__ ) for t in timesteps]
_UpperCamelCase : Optional[int] = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
_UpperCamelCase : Tuple = sigma.unsqueeze(-1 )
_UpperCamelCase : Tuple = original_samples + noise * sigma
return noisy_samples
def __len__( self : int ):
'''simple docstring'''
return self.config.num_train_timesteps
| 358 |
'''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
snake_case_ : int = logging.get_logger(__name__)
snake_case_ : List[str] = {
'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 lowercase__ ( lowercase ):
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 : Any ,lowerCamelCase__ : Optional[Any]=50400 ,lowerCamelCase__ : Tuple=2048 ,lowerCamelCase__ : Tuple=4096 ,lowerCamelCase__ : int=28 ,lowerCamelCase__ : Optional[Any]=16 ,lowerCamelCase__ : Optional[Any]=64 ,lowerCamelCase__ : List[Any]=None ,lowerCamelCase__ : List[Any]="gelu_new" ,lowerCamelCase__ : Optional[Any]=0.0 ,lowerCamelCase__ : List[str]=0.0 ,lowerCamelCase__ : List[Any]=0.0 ,lowerCamelCase__ : Tuple=1E-5 ,lowerCamelCase__ : int=0.0_2 ,lowerCamelCase__ : Any=True ,lowerCamelCase__ : str=50256 ,lowerCamelCase__ : Any=50256 ,lowerCamelCase__ : Tuple=False ,**lowerCamelCase__ : Optional[Any] ,):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = vocab_size
_UpperCamelCase : Optional[Any] = n_positions
_UpperCamelCase : Union[str, Any] = n_embd
_UpperCamelCase : Any = n_layer
_UpperCamelCase : Optional[int] = n_head
_UpperCamelCase : List[str] = n_inner
_UpperCamelCase : List[Any] = rotary_dim
_UpperCamelCase : int = activation_function
_UpperCamelCase : Dict = resid_pdrop
_UpperCamelCase : Any = embd_pdrop
_UpperCamelCase : Union[str, Any] = attn_pdrop
_UpperCamelCase : Union[str, Any] = layer_norm_epsilon
_UpperCamelCase : Optional[Any] = initializer_range
_UpperCamelCase : str = use_cache
_UpperCamelCase : Union[str, Any] = bos_token_id
_UpperCamelCase : Any = eos_token_id
super().__init__(
bos_token_id=lowerCamelCase__ ,eos_token_id=lowerCamelCase__ ,tie_word_embeddings=lowerCamelCase__ ,**lowerCamelCase__ )
class lowercase__ ( lowercase ):
def __init__( self : Tuple ,lowerCamelCase__ : PretrainedConfig ,lowerCamelCase__ : str = "default" ,lowerCamelCase__ : List[PatchingSpec] = None ,lowerCamelCase__ : bool = False ,):
'''simple docstring'''
super().__init__(lowerCamelCase__ ,task=lowerCamelCase__ ,patching_specs=lowerCamelCase__ ,use_past=lowerCamelCase__ )
if not getattr(self._config ,'pad_token_id' ,lowerCamelCase__ ):
# TODO: how to do that better?
_UpperCamelCase : int = 0
@property
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
_UpperCamelCase : List[str] = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
if self.use_past:
self.fill_with_past_key_values_(lowerCamelCase__ ,direction='inputs' )
_UpperCamelCase : Tuple = {0: 'batch', 1: 'past_sequence + sequence'}
else:
_UpperCamelCase : Any = {0: 'batch', 1: 'sequence'}
return common_inputs
@property
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
return self._config.n_layer
@property
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
return self._config.n_head
def UpperCamelCase_ ( self : List[str] ,lowerCamelCase__ : PreTrainedTokenizer ,lowerCamelCase__ : int = -1 ,lowerCamelCase__ : int = -1 ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : Optional[TensorType] = None ,):
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = super(lowerCamelCase__ ,self ).generate_dummy_inputs(
lowerCamelCase__ ,batch_size=lowerCamelCase__ ,seq_length=lowerCamelCase__ ,is_pair=lowerCamelCase__ ,framework=lowerCamelCase__ )
# We need to order the input in the way they appears in the forward()
_UpperCamelCase : Tuple = 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 : str = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
_UpperCamelCase : Optional[int] = seqlen + 2
_UpperCamelCase : List[Any] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_UpperCamelCase : Optional[Any] = [
(torch.zeros(lowerCamelCase__ ), torch.zeros(lowerCamelCase__ )) for _ in range(self.num_layers )
]
_UpperCamelCase : Union[str, Any] = common_inputs['attention_mask']
if self.use_past:
_UpperCamelCase : Any = ordered_inputs['attention_mask'].dtype
_UpperCamelCase : List[str] = torch.cat(
[ordered_inputs['attention_mask'], torch.ones(lowerCamelCase__ ,lowerCamelCase__ ,dtype=lowerCamelCase__ )] ,dim=1 )
return ordered_inputs
@property
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
return 13
| 236 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( lowercase ,lowercase = False ):
"""simple docstring"""
if not isinstance(lowercase ,lowercase ):
_UpperCAmelCase = f'''Expected string as input, found {type(lowercase )}'''
raise ValueError(lowercase )
if not isinstance(lowercase ,lowercase ):
_UpperCAmelCase = f'''Expected boolean as use_pascal parameter, found {type(lowercase )}'''
raise ValueError(lowercase )
_UpperCAmelCase = input_str.split("""_""" )
_UpperCAmelCase = 0 if use_pascal else 1
_UpperCAmelCase = words[start_index:]
_UpperCAmelCase = [word[0].upper() + word[1:] for word in words_to_capitalize]
_UpperCAmelCase = """""" if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 289 | """simple docstring"""
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import 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 (
MODEL_FOR_PRETRAINING_MAPPING,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
)
from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class a :
def __init__( self : Optional[int] , __lowerCAmelCase : int , __lowerCAmelCase : Union[str, Any]=13 , __lowerCAmelCase : str=7 , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : int=True , __lowerCAmelCase : List[Any]=True , __lowerCAmelCase : int=True , __lowerCAmelCase : List[Any]=99 , __lowerCAmelCase : Optional[int]=16 , __lowerCAmelCase : Dict=36 , __lowerCAmelCase : Optional[Any]=6 , __lowerCAmelCase : List[str]=6 , __lowerCAmelCase : Union[str, Any]=6 , __lowerCAmelCase : str=37 , __lowerCAmelCase : Optional[int]="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : Dict=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : Optional[Any]=16 , __lowerCAmelCase : int=2 , __lowerCAmelCase : List[str]=0.02 , __lowerCAmelCase : Optional[int]=3 , __lowerCAmelCase : List[str]=4 , __lowerCAmelCase : Any=None , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_input_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = embedding_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_hidden_groups
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_labels
_UpperCAmelCase = num_choices
_UpperCAmelCase = scope
def lowerCAmelCase_ ( self : Union[str, Any] ):
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = None
if self.use_input_mask:
_UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase = None
if self.use_token_type_ids:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase_ ( self : Union[str, Any] ):
return AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , num_hidden_groups=self.num_hidden_groups , )
def lowerCAmelCase_ ( self : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : Any ):
_UpperCAmelCase = AlbertModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_UpperCAmelCase = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
_UpperCAmelCase = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
_UpperCAmelCase = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowerCAmelCase_ ( self : Any , __lowerCAmelCase : List[str] , __lowerCAmelCase : Any , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : int ):
_UpperCAmelCase = AlbertForPreTraining(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_UpperCAmelCase = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , sentence_order_label=__lowerCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.sop_logits.shape , (self.batch_size, config.num_labels) )
def lowerCAmelCase_ ( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Tuple ):
_UpperCAmelCase = AlbertForMaskedLM(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_UpperCAmelCase = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase_ ( self : str , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple ):
_UpperCAmelCase = AlbertForQuestionAnswering(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_UpperCAmelCase = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , start_positions=__lowerCAmelCase , end_positions=__lowerCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCAmelCase_ ( self : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any ):
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = AlbertForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_UpperCAmelCase = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase_ ( self : Optional[int] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] ):
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = AlbertForTokenClassification(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_UpperCAmelCase = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCAmelCase_ ( self : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[Any] , __lowerCAmelCase : str , __lowerCAmelCase : Dict ):
_UpperCAmelCase = self.num_choices
_UpperCAmelCase = AlbertForMultipleChoice(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_UpperCAmelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCAmelCase_ ( self : List[str] ):
_UpperCAmelCase = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = config_and_inputs
_UpperCAmelCase = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class a ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
_snake_case : str = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
_snake_case : Tuple = (
{
'feature-extraction': AlbertModel,
'fill-mask': AlbertForMaskedLM,
'question-answering': AlbertForQuestionAnswering,
'text-classification': AlbertForSequenceClassification,
'token-classification': AlbertForTokenClassification,
'zero-shot': AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
_snake_case : Dict = True
def lowerCAmelCase_ ( self : str , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[Any]=False ):
_UpperCAmelCase = super()._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
if return_labels:
if model_class in get_values(__lowerCAmelCase ):
_UpperCAmelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowerCAmelCase )
_UpperCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowerCAmelCase )
return inputs_dict
def lowerCAmelCase_ ( self : Optional[Any] ):
_UpperCAmelCase = AlbertModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37 )
def lowerCAmelCase_ ( self : Optional[int] ):
self.config_tester.run_common_tests()
def lowerCAmelCase_ ( self : int ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__lowerCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase )
def lowerCAmelCase_ ( self : Dict ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__lowerCAmelCase )
def lowerCAmelCase_ ( self : str ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase )
def lowerCAmelCase_ ( self : List[Any] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase )
def lowerCAmelCase_ ( self : Optional[int] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_UpperCAmelCase = type
self.model_tester.create_and_check_model(*__lowerCAmelCase )
@slow
def lowerCAmelCase_ ( self : Dict ):
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = AlbertModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
@require_torch
class a ( unittest.TestCase ):
@slow
def lowerCAmelCase_ ( self : Tuple ):
_UpperCAmelCase = AlbertModel.from_pretrained("""albert-base-v2""" )
_UpperCAmelCase = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
_UpperCAmelCase = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
_UpperCAmelCase = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase )[0]
_UpperCAmelCase = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , __lowerCAmelCase )
_UpperCAmelCase = torch.tensor(
[[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __lowerCAmelCase , atol=1e-4 ) )
| 289 | 1 |
import string
def __lowerCamelCase ( UpperCAmelCase_ : str ):
"""simple docstring"""
a :Optional[Any] = ''''''
for i in sequence:
a :List[Any] = ord(UpperCAmelCase_ )
if 65 <= extract <= 90:
output += chr(155 - extract )
elif 97 <= extract <= 122:
output += chr(219 - extract )
else:
output += i
return output
def __lowerCamelCase ( UpperCAmelCase_ : str ):
"""simple docstring"""
a :Union[str, Any] = string.ascii_letters
a :Optional[int] = string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
return "".join(
letters_reversed[letters.index(UpperCAmelCase_ )] if c in letters else c for c in sequence )
def __lowerCamelCase ( ):
"""simple docstring"""
from timeit import timeit
print('''Running performance benchmarks...''' )
a :Optional[Any] = '''from string import printable ; from __main__ import atbash, atbash_slow'''
print(F'''> atbash_slow(): {timeit('atbash_slow(printable)' , setup=UpperCAmelCase_ )} seconds''' )
print(F'''> atbash(): {timeit('atbash(printable)' , setup=UpperCAmelCase_ )} seconds''' )
if __name__ == "__main__":
for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
print(F"""{example} encrypted in atbash: {atbash(example)}""")
benchmark()
| 281 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case : Union[str, Any] = logging.get_logger(__name__)
snake_case : List[str] = {
'''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = 'efficientnet'
def __init__( self , _lowerCamelCase = 3 , _lowerCamelCase = 600 , _lowerCamelCase = 2.0 , _lowerCamelCase = 3.1 , _lowerCamelCase = 8 , _lowerCamelCase = [3, 3, 5, 3, 5, 5, 3] , _lowerCamelCase = [32, 16, 24, 40, 80, 112, 192] , _lowerCamelCase = [16, 24, 40, 80, 112, 192, 320] , _lowerCamelCase = [] , _lowerCamelCase = [1, 2, 2, 2, 1, 2, 1] , _lowerCamelCase = [1, 2, 2, 3, 3, 4, 1] , _lowerCamelCase = [1, 6, 6, 6, 6, 6, 6] , _lowerCamelCase = 0.25 , _lowerCamelCase = "swish" , _lowerCamelCase = 2560 , _lowerCamelCase = "mean" , _lowerCamelCase = 0.02 , _lowerCamelCase = 0.001 , _lowerCamelCase = 0.99 , _lowerCamelCase = 0.5 , _lowerCamelCase = 0.2 , **_lowerCamelCase , ):
super().__init__(**_lowerCamelCase )
a :Optional[int] = num_channels
a :List[str] = image_size
a :int = width_coefficient
a :Optional[Any] = depth_coefficient
a :Any = depth_divisor
a :Any = kernel_sizes
a :Tuple = in_channels
a :Union[str, Any] = out_channels
a :Any = depthwise_padding
a :Any = strides
a :Optional[Any] = num_block_repeats
a :Tuple = expand_ratios
a :Dict = squeeze_expansion_ratio
a :int = hidden_act
a :Dict = hidden_dim
a :Tuple = pooling_type
a :Any = initializer_range
a :Tuple = batch_norm_eps
a :Optional[int] = batch_norm_momentum
a :List[Any] = dropout_rate
a :Optional[int] = drop_connect_rate
a :Tuple = sum(_lowerCamelCase ) * 4
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = version.parse('1.11' )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return 1e-5
| 281 | 1 |
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 _lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase , UpperCAmelCase=99 , UpperCAmelCase=13 , UpperCAmelCase=7 , UpperCAmelCase=9 , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=False , UpperCAmelCase=32 , UpperCAmelCase=5 , UpperCAmelCase=4 , UpperCAmelCase=37 , UpperCAmelCase=8 , UpperCAmelCase=0.1 , UpperCAmelCase=0.002 , UpperCAmelCase=1 , UpperCAmelCase=0 , UpperCAmelCase=0 , UpperCAmelCase=None , UpperCAmelCase=None , ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[str] = parent
__snake_case : str = batch_size
__snake_case : Tuple = encoder_seq_length
__snake_case : Dict = decoder_seq_length
# For common tests
__snake_case : Dict = self.decoder_seq_length
__snake_case : Optional[int] = is_training
__snake_case : int = use_attention_mask
__snake_case : Union[str, Any] = use_labels
__snake_case : str = vocab_size
__snake_case : int = hidden_size
__snake_case : str = num_hidden_layers
__snake_case : List[str] = num_attention_heads
__snake_case : List[Any] = d_ff
__snake_case : Optional[int] = relative_attention_num_buckets
__snake_case : List[Any] = dropout_rate
__snake_case : Tuple = initializer_factor
__snake_case : int = eos_token_id
__snake_case : Optional[int] = pad_token_id
__snake_case : str = decoder_start_token_id
__snake_case : Dict = None
__snake_case : Union[str, Any] = decoder_layers
def UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
return TaConfig.from_pretrained("google/umt5-base" )
def UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , ) -> Union[str, Any]:
'''simple docstring'''
if attention_mask is None:
__snake_case : int = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
__snake_case : List[Any] = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
__snake_case : int = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=UpperCAmelCase )
if decoder_head_mask is None:
__snake_case : int = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=UpperCAmelCase )
if cross_attn_head_mask is None:
__snake_case : List[Any] = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=UpperCAmelCase )
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 UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
__snake_case : Optional[int] = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
__snake_case : List[str] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
__snake_case : Any = input_ids.clamp(self.pad_token_id + 1 )
__snake_case : List[Any] = decoder_input_ids.clamp(self.pad_token_id + 1 )
__snake_case : Optional[Any] = self.get_config()
__snake_case : Tuple = config.num_attention_heads
__snake_case : Any = self.prepare_inputs_dict(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
return config, input_dict
def UpperCAmelCase ( self ) -> Optional[int]:
'''simple docstring'''
__snake_case , __snake_case : List[Any] = self.prepare_config_and_inputs()
return config, inputs_dict
def UpperCAmelCase ( self ) -> Dict:
'''simple docstring'''
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 UpperCAmelCase ( self ) -> int:
'''simple docstring'''
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 UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[int] = UMTaModel(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__snake_case : Tuple = model(
input_ids=UpperCAmelCase , decoder_input_ids=UpperCAmelCase , attention_mask=UpperCAmelCase , decoder_attention_mask=UpperCAmelCase , )
__snake_case : Union[str, Any] = model(input_ids=UpperCAmelCase , decoder_input_ids=UpperCAmelCase )
__snake_case : Any = result.last_hidden_state
__snake_case : str = result.past_key_values
__snake_case : List[Any] = 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(UpperCAmelCase ) , 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 UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = UMTaModel(config=UpperCAmelCase ).get_decoder().to(UpperCAmelCase ).eval()
# first forward pass
__snake_case : Any = model(UpperCAmelCase , use_cache=UpperCAmelCase )
__snake_case : str = model(UpperCAmelCase )
__snake_case : List[Any] = model(UpperCAmelCase , use_cache=UpperCAmelCase )
self.parent.assertTrue(len(UpperCAmelCase ) == len(UpperCAmelCase ) )
self.parent.assertTrue(len(UpperCAmelCase ) == len(UpperCAmelCase ) + 1 )
__snake_case , __snake_case : Optional[Any] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__snake_case : List[str] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
__snake_case : List[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
__snake_case : Tuple = model(UpperCAmelCase )["last_hidden_state"]
__snake_case : Optional[int] = model(UpperCAmelCase , past_key_values=UpperCAmelCase )["last_hidden_state"]
# select random slice
__snake_case : Optional[int] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__snake_case : Any = output_from_no_past[:, -1, random_slice_idx].detach()
__snake_case : str = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(UpperCAmelCase , UpperCAmelCase , atol=1E-3 ) )
def UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase , ) -> List[str]:
'''simple docstring'''
__snake_case : str = UMTaModel(config=UpperCAmelCase ).to(UpperCAmelCase ).half().eval()
__snake_case : int = model(**UpperCAmelCase )["last_hidden_state"]
self.parent.assertFalse(torch.isnan(UpperCAmelCase ).any().item() )
@require_torch
class _lowerCamelCase ( a , a , a , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase_ : Any =(
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
UpperCAmelCase_ : List[Any] =(UMTaForConditionalGeneration,) if is_torch_available() else ()
UpperCAmelCase_ : int =(
{
"conversational": UMTaForConditionalGeneration,
"feature-extraction": UMTaModel,
"summarization": UMTaForConditionalGeneration,
"text2text-generation": UMTaForConditionalGeneration,
"translation": UMTaForConditionalGeneration,
"question-answering": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
UpperCAmelCase_ : int =True
UpperCAmelCase_ : Optional[int] =False
UpperCAmelCase_ : int =False
UpperCAmelCase_ : Optional[int] =True
UpperCAmelCase_ : int =True
# The small UMT5 model needs higher percentages for CPU/MP tests
UpperCAmelCase_ : List[str] =[0.8, 0.9]
def UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
__snake_case : str = UMTaModelTester(self )
@unittest.skip("Test has a segmentation fault on torch 1.8.0" )
def UpperCAmelCase ( self ) -> Optional[int]:
'''simple docstring'''
__snake_case : Any = self.model_tester.prepare_config_and_inputs()
__snake_case : str = UMTaModel(config_and_inputs[0] ).to(UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
UpperCAmelCase , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , F"""{tmpdirname}/t5_test.onnx""" , export_params=UpperCAmelCase , opset_version=9 , input_names=["input_ids", "decoder_input_ids"] , )
@unittest.skipIf(torch_device == "cpu" , "Cant do half precision" )
def UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
__snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*UpperCAmelCase )
def UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
__snake_case : Union[str, Any] = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
__snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs()
__snake_case : Any = config_and_inputs[0]
__snake_case : Union[str, Any] = UMTaForConditionalGeneration(UpperCAmelCase ).eval()
model.to(UpperCAmelCase )
__snake_case : Union[str, Any] = {
"head_mask": torch.zeros(config.num_layers , config.num_heads , device=UpperCAmelCase ),
"decoder_head_mask": torch.zeros(config.num_decoder_layers , config.num_heads , device=UpperCAmelCase ),
"cross_attn_head_mask": torch.zeros(config.num_decoder_layers , config.num_heads , device=UpperCAmelCase ),
}
for attn_name, (name, mask) in zip(UpperCAmelCase , head_masking.items() ):
__snake_case : Tuple = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
__snake_case : Dict = torch.ones(
config.num_decoder_layers , config.num_heads , device=UpperCAmelCase )
__snake_case : List[Any] = model.generate(
config_and_inputs[1]["input_ids"] , num_beams=1 , max_length=3 , output_attentions=UpperCAmelCase , return_dict_in_generate=UpperCAmelCase , **UpperCAmelCase , )
# We check the state of decoder_attentions and cross_attentions just from the last step
__snake_case : Tuple = 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 UpperCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
@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 UpperCAmelCase ( self ) -> List[str]:
'''simple docstring'''
__snake_case : Union[str, Any] = UMTaForConditionalGeneration.from_pretrained("google/umt5-small" , return_dict=UpperCAmelCase ).to(UpperCAmelCase )
__snake_case : int = AutoTokenizer.from_pretrained("google/umt5-small" , use_fast=UpperCAmelCase , legacy=UpperCAmelCase )
__snake_case : Union[str, Any] = [
"Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
"No se como puedo <extra_id_0>.",
"This is the reason why we <extra_id_0> them.",
"The <extra_id_0> walks in <extra_id_1>, seats",
"A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
]
__snake_case : List[Any] = tokenizer(UpperCAmelCase , return_tensors="pt" , padding=UpperCAmelCase ).input_ids
# fmt: off
__snake_case : List[str] = 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(UpperCAmelCase , UpperCAmelCase )
__snake_case : str = model.generate(input_ids.to(UpperCAmelCase ) )
__snake_case : List[str] = [
"<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
"<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
"<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
"<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
"<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
]
__snake_case : Optional[Any] = tokenizer.batch_decode(UpperCAmelCase )
self.assertEqual(UpperCAmelCase , UpperCAmelCase )
| 326 |
def lowerCAmelCase__( lowercase : int = 100_0000 ) -> int:
__snake_case : List[Any] = limit + 1
__snake_case : List[str] = [0] * limit
for first_term in range(1 , lowercase ):
for n in range(lowercase , lowercase , lowercase ):
__snake_case : Union[str, Any] = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
__snake_case : Tuple = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(F'''{solution() = }''')
| 326 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegatronBertForCausalLM',
'MegatronBertForMaskedLM',
'MegatronBertForMultipleChoice',
'MegatronBertForNextSentencePrediction',
'MegatronBertForPreTraining',
'MegatronBertForQuestionAnswering',
'MegatronBertForSequenceClassification',
'MegatronBertForTokenClassification',
'MegatronBertModel',
'MegatronBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 222 |
'''simple docstring'''
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
a_ = get_logger(__name__)
a_ = R'\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n Indices of input sequence tokens in the vocabulary.\n\n Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details.\n\n [What are input IDs?](../glossary#input-ids)\n scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n search or log softmax for each vocabulary token when using beam search\n kwargs (`Dict[str, Any]`, *optional*):\n Additional logits processor specific kwargs.\n\n Return:\n `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n'
class __SCREAMING_SNAKE_CASE :
@add_start_docstrings(__lowercase )
def __call__( self : Any , __lowercase : jnp.ndarray , __lowercase : jnp.ndarray ) -> jnp.ndarray:
raise NotImplementedError(
F"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )
class __SCREAMING_SNAKE_CASE :
@add_start_docstrings(__lowercase )
def __call__( self : List[str] , __lowercase : jnp.ndarray , __lowercase : jnp.ndarray ) -> jnp.ndarray:
raise NotImplementedError(
F"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
@add_start_docstrings(__lowercase )
def __call__( self : Any , __lowercase : jnp.ndarray , __lowercase : jnp.ndarray , __lowercase : int , **__lowercase : List[str] ) -> jnp.ndarray:
for processor in self:
SCREAMING_SNAKE_CASE__ : Optional[Any] =inspect.signature(processor.__call__ ).parameters
if len(__lowercase ) > 3:
if not all(arg in kwargs for arg in list(function_args.keys() )[2:] ):
raise ValueError(
F"Make sure that all the required parameters: {list(function_args.keys() )} for "
F"{processor.__class__} are passed to the logits processor." )
SCREAMING_SNAKE_CASE__ : List[Any] =processor(__lowercase , __lowercase , __lowercase , **__lowercase )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =processor(__lowercase , __lowercase , __lowercase )
return scores
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : List[str] , __lowercase : float ) -> Tuple:
if not isinstance(__lowercase , __lowercase ) or not (temperature > 0):
raise ValueError(F"`temperature` has to be a strictly positive float, but is {temperature}" )
SCREAMING_SNAKE_CASE__ : Optional[int] =temperature
def __call__( self : Optional[int] , __lowercase : jnp.ndarray , __lowercase : jnp.ndarray , __lowercase : int ) -> jnp.ndarray:
SCREAMING_SNAKE_CASE__ : int =scores / self.temperature
return scores
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : Tuple , __lowercase : float , __lowercase : float = -float('''Inf''' ) , __lowercase : int = 1 ) -> List[str]:
if not isinstance(__lowercase , __lowercase ) or (top_p < 0 or top_p > 1.0):
raise ValueError(F"`top_p` has to be a float > 0 and < 1, but is {top_p}" )
if not isinstance(__lowercase , __lowercase ) or (min_tokens_to_keep < 1):
raise ValueError(F"`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}" )
SCREAMING_SNAKE_CASE__ : Tuple =top_p
SCREAMING_SNAKE_CASE__ : List[str] =filter_value
SCREAMING_SNAKE_CASE__ : Optional[Any] =min_tokens_to_keep
def __call__( self : int , __lowercase : jnp.ndarray , __lowercase : jnp.ndarray , __lowercase : int ) -> jnp.ndarray:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] =lax.top_k(__lowercase , scores.shape[-1] )
SCREAMING_SNAKE_CASE__ : List[str] =jnp.full_like(__lowercase , self.filter_value )
SCREAMING_SNAKE_CASE__ : Tuple =jax.nn.softmax(__lowercase , axis=-1 ).cumsum(axis=-1 )
SCREAMING_SNAKE_CASE__ : Dict =cumulative_probs < self.top_p
# include the token that is higher than top_p as well
SCREAMING_SNAKE_CASE__ : Optional[Any] =jnp.roll(__lowercase , 1 )
score_mask |= score_mask.at[:, 0].set(__lowercase )
# min tokens to keep
SCREAMING_SNAKE_CASE__ : Optional[Any] =score_mask.at[:, : self.min_tokens_to_keep].set(__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =jnp.where(__lowercase , __lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : int =jax.lax.sort_key_val(__lowercase , __lowercase )[-1]
return next_scores
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : List[str] , __lowercase : int , __lowercase : float = -float('''Inf''' ) , __lowercase : int = 1 ) -> List[Any]:
if not isinstance(__lowercase , __lowercase ) or top_k <= 0:
raise ValueError(F"`top_k` has to be a strictly positive integer, but is {top_k}" )
SCREAMING_SNAKE_CASE__ : str =max(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =filter_value
def __call__( self : List[Any] , __lowercase : jnp.ndarray , __lowercase : jnp.ndarray , __lowercase : int ) -> jnp.ndarray:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict =scores.shape
SCREAMING_SNAKE_CASE__ : str =jnp.full(batch_size * vocab_size , self.filter_value )
SCREAMING_SNAKE_CASE__ : List[Any] =min(self.top_k , scores.shape[-1] ) # Safety check
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] =lax.top_k(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : int =jnp.broadcast_to((jnp.arange(__lowercase ) * vocab_size)[:, None] , (batch_size, topk) ).flatten()
SCREAMING_SNAKE_CASE__ : str =topk_scores.flatten()
SCREAMING_SNAKE_CASE__ : int =topk_indices.flatten() + shift
SCREAMING_SNAKE_CASE__ : str =next_scores_flat.at[topk_indices_flat].set(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =next_scores_flat.reshape(__lowercase , __lowercase )
return next_scores
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : Any , __lowercase : int ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[str] =bos_token_id
def __call__( self : Tuple , __lowercase : jnp.ndarray , __lowercase : jnp.ndarray , __lowercase : int ) -> jnp.ndarray:
SCREAMING_SNAKE_CASE__ : Tuple =jnp.full(scores.shape , -float('''inf''' ) )
SCREAMING_SNAKE_CASE__ : str =1 - jnp.bool_(cur_len - 1 )
SCREAMING_SNAKE_CASE__ : Optional[int] =jnp.where(__lowercase , new_scores.at[:, self.bos_token_id].set(0 ) , __lowercase )
return scores
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : Union[str, Any] , __lowercase : int , __lowercase : int ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Optional[int] =max_length
SCREAMING_SNAKE_CASE__ : int =eos_token_id
def __call__( self : Any , __lowercase : jnp.ndarray , __lowercase : jnp.ndarray , __lowercase : int ) -> jnp.ndarray:
SCREAMING_SNAKE_CASE__ : Optional[int] =jnp.full(scores.shape , -float('''inf''' ) )
SCREAMING_SNAKE_CASE__ : str =1 - jnp.bool_(cur_len - self.max_length + 1 )
SCREAMING_SNAKE_CASE__ : Optional[int] =jnp.where(__lowercase , new_scores.at[:, self.eos_token_id].set(0 ) , __lowercase )
return scores
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : Union[str, Any] , __lowercase : int , __lowercase : int ) -> Tuple:
if not isinstance(__lowercase , __lowercase ) or min_length < 0:
raise ValueError(F"`min_length` has to be a positive integer, but is {min_length}" )
if not isinstance(__lowercase , __lowercase ) or eos_token_id < 0:
raise ValueError(F"`eos_token_id` has to be a positive integer, but is {eos_token_id}" )
SCREAMING_SNAKE_CASE__ : str =min_length
SCREAMING_SNAKE_CASE__ : Tuple =eos_token_id
def __call__( self : Tuple , __lowercase : jnp.ndarray , __lowercase : jnp.ndarray , __lowercase : int ) -> jnp.ndarray:
# create boolean flag to decide if min length penalty should be applied
SCREAMING_SNAKE_CASE__ : List[str] =1 - jnp.clip(cur_len - self.min_length , 0 , 1 )
SCREAMING_SNAKE_CASE__ : Tuple =jnp.where(__lowercase , scores.at[:, self.eos_token_id].set(-float('''inf''' ) ) , __lowercase )
return scores
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : int , __lowercase : Optional[Any] , __lowercase : Tuple ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Dict =list(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =begin_index
def __call__( self : Optional[Any] , __lowercase : Optional[Any] , __lowercase : Dict , __lowercase : int ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Any =1 - jnp.bool_(cur_len - self.begin_index )
SCREAMING_SNAKE_CASE__ : Optional[int] =jnp.where(__lowercase , scores.at[:, self.begin_suppress_tokens].set(-float('''inf''' ) ) , __lowercase )
return scores
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : Optional[Any] , __lowercase : list ) -> Dict:
SCREAMING_SNAKE_CASE__ : int =list(__lowercase )
def __call__( self : str , __lowercase : jnp.ndarray , __lowercase : jnp.ndarray , __lowercase : int ) -> jnp.ndarray:
SCREAMING_SNAKE_CASE__ : Optional[int] =scores.at[..., self.suppress_tokens].set(-float('''inf''' ) )
return scores
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : List[Any] , __lowercase : List[str] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : List[str] =dict(__lowercase )
# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
# Indexes without forced tokens will have a negative value.
SCREAMING_SNAKE_CASE__ : Any =jnp.ones((max(force_token_map.keys() ) + 1) , dtype=jnp.intaa ) * -1
for index, token in force_token_map.items():
if token is not None:
SCREAMING_SNAKE_CASE__ : Tuple =force_token_array.at[index].set(__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =jnp.intaa(__lowercase )
def __call__( self : Optional[Any] , __lowercase : jnp.ndarray , __lowercase : jnp.ndarray , __lowercase : int ) -> jnp.ndarray:
def _force_token(__lowercase : Union[str, Any] ):
SCREAMING_SNAKE_CASE__ : Dict =scores.shape[0]
SCREAMING_SNAKE_CASE__ : Optional[int] =self.force_token_array[generation_idx]
SCREAMING_SNAKE_CASE__ : Optional[int] =jnp.ones_like(__lowercase , dtype=scores.dtype ) * -float('''inf''' )
SCREAMING_SNAKE_CASE__ : Tuple =jnp.zeros((batch_size, 1) , dtype=scores.dtype )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =lax.dynamic_update_slice(__lowercase , __lowercase , (0, current_token) )
return new_scores
SCREAMING_SNAKE_CASE__ : Tuple =lax.cond(
cur_len >= self.force_token_array.shape[0] , lambda: scores , lambda: lax.cond(
self.force_token_array[cur_len] >= 0 , lambda: _force_token(__lowercase ) , lambda: scores , ) , )
return scores
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : List[str] , __lowercase : List[Any] , __lowercase : List[str] , __lowercase : Any ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =generate_config.eos_token_id
SCREAMING_SNAKE_CASE__ : List[Any] =generate_config.no_timestamps_token_id
SCREAMING_SNAKE_CASE__ : List[Any] =generate_config.no_timestamps_token_id + 1
SCREAMING_SNAKE_CASE__ : Dict =decoder_input_length + 1
if generate_config.is_multilingual:
# room for language token and task token
self.begin_index += 2
if hasattr(__lowercase , '''max_initial_timestamp_index''' ):
SCREAMING_SNAKE_CASE__ : int =generate_config.max_initial_timestamp_index
else:
SCREAMING_SNAKE_CASE__ : Optional[int] =model_config.vocab_size
if self.max_initial_timestamp_index is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model_config.vocab_size
def __call__( self : List[Any] , __lowercase : Union[str, Any] , __lowercase : Any , __lowercase : str ) -> Optional[Any]:
# suppress <|notimestamps|> which is handled by without_timestamps
SCREAMING_SNAKE_CASE__ : Any =scores.at[:, self.no_timestamps_token_id].set(-float('''inf''' ) )
def handle_pairs(__lowercase : Any , __lowercase : Optional[int] ):
SCREAMING_SNAKE_CASE__ : Optional[int] =jnp.where((cur_len - self.begin_index) >= 1 , __lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =jnp.where(
input_ids_k[cur_len - 1] >= self.timestamp_begin , True and last_was_timestamp , __lowercase , )
SCREAMING_SNAKE_CASE__ : List[str] =jnp.where((cur_len - self.begin_index) < 2 , __lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : Any =jnp.where(
input_ids_k[cur_len - 2] >= self.timestamp_begin , __lowercase , __lowercase , )
return jnp.where(
__lowercase , jnp.where(
penultimate_was_timestamp > 0 , scores_k.at[self.timestamp_begin :].set(-float('''inf''' ) ) , scores_k.at[: self.eos_token_id].set(-float('''inf''' ) ) , ) , __lowercase , )
SCREAMING_SNAKE_CASE__ : List[str] =jax.vmap(__lowercase )(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =jnp.where(cur_len == self.begin_index , __lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : int =jnp.where(
self.max_initial_timestamp_index is not None , True and apply_max_initial_timestamp , __lowercase , )
SCREAMING_SNAKE_CASE__ : Optional[int] =self.timestamp_begin + self.max_initial_timestamp_index
SCREAMING_SNAKE_CASE__ : int =jnp.where(
__lowercase , scores.at[:, last_allowed + 1 :].set(-float('''inf''' ) ) , __lowercase , )
# if sum of probability over timestamps is above any other token, sample timestamp
SCREAMING_SNAKE_CASE__ : Any =jax.nn.log_softmax(__lowercase , axis=-1 )
def handle_cumulative_probs(__lowercase : Optional[int] , __lowercase : str ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] =jax.nn.logsumexp(logprobs_k[self.timestamp_begin :] , axis=-1 )
SCREAMING_SNAKE_CASE__ : List[Any] =jnp.max(logprobs_k[: self.timestamp_begin] )
return jnp.where(
timestamp_logprob > max_text_token_logprob , scores_k.at[: self.timestamp_begin].set(-float('''inf''' ) ) , __lowercase , )
SCREAMING_SNAKE_CASE__ : Any =jax.vmap(__lowercase )(__lowercase , __lowercase )
return scores | 222 | 1 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( lowerCAmelCase__ ) -> str:
return "".join(sorted(lowerCAmelCase__ ) )
def a__ ( lowerCAmelCase__ ) -> list[str]:
return word_by_signature[signature(lowerCAmelCase__ )]
UpperCamelCase__ = Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
UpperCamelCase__ = sorted({word.strip().lower() for word in data.splitlines()})
UpperCamelCase__ = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
UpperCamelCase__ = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open('''anagrams.txt''', '''w''') as file:
file.write('''all_anagrams = \n ''')
file.write(pprint.pformat(all_anagrams))
| 181 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase__ = {
'''vocab_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
},
'''merges_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
},
}
UpperCamelCase__ = {
'''facebook/bart-base''': 1_0_2_4,
'''facebook/bart-large''': 1_0_2_4,
'''facebook/bart-large-mnli''': 1_0_2_4,
'''facebook/bart-large-cnn''': 1_0_2_4,
'''facebook/bart-large-xsum''': 1_0_2_4,
'''yjernite/bart_eli5''': 1_0_2_4,
}
@lru_cache()
def a__ ( ) -> List[Any]:
UpperCAmelCase__ : int = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
UpperCAmelCase__ : Optional[int] = bs[:]
UpperCAmelCase__ : List[str] = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowerCAmelCase__ )
cs.append(2**8 + n )
n += 1
UpperCAmelCase__ : Any = [chr(lowerCAmelCase__ ) for n in cs]
return dict(zip(lowerCAmelCase__ , lowerCAmelCase__ ) )
def a__ ( lowerCAmelCase__ ) -> Union[str, Any]:
UpperCAmelCase__ : str = set()
UpperCAmelCase__ : str = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
UpperCAmelCase__ : Optional[int] = char
return pairs
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = VOCAB_FILES_NAMES
lowerCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ = ['input_ids', 'attention_mask']
def __init__( self : Optional[int] , _A : Optional[int] , _A : List[Any] , _A : int="replace" , _A : List[Any]="<s>" , _A : List[Any]="</s>" , _A : List[Any]="</s>" , _A : Optional[int]="<s>" , _A : List[str]="<unk>" , _A : List[str]="<pad>" , _A : Union[str, Any]="<mask>" , _A : Any=False , **_A : Dict , ):
'''simple docstring'''
UpperCAmelCase__ : Dict = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else bos_token
UpperCAmelCase__ : Any = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else eos_token
UpperCAmelCase__ : Optional[int] = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else sep_token
UpperCAmelCase__ : Tuple = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else cls_token
UpperCAmelCase__ : int = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else unk_token
UpperCAmelCase__ : Optional[Any] = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase__ : Optional[int] = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else mask_token
super().__init__(
errors=_A , bos_token=_A , eos_token=_A , unk_token=_A , sep_token=_A , cls_token=_A , pad_token=_A , mask_token=_A , add_prefix_space=_A , **_A , )
with open(_A , encoding='''utf-8''' ) as vocab_handle:
UpperCAmelCase__ : Optional[Any] = json.load(_A )
UpperCAmelCase__ : Any = {v: k for k, v in self.encoder.items()}
UpperCAmelCase__ : List[str] = errors # how to handle errors in decoding
UpperCAmelCase__ : str = bytes_to_unicode()
UpperCAmelCase__ : Dict = {v: k for k, v in self.byte_encoder.items()}
with open(_A , encoding='''utf-8''' ) as merges_handle:
UpperCAmelCase__ : str = merges_handle.read().split('''\n''' )[1:-1]
UpperCAmelCase__ : Union[str, Any] = [tuple(merge.split() ) for merge in bpe_merges]
UpperCAmelCase__ : Optional[Any] = dict(zip(_A , range(len(_A ) ) ) )
UpperCAmelCase__ : Optional[int] = {}
UpperCAmelCase__ : int = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
UpperCAmelCase__ : List[Any] = re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
def lowercase_ ( self : int ):
'''simple docstring'''
return len(self.encoder )
def lowercase_ ( self : Tuple ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def lowercase_ ( self : List[Any] , _A : Tuple ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
UpperCAmelCase__ : Optional[Any] = tuple(_A )
UpperCAmelCase__ : Dict = get_pairs(_A )
if not pairs:
return token
while True:
UpperCAmelCase__ : Optional[Any] = min(_A , key=lambda _A : self.bpe_ranks.get(_A , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
UpperCAmelCase__ , UpperCAmelCase__ : str = bigram
UpperCAmelCase__ : int = []
UpperCAmelCase__ : Tuple = 0
while i < len(_A ):
try:
UpperCAmelCase__ : Optional[int] = word.index(_A , _A )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
UpperCAmelCase__ : Tuple = j
if word[i] == first and i < len(_A ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
UpperCAmelCase__ : Optional[Any] = tuple(_A )
UpperCAmelCase__ : List[Any] = new_word
if len(_A ) == 1:
break
else:
UpperCAmelCase__ : Union[str, Any] = get_pairs(_A )
UpperCAmelCase__ : Optional[Any] = ''' '''.join(_A )
UpperCAmelCase__ : List[Any] = word
return word
def lowercase_ ( self : str , _A : str ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = []
for token in re.findall(self.pat , _A ):
UpperCAmelCase__ : str = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_A ).split(''' ''' ) )
return bpe_tokens
def lowercase_ ( self : List[str] , _A : Any ):
'''simple docstring'''
return self.encoder.get(_A , self.encoder.get(self.unk_token ) )
def lowercase_ ( self : int , _A : List[str] ):
'''simple docstring'''
return self.decoder.get(_A )
def lowercase_ ( self : Tuple , _A : Any ):
'''simple docstring'''
UpperCAmelCase__ : Any = ''''''.join(_A )
UpperCAmelCase__ : List[str] = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def lowercase_ ( self : int , _A : str , _A : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(_A ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase__ : Tuple = os.path.join(
_A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
UpperCAmelCase__ : Any = 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''' )
UpperCAmelCase__ : Union[str, Any] = 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!''' )
UpperCAmelCase__ : List[str] = token_index
writer.write(''' '''.join(_A ) + '''\n''' )
index += 1
return vocab_file, merge_file
def lowercase_ ( self : str , _A : List[int] , _A : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase__ : List[str] = [self.cls_token_id]
UpperCAmelCase__ : Union[str, Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowercase_ ( self : Optional[int] , _A : List[int] , _A : Optional[List[int]] = None , _A : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_A , token_ids_a=_A , already_has_special_tokens=_A )
if token_ids_a is None:
return [1] + ([0] * len(_A )) + [1]
return [1] + ([0] * len(_A )) + [1, 1] + ([0] * len(_A )) + [1]
def lowercase_ ( self : Dict , _A : List[int] , _A : Optional[List[int]] = None ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = [self.sep_token_id]
UpperCAmelCase__ : Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowercase_ ( self : Optional[Any] , _A : Any , _A : Dict=False , **_A : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Any = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(_A ) > 0 and not text[0].isspace()):
UpperCAmelCase__ : Tuple = ''' ''' + text
return (text, kwargs)
| 181 | 1 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCamelCase : Any =logging.getLogger(__name__)
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
UpperCamelCase__ : List[Any] = np.argmax(__lowerCAmelCase , axis=1 )
return np.sum(outputs == labels )
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> List[str]:
with open(__lowerCAmelCase , encoding="utf_8" ) as f:
UpperCamelCase__ : Tuple = csv.reader(__lowerCAmelCase )
UpperCamelCase__ : Optional[Any] = []
next(__lowerCAmelCase ) # skip the first line
for line in tqdm(__lowerCAmelCase ):
output.append((" ".join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
UpperCamelCase__ : Optional[int] = []
for dataset in encoded_datasets:
UpperCamelCase__ : List[Any] = len(__lowerCAmelCase )
UpperCamelCase__ : List[str] = np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
UpperCamelCase__ : Tuple = np.zeros((n_batch, 2) , dtype=np.intaa )
UpperCamelCase__ : str = np.full((n_batch, 2, input_len) , fill_value=-100 , dtype=np.intaa )
UpperCamelCase__ : Union[str, Any] = np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(__lowerCAmelCase ):
UpperCamelCase__ : Union[str, Any] = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
UpperCamelCase__ : List[Any] = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
UpperCamelCase__ : str = with_conta
UpperCamelCase__ : Union[str, Any] = with_conta
UpperCamelCase__ : str = len(__lowerCAmelCase ) - 1
UpperCamelCase__ : List[Any] = len(__lowerCAmelCase ) - 1
UpperCamelCase__ : Dict = with_conta
UpperCamelCase__ : Tuple = with_conta
UpperCamelCase__ : Dict = mc_label
UpperCamelCase__ : List[str] = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(__lowerCAmelCase ) for t in all_inputs ) )
return tensor_datasets
def SCREAMING_SNAKE_CASE ( ) -> Tuple:
UpperCamelCase__ : int = argparse.ArgumentParser()
parser.add_argument("--model_name" , type=__lowerCAmelCase , default="openai-gpt" , help="pretrained model name" )
parser.add_argument("--do_train" , action="store_true" , help="Whether to run training." )
parser.add_argument("--do_eval" , action="store_true" , help="Whether to run eval on the dev set." )
parser.add_argument(
"--output_dir" , default=__lowerCAmelCase , type=__lowerCAmelCase , required=__lowerCAmelCase , help="The output directory where the model predictions and checkpoints will be written." , )
parser.add_argument("--train_dataset" , type=__lowerCAmelCase , default="" )
parser.add_argument("--eval_dataset" , type=__lowerCAmelCase , default="" )
parser.add_argument("--seed" , type=__lowerCAmelCase , default=42 )
parser.add_argument("--num_train_epochs" , type=__lowerCAmelCase , default=3 )
parser.add_argument("--train_batch_size" , type=__lowerCAmelCase , default=8 )
parser.add_argument("--eval_batch_size" , type=__lowerCAmelCase , default=16 )
parser.add_argument("--adam_epsilon" , default=1E-8 , type=__lowerCAmelCase , help="Epsilon for Adam optimizer." )
parser.add_argument("--max_grad_norm" , type=__lowerCAmelCase , default=1 )
parser.add_argument(
"--max_steps" , default=-1 , type=__lowerCAmelCase , help=(
"If > 0: set total number of training steps to perform. Override num_train_epochs."
) , )
parser.add_argument(
"--gradient_accumulation_steps" , type=__lowerCAmelCase , default=1 , help="Number of updates steps to accumulate before performing a backward/update pass." , )
parser.add_argument("--learning_rate" , type=__lowerCAmelCase , default=6.2_5E-5 )
parser.add_argument("--warmup_steps" , default=0 , type=__lowerCAmelCase , help="Linear warmup over warmup_steps." )
parser.add_argument("--lr_schedule" , type=__lowerCAmelCase , default="warmup_linear" )
parser.add_argument("--weight_decay" , type=__lowerCAmelCase , default=0.0_1 )
parser.add_argument("--lm_coef" , type=__lowerCAmelCase , default=0.9 )
parser.add_argument("--n_valid" , type=__lowerCAmelCase , default=374 )
parser.add_argument("--server_ip" , type=__lowerCAmelCase , default="" , help="Can be used for distant debugging." )
parser.add_argument("--server_port" , type=__lowerCAmelCase , default="" , help="Can be used for distant debugging." )
UpperCamelCase__ : List[Any] = parser.parse_args()
print(__lowerCAmelCase )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__lowerCAmelCase )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
UpperCamelCase__ : Any = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
UpperCamelCase__ : Tuple = torch.cuda.device_count()
logger.info("device: {}, n_gpu {}".format(__lowerCAmelCase , __lowerCAmelCase ) )
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True." )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
UpperCamelCase__ : Optional[int] = ["_start_", "_delimiter_", "_classify_"]
UpperCamelCase__ : Tuple = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(__lowerCAmelCase )
UpperCamelCase__ : str = tokenizer.convert_tokens_to_ids(__lowerCAmelCase )
UpperCamelCase__ : List[str] = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(__lowerCAmelCase ) )
model.to(__lowerCAmelCase )
# Load and encode the datasets
def tokenize_and_encode(__lowerCAmelCase ):
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__lowerCAmelCase ) )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
return obj
return [tokenize_and_encode(__lowerCAmelCase ) for o in obj]
logger.info("Encoding dataset..." )
UpperCamelCase__ : List[Any] = load_rocstories_dataset(args.train_dataset )
UpperCamelCase__ : Optional[int] = load_rocstories_dataset(args.eval_dataset )
UpperCamelCase__ : str = (train_dataset, eval_dataset)
UpperCamelCase__ : List[str] = tokenize_and_encode(__lowerCAmelCase )
# Compute the max input length for the Transformer
UpperCamelCase__ : Optional[Any] = model.config.n_positions // 2 - 2
UpperCamelCase__ : Optional[Any] = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
UpperCamelCase__ : Union[str, Any] = min(__lowerCAmelCase , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
UpperCamelCase__ : Dict = pre_process_datasets(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , *__lowerCAmelCase )
UpperCamelCase__ , UpperCamelCase__ : Tuple = tensor_datasets[0], tensor_datasets[1]
UpperCamelCase__ : Any = TensorDataset(*__lowerCAmelCase )
UpperCamelCase__ : List[Any] = RandomSampler(__lowerCAmelCase )
UpperCamelCase__ : Dict = DataLoader(__lowerCAmelCase , sampler=__lowerCAmelCase , batch_size=args.train_batch_size )
UpperCamelCase__ : Optional[Any] = TensorDataset(*__lowerCAmelCase )
UpperCamelCase__ : Optional[Any] = SequentialSampler(__lowerCAmelCase )
UpperCamelCase__ : int = DataLoader(__lowerCAmelCase , sampler=__lowerCAmelCase , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
UpperCamelCase__ : str = args.max_steps
UpperCamelCase__ : int = args.max_steps // (len(__lowerCAmelCase ) // args.gradient_accumulation_steps) + 1
else:
UpperCamelCase__ : Optional[Any] = len(__lowerCAmelCase ) // args.gradient_accumulation_steps * args.num_train_epochs
UpperCamelCase__ : Optional[int] = list(model.named_parameters() )
UpperCamelCase__ : List[str] = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
UpperCamelCase__ : Tuple = [
{
"params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
"weight_decay": args.weight_decay,
},
{"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], "weight_decay": 0.0},
]
UpperCamelCase__ : Union[str, Any] = AdamW(__lowerCAmelCase , lr=args.learning_rate , eps=args.adam_epsilon )
UpperCamelCase__ : Any = get_linear_schedule_with_warmup(
__lowerCAmelCase , num_warmup_steps=args.warmup_steps , num_training_steps=__lowerCAmelCase )
if args.do_train:
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Union[str, Any] = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc="Epoch" ):
UpperCamelCase__ : List[Any] = 0
UpperCamelCase__ : str = 0
UpperCamelCase__ : Tuple = tqdm(__lowerCAmelCase , desc="Training" )
for step, batch in enumerate(__lowerCAmelCase ):
UpperCamelCase__ : Any = tuple(t.to(__lowerCAmelCase ) for t in batch )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : int = batch
UpperCamelCase__ : Optional[int] = model(__lowerCAmelCase , mc_token_ids=__lowerCAmelCase , lm_labels=__lowerCAmelCase , mc_labels=__lowerCAmelCase )
UpperCamelCase__ : List[Any] = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
UpperCamelCase__ : str = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
UpperCamelCase__ : Union[str, Any] = "Training loss: {:.2e} lr: {:.2e}".format(__lowerCAmelCase , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
UpperCamelCase__ : Any = model.module if hasattr(__lowerCAmelCase , "module" ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
UpperCamelCase__ : Any = os.path.join(args.output_dir , __lowerCAmelCase )
UpperCamelCase__ : List[str] = os.path.join(args.output_dir , __lowerCAmelCase )
torch.save(model_to_save.state_dict() , __lowerCAmelCase )
model_to_save.config.to_json_file(__lowerCAmelCase )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
UpperCamelCase__ : Tuple = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
UpperCamelCase__ : Optional[Any] = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(__lowerCAmelCase )
if args.do_eval:
model.eval()
UpperCamelCase__ , UpperCamelCase__ : Union[str, Any] = 0, 0
UpperCamelCase__ , UpperCamelCase__ : Dict = 0, 0
for batch in tqdm(__lowerCAmelCase , desc="Evaluating" ):
UpperCamelCase__ : Tuple = tuple(t.to(__lowerCAmelCase ) for t in batch )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Any = batch
with torch.no_grad():
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : int = model(
__lowerCAmelCase , mc_token_ids=__lowerCAmelCase , lm_labels=__lowerCAmelCase , mc_labels=__lowerCAmelCase )
UpperCamelCase__ : int = mc_logits.detach().cpu().numpy()
UpperCamelCase__ : Optional[int] = mc_labels.to("cpu" ).numpy()
UpperCamelCase__ : Optional[int] = accuracy(__lowerCAmelCase , __lowerCAmelCase )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
UpperCamelCase__ : Any = eval_loss / nb_eval_steps
UpperCamelCase__ : Any = eval_accuracy / nb_eval_examples
UpperCamelCase__ : List[str] = tr_loss / nb_tr_steps if args.do_train else None
UpperCamelCase__ : Dict = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}
UpperCamelCase__ : Dict = os.path.join(args.output_dir , "eval_results.txt" )
with open(__lowerCAmelCase , "w" ) as writer:
logger.info("***** Eval results *****" )
for key in sorted(result.keys() ):
logger.info(" %s = %s" , __lowerCAmelCase , str(result[key] ) )
writer.write("%s = %s\n" % (key, str(result[key] )) )
if __name__ == "__main__":
main() | 196 |
import unittest
from transformers import BigBirdConfig, 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
from transformers.models.big_bird.modeling_flax_big_bird import (
FlaxBigBirdForCausalLM,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForPreTraining,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
FlaxBigBirdModel,
)
class __a ( unittest.TestCase ):
def __init__( self : int , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Dict=2 , SCREAMING_SNAKE_CASE : List[Any]=56 , SCREAMING_SNAKE_CASE : List[str]=True , SCREAMING_SNAKE_CASE : str=True , SCREAMING_SNAKE_CASE : Any=True , SCREAMING_SNAKE_CASE : int=True , SCREAMING_SNAKE_CASE : List[str]=99 , SCREAMING_SNAKE_CASE : str=32 , SCREAMING_SNAKE_CASE : Optional[int]=2 , SCREAMING_SNAKE_CASE : Union[str, Any]=2 , SCREAMING_SNAKE_CASE : Dict=7 , SCREAMING_SNAKE_CASE : List[Any]="gelu_new" , SCREAMING_SNAKE_CASE : List[Any]=0.1 , SCREAMING_SNAKE_CASE : int=0.1 , SCREAMING_SNAKE_CASE : Any=5_12 , SCREAMING_SNAKE_CASE : Dict=16 , SCREAMING_SNAKE_CASE : Union[str, Any]=2 , SCREAMING_SNAKE_CASE : Any=0.0_2 , SCREAMING_SNAKE_CASE : Optional[Any]=4 , SCREAMING_SNAKE_CASE : int="block_sparse" , SCREAMING_SNAKE_CASE : int=True , SCREAMING_SNAKE_CASE : Optional[Any]=False , SCREAMING_SNAKE_CASE : Tuple=2 , SCREAMING_SNAKE_CASE : Dict=3 , ):
'''simple docstring'''
UpperCamelCase__ : List[str] = parent
UpperCamelCase__ : Union[str, Any] = batch_size
UpperCamelCase__ : Union[str, Any] = seq_length
UpperCamelCase__ : Dict = is_training
UpperCamelCase__ : Optional[int] = use_attention_mask
UpperCamelCase__ : List[str] = use_token_type_ids
UpperCamelCase__ : Dict = use_labels
UpperCamelCase__ : Optional[int] = vocab_size
UpperCamelCase__ : List[Any] = hidden_size
UpperCamelCase__ : List[str] = num_hidden_layers
UpperCamelCase__ : List[str] = num_attention_heads
UpperCamelCase__ : int = intermediate_size
UpperCamelCase__ : str = hidden_act
UpperCamelCase__ : Tuple = hidden_dropout_prob
UpperCamelCase__ : Any = attention_probs_dropout_prob
UpperCamelCase__ : str = max_position_embeddings
UpperCamelCase__ : Tuple = type_vocab_size
UpperCamelCase__ : Dict = type_sequence_label_size
UpperCamelCase__ : Optional[Any] = initializer_range
UpperCamelCase__ : Any = num_choices
UpperCamelCase__ : Dict = rescale_embeddings
UpperCamelCase__ : Union[str, Any] = attention_type
UpperCamelCase__ : int = use_bias
UpperCamelCase__ : List[Any] = block_size
UpperCamelCase__ : Union[str, Any] = num_random_blocks
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCamelCase__ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase__ : Any = None
if self.use_attention_mask:
UpperCamelCase__ : str = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase__ : Any = None
if self.use_token_type_ids:
UpperCamelCase__ : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase__ : List[Any] = BigBirdConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , attention_type=self.attention_type , block_size=self.block_size , num_random_blocks=self.num_random_blocks , use_bias=self.use_bias , rescale_embeddings=self.rescale_embeddings , )
return config, input_ids, token_type_ids, attention_mask
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCamelCase__ : int = self.prepare_config_and_inputs()
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Tuple = config_and_inputs
UpperCamelCase__ : int = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_flax
class __a ( A__ , unittest.TestCase ):
_lowerCAmelCase : Optional[Any] = (
(
FlaxBigBirdForCausalLM,
FlaxBigBirdModel,
FlaxBigBirdForPreTraining,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
)
if is_flax_available()
else ()
)
_lowerCAmelCase : Dict = False
_lowerCAmelCase : Optional[int] = False
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCamelCase__ : Any = FlaxBigBirdModelTester(self )
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def __lowercase ( self : List[Any] ):
'''simple docstring'''
super().test_from_pretrained_save_pretrained()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
super().test_from_pretrained_with_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def __lowercase ( self : List[Any] ):
'''simple docstring'''
super().test_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
super().test_hidden_states_output()
@slow
def __lowercase ( self : str ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCamelCase__ : Any = model_class_name.from_pretrained("google/bigbird-roberta-base" )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
if self.test_attn_probs:
super().test_attention_outputs()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCamelCase__ : Dict = self._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Union[str, Any] = model_class(SCREAMING_SNAKE_CASE )
@jax.jit
def model_jitted(SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : List[str]=None , **SCREAMING_SNAKE_CASE : List[Any] ):
return model(input_ids=SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
with self.subTest("JIT Enabled" ):
UpperCamelCase__ : Tuple = model_jitted(**SCREAMING_SNAKE_CASE ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
UpperCamelCase__ : List[Any] = model_jitted(**SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , len(SCREAMING_SNAKE_CASE ) )
for jitted_output, output in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
self.assertEqual(jitted_output.shape , output.shape )
def __lowercase ( self : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Any=1e-5 , SCREAMING_SNAKE_CASE : Tuple="outputs" , SCREAMING_SNAKE_CASE : Optional[Any]=None ):
'''simple docstring'''
if name.startswith("outputs.attentions" ):
return
else:
super().check_pt_flax_outputs(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) | 196 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..utils import cached_file
# docstyle-ignore
a_ = '\nHuman: <<task>>\n\nAssistant: '
a_ = 'huggingface-tools/default-prompts'
a_ = {'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase="run" ):
if prompt_or_repo_id is None:
__lowercase : Optional[int] = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search('''\\s''' , __UpperCamelCase ) is not None:
return prompt_or_repo_id
__lowercase : List[Any] = cached_file(
__UpperCamelCase , PROMPT_FILES[mode] , repo_type='''dataset''' , user_agent={'''agent''': agent_name} )
with open(__UpperCamelCase , '''r''' , encoding='''utf-8''' ) as f:
return f.read()
| 249 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'configuration_xlm_roberta': [
'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaConfig',
'XLMRobertaOnnxConfig',
],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['XLMRobertaTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['XLMRobertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLMRobertaForCausalLM',
'XLMRobertaForMaskedLM',
'XLMRobertaForMultipleChoice',
'XLMRobertaForQuestionAnswering',
'XLMRobertaForSequenceClassification',
'XLMRobertaForTokenClassification',
'XLMRobertaModel',
'XLMRobertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFXLMRobertaForCausalLM',
'TFXLMRobertaForMaskedLM',
'TFXLMRobertaForMultipleChoice',
'TFXLMRobertaForQuestionAnswering',
'TFXLMRobertaForSequenceClassification',
'TFXLMRobertaForTokenClassification',
'TFXLMRobertaModel',
'TFXLMRobertaPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'FlaxXLMRobertaForMaskedLM',
'FlaxXLMRobertaForCausalLM',
'FlaxXLMRobertaForMultipleChoice',
'FlaxXLMRobertaForQuestionAnswering',
'FlaxXLMRobertaForSequenceClassification',
'FlaxXLMRobertaForTokenClassification',
'FlaxXLMRobertaModel',
'FlaxXLMRobertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaConfig,
XLMRobertaOnnxConfig,
)
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta import XLMRobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaForCausalLM,
XLMRobertaForMaskedLM,
XLMRobertaForMultipleChoice,
XLMRobertaForQuestionAnswering,
XLMRobertaForSequenceClassification,
XLMRobertaForTokenClassification,
XLMRobertaModel,
XLMRobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm_roberta import (
TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMRobertaForCausalLM,
TFXLMRobertaForMaskedLM,
TFXLMRobertaForMultipleChoice,
TFXLMRobertaForQuestionAnswering,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TFXLMRobertaModel,
TFXLMRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xlm_roberta import (
FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxXLMRobertaForCausalLM,
FlaxXLMRobertaForMaskedLM,
FlaxXLMRobertaForMultipleChoice,
FlaxXLMRobertaForQuestionAnswering,
FlaxXLMRobertaForSequenceClassification,
FlaxXLMRobertaForTokenClassification,
FlaxXLMRobertaModel,
FlaxXLMRobertaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 249 | 1 |
"""simple docstring"""
import random
def __a ( __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Dict = [], [], []
for element in data:
if element < pivot:
less.append(__lowerCamelCase )
elif element > pivot:
greater.append(__lowerCamelCase )
else:
equal.append(__lowerCamelCase )
return less, equal, greater
def __a ( __lowerCamelCase, __lowerCamelCase ):
# index = len(items) // 2 when trying to find the median
# (value of index when items is sorted)
# invalid input
if index >= len(__lowerCamelCase ) or index < 0:
return None
UpperCAmelCase_ : int = items[random.randint(0, len(__lowerCamelCase ) - 1 )]
UpperCAmelCase_ : List[str] = 0
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Dict = _partition(__lowerCamelCase, __lowerCamelCase )
UpperCAmelCase_ : int = len(__lowerCamelCase )
UpperCAmelCase_ : Union[str, Any] = len(__lowerCamelCase )
# index is the pivot
if m <= index < m + count:
return pivot
# must be in smaller
elif m > index:
return quick_select(__lowerCamelCase, __lowerCamelCase )
# must be in larger
else:
return quick_select(__lowerCamelCase, index - (m + count) )
| 23 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_a = logging.get_logger(__name__)
def __a ( __lowerCamelCase, __lowerCamelCase=False ):
UpperCAmelCase_ : Optional[int] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""deit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""deit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""deit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""deit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""deit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""deit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""deit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""deit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""deit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""deit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
("cls_token", "deit.embeddings.cls_token"),
("dist_token", "deit.embeddings.distillation_token"),
("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
("pos_embed", "deit.embeddings.position_embeddings"),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
("pre_logits.fc.weight", "pooler.dense.weight"),
("pre_logits.fc.bias", "pooler.dense.bias"),
] )
# if just the base model, we should remove "deit" from all keys that start with "deit"
UpperCAmelCase_ : Dict = [(pair[0], pair[1][4:]) if pair[1].startswith("deit" ) else pair for pair in rename_keys]
else:
# layernorm + classification heads
rename_keys.extend(
[
("norm.weight", "deit.layernorm.weight"),
("norm.bias", "deit.layernorm.bias"),
("head.weight", "cls_classifier.weight"),
("head.bias", "cls_classifier.bias"),
("head_dist.weight", "distillation_classifier.weight"),
("head_dist.bias", "distillation_classifier.bias"),
] )
return rename_keys
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase=False ):
for i in range(config.num_hidden_layers ):
if base_model:
UpperCAmelCase_ : int = ""
else:
UpperCAmelCase_ : Union[str, Any] = "deit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
UpperCAmelCase_ : Tuple = state_dict.pop(f"""blocks.{i}.attn.qkv.weight""" )
UpperCAmelCase_ : Dict = state_dict.pop(f"""blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
UpperCAmelCase_ : Union[str, Any] = in_proj_weight[
: config.hidden_size, :
]
UpperCAmelCase_ : Any = in_proj_bias[: config.hidden_size]
UpperCAmelCase_ : Optional[Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCAmelCase_ : Dict = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
UpperCAmelCase_ : List[Any] = in_proj_weight[
-config.hidden_size :, :
]
UpperCAmelCase_ : str = in_proj_bias[-config.hidden_size :]
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : Tuple = dct.pop(__lowerCamelCase )
UpperCAmelCase_ : Tuple = val
def __a ( ):
UpperCAmelCase_ : Union[str, Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
UpperCAmelCase_ : str = Image.open(requests.get(__lowerCamelCase, stream=__lowerCamelCase ).raw )
return im
@torch.no_grad()
def __a ( __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : List[str] = DeiTConfig()
# all deit models have fine-tuned heads
UpperCAmelCase_ : Optional[int] = False
# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
UpperCAmelCase_ : Tuple = 1000
UpperCAmelCase_ : str = "huggingface/label-files"
UpperCAmelCase_ : str = "imagenet-1k-id2label.json"
UpperCAmelCase_ : List[Any] = json.load(open(hf_hub_download(__lowerCamelCase, __lowerCamelCase, repo_type="dataset" ), "r" ) )
UpperCAmelCase_ : List[str] = {int(__lowerCamelCase ): v for k, v in idalabel.items()}
UpperCAmelCase_ : Any = idalabel
UpperCAmelCase_ : int = {v: k for k, v in idalabel.items()}
UpperCAmelCase_ : Any = int(deit_name[-6:-4] )
UpperCAmelCase_ : Dict = int(deit_name[-3:] )
# size of the architecture
if deit_name[9:].startswith("tiny" ):
UpperCAmelCase_ : Any = 192
UpperCAmelCase_ : Union[str, Any] = 768
UpperCAmelCase_ : Union[str, Any] = 12
UpperCAmelCase_ : int = 3
elif deit_name[9:].startswith("small" ):
UpperCAmelCase_ : List[str] = 384
UpperCAmelCase_ : List[str] = 1536
UpperCAmelCase_ : Dict = 12
UpperCAmelCase_ : Any = 6
if deit_name[9:].startswith("base" ):
pass
elif deit_name[4:].startswith("large" ):
UpperCAmelCase_ : int = 1024
UpperCAmelCase_ : List[Any] = 4096
UpperCAmelCase_ : Optional[int] = 24
UpperCAmelCase_ : int = 16
# load original model from timm
UpperCAmelCase_ : Union[str, Any] = timm.create_model(__lowerCamelCase, pretrained=__lowerCamelCase )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
UpperCAmelCase_ : Optional[Any] = timm_model.state_dict()
UpperCAmelCase_ : Tuple = create_rename_keys(__lowerCamelCase, __lowerCamelCase )
for src, dest in rename_keys:
rename_key(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
read_in_q_k_v(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
# load HuggingFace model
UpperCAmelCase_ : str = DeiTForImageClassificationWithTeacher(__lowerCamelCase ).eval()
model.load_state_dict(__lowerCamelCase )
# Check outputs on an image, prepared by DeiTImageProcessor
UpperCAmelCase_ : Union[str, Any] = int(
(256 / 224) * config.image_size ) # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
UpperCAmelCase_ : Optional[Any] = DeiTImageProcessor(size=__lowerCamelCase, crop_size=config.image_size )
UpperCAmelCase_ : Any = image_processor(images=prepare_img(), return_tensors="pt" )
UpperCAmelCase_ : int = encoding["pixel_values"]
UpperCAmelCase_ : Optional[Any] = model(__lowerCamelCase )
UpperCAmelCase_ : Any = timm_model(__lowerCamelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(__lowerCamelCase, outputs.logits, atol=1E-3 )
Path(__lowerCamelCase ).mkdir(exist_ok=__lowerCamelCase )
print(f"""Saving model {deit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__lowerCamelCase )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(__lowerCamelCase )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--deit_name',
default='vit_deit_base_distilled_patch16_224',
type=str,
help='Name of the DeiT timm model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
_a = parser.parse_args()
convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)
| 23 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase__ : Tuple = {
'''configuration_mask2former''': [
'''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Mask2FormerConfig''',
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : Tuple = ['''Mask2FormerImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : Any = [
'''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Mask2FormerForUniversalSegmentation''',
'''Mask2FormerModel''',
'''Mask2FormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_maskaformer import MaskaFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskaformer import (
MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskaFormerForUniversalSegmentation,
MaskaFormerModel,
MaskaFormerPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 143 |
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 lowercase_ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
A__ : Dict = BarthezTokenizer
A__ : List[Any] = BarthezTokenizerFast
A__ : int = True
A__ : str = True
def lowerCamelCase_ ( self ):
"""simple docstring"""
super().setUp()
UpperCamelCase_ = BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez""" )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=__UpperCamelCase )
UpperCamelCase_ = tokenizer
def lowerCamelCase_ ( self ):
"""simple docstring"""
UpperCamelCase_ = """<pad>"""
UpperCamelCase_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCamelCase ) , __UpperCamelCase )
def lowerCamelCase_ ( self ):
"""simple docstring"""
UpperCamelCase_ = 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(__UpperCamelCase ) , 1_0_1_1_2_2 )
def lowerCamelCase_ ( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_1_1_2_2 )
@require_torch
def lowerCamelCase_ ( self ):
"""simple docstring"""
UpperCamelCase_ = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
UpperCamelCase_ = [0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]
UpperCamelCase_ = self.tokenizer(
__UpperCamelCase , max_length=len(__UpperCamelCase ) , padding=__UpperCamelCase , truncation=__UpperCamelCase , return_tensors="""pt""" )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
UpperCamelCase_ = batch.input_ids.tolist()[0]
self.assertListEqual(__UpperCamelCase , __UpperCamelCase )
def lowerCamelCase_ ( self ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
UpperCamelCase_ = self.get_tokenizer()
UpperCamelCase_ = self.get_rust_tokenizer()
UpperCamelCase_ = """I was born in 92000, and this is falsé."""
UpperCamelCase_ = tokenizer.tokenize(__UpperCamelCase )
UpperCamelCase_ = rust_tokenizer.tokenize(__UpperCamelCase )
self.assertListEqual(__UpperCamelCase , __UpperCamelCase )
UpperCamelCase_ = tokenizer.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase )
UpperCamelCase_ = rust_tokenizer.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase )
self.assertListEqual(__UpperCamelCase , __UpperCamelCase )
UpperCamelCase_ = self.get_rust_tokenizer()
UpperCamelCase_ = tokenizer.encode(__UpperCamelCase )
UpperCamelCase_ = rust_tokenizer.encode(__UpperCamelCase )
self.assertListEqual(__UpperCamelCase , __UpperCamelCase )
@slow
def lowerCamelCase_ ( self ):
"""simple docstring"""
UpperCamelCase_ = {"""input_ids""": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 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_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 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.
UpperCamelCase_ = [
"""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=__UpperCamelCase , model_name="""moussaKam/mbarthez""" , revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6""" , sequences=__UpperCamelCase , )
| 122 | 0 |
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
_a : str = TypeVar('T')
_a : Any = Union[List[T], Tuple[T, ...]]
_a : Optional[Any] = Union[T, List[T], Dict[str, T]]
_a : Optional[int] = Union[str, bytes, os.PathLike]
| 361 | """simple docstring"""
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
_a : List[Any] = logging.get_logger(__name__)
class __A :
_UpperCamelCase : str = None
@experimental
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[int] ,_lowerCamelCase : Any ,_lowerCamelCase : Optional[int] ,_lowerCamelCase : List[str] ,_lowerCamelCase : List[Any] ,_lowerCamelCase : Tuple ,_lowerCamelCase : Optional[int] ) -> List[Any]:
if ParallelBackendConfig.backend_name is None:
return _map_with_multiprocessing_pool(
_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase )
return _map_with_joblib(_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase )
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : List[str] ,_lowerCamelCase : Optional[Any] ,_lowerCamelCase : str ,_lowerCamelCase : Tuple ,_lowerCamelCase : Any ,_lowerCamelCase : Optional[int] ,_lowerCamelCase : Tuple ) -> Union[str, Any]:
_lowerCAmelCase : int = num_proc if num_proc <= len(_lowerCamelCase ) else len(_lowerCamelCase )
_lowerCAmelCase : Union[str, Any] = [] # We organize the splits ourselve (contiguous splits)
for index in range(_lowerCamelCase ):
_lowerCAmelCase : List[str] = len(_lowerCamelCase ) // num_proc
_lowerCAmelCase : str = len(_lowerCamelCase ) % num_proc
_lowerCAmelCase : Tuple = div * index + min(_lowerCamelCase ,_lowerCamelCase )
_lowerCAmelCase : int = start + div + (1 if index < mod else 0)
split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc) )
if len(_lowerCamelCase ) != sum(len(i[1] ) for i in split_kwds ):
raise ValueError(
f"Error dividing inputs iterable among processes. "
f"Total number of objects {len(_lowerCamelCase )}, "
f"length: {sum(len(i[1] ) for i in split_kwds )}" )
logger.info(
f"Spawning {num_proc} processes for {len(_lowerCamelCase )} objects in slices of {[len(i[1] ) for i in split_kwds]}" )
_lowerCAmelCase , _lowerCAmelCase : List[str] = None, None
if not disable_tqdm:
_lowerCAmelCase , _lowerCAmelCase : Optional[Any] = (RLock(),), tqdm.set_lock
with Pool(_lowerCamelCase ,initargs=_lowerCamelCase ,initializer=_lowerCamelCase ) as pool:
_lowerCAmelCase : str = pool.map(_lowerCamelCase ,_lowerCamelCase )
logger.info(f"Finished {num_proc} processes" )
_lowerCAmelCase : int = [obj for proc_res in mapped for obj in proc_res]
logger.info(f"Unpacked {len(_lowerCamelCase )} objects" )
return mapped
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : List[str] ,_lowerCamelCase : Union[str, Any] ,_lowerCamelCase : int ,_lowerCamelCase : Dict ,_lowerCamelCase : Tuple ,_lowerCamelCase : Any ,_lowerCamelCase : str ) -> Optional[Any]:
# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
# and it requires monkey-patching joblib internal classes which is subject to change
import joblib
with joblib.parallel_backend(ParallelBackendConfig.backend_name ,n_jobs=_lowerCamelCase ):
return joblib.Parallel()(
joblib.delayed(_lowerCamelCase )((function, obj, types, None, True, None) ) for obj in iterable )
@experimental
@contextlib.contextmanager
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : str ) -> Any:
_lowerCAmelCase : List[Any] = backend_name
if backend_name == "spark":
from joblibspark import register_spark
register_spark()
# TODO: call create_cache_and_write_probe if "download" in steps
# TODO: raise NotImplementedError when Dataset.map etc is called
try:
yield
finally:
_lowerCAmelCase : Optional[Any] = None
| 126 | 0 |
"""simple docstring"""
def UpperCamelCase__ ( lowercase__ : float ):
if edge <= 0 or not isinstance(lowercase__ , lowercase__ ):
raise ValueError("Length must be a positive." )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def UpperCamelCase__ ( lowercase__ : float ):
if edge <= 0 or not isinstance(lowercase__ , lowercase__ ):
raise ValueError("Length must be a positive." )
return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 148 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
__A = logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase_ )
class lowerCamelCase__ ( lowerCamelCase_ ):
def __init__( self , *SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ):
"""simple docstring"""
super().__init__(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
self.check_model_type(SCREAMING_SNAKE_CASE )
def lowerCamelCase_ ( self , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , **SCREAMING_SNAKE_CASE ):
"""simple docstring"""
snake_case , snake_case : Optional[Any] = {}, {}
if padding is not None:
snake_case : Optional[Any] = padding
if truncation is not None:
snake_case : Union[str, Any] = truncation
if top_k is not None:
snake_case : str = top_k
return preprocess_params, {}, postprocess_params
def __call__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , **SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE , (Image.Image, str) ) and isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
snake_case : Tuple = {"image": image, "question": question}
else:
snake_case : List[str] = image
snake_case : Optional[int] = super().__call__(SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
return results
def lowerCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=False ):
"""simple docstring"""
snake_case : List[Any] = load_image(inputs["image"] )
snake_case : Tuple = self.tokenizer(
inputs["question"] , return_tensors=self.framework , padding=SCREAMING_SNAKE_CASE , truncation=SCREAMING_SNAKE_CASE )
snake_case : Optional[int] = self.image_processor(images=SCREAMING_SNAKE_CASE , return_tensors=self.framework )
model_inputs.update(SCREAMING_SNAKE_CASE )
return model_inputs
def lowerCamelCase_ ( self , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
snake_case : Optional[Any] = self.model(**SCREAMING_SNAKE_CASE )
return model_outputs
def lowerCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=5 ):
"""simple docstring"""
if top_k > self.model.config.num_labels:
snake_case : List[Any] = self.model.config.num_labels
if self.framework == "pt":
snake_case : Optional[int] = model_outputs.logits.sigmoid()[0]
snake_case , snake_case : Any = probs.topk(SCREAMING_SNAKE_CASE )
else:
raise ValueError(F'''Unsupported framework: {self.framework}''' )
snake_case : Optional[Any] = scores.tolist()
snake_case : List[Any] = ids.tolist()
return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )]
| 148 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
__SCREAMING_SNAKE_CASE :List[Any] = 42
class _SCREAMING_SNAKE_CASE ( snake_case_ ,snake_case_ ):
@register_to_config
def __init__( self : str , a__ : int = 6_5536 , a__ : Optional[int] = None , a__ : int = 2 , a__ : int = 2 , a__ : int = 0 , a__ : str = "fourier" , a__ : bool = True , a__ : bool = False , a__ : float = 0.0 , a__ : Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") , a__ : Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") , a__ : Tuple[str] = "UNetMidBlock1D" , a__ : str = None , a__ : Tuple[int] = (32, 32, 64) , a__ : str = None , a__ : int = 8 , a__ : int = 1 , a__ : bool = False , ):
super().__init__()
__magic_name__ = sample_size
# time
if time_embedding_type == "fourier":
__magic_name__ = GaussianFourierProjection(
embedding_size=8 , set_W_to_weight=a__ , log=a__ , flip_sin_to_cos=a__ )
__magic_name__ = 2 * block_out_channels[0]
elif time_embedding_type == "positional":
__magic_name__ = Timesteps(
block_out_channels[0] , flip_sin_to_cos=a__ , downscale_freq_shift=a__ )
__magic_name__ = block_out_channels[0]
if use_timestep_embedding:
__magic_name__ = block_out_channels[0] * 4
__magic_name__ = TimestepEmbedding(
in_channels=a__ , time_embed_dim=a__ , act_fn=a__ , out_dim=block_out_channels[0] , )
__magic_name__ = nn.ModuleList([] )
__magic_name__ = None
__magic_name__ = nn.ModuleList([] )
__magic_name__ = None
# down
__magic_name__ = in_channels
for i, down_block_type in enumerate(a__ ):
__magic_name__ = output_channel
__magic_name__ = block_out_channels[i]
if i == 0:
input_channel += extra_in_channels
__magic_name__ = i == len(a__ ) - 1
__magic_name__ = get_down_block(
a__ , num_layers=a__ , in_channels=a__ , out_channels=a__ , temb_channels=block_out_channels[0] , add_downsample=not is_final_block or downsample_each_block , )
self.down_blocks.append(a__ )
# mid
__magic_name__ = get_mid_block(
a__ , in_channels=block_out_channels[-1] , mid_channels=block_out_channels[-1] , out_channels=block_out_channels[-1] , embed_dim=block_out_channels[0] , num_layers=a__ , add_downsample=a__ , )
# up
__magic_name__ = list(reversed(a__ ) )
__magic_name__ = reversed_block_out_channels[0]
if out_block_type is None:
__magic_name__ = out_channels
else:
__magic_name__ = block_out_channels[0]
for i, up_block_type in enumerate(a__ ):
__magic_name__ = output_channel
__magic_name__ = (
reversed_block_out_channels[i + 1] if i < len(a__ ) - 1 else final_upsample_channels
)
__magic_name__ = i == len(a__ ) - 1
__magic_name__ = get_up_block(
a__ , num_layers=a__ , in_channels=a__ , out_channels=a__ , temb_channels=block_out_channels[0] , add_upsample=not is_final_block , )
self.up_blocks.append(a__ )
__magic_name__ = output_channel
# out
__magic_name__ = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 , 32 )
__magic_name__ = get_out_block(
out_block_type=a__ , num_groups_out=a__ , embed_dim=block_out_channels[0] , out_channels=a__ , act_fn=a__ , fc_dim=block_out_channels[-1] // 4 , )
def snake_case__ ( self : Dict , a__ : torch.FloatTensor , a__ : Union[torch.Tensor, float, int] , a__ : bool = True , ):
__magic_name__ = timestep
if not torch.is_tensor(a__ ):
__magic_name__ = torch.tensor([timesteps] , dtype=torch.long , device=sample.device )
elif torch.is_tensor(a__ ) and len(timesteps.shape ) == 0:
__magic_name__ = timesteps[None].to(sample.device )
__magic_name__ = self.time_proj(a__ )
if self.config.use_timestep_embedding:
__magic_name__ = self.time_mlp(a__ )
else:
__magic_name__ = timestep_embed[..., None]
__magic_name__ = timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
__magic_name__ = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )
# 2. down
__magic_name__ = ()
for downsample_block in self.down_blocks:
__magic_name__ = downsample_block(hidden_states=a__ , temb=a__ )
down_block_res_samples += res_samples
# 3. mid
if self.mid_block:
__magic_name__ = self.mid_block(a__ , a__ )
# 4. up
for i, upsample_block in enumerate(self.up_blocks ):
__magic_name__ = down_block_res_samples[-1:]
__magic_name__ = down_block_res_samples[:-1]
__magic_name__ = upsample_block(a__ , res_hidden_states_tuple=a__ , temb=a__ )
# 5. post-process
if self.out_block:
__magic_name__ = self.out_block(a__ , a__ )
if not return_dict:
return (sample,)
return UNetaDOutput(sample=a__ )
| 356 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def UpperCamelCase ( a , a ) -> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def UpperCamelCase ( a ) -> list[str]:
'''simple docstring'''
__magic_name__ = []
__magic_name__ = 11
__magic_name__ = int('''1''' + '''0''' * digit_len )
for num in range(a , a ):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(a , a ):
solutions.append(F'''{num}/{den}''' )
den += 1
num += 1
__magic_name__ = 10
return solutions
def UpperCamelCase ( a = 2 ) -> int:
'''simple docstring'''
__magic_name__ = 1.0
for fraction in fraction_list(a ):
__magic_name__ = Fraction(a )
result *= frac.denominator / frac.numerator
return int(a )
if __name__ == "__main__":
print(solution())
| 98 | 0 |
import logging
import os
from .state import PartialState
class _UpperCamelCase ( logging.LoggerAdapter ):
@staticmethod
def UpperCAmelCase_ ( lowerCamelCase :Dict ) -> List[Any]:
UpperCAmelCase__ = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def UpperCAmelCase_ ( self :List[Any] , lowerCamelCase :List[Any] , lowerCamelCase :List[str] , *lowerCamelCase :Optional[int] , **lowerCamelCase :List[Any] ) -> Optional[Any]:
if PartialState._shared_state == {}:
raise RuntimeError(
"You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility." )
UpperCAmelCase__ = kwargs.pop("main_process_only" , lowerCamelCase )
UpperCAmelCase__ = kwargs.pop("in_order" , lowerCamelCase )
if self.isEnabledFor(lowerCamelCase ):
if self._should_log(lowerCamelCase ):
UpperCAmelCase__ , UpperCAmelCase__ = self.process(lowerCamelCase , lowerCamelCase )
self.logger.log(lowerCamelCase , lowerCamelCase , *lowerCamelCase , **lowerCamelCase )
elif in_order:
UpperCAmelCase__ = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
UpperCAmelCase__ , UpperCAmelCase__ = self.process(lowerCamelCase , lowerCamelCase )
self.logger.log(lowerCamelCase , lowerCamelCase , *lowerCamelCase , **lowerCamelCase )
state.wait_for_everyone()
def lowerCAmelCase ( _lowerCAmelCase : str , _lowerCAmelCase : str = None ):
"""simple docstring"""
if log_level is None:
UpperCAmelCase__ = os.environ.get("ACCELERATE_LOG_LEVEL" , _lowerCAmelCase )
UpperCAmelCase__ = logging.getLogger(_lowerCAmelCase )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(_lowerCAmelCase , {} )
| 169 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
_lowerCAmelCase : List[str] = [
"cross_validation.py",
"gradient_accumulation.py",
"local_sgd.py",
"multi_process_metrics.py",
"memory.py",
"automatic_gradient_accumulation.py",
"fsdp_with_peak_mem_tracking.py",
"deepspeed_with_config_support.py",
"megatron_lm_gpt_pretraining.py",
]
class _UpperCamelCase ( unittest.TestCase ):
def UpperCAmelCase_ ( self :Dict , lowerCamelCase :str , lowerCamelCase :bool , lowerCamelCase :str = None , lowerCamelCase :list = None ) -> Tuple:
UpperCAmelCase__ = None
UpperCAmelCase__ = os.path.abspath(os.path.join("examples" , "by_feature" ) )
UpperCAmelCase__ = os.path.abspath("examples" )
for item in os.listdir(lowerCamelCase ):
if item not in EXCLUDE_EXAMPLES:
UpperCAmelCase__ = os.path.join(lowerCamelCase , lowerCamelCase )
if os.path.isfile(lowerCamelCase ) and ".py" in item_path:
with self.subTest(
tested_script=lowerCamelCase , feature_script=lowerCamelCase , tested_section="main()" if parser_only else "training_function()" , ):
UpperCAmelCase__ = compare_against_test(
os.path.join(lowerCamelCase , lowerCamelCase ) , lowerCamelCase , lowerCamelCase , lowerCamelCase )
UpperCAmelCase__ = "\n".join(lowerCamelCase )
if special_strings is not None:
for string in special_strings:
UpperCAmelCase__ = diff.replace(lowerCamelCase , "" )
self.assertEqual(lowerCamelCase , "" )
def UpperCAmelCase_ ( self :List[str] ) -> Any:
self.one_complete_example("complete_nlp_example.py" , lowerCamelCase )
self.one_complete_example("complete_nlp_example.py" , lowerCamelCase )
def UpperCAmelCase_ ( self :str ) -> int:
UpperCAmelCase__ = os.path.abspath(os.path.join("examples" , "cv_example.py" ) )
UpperCAmelCase__ = [
" " * 16 + "{\n\n",
" " * 20 + "\"accuracy\": eval_metric[\"accuracy\"],\n\n",
" " * 20 + "\"f1\": eval_metric[\"f1\"],\n\n",
" " * 20 + "\"train_loss\": total_loss.item() / len(train_dataloader),\n\n",
" " * 20 + "\"epoch\": epoch,\n\n",
" " * 16 + "},\n\n",
" " * 16 + "step=epoch,\n",
" " * 12,
" " * 8 + "for step, batch in enumerate(active_dataloader):\n",
]
self.one_complete_example("complete_cv_example.py" , lowerCamelCase , lowerCamelCase , lowerCamelCase )
self.one_complete_example("complete_cv_example.py" , lowerCamelCase , lowerCamelCase , lowerCamelCase )
@mock.patch.dict(os.environ , {"""TESTING_MOCKED_DATALOADERS""": """1"""} )
class _UpperCamelCase ( lowerCAmelCase ):
UpperCAmelCase_ = False
@classmethod
def UpperCAmelCase_ ( cls :List[Any] ) -> Any:
super().setUpClass()
UpperCAmelCase__ = tempfile.mkdtemp()
UpperCAmelCase__ = os.path.join(cls._tmpdir , "default_config.yml" )
write_basic_config(save_location=cls.configPath )
UpperCAmelCase__ = ["accelerate", "launch", "--config_file", cls.configPath]
@classmethod
def UpperCAmelCase_ ( cls :Union[str, Any] ) -> Optional[int]:
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def UpperCAmelCase_ ( self :Dict ) -> Dict:
UpperCAmelCase__ = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps epoch
--output_dir {self.tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , "epoch_0" ) ) )
def UpperCAmelCase_ ( self :Optional[int] ) -> Any:
UpperCAmelCase__ = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps 1
--output_dir {self.tmpdir}
'''.split()
UpperCAmelCase__ = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , "step_2" ) ) )
def UpperCAmelCase_ ( self :Tuple ) -> Dict:
UpperCAmelCase__ = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , "epoch_0" )}
'''.split()
UpperCAmelCase__ = run_command(self._launch_args + testargs , return_stdout=lowerCamelCase )
self.assertNotIn("epoch 0:" , lowerCamelCase )
self.assertIn("epoch 1:" , lowerCamelCase )
def UpperCAmelCase_ ( self :Dict ) -> int:
UpperCAmelCase__ = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , "step_2" )}
'''.split()
UpperCAmelCase__ = run_command(self._launch_args + testargs , return_stdout=lowerCamelCase )
if torch.cuda.is_available():
UpperCAmelCase__ = torch.cuda.device_count()
else:
UpperCAmelCase__ = 1
if num_processes > 1:
self.assertNotIn("epoch 0:" , lowerCamelCase )
self.assertIn("epoch 1:" , lowerCamelCase )
else:
self.assertIn("epoch 0:" , lowerCamelCase )
self.assertIn("epoch 1:" , lowerCamelCase )
@slow
def UpperCAmelCase_ ( self :Dict ) -> Optional[int]:
UpperCAmelCase__ = "\n examples/by_feature/cross_validation.py\n --num_folds 2\n ".split()
with mock.patch.dict(os.environ , {"TESTING_MOCKED_DATALOADERS": "0"} ):
UpperCAmelCase__ = run_command(self._launch_args + testargs , return_stdout=lowerCamelCase )
UpperCAmelCase__ = re.findall("({.+})" , lowerCamelCase )
UpperCAmelCase__ = [r for r in results if "accuracy" in r][-1]
UpperCAmelCase__ = ast.literal_eval(lowerCamelCase )
self.assertGreaterEqual(results["accuracy"] , 0.75 )
def UpperCAmelCase_ ( self :int ) -> Optional[int]:
UpperCAmelCase__ = ["examples/by_feature/multi_process_metrics.py"]
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def UpperCAmelCase_ ( self :List[Any] ) -> Dict:
with tempfile.TemporaryDirectory() as tmpdir:
UpperCAmelCase__ = f'''
examples/by_feature/tracking.py
--with_tracking
--project_dir {tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(lowerCamelCase , "tracking" ) ) )
def UpperCAmelCase_ ( self :Any ) -> Dict:
UpperCAmelCase__ = ["examples/by_feature/gradient_accumulation.py"]
run_command(self._launch_args + testargs )
def UpperCAmelCase_ ( self :Any ) -> Optional[int]:
UpperCAmelCase__ = ["examples/by_feature/local_sgd.py"]
run_command(self._launch_args + testargs )
| 169 | 1 |
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
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {'vocab_file': 'spiece.model'}
UpperCamelCase__ = {
'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',
}
}
UpperCamelCase__ = {
'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 ( a__ ):
__UpperCAmelCase : Union[str, Any] = VOCAB_FILES_NAMES
__UpperCAmelCase : Any = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase : Optional[int] = ["input_ids", "attention_mask"]
def __init__(self : Optional[Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : Any=False , __UpperCAmelCase : Optional[Any]=False , __UpperCAmelCase : str=False , __UpperCAmelCase : Tuple=None , __UpperCAmelCase : int=None , __UpperCAmelCase : Any=None , __UpperCAmelCase : List[Any]=None , __UpperCAmelCase : List[Any] = None , **__UpperCAmelCase : Optional[Any] , ) -> None:
"""simple docstring"""
UpperCAmelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs
UpperCAmelCase__ = kwargs.get("name_or_path" )
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored" )
UpperCAmelCase__ = 'None'
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
UpperCAmelCase__ = '<|endoftext|>' if eos_token is None else eos_token
UpperCAmelCase__ = '<unk>' if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
UpperCAmelCase__ = unk_token if pad_token is None else pad_token
UpperCAmelCase__ = eos_token if bos_token is None else bos_token
else:
UpperCAmelCase__ = '<pad>' if pad_token is None else pad_token
UpperCAmelCase__ = '<s>' if bos_token is None else bos_token
super().__init__(
do_lower_case=SCREAMING_SNAKE_CASE_ , remove_space=SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , sp_model_kwargs=self.sp_model_kwargs , **SCREAMING_SNAKE_CASE_ , )
UpperCAmelCase__ = do_lower_case
UpperCAmelCase__ = remove_space
UpperCAmelCase__ = keep_accents
UpperCAmelCase__ = vocab_file
UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(SCREAMING_SNAKE_CASE_ )
# Used for whitespace normalization in input texts
# fmt : off
UpperCAmelCase__ = {' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '', ''}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
UpperCAmelCase__ = re.compile(
f"""[{"".join(map(SCREAMING_SNAKE_CASE_ , list(range(0 , 9 ) ) + list(range(1_1 , 3_2 ) ) + list(range(1_2_7 , 1_6_0 ) ) + [1_6_0, 1_7_3, 8_2_0_3] ) )}]""" )
def __getstate__(self : Any ) -> Tuple:
"""simple docstring"""
UpperCAmelCase__ = self.__dict__.copy()
UpperCAmelCase__ = None
return state
def __setstate__(self : Optional[int] , __UpperCAmelCase : Optional[Any] ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
UpperCAmelCase__ = {}
UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def lowercase_ (self : Optional[int] ) -> int:
"""simple docstring"""
return len(self.sp_model )
def lowercase_ (self : List[str] , __UpperCAmelCase : Any ) -> str:
"""simple docstring"""
UpperCAmelCase__ = self.non_printing_characters_re.sub("" , SCREAMING_SNAKE_CASE_ )
# Normalize whitespaces
UpperCAmelCase__ = ''.join([char if char not in self.whitespaces else " " for char in text] )
# NFC Unicode normalization
UpperCAmelCase__ = unicodedata.normalize("NFC" , SCREAMING_SNAKE_CASE_ )
return text
def lowercase_ (self : str , __UpperCAmelCase : List[Any] , **__UpperCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = self.preprocess_text(SCREAMING_SNAKE_CASE_ )
return self.sp_model.encode(SCREAMING_SNAKE_CASE_ , out_type=SCREAMING_SNAKE_CASE_ )
def lowercase_ (self : List[Any] , __UpperCAmelCase : Union[str, Any] ) -> int:
"""simple docstring"""
return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_ )
def lowercase_ (self : List[Any] , __UpperCAmelCase : str ) -> str:
"""simple docstring"""
return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_ )
@staticmethod
def lowercase_ (__UpperCAmelCase : Optional[Any] ) -> str:
"""simple docstring"""
return out_string
def lowercase_ (self : Union[str, Any] , __UpperCAmelCase : List[str] ) -> str:
"""simple docstring"""
UpperCAmelCase__ = []
UpperCAmelCase__ = ''
UpperCAmelCase__ = 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(SCREAMING_SNAKE_CASE_ ) + token
UpperCAmelCase__ = True
UpperCAmelCase__ = []
else:
current_sub_tokens.append(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase__ = False
out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_ )
return out_string
def lowercase_ (self : List[Any] ) -> Dict[str, int]:
"""simple docstring"""
UpperCAmelCase__ = {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowercase_ (self : Optional[Any] , __UpperCAmelCase : Tuple , __UpperCAmelCase : int = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase__ = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , SCREAMING_SNAKE_CASE_ )
elif not os.path.isfile(self.vocab_file ):
with open(SCREAMING_SNAKE_CASE_ , "wb" ) as fi:
UpperCAmelCase__ = self.sp_model.serialized_model_proto()
fi.write(SCREAMING_SNAKE_CASE_ )
return (out_vocab_file,)
def lowercase_ (self : str , __UpperCAmelCase : str , __UpperCAmelCase : List[Any] = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCAmelCase__ = self.preprocess_text(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase__ = self.sp_model.encode(SCREAMING_SNAKE_CASE_ )
else:
UpperCAmelCase__ = [self.preprocess_text(SCREAMING_SNAKE_CASE_ ) for t in text]
UpperCAmelCase__ = self.sp_model.encode(SCREAMING_SNAKE_CASE_ )
if return_tensors is True or return_tensors == "pt":
UpperCAmelCase__ = torch.tensor(SCREAMING_SNAKE_CASE_ )
return token_ids
def lowercase_ (self : str , __UpperCAmelCase : Dict ) -> str:
"""simple docstring"""
return self.sp_model.decode(SCREAMING_SNAKE_CASE_ )
def lowercase_ (self : List[Any] , __UpperCAmelCase : Optional[Any] ) -> List[int]:
"""simple docstring"""
UpperCAmelCase__ = [f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
UpperCAmelCase__ = (
f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(SCREAMING_SNAKE_CASE_ ) + f"""{self.bos_token}Bot:"""
)
return self.encode(text=SCREAMING_SNAKE_CASE_ )
| 365 | # Lint as: python3
import itertools
import os
import re
UpperCamelCase__ = re.compile(R'([A-Z]+)([A-Z][a-z])')
UpperCamelCase__ = re.compile(R'([a-z\d])([A-Z])')
UpperCamelCase__ = re.compile(R'(?<!_)_(?!_)')
UpperCamelCase__ = re.compile(R'(_{2,})')
UpperCamelCase__ = R'^\w+(\.\w+)*$'
UpperCamelCase__ = R'<>:/\|?*'
def lowerCAmelCase_ ( __A ) -> Dict:
'''simple docstring'''
UpperCAmelCase__ = _uppercase_uppercase_re.sub(r"\1_\2", __A )
UpperCAmelCase__ = _lowercase_uppercase_re.sub(r"\1_\2", __A )
return name.lower()
def lowerCAmelCase_ ( __A ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ = _single_underscore_re.split(__A )
UpperCAmelCase__ = [_multiple_underscores_re.split(__A ) for n in name]
return "".join(n.capitalize() for n in itertools.chain.from_iterable(__A ) if n != "" )
def lowerCAmelCase_ ( __A ) -> List[str]:
'''simple docstring'''
if os.path.basename(__A ) != name:
raise ValueError(f"""Should be a dataset name, not a path: {name}""" )
return camelcase_to_snakecase(__A )
def lowerCAmelCase_ ( __A, __A ) -> Optional[int]:
'''simple docstring'''
if os.path.basename(__A ) != name:
raise ValueError(f"""Should be a dataset name, not a path: {name}""" )
if not re.match(_split_re, __A ):
raise ValueError(f"""Split name should match '{_split_re}'' but got '{split}'.""" )
return f"""{filename_prefix_for_name(__A )}-{split}"""
def lowerCAmelCase_ ( __A, __A, __A, __A=None ) -> str:
'''simple docstring'''
UpperCAmelCase__ = filename_prefix_for_split(__A, __A )
if filetype_suffix:
prefix += f""".{filetype_suffix}"""
UpperCAmelCase__ = os.path.join(__A, __A )
return f"""{filepath}*"""
def lowerCAmelCase_ ( __A, __A, __A, __A=None, __A=None ) -> Any:
'''simple docstring'''
UpperCAmelCase__ = filename_prefix_for_split(__A, __A )
UpperCAmelCase__ = os.path.join(__A, __A )
if shard_lengths:
UpperCAmelCase__ = len(__A )
UpperCAmelCase__ = [f"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(__A )]
if filetype_suffix:
UpperCAmelCase__ = [filename + f""".{filetype_suffix}""" for filename in filenames]
return filenames
else:
UpperCAmelCase__ = prefix
if filetype_suffix:
filename += f""".{filetype_suffix}"""
return [filename]
| 143 | 0 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 343 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
_UpperCAmelCase : Tuple = {
"susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
"susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}
class __lowerCAmelCase ( lowerCAmelCase):
_a = '''ernie_m'''
_a = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self: List[Any] , _lowerCAmelCase: int = 25_00_02 , _lowerCAmelCase: int = 7_68 , _lowerCAmelCase: int = 12 , _lowerCAmelCase: int = 12 , _lowerCAmelCase: int = 30_72 , _lowerCAmelCase: str = "gelu" , _lowerCAmelCase: float = 0.1 , _lowerCAmelCase: float = 0.1 , _lowerCAmelCase: int = 5_14 , _lowerCAmelCase: float = 0.02 , _lowerCAmelCase: int = 1 , _lowerCAmelCase: float = 1e-0_5 , _lowerCAmelCase: Dict=None , _lowerCAmelCase: Optional[int]=False , _lowerCAmelCase: List[str]=0.0 , **_lowerCAmelCase: Tuple , ):
super().__init__(pad_token_id=_lowerCAmelCase , **_lowerCAmelCase )
lowercase :Tuple = vocab_size
lowercase :List[str] = hidden_size
lowercase :Optional[int] = num_hidden_layers
lowercase :Optional[Any] = num_attention_heads
lowercase :Optional[Any] = intermediate_size
lowercase :Optional[Any] = hidden_act
lowercase :Any = hidden_dropout_prob
lowercase :int = attention_probs_dropout_prob
lowercase :Dict = max_position_embeddings
lowercase :Optional[Any] = initializer_range
lowercase :Any = layer_norm_eps
lowercase :Union[str, Any] = classifier_dropout
lowercase :int = is_decoder
lowercase :List[str] = act_dropout
| 236 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : Tuple , UpperCAmelCase_ : int ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[Any] = 0
__snake_case : Any = len(UpperCAmelCase_ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
__snake_case : Optional[Any] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCAmelCase_ ):
return None
__snake_case : int = sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
__snake_case : Union[str, Any] = left
__snake_case : List[str] = point
elif point > right:
__snake_case : int = right
__snake_case : int = point
else:
if item < current_item:
__snake_case : str = point - 1
else:
__snake_case : Any = point + 1
return None
def __UpperCAmelCase ( UpperCAmelCase_ : Any , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : Dict ) -> Any:
'''simple docstring'''
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
__snake_case : int = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCAmelCase_ ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
elif point > right:
return interpolation_search_by_recursion(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , point - 1 )
else:
return interpolation_search_by_recursion(
UpperCAmelCase_ , UpperCAmelCase_ , point + 1 , UpperCAmelCase_ )
def __UpperCAmelCase ( UpperCAmelCase_ : Optional[Any] ) -> Dict:
'''simple docstring'''
if collection != sorted(UpperCAmelCase_ ):
raise ValueError('Collection must be ascending sorted' )
return True
if __name__ == "__main__":
import sys
_a : Any= 0
if debug == 1:
_a : List[Any]= [10, 30, 40, 45, 50, 66, 77, 93]
try:
__assert_sorted(collection)
except ValueError:
sys.exit("Sequence must be ascending sorted to apply interpolation search")
_a : Tuple= 67
_a : Optional[Any]= interpolation_search(collection, target)
if result is not None:
print(f'''{target} found at positions: {result}''')
else:
print("Not found")
| 95 | """simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_a : Any= logging.get_logger(__name__)
_a : str= {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
_a : Optional[Any]= [
"small",
"small-base",
"medium",
"medium-base",
"intermediate",
"intermediate-base",
"large",
"large-base",
"xlarge",
"xlarge-base",
]
_a : List[Any]= {
"vocab_file": {
"funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt",
"funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt",
"funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt",
"funnel-transformer/medium-base": (
"https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"
),
"funnel-transformer/intermediate": (
"https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"
),
"funnel-transformer/intermediate-base": (
"https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"
),
"funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt",
"funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt",
"funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt",
"funnel-transformer/xlarge-base": (
"https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json",
"funnel-transformer/small-base": (
"https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"
),
"funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json",
"funnel-transformer/medium-base": (
"https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"
),
"funnel-transformer/intermediate": (
"https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"
),
"funnel-transformer/intermediate-base": (
"https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"
),
"funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json",
"funnel-transformer/large-base": (
"https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"
),
"funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json",
"funnel-transformer/xlarge-base": (
"https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"
),
},
}
_a : str= {f'''funnel-transformer/{name}''': 512 for name in _model_names}
_a : List[Any]= {f'''funnel-transformer/{name}''': {"do_lower_case": True} for name in _model_names}
class UpperCamelCase ( lowercase ):
UpperCAmelCase : List[str] = VOCAB_FILES_NAMES
UpperCAmelCase : Tuple = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase : int = PRETRAINED_INIT_CONFIGURATION
UpperCAmelCase : Tuple = FunnelTokenizer
UpperCAmelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase : int = 2
def __init__(self : int , _A : Any=None , _A : Union[str, Any]=None , _A : Union[str, Any]=True , _A : List[str]="<unk>" , _A : Any="<sep>" , _A : Dict="<pad>" , _A : Tuple="<cls>" , _A : Dict="<mask>" , _A : Optional[Any]="<s>" , _A : List[Any]="</s>" , _A : Optional[int]=True , _A : Dict=True , _A : Tuple=None , _A : int="##" , **_A : Any , ) -> str:
super().__init__(
_A , tokenizer_file=_A , do_lower_case=_A , unk_token=_A , sep_token=_A , pad_token=_A , cls_token=_A , mask_token=_A , bos_token=_A , eos_token=_A , clean_text=_A , tokenize_chinese_chars=_A , strip_accents=_A , wordpieces_prefix=_A , **_A , )
__snake_case : Optional[int] = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get('lowercase' , _A) != do_lower_case
or normalizer_state.get('strip_accents' , _A) != strip_accents
or normalizer_state.get('handle_chinese_chars' , _A) != tokenize_chinese_chars
):
__snake_case : List[str] = getattr(_A , normalizer_state.pop('type'))
__snake_case : int = do_lower_case
__snake_case : Optional[int] = strip_accents
__snake_case : str = tokenize_chinese_chars
__snake_case : Optional[int] = normalizer_class(**_A)
__snake_case : str = do_lower_case
def _lowercase (self : Optional[Any] , _A : Dict , _A : Tuple=None) -> Any:
__snake_case : List[Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _lowercase (self : str , _A : List[int] , _A : Optional[List[int]] = None) -> List[int]:
__snake_case : Union[str, Any] = [self.sep_token_id]
__snake_case : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls) * [self.cls_token_type_id] + len(token_ids_a + sep) * [0]
return len(cls) * [self.cls_token_type_id] + len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]
def _lowercase (self : Tuple , _A : str , _A : Optional[str] = None) -> Tuple[str]:
__snake_case : int = self._tokenizer.model.save(_A , name=_A)
return tuple(_A)
| 95 | 1 |
import argparse
from collections import defaultdict
def A_ ( a , a , a , a , a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = f"{file}_{class_name}_{test_name}"
done_test[_id] += 1
with open(a , 'r' ) as f:
SCREAMING_SNAKE_CASE_ : List[Any] = f.readlines()
SCREAMING_SNAKE_CASE_ : List[str] = f"class {class_name}("
SCREAMING_SNAKE_CASE_ : List[str] = f"{4 * ' '}def {test_name}("
SCREAMING_SNAKE_CASE_ : Optional[int] = f"{8 * ' '}{correct_line.split()[0]}"
SCREAMING_SNAKE_CASE_ : Any = f"{1_6 * ' '}{correct_line.split()[0]}"
SCREAMING_SNAKE_CASE_ : Optional[Any] = False
SCREAMING_SNAKE_CASE_ : Union[str, Any] = False
SCREAMING_SNAKE_CASE_ : int = False
SCREAMING_SNAKE_CASE_ : str = False
SCREAMING_SNAKE_CASE_ : int = 0
SCREAMING_SNAKE_CASE_ : Tuple = 0
SCREAMING_SNAKE_CASE_ : int = []
for line in lines:
if line.startswith(a ):
SCREAMING_SNAKE_CASE_ : List[Any] = True
elif in_class and line.startswith(a ):
SCREAMING_SNAKE_CASE_ : Dict = True
elif in_class and in_func and (line.startswith(a ) or line.startswith(a )):
SCREAMING_SNAKE_CASE_ : Optional[Any] = len(line.split(correct_line.split()[0] )[0] )
count += 1
if count == done_test[_id]:
SCREAMING_SNAKE_CASE_ : Tuple = True
if in_class and in_func and in_line:
if ")" not in line:
continue
else:
SCREAMING_SNAKE_CASE_ : Optional[Any] = True
if in_class and in_func and in_line and insert_line:
new_lines.append(f"{spaces * ' '}{correct_line}" )
SCREAMING_SNAKE_CASE_ : Optional[Any] = False
else:
new_lines.append(a )
with open(a , 'w' ) as f:
for line in new_lines:
f.write(a )
def A_ ( a , a=None ):
"""simple docstring"""
if fail is not None:
with open(a , 'r' ) as f:
SCREAMING_SNAKE_CASE_ : List[Any] = {l.strip() for l in f.readlines()}
else:
SCREAMING_SNAKE_CASE_ : Tuple = None
with open(a , 'r' ) as f:
SCREAMING_SNAKE_CASE_ : Any = f.readlines()
SCREAMING_SNAKE_CASE_ : List[str] = defaultdict(a )
for line in correct_lines:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Union[str, Any] = line.split(';' )
if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
overwrite_file(a , a , a , a , a )
if __name__ == "__main__":
lowerCAmelCase : List[str] = argparse.ArgumentParser()
parser.add_argument('--correct_filename', help='filename of tests with expected result')
parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
lowerCAmelCase : int = parser.parse_args()
main(args.correct_filename, args.fail_filename)
| 253 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
lowerCAmelCase : Any = logging.get_logger(__name__)
def A_ ( a , a , a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = WavaVecaForSequenceClassification.from_pretrained(a , config=a )
SCREAMING_SNAKE_CASE_ : Optional[Any] = downstream_dict['projector.weight']
SCREAMING_SNAKE_CASE_ : Union[str, Any] = downstream_dict['projector.bias']
SCREAMING_SNAKE_CASE_ : List[str] = downstream_dict['model.post_net.linear.weight']
SCREAMING_SNAKE_CASE_ : Optional[Any] = downstream_dict['model.post_net.linear.bias']
return model
def A_ ( a , a , a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = WavaVecaForAudioFrameClassification.from_pretrained(a , config=a )
SCREAMING_SNAKE_CASE_ : Dict = downstream_dict['model.linear.weight']
SCREAMING_SNAKE_CASE_ : List[Any] = downstream_dict['model.linear.bias']
return model
def A_ ( a , a , a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = WavaVecaForXVector.from_pretrained(a , config=a )
SCREAMING_SNAKE_CASE_ : Tuple = downstream_dict['connector.weight']
SCREAMING_SNAKE_CASE_ : Any = downstream_dict['connector.bias']
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = downstream_dict[
f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
]
SCREAMING_SNAKE_CASE_ : Optional[Any] = downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]
SCREAMING_SNAKE_CASE_ : Optional[Any] = downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
SCREAMING_SNAKE_CASE_ : Tuple = downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
SCREAMING_SNAKE_CASE_ : Any = downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
SCREAMING_SNAKE_CASE_ : List[str] = downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
SCREAMING_SNAKE_CASE_ : Optional[int] = downstream_dict['objective.W']
return model
@torch.no_grad()
def A_ ( a , a , a , a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = torch.load(a , map_location='cpu' )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = checkpoint['Downstream']
SCREAMING_SNAKE_CASE_ : Optional[int] = WavaVecaConfig.from_pretrained(a )
SCREAMING_SNAKE_CASE_ : Optional[Any] = WavaVecaFeatureExtractor.from_pretrained(
a , return_attention_mask=a , do_normalize=a )
SCREAMING_SNAKE_CASE_ : Tuple = hf_config.architectures[0]
if arch.endswith('ForSequenceClassification' ):
SCREAMING_SNAKE_CASE_ : Tuple = convert_classification(a , a , a )
elif arch.endswith('ForAudioFrameClassification' ):
SCREAMING_SNAKE_CASE_ : str = convert_diarization(a , a , a )
elif arch.endswith('ForXVector' ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = convert_xvector(a , a , a )
else:
raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}" )
if hf_config.use_weighted_layer_sum:
SCREAMING_SNAKE_CASE_ : Dict = checkpoint['Featurizer']['weights']
hf_feature_extractor.save_pretrained(a )
hf_model.save_pretrained(a )
if __name__ == "__main__":
lowerCAmelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument(
'--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
)
parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
lowerCAmelCase : List[str] = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 253 | 1 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__lowercase : List[Any] = TypeVar('KEY')
__lowercase : str = TypeVar('VAL')
@dataclass(frozen=lowerCAmelCase_ , slots=lowerCAmelCase_ )
class __UpperCamelCase ( Generic[KEY, VAL] ):
A_ = 42
A_ = 42
class __UpperCamelCase ( _Item ):
def __init__( self ):
'''simple docstring'''
super().__init__(__a , __a )
def __bool__( self ):
'''simple docstring'''
return False
__lowercase : Union[str, Any] = _DeletedItem()
class __UpperCamelCase ( MutableMapping[KEY, VAL] ):
def __init__( self , __a = 8 , __a = 0.75 ):
'''simple docstring'''
__a : Dict = initial_block_size
__a : list[_Item | None] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
__a : Optional[Any] = capacity_factor
__a : str = 0
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
return hash(__a ) % len(self._buckets )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
return (ind + 1) % len(self._buckets )
def __UpperCAmelCase ( self , __a , __a , __a ):
'''simple docstring'''
__a : List[Any] = self._buckets[ind]
if not stored:
__a : Union[str, Any] = _Item(__a , __a )
self._len += 1
return True
elif stored.key == key:
__a : Optional[Any] = _Item(__a , __a )
return True
else:
return False
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
if len(self._buckets ) <= self._initial_block_size:
return False
__a : str = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : Optional[int] = self._buckets
__a : int = [None] * new_size
__a : str = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self._resize(len(self._buckets ) * 2 )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self._resize(len(self._buckets ) // 2 )
def __UpperCAmelCase ( self , __a ):
'''simple docstring'''
__a : Dict = self._get_bucket_index(__a )
for _ in range(len(self._buckets ) ):
yield ind
__a : Optional[int] = self._get_next_ind(__a )
def __UpperCAmelCase ( self , __a , __a ):
'''simple docstring'''
for ind in self._iterate_buckets(__a ):
if self._try_set(__a , __a , __a ):
break
def __setitem__( self , __a , __a ):
'''simple docstring'''
if self._is_full():
self._size_up()
self._add_item(__a , __a )
def __delitem__( self , __a ):
'''simple docstring'''
for ind in self._iterate_buckets(__a ):
__a : Tuple = self._buckets[ind]
if item is None:
raise KeyError(__a )
if item is _deleted:
continue
if item.key == key:
__a : Tuple = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self , __a ):
'''simple docstring'''
for ind in self._iterate_buckets(__a ):
__a : List[Any] = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(__a )
def __len__( self ):
'''simple docstring'''
return self._len
def __iter__( self ):
'''simple docstring'''
yield from (item.key for item in self._buckets if item)
def __repr__( self ):
'''simple docstring'''
__a : Any = ' ,'.join(
f"""{item.key}: {item.val}""" for item in self._buckets if item )
return f"""HashMap({val_string})"""
| 294 |
'''simple docstring'''
__lowercase : Optional[Any] = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
__lowercase : List[str] = ['a', 'b', 'c', 'd', 'e']
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : List[str] ):
__a : Any = start
# add current to visited
visited.append(_SCREAMING_SNAKE_CASE )
__a : Union[str, Any] = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
__a : Dict = topological_sort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# if all neighbors visited add current to sort
sort.append(_SCREAMING_SNAKE_CASE )
# if all vertices haven't been visited select a new one to visit
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
for vertice in vertices:
if vertice not in visited:
__a : List[Any] = topological_sort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# return sort
return sort
if __name__ == "__main__":
__lowercase : Union[str, Any] = topological_sort('a', [], [])
print(sort)
| 294 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_UpperCAmelCase : str = logging.get_logger(__name__)
class lowercase ( _SCREAMING_SNAKE_CASE ):
__lowercase : Tuple = ["pixel_values"]
def __init__( self , A_ = True , A_ = None , A_ = None , A_ = PILImageResampling.BILINEAR , A_ = True , A_ = 1 / 255 , A_ = True , A_ = None , A_ = None , **A_ , ) -> None:
"""simple docstring"""
super().__init__(**A_ )
UpperCamelCase = size if size is not None else {'shortest_edge': 384}
UpperCamelCase = get_size_dict(A_ , default_to_square=A_ )
UpperCamelCase = do_resize
UpperCamelCase = size
# Default value set here for backwards compatibility where the value in config is None
UpperCamelCase = crop_pct if crop_pct is not None else 224 / 256
UpperCamelCase = resample
UpperCamelCase = do_rescale
UpperCamelCase = rescale_factor
UpperCamelCase = do_normalize
UpperCamelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCamelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __UpperCamelCase ( self , A_ , A_ , A_ , A_ = PILImageResampling.BICUBIC , A_ = None , **A_ , ) -> np.ndarray:
"""simple docstring"""
UpperCamelCase = get_size_dict(A_ , default_to_square=A_ )
if "shortest_edge" not in size:
raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}''' )
UpperCamelCase = size['shortest_edge']
if shortest_edge < 384:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
UpperCamelCase = int(shortest_edge / crop_pct )
UpperCamelCase = get_resize_output_image_size(A_ , size=A_ , default_to_square=A_ )
UpperCamelCase = resize(image=A_ , size=A_ , resample=A_ , data_format=A_ , **A_ )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=A_ , size=(shortest_edge, shortest_edge) , data_format=A_ , **A_ )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
A_ , size=(shortest_edge, shortest_edge) , resample=A_ , data_format=A_ , **A_ )
def __UpperCamelCase ( self , A_ , A_ , A_ = None , **A_ , ) -> Dict:
"""simple docstring"""
return rescale(A_ , scale=A_ , data_format=A_ , **A_ )
def __UpperCamelCase ( self , A_ , A_ , A_ , A_ = None , **A_ , ) -> np.ndarray:
"""simple docstring"""
return normalize(A_ , mean=A_ , std=A_ , data_format=A_ , **A_ )
def __UpperCamelCase ( self , A_ , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = ChannelDimension.FIRST , **A_ , ) -> PIL.Image.Image:
"""simple docstring"""
UpperCamelCase = do_resize if do_resize is not None else self.do_resize
UpperCamelCase = crop_pct if crop_pct is not None else self.crop_pct
UpperCamelCase = resample if resample is not None else self.resample
UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase = image_mean if image_mean is not None else self.image_mean
UpperCamelCase = image_std if image_std is not None else self.image_std
UpperCamelCase = size if size is not None else self.size
UpperCamelCase = get_size_dict(A_ , default_to_square=A_ )
UpperCamelCase = make_list_of_images(A_ )
if not valid_images(A_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
raise ValueError('crop_pct must be specified if size < 384.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
UpperCamelCase = [to_numpy_array(A_ ) for image in images]
if do_resize:
UpperCamelCase = [self.resize(image=A_ , size=A_ , crop_pct=A_ , resample=A_ ) for image in images]
if do_rescale:
UpperCamelCase = [self.rescale(image=A_ , scale=A_ ) for image in images]
if do_normalize:
UpperCamelCase = [self.normalize(image=A_ , mean=A_ , std=A_ ) for image in images]
UpperCamelCase = [to_channel_dimension_format(A_ , A_ ) for image in images]
UpperCamelCase = {'pixel_values': images}
return BatchFeature(data=A_ , tensor_type=A_ )
| 222 |
def A ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> int:
'''simple docstring'''
if index == number_of_items:
return 0
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = knapsack(lowercase , lowercase , lowercase , lowercase , index + 1 )
if weights[index] <= max_weight:
UpperCamelCase = values[index] + knapsack(
lowercase , lowercase , lowercase , max_weight - weights[index] , index + 1 )
return max(lowercase , lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 222 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class snake_case ( _a , unittest.TestCase ):
a_ : List[Any] = RoCBertTokenizer
a_ : Optional[Any] = None
a_ : str = False
a_ : Dict = True
a_ : str = filter_non_english
def UpperCAmelCase__ ( self) ->Union[str, Any]:
super().setUp()
a_ = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"]
a_ = {}
a_ = {}
for i, value in enumerate(__lowerCAmelCase):
a_ = i
a_ = i
a_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"])
a_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["word_shape_file"])
a_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["word_pronunciation_file"])
with open(self.vocab_file , "w" , encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
with open(self.word_shape_file , "w" , encoding="utf-8") as word_shape_writer:
json.dump(__lowerCAmelCase , __lowerCAmelCase , ensure_ascii=__lowerCAmelCase)
with open(self.word_pronunciation_file , "w" , encoding="utf-8") as word_pronunciation_writer:
json.dump(__lowerCAmelCase , __lowerCAmelCase , ensure_ascii=__lowerCAmelCase)
def UpperCAmelCase__ ( self) ->Optional[int]:
a_ = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file)
a_ = tokenizer.tokenize("你好[SEP]你是谁")
self.assertListEqual(__lowerCAmelCase , ["你", "好", "[SEP]", "你", "是", "谁"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase) , [5, 6, 2, 5, 7, 8])
self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(__lowerCAmelCase) , [5, 6, 2, 5, 7, 8])
self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(__lowerCAmelCase) , [5, 6, 2, 5, 7, 8])
def UpperCAmelCase__ ( self) ->Dict:
a_ = RoCBertBasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz") , ["ah", "\u535A", "\u63A8", "zz"])
def UpperCAmelCase__ ( self) ->Tuple:
a_ = RoCBertBasicTokenizer(do_lower_case=__lowerCAmelCase)
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? ") , ["hello", "!", "how", "are", "you", "?"])
self.assertListEqual(tokenizer.tokenize("H\u00E9llo") , ["hello"])
def UpperCAmelCase__ ( self) ->Tuple:
a_ = RoCBertBasicTokenizer(do_lower_case=__lowerCAmelCase , strip_accents=__lowerCAmelCase)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["hällo", "!", "how", "are", "you", "?"])
self.assertListEqual(tokenizer.tokenize("H\u00E9llo") , ["h\u00E9llo"])
def UpperCAmelCase__ ( self) ->int:
a_ = RoCBertBasicTokenizer(do_lower_case=__lowerCAmelCase , strip_accents=__lowerCAmelCase)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["hallo", "!", "how", "are", "you", "?"])
self.assertListEqual(tokenizer.tokenize("H\u00E9llo") , ["hello"])
def UpperCAmelCase__ ( self) ->int:
a_ = RoCBertBasicTokenizer(do_lower_case=__lowerCAmelCase)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["hallo", "!", "how", "are", "you", "?"])
self.assertListEqual(tokenizer.tokenize("H\u00E9llo") , ["hello"])
def UpperCAmelCase__ ( self) ->Union[str, Any]:
a_ = RoCBertBasicTokenizer(do_lower_case=__lowerCAmelCase)
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? ") , ["HeLLo", "!", "how", "Are", "yoU", "?"])
def UpperCAmelCase__ ( self) ->List[Any]:
a_ = RoCBertBasicTokenizer(do_lower_case=__lowerCAmelCase , strip_accents=__lowerCAmelCase)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["HäLLo", "!", "how", "Are", "yoU", "?"])
def UpperCAmelCase__ ( self) ->List[str]:
a_ = RoCBertBasicTokenizer(do_lower_case=__lowerCAmelCase , strip_accents=__lowerCAmelCase)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["HaLLo", "!", "how", "Are", "yoU", "?"])
def UpperCAmelCase__ ( self) ->Any:
a_ = RoCBertBasicTokenizer(do_lower_case=__lowerCAmelCase , never_split=["[UNK]"])
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]") , ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"])
def UpperCAmelCase__ ( self) ->List[str]:
a_ = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
a_ = {}
for i, token in enumerate(__lowerCAmelCase):
a_ = i
a_ = RoCBertWordpieceTokenizer(vocab=__lowerCAmelCase , unk_token="[UNK]")
self.assertListEqual(tokenizer.tokenize("") , [])
self.assertListEqual(tokenizer.tokenize("unwanted running") , ["un", "##want", "##ed", "runn", "##ing"])
self.assertListEqual(tokenizer.tokenize("unwantedX running") , ["[UNK]", "runn", "##ing"])
def UpperCAmelCase__ ( self) ->List[Any]:
self.assertTrue(_is_whitespace(" "))
self.assertTrue(_is_whitespace("\t"))
self.assertTrue(_is_whitespace("\r"))
self.assertTrue(_is_whitespace("\n"))
self.assertTrue(_is_whitespace("\u00A0"))
self.assertFalse(_is_whitespace("A"))
self.assertFalse(_is_whitespace("-"))
def UpperCAmelCase__ ( self) ->Union[str, Any]:
self.assertTrue(_is_control("\u0005"))
self.assertFalse(_is_control("A"))
self.assertFalse(_is_control(" "))
self.assertFalse(_is_control("\t"))
self.assertFalse(_is_control("\r"))
def UpperCAmelCase__ ( self) ->Optional[Any]:
self.assertTrue(_is_punctuation("-"))
self.assertTrue(_is_punctuation("$"))
self.assertTrue(_is_punctuation("`"))
self.assertTrue(_is_punctuation("."))
self.assertFalse(_is_punctuation("A"))
self.assertFalse(_is_punctuation(" "))
def UpperCAmelCase__ ( self) ->Any:
a_ = self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(__lowerCAmelCase) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]])
if self.test_rust_tokenizer:
a_ = self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(__lowerCAmelCase) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]])
def UpperCAmelCase__ ( self) ->List[Any]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
a_ = self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase , **__lowerCAmelCase)
a_ = F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
a_ = tokenizer_r.encode_plus(
__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , return_token_type_ids=__lowerCAmelCase , return_offsets_mapping=__lowerCAmelCase , add_special_tokens=__lowerCAmelCase , )
a_ = tokenizer_r.do_lower_case if hasattr(__lowerCAmelCase , "do_lower_case") else False
a_ = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), "A"),
((1, 2), ","),
((3, 5), "na"),
((5, 6), "##ï"),
((6, 8), "##ve"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "Allen"),
((21, 23), "##NL"),
((23, 24), "##P"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), "a"),
((1, 2), ","),
((3, 8), "naive"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "allen"),
((21, 23), "##nl"),
((23, 24), "##p"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]))
self.assertEqual([e[0] for e in expected_results] , tokens["offset_mapping"])
def UpperCAmelCase__ ( self) ->Optional[int]:
a_ = ["的", "人", "有"]
a_ = "".join(__lowerCAmelCase)
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
a_ = True
a_ = self.tokenizer_class.from_pretrained(__lowerCAmelCase , **__lowerCAmelCase)
a_ = self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase , **__lowerCAmelCase)
a_ = tokenizer_p.encode(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase)
a_ = tokenizer_r.encode(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase)
a_ = tokenizer_r.convert_ids_to_tokens(__lowerCAmelCase)
a_ = tokenizer_p.convert_ids_to_tokens(__lowerCAmelCase)
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase)
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase)
a_ = False
a_ = self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase , **__lowerCAmelCase)
a_ = self.tokenizer_class.from_pretrained(__lowerCAmelCase , **__lowerCAmelCase)
a_ = tokenizer_r.encode(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase)
a_ = tokenizer_p.encode(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase)
a_ = tokenizer_r.convert_ids_to_tokens(__lowerCAmelCase)
a_ = tokenizer_p.convert_ids_to_tokens(__lowerCAmelCase)
# it is expected that only the first Chinese character is not preceded by "##".
a_ = [
F'''##{token}''' if idx != 0 else token for idx, token in enumerate(__lowerCAmelCase)
]
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase)
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase)
@slow
def UpperCAmelCase__ ( self) ->Optional[Any]:
a_ = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file)
a_ = tokenizer.encode("你好" , add_special_tokens=__lowerCAmelCase)
a_ = tokenizer.encode("你是谁" , add_special_tokens=__lowerCAmelCase)
a_ = tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase)
a_ = tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase , __lowerCAmelCase)
assert encoded_sentence == [1] + text + [2]
assert encoded_pair == [1] + text + [2] + text_a + [2]
def UpperCAmelCase__ ( self) ->Optional[Any]:
a_ = self.get_tokenizers(do_lower_case=__lowerCAmelCase)
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}'''):
a_ = "你好，你是谁"
a_ = tokenizer.tokenize(__lowerCAmelCase)
a_ = tokenizer.convert_tokens_to_ids(__lowerCAmelCase)
a_ = tokenizer.convert_tokens_to_shape_ids(__lowerCAmelCase)
a_ = tokenizer.convert_tokens_to_pronunciation_ids(__lowerCAmelCase)
a_ = tokenizer.prepare_for_model(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , add_special_tokens=__lowerCAmelCase)
a_ = tokenizer.encode_plus(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase)
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase) | 371 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase_ = {
'configuration_swiftformer': [
'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SwiftFormerConfig',
'SwiftFormerOnnxConfig',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'SwiftFormerForImageClassification',
'SwiftFormerModel',
'SwiftFormerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 303 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class __a ( __UpperCamelCase ):
__lowercase : Optional[Any] = 'decision_transformer'
__lowercase : List[Any] = ['past_key_values']
__lowercase : Dict = {
'max_position_embeddings': 'n_positions',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self , lowerCAmelCase__=17 , lowerCAmelCase__=4 , lowerCAmelCase__=128 , lowerCAmelCase__=4_096 , lowerCAmelCase__=True , lowerCAmelCase__=1 , lowerCAmelCase__=1_024 , lowerCAmelCase__=3 , lowerCAmelCase__=1 , lowerCAmelCase__=None , lowerCAmelCase__="relu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=1E-5 , lowerCAmelCase__=0.0_2 , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=50_256 , lowerCAmelCase__=50_256 , lowerCAmelCase__=False , lowerCAmelCase__=False , **lowerCAmelCase__ , ) -> str:
'''simple docstring'''
lowercase__: Tuple = state_dim
lowercase__: Optional[Any] = act_dim
lowercase__: str = hidden_size
lowercase__: Optional[int] = max_ep_len
lowercase__: Optional[Any] = action_tanh
lowercase__: Union[str, Any] = vocab_size
lowercase__: List[Any] = n_positions
lowercase__: str = n_layer
lowercase__: int = n_head
lowercase__: Optional[int] = n_inner
lowercase__: Optional[int] = activation_function
lowercase__: List[Any] = resid_pdrop
lowercase__: Dict = embd_pdrop
lowercase__: Optional[Any] = attn_pdrop
lowercase__: int = layer_norm_epsilon
lowercase__: Union[str, Any] = initializer_range
lowercase__: Optional[Any] = scale_attn_weights
lowercase__: List[str] = use_cache
lowercase__: Any = scale_attn_by_inverse_layer_idx
lowercase__: List[str] = reorder_and_upcast_attn
lowercase__: List[Any] = bos_token_id
lowercase__: Union[str, Any] = eos_token_id
super().__init__(bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
| 196 |
from __future__ import annotations
def snake_case_ ( snake_case , snake_case ) -> bool:
if len(snake_case ) == 0:
return False
lowercase__: Any = len(snake_case ) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] , snake_case )
else:
return binary_search(a_list[midpoint + 1 :] , snake_case )
if __name__ == "__main__":
__lowerCAmelCase = input('''Enter numbers separated by comma:\n''').strip()
__lowerCAmelCase = [int(item.strip()) for item in user_input.split(''',''')]
__lowerCAmelCase = int(input('''Enter the number to be found in the list:\n''').strip())
__lowerCAmelCase = '''''' if binary_search(sequence, target) else '''not '''
print(F'''{target} was {not_str}found in {sequence}''')
| 196 | 1 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCAmelCase (_UpperCAmelCase ,unittest.TestCase ):
__snake_case : Optional[Any] = OpenAIGPTTokenizer
__snake_case : Dict = OpenAIGPTTokenizerFast
__snake_case : Optional[Any] = True
__snake_case : Dict = False
def UpperCamelCase ( self: Union[str, Any] ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_SCREAMING_SNAKE_CASE = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""w</w>""",
"""r</w>""",
"""t</w>""",
"""lo""",
"""low""",
"""er</w>""",
"""low</w>""",
"""lowest</w>""",
"""newer</w>""",
"""wider</w>""",
"""<unk>""",
]
_SCREAMING_SNAKE_CASE = dict(zip(UpperCAmelCase_ , range(len(UpperCAmelCase_ ) ) ) )
_SCREAMING_SNAKE_CASE = ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" ) as fp:
fp.write(json.dumps(UpperCAmelCase_ ) )
with open(self.merges_file , """w""" ) as fp:
fp.write("""\n""".join(UpperCAmelCase_ ) )
def UpperCamelCase ( self: List[Any] , UpperCAmelCase_: Dict ):
'''simple docstring'''
return "lower newer", "lower newer"
def UpperCamelCase ( self: Any ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = OpenAIGPTTokenizer(self.vocab_file , self.merges_file )
_SCREAMING_SNAKE_CASE = """lower"""
_SCREAMING_SNAKE_CASE = ["""low""", """er</w>"""]
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = tokens + ["""<unk>"""]
_SCREAMING_SNAKE_CASE = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_ ) , UpperCAmelCase_ )
def UpperCamelCase ( self: Optional[Any] , UpperCAmelCase_: Union[str, Any]=15 ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
# Simple input
_SCREAMING_SNAKE_CASE = """This is a simple input"""
_SCREAMING_SNAKE_CASE = ["""This is a simple input 1""", """This is a simple input 2"""]
_SCREAMING_SNAKE_CASE = ("""This is a simple input""", """This is a pair""")
_SCREAMING_SNAKE_CASE = [
("""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(UpperCAmelCase_ , tokenizer_r.encode , UpperCAmelCase_ , max_length=UpperCAmelCase_ , padding="""max_length""" )
# Simple input
self.assertRaises(UpperCAmelCase_ , tokenizer_r.encode_plus , UpperCAmelCase_ , max_length=UpperCAmelCase_ , padding="""max_length""" )
# Simple input
self.assertRaises(
UpperCAmelCase_ , tokenizer_r.batch_encode_plus , UpperCAmelCase_ , max_length=UpperCAmelCase_ , padding="""max_length""" , )
# Pair input
self.assertRaises(UpperCAmelCase_ , tokenizer_r.encode , UpperCAmelCase_ , max_length=UpperCAmelCase_ , padding="""max_length""" )
# Pair input
self.assertRaises(UpperCAmelCase_ , tokenizer_r.encode_plus , UpperCAmelCase_ , max_length=UpperCAmelCase_ , padding="""max_length""" )
# Pair input
self.assertRaises(
UpperCAmelCase_ , tokenizer_r.batch_encode_plus , UpperCAmelCase_ , max_length=UpperCAmelCase_ , padding="""max_length""" , )
def UpperCamelCase ( self: List[Any] ):
'''simple docstring'''
pass
@require_ftfy
@require_spacy
@require_tokenizers
class __UpperCAmelCase (_UpperCAmelCase ):
pass
| 125 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCAmelCase (_UpperCAmelCase ):
__snake_case : Optional[Any] = (UnCLIPScheduler,)
def UpperCamelCase ( self: int , **UpperCAmelCase_: Dict ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = {
"""num_train_timesteps""": 1_000,
"""variance_type""": """fixed_small_log""",
"""clip_sample""": True,
"""clip_sample_range""": 1.0,
"""prediction_type""": """epsilon""",
}
config.update(**UpperCAmelCase_ )
return config
def UpperCamelCase ( self: Optional[Any] ):
'''simple docstring'''
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase_ )
def UpperCamelCase ( self: Any ):
'''simple docstring'''
for variance in ["fixed_small_log", "learned_range"]:
self.check_over_configs(variance_type=UpperCAmelCase_ )
def UpperCamelCase ( self: List[Any] ):
'''simple docstring'''
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=UpperCAmelCase_ )
def UpperCamelCase ( self: Optional[int] ):
'''simple docstring'''
for clip_sample_range in [1, 5, 10, 20]:
self.check_over_configs(clip_sample_range=UpperCAmelCase_ )
def UpperCamelCase ( self: Union[str, Any] ):
'''simple docstring'''
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(prediction_type=UpperCAmelCase_ )
def UpperCamelCase ( self: Optional[Any] ):
'''simple docstring'''
for time_step in [0, 500, 999]:
for prev_timestep in [None, 5, 100, 250, 500, 750]:
if prev_timestep is not None and prev_timestep >= time_step:
continue
self.check_over_forward(time_step=UpperCAmelCase_ , prev_timestep=UpperCAmelCase_ )
def UpperCamelCase ( self: Any ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
_SCREAMING_SNAKE_CASE = self.get_scheduler_config(variance_type="""fixed_small_log""" )
_SCREAMING_SNAKE_CASE = scheduler_class(**UpperCAmelCase_ )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.0_000E-10 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_54_96_25 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.9_99_49_87 ) ) < 1E-5
def UpperCamelCase ( self: int ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
_SCREAMING_SNAKE_CASE = self.get_scheduler_config(variance_type="""learned_range""" )
_SCREAMING_SNAKE_CASE = scheduler_class(**UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = 0.5
assert scheduler._get_variance(1 , predicted_variance=UpperCAmelCase_ ) - -10.1_71_27_90 < 1E-5
assert scheduler._get_variance(487 , predicted_variance=UpperCAmelCase_ ) - -5.7_99_80_52 < 1E-5
assert scheduler._get_variance(999 , predicted_variance=UpperCAmelCase_ ) - -0.0_01_00_11 < 1E-5
def UpperCamelCase ( self: List[Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
_SCREAMING_SNAKE_CASE = self.get_scheduler_config()
_SCREAMING_SNAKE_CASE = scheduler_class(**UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = scheduler.timesteps
_SCREAMING_SNAKE_CASE = self.dummy_model()
_SCREAMING_SNAKE_CASE = self.dummy_sample_deter
_SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
for i, t in enumerate(UpperCAmelCase_ ):
# 1. predict noise residual
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase_ , UpperCAmelCase_ )
# 2. predict previous mean of sample x_t-1
_SCREAMING_SNAKE_CASE = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_ ).prev_sample
_SCREAMING_SNAKE_CASE = pred_prev_sample
_SCREAMING_SNAKE_CASE = torch.sum(torch.abs(UpperCAmelCase_ ) )
_SCREAMING_SNAKE_CASE = torch.mean(torch.abs(UpperCAmelCase_ ) )
assert abs(result_sum.item() - 2_52.2_68_24_95 ) < 1E-2
assert abs(result_mean.item() - 0.3_28_47_43 ) < 1E-3
def UpperCamelCase ( self: List[str] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
_SCREAMING_SNAKE_CASE = self.get_scheduler_config()
_SCREAMING_SNAKE_CASE = scheduler_class(**UpperCAmelCase_ )
scheduler.set_timesteps(25 )
_SCREAMING_SNAKE_CASE = scheduler.timesteps
_SCREAMING_SNAKE_CASE = self.dummy_model()
_SCREAMING_SNAKE_CASE = self.dummy_sample_deter
_SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
for i, t in enumerate(UpperCAmelCase_ ):
# 1. predict noise residual
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase_ , UpperCAmelCase_ )
if i + 1 == timesteps.shape[0]:
_SCREAMING_SNAKE_CASE = None
else:
_SCREAMING_SNAKE_CASE = timesteps[i + 1]
# 2. predict previous mean of sample x_t-1
_SCREAMING_SNAKE_CASE = scheduler.step(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , prev_timestep=UpperCAmelCase_ , generator=UpperCAmelCase_ ).prev_sample
_SCREAMING_SNAKE_CASE = pred_prev_sample
_SCREAMING_SNAKE_CASE = torch.sum(torch.abs(UpperCAmelCase_ ) )
_SCREAMING_SNAKE_CASE = torch.mean(torch.abs(UpperCAmelCase_ ) )
assert abs(result_sum.item() - 2_58.2_04_49_83 ) < 1E-2
assert abs(result_mean.item() - 0.3_36_20_38 ) < 1E-3
def UpperCamelCase ( self: List[Any] ):
'''simple docstring'''
pass
def UpperCamelCase ( self: str ):
'''simple docstring'''
pass
| 125 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class SCREAMING_SNAKE_CASE( A__ , A__ , A__ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = AltDiffusionPipeline
lowerCamelCase__ = TEXT_TO_IMAGE_PARAMS
lowerCamelCase__ = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCamelCase__ = TEXT_TO_IMAGE_IMAGE_PARAMS
lowerCamelCase__ = TEXT_TO_IMAGE_IMAGE_PARAMS
def A ( self : Dict ) -> int:
torch.manual_seed(0 )
UpperCAmelCase : str = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
UpperCAmelCase : Dict = DDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , clip_sample=__snake_case , set_alpha_to_one=__snake_case , )
torch.manual_seed(0 )
UpperCAmelCase : Union[str, Any] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
# TODO: address the non-deterministic text encoder (fails for save-load tests)
# torch.manual_seed(0)
# text_encoder_config = RobertaSeriesConfig(
# hidden_size=32,
# project_dim=32,
# intermediate_size=37,
# layer_norm_eps=1e-05,
# num_attention_heads=4,
# num_hidden_layers=5,
# vocab_size=5002,
# )
# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)
torch.manual_seed(0 )
UpperCAmelCase : Dict = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5002 , )
UpperCAmelCase : List[Any] = CLIPTextModel(__snake_case )
UpperCAmelCase : Optional[Any] = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''' )
UpperCAmelCase : Optional[int] = 77
UpperCAmelCase : Optional[int] = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def A ( self : Optional[Any] , __snake_case : Dict , __snake_case : List[str]=0 ) -> Union[str, Any]:
if str(__snake_case ).startswith('''mps''' ):
UpperCAmelCase : str = torch.manual_seed(__snake_case )
else:
UpperCAmelCase : Tuple = torch.Generator(device=__snake_case ).manual_seed(__snake_case )
UpperCAmelCase : Dict = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def A ( self : Union[str, Any] ) -> List[str]:
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def A ( self : Tuple ) -> List[str]:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def A ( self : Tuple ) -> Optional[int]:
UpperCAmelCase : List[str] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase : Any = self.get_dummy_components()
torch.manual_seed(0 )
UpperCAmelCase : Optional[int] = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , )
# TODO: remove after fixing the non-deterministic text encoder
UpperCAmelCase : List[str] = RobertaSeriesModelWithTransformation(__snake_case )
UpperCAmelCase : str = text_encoder
UpperCAmelCase : Optional[int] = AltDiffusionPipeline(**__snake_case )
UpperCAmelCase : str = alt_pipe.to(__snake_case )
alt_pipe.set_progress_bar_config(disable=__snake_case )
UpperCAmelCase : Optional[int] = self.get_dummy_inputs(__snake_case )
UpperCAmelCase : Optional[int] = '''A photo of an astronaut'''
UpperCAmelCase : List[Any] = alt_pipe(**__snake_case )
UpperCAmelCase : Optional[Any] = output.images
UpperCAmelCase : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase : List[str] = np.array(
[0.5_74_81_62, 0.60_44_71_45, 0.48_82_12_17, 0.50_10_06_36, 0.5_43_11_85, 0.45_76_36_83, 0.49_65_76_96, 0.48_13_27_33, 0.47_57_30_93] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A ( self : int ) -> Union[str, Any]:
UpperCAmelCase : Optional[int] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase : int = self.get_dummy_components()
UpperCAmelCase : int = PNDMScheduler(skip_prk_steps=__snake_case )
torch.manual_seed(0 )
UpperCAmelCase : int = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , )
# TODO: remove after fixing the non-deterministic text encoder
UpperCAmelCase : Union[str, Any] = RobertaSeriesModelWithTransformation(__snake_case )
UpperCAmelCase : Union[str, Any] = text_encoder
UpperCAmelCase : Optional[int] = AltDiffusionPipeline(**__snake_case )
UpperCAmelCase : Dict = alt_pipe.to(__snake_case )
alt_pipe.set_progress_bar_config(disable=__snake_case )
UpperCAmelCase : int = self.get_dummy_inputs(__snake_case )
UpperCAmelCase : Optional[int] = alt_pipe(**__snake_case )
UpperCAmelCase : Optional[int] = output.images
UpperCAmelCase : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase : Optional[int] = np.array(
[0.51_60_50_93, 0.5_70_72_41, 0.47_36_55_07, 0.50_57_88_86, 0.5_63_38_77, 0.4_64_25_03, 0.5_18_20_81, 0.48_76_34_84, 0.49_08_42_37] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE( unittest.TestCase ):
"""simple docstring"""
def A ( self : str ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : List[Any] ) -> Any:
# make sure here that pndm scheduler skips prk
UpperCAmelCase : List[Any] = AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , safety_checker=__snake_case )
UpperCAmelCase : Tuple = alt_pipe.to(__snake_case )
alt_pipe.set_progress_bar_config(disable=__snake_case )
UpperCAmelCase : List[Any] = '''A painting of a squirrel eating a burger'''
UpperCAmelCase : Any = torch.manual_seed(0 )
UpperCAmelCase : Optional[int] = alt_pipe([prompt] , generator=__snake_case , guidance_scale=6.0 , num_inference_steps=20 , output_type='''np''' )
UpperCAmelCase : Dict = output.images
UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
UpperCAmelCase : List[str] = np.array([0.10_10, 0.08_00, 0.07_94, 0.08_85, 0.08_43, 0.07_62, 0.07_69, 0.07_29, 0.05_86] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A ( self : Tuple ) -> int:
UpperCAmelCase : int = DDIMScheduler.from_pretrained('''BAAI/AltDiffusion''' , subfolder='''scheduler''' )
UpperCAmelCase : Tuple = AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , scheduler=__snake_case , safety_checker=__snake_case )
UpperCAmelCase : Dict = alt_pipe.to(__snake_case )
alt_pipe.set_progress_bar_config(disable=__snake_case )
UpperCAmelCase : Tuple = '''A painting of a squirrel eating a burger'''
UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
UpperCAmelCase : List[Any] = alt_pipe([prompt] , generator=__snake_case , num_inference_steps=2 , output_type='''numpy''' )
UpperCAmelCase : Dict = output.images
UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
UpperCAmelCase : Union[str, Any] = np.array([0.40_19, 0.40_52, 0.38_10, 0.41_19, 0.39_16, 0.39_82, 0.46_51, 0.41_95, 0.53_23] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 23 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
"The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
)
UpperCamelCase__: str = None
UpperCamelCase__: int = {
"7B": 11008,
"13B": 13824,
"30B": 17920,
"65B": 22016,
"70B": 28672,
}
UpperCamelCase__: List[Any] = {
"7B": 1,
"7Bf": 1,
"13B": 2,
"13Bf": 2,
"30B": 4,
"65B": 8,
"70B": 8,
"70Bf": 8,
}
def snake_case_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Tuple=1 , _lowerCAmelCase : List[Any]=256 ) -> Optional[Any]:
return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of)
def snake_case_ ( _lowerCAmelCase : List[str] ) -> str:
with open(_lowerCAmelCase , '''r''' ) as f:
return json.load(_lowerCAmelCase )
def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any ) -> Optional[Any]:
with open(_lowerCAmelCase , '''w''' ) as f:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
def snake_case_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Any=True ) -> List[Any]:
os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase )
UpperCAmelCase : int = os.path.join(_lowerCAmelCase , '''tmp''' )
os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase )
UpperCAmelCase : List[str] = read_json(os.path.join(_lowerCAmelCase , '''params.json''' ) )
UpperCAmelCase : str = NUM_SHARDS[model_size]
UpperCAmelCase : Any = params['''n_layers''']
UpperCAmelCase : str = params['''n_heads''']
UpperCAmelCase : Any = n_heads // num_shards
UpperCAmelCase : List[str] = params['''dim''']
UpperCAmelCase : Optional[Any] = dim // n_heads
UpperCAmelCase : str = 1_0_0_0_0.0
UpperCAmelCase : Optional[int] = 1.0 / (base ** (torch.arange(0 , _lowerCAmelCase , 2 ).float() / dims_per_head))
if "n_kv_heads" in params:
UpperCAmelCase : Tuple = params['''n_kv_heads'''] # for GQA / MQA
UpperCAmelCase : Optional[int] = n_heads_per_shard // num_key_value_heads
UpperCAmelCase : Optional[Any] = dim // num_key_value_heads
else: # compatibility with other checkpoints
UpperCAmelCase : List[str] = n_heads
UpperCAmelCase : Optional[int] = n_heads_per_shard
UpperCAmelCase : List[str] = dim
# permute for sliced rotary
def permute(_lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any]=n_heads , _lowerCAmelCase : int=dim , _lowerCAmelCase : Dict=dim ):
return w.view(_lowerCAmelCase , dima // n_heads // 2 , 2 , _lowerCAmelCase ).transpose(1 , 2 ).reshape(_lowerCAmelCase , _lowerCAmelCase )
print(f"""Fetching all parameters from the checkpoint at {input_base_path}.""" )
# Load weights
if model_size == "7B":
# Not sharded
# (The sharded implementation would also work, but this is simpler.)
UpperCAmelCase : int = torch.load(os.path.join(_lowerCAmelCase , '''consolidated.00.pth''' ) , map_location='''cpu''' )
else:
# Sharded
UpperCAmelCase : Optional[Any] = [
torch.load(os.path.join(_lowerCAmelCase , f"""consolidated.{i:02d}.pth""" ) , map_location='''cpu''' )
for i in range(_lowerCAmelCase )
]
UpperCAmelCase : Any = 0
UpperCAmelCase : str = {'''weight_map''': {}}
for layer_i in range(_lowerCAmelCase ):
UpperCAmelCase : Optional[Any] = f"""pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"""
if model_size == "7B":
# Unsharded
UpperCAmelCase : Optional[int] = {
f"""model.layers.{layer_i}.self_attn.q_proj.weight""": permute(
loaded[f"""layers.{layer_i}.attention.wq.weight"""] ),
f"""model.layers.{layer_i}.self_attn.k_proj.weight""": permute(
loaded[f"""layers.{layer_i}.attention.wk.weight"""] ),
f"""model.layers.{layer_i}.self_attn.v_proj.weight""": loaded[f"""layers.{layer_i}.attention.wv.weight"""],
f"""model.layers.{layer_i}.self_attn.o_proj.weight""": loaded[f"""layers.{layer_i}.attention.wo.weight"""],
f"""model.layers.{layer_i}.mlp.gate_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w1.weight"""],
f"""model.layers.{layer_i}.mlp.down_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w2.weight"""],
f"""model.layers.{layer_i}.mlp.up_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w3.weight"""],
f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[f"""layers.{layer_i}.attention_norm.weight"""],
f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[f"""layers.{layer_i}.ffn_norm.weight"""],
}
else:
# Sharded
# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
UpperCAmelCase : List[str] = {
f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[0][
f"""layers.{layer_i}.attention_norm.weight"""
].clone(),
f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[0][
f"""layers.{layer_i}.ffn_norm.weight"""
].clone(),
}
UpperCAmelCase : Union[str, Any] = permute(
torch.cat(
[
loaded[i][f"""layers.{layer_i}.attention.wq.weight"""].view(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
for i in range(_lowerCAmelCase )
] , dim=0 , ).reshape(_lowerCAmelCase , _lowerCAmelCase ) )
UpperCAmelCase : Optional[Any] = permute(
torch.cat(
[
loaded[i][f"""layers.{layer_i}.attention.wk.weight"""].view(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
for i in range(_lowerCAmelCase )
] , dim=0 , ).reshape(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , )
UpperCAmelCase : str = torch.cat(
[
loaded[i][f"""layers.{layer_i}.attention.wv.weight"""].view(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
for i in range(_lowerCAmelCase )
] , dim=0 , ).reshape(_lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase : Optional[int] = torch.cat(
[loaded[i][f"""layers.{layer_i}.attention.wo.weight"""] for i in range(_lowerCAmelCase )] , dim=1 )
UpperCAmelCase : Any = torch.cat(
[loaded[i][f"""layers.{layer_i}.feed_forward.w1.weight"""] for i in range(_lowerCAmelCase )] , dim=0 )
UpperCAmelCase : str = torch.cat(
[loaded[i][f"""layers.{layer_i}.feed_forward.w2.weight"""] for i in range(_lowerCAmelCase )] , dim=1 )
UpperCAmelCase : Tuple = torch.cat(
[loaded[i][f"""layers.{layer_i}.feed_forward.w3.weight"""] for i in range(_lowerCAmelCase )] , dim=0 )
UpperCAmelCase : Any = inv_freq
for k, v in state_dict.items():
UpperCAmelCase : List[Any] = filename
param_count += v.numel()
torch.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , _lowerCAmelCase ) )
UpperCAmelCase : Optional[int] = f"""pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"""
if model_size == "7B":
# Unsharded
UpperCAmelCase : str = {
'''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''],
'''model.norm.weight''': loaded['''norm.weight'''],
'''lm_head.weight''': loaded['''output.weight'''],
}
else:
UpperCAmelCase : Any = {
'''model.norm.weight''': loaded[0]['''norm.weight'''],
'''model.embed_tokens.weight''': torch.cat(
[loaded[i]['''tok_embeddings.weight'''] for i in range(_lowerCAmelCase )] , dim=1 ),
'''lm_head.weight''': torch.cat([loaded[i]['''output.weight'''] for i in range(_lowerCAmelCase )] , dim=0 ),
}
for k, v in state_dict.items():
UpperCAmelCase : Optional[int] = filename
param_count += v.numel()
torch.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , _lowerCAmelCase ) )
# Write configs
UpperCAmelCase : Union[str, Any] = {'''total_size''': param_count * 2}
write_json(_lowerCAmelCase , os.path.join(_lowerCAmelCase , '''pytorch_model.bin.index.json''' ) )
UpperCAmelCase : int = params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
UpperCAmelCase : Tuple = params['''multiple_of'''] if '''multiple_of''' in params else 256
UpperCAmelCase : Any = LlamaConfig(
hidden_size=_lowerCAmelCase , intermediate_size=compute_intermediate_size(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) , num_attention_heads=params['''n_heads'''] , num_hidden_layers=params['''n_layers'''] , rms_norm_eps=params['''norm_eps'''] , num_key_value_heads=_lowerCAmelCase , )
config.save_pretrained(_lowerCAmelCase )
# Make space so we can load the model properly now.
del state_dict
del loaded
gc.collect()
print('''Loading the checkpoint in a Llama model.''' )
UpperCAmelCase : Optional[int] = LlamaForCausalLM.from_pretrained(_lowerCAmelCase , torch_dtype=torch.floataa , low_cpu_mem_usage=_lowerCAmelCase )
# Avoid saving this as part of the config.
del model.config._name_or_path
print('''Saving in the Transformers format.''' )
model.save_pretrained(_lowerCAmelCase , safe_serialization=_lowerCAmelCase )
shutil.rmtree(_lowerCAmelCase )
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Union[str, Any] ) -> List[str]:
# Initialize the tokenizer based on the `spm` model
UpperCAmelCase : Dict = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
print(f"""Saving a {tokenizer_class.__name__} to {tokenizer_path}.""" )
UpperCAmelCase : List[Any] = tokenizer_class(_lowerCAmelCase )
tokenizer.save_pretrained(_lowerCAmelCase )
def snake_case_ ( ) -> List[Any]:
UpperCAmelCase : int = argparse.ArgumentParser()
parser.add_argument(
'''--input_dir''' , help='''Location of LLaMA weights, which contains tokenizer.model and model folders''' , )
parser.add_argument(
'''--model_size''' , choices=['''7B''', '''7Bf''', '''13B''', '''13Bf''', '''30B''', '''65B''', '''70B''', '''70Bf''', '''tokenizer_only'''] , )
parser.add_argument(
'''--output_dir''' , help='''Location to write HF model and tokenizer''' , )
parser.add_argument('''--safe_serialization''' , type=_lowerCAmelCase , help='''Whether or not to save using `safetensors`.''' )
UpperCAmelCase : List[Any] = parser.parse_args()
if args.model_size != "tokenizer_only":
write_model(
model_path=args.output_dir , input_base_path=os.path.join(args.input_dir , args.model_size ) , model_size=args.model_size , safe_serialization=args.safe_serialization , )
UpperCAmelCase : Optional[int] = os.path.join(args.input_dir , '''tokenizer.model''' )
write_tokenizer(args.output_dir , _lowerCAmelCase )
if __name__ == "__main__":
main()
| 23 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
@dataclass
class A__ ( __snake_case ):
_UpperCAmelCase :Any = [
'no_inference',
'no_cuda',
'no_tpu',
'no_speed',
'no_memory',
'no_env_print',
'no_multi_process',
]
def __init__( self , **A_ ):
'''simple docstring'''
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
UpperCamelCase : List[str] = deprecated_arg[3:]
UpperCamelCase : Union[str, Any] = not kwargs.pop(A_ )
logger.warning(
F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
F""" {positive_arg}={kwargs[positive_arg]}""" )
UpperCamelCase : Dict = kwargs.pop("tpu_name" , self.tpu_name )
UpperCamelCase : str = kwargs.pop("device_idx" , self.device_idx )
UpperCamelCase : List[str] = kwargs.pop("eager_mode" , self.eager_mode )
UpperCamelCase : List[str] = kwargs.pop("use_xla" , self.use_xla )
super().__init__(**A_ )
_UpperCAmelCase :str = field(
default=__snake_case , metadata={'help': 'Name of TPU'} , )
_UpperCAmelCase :int = field(
default=0 , metadata={'help': 'CPU / GPU device index. Defaults to 0.'} , )
_UpperCAmelCase :bool = field(default=__snake_case , metadata={'help': 'Benchmark models in eager model.'} )
_UpperCAmelCase :bool = field(
default=__snake_case , metadata={
'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
} , )
@cached_property
def __UpperCamelCase( self ):
'''simple docstring'''
requires_backends(self , ["tf"] )
UpperCamelCase : Union[str, Any] = None
if self.tpu:
try:
if self.tpu_name:
UpperCamelCase : str = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
UpperCamelCase : List[Any] = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
UpperCamelCase : Tuple = None
return tpu
@cached_property
def __UpperCamelCase( self ):
'''simple docstring'''
requires_backends(self , ["tf"] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
UpperCamelCase : int = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , "GPU" )
UpperCamelCase : Any = tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}""" )
else:
tf.config.set_visible_devices([] , "GPU" ) # disable GPU
UpperCamelCase : Union[str, Any] = tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}""" )
return strategy
@property
def __UpperCamelCase( self ):
'''simple docstring'''
requires_backends(self , ["tf"] )
return self._setup_tpu is not None
@property
def __UpperCamelCase( self ):
'''simple docstring'''
requires_backends(self , ["tf"] )
return self._setup_strategy
@property
def __UpperCamelCase( self ):
'''simple docstring'''
requires_backends(self , ["tf"] )
return tf.config.list_physical_devices("GPU" )
@property
def __UpperCamelCase( self ):
'''simple docstring'''
requires_backends(self , ["tf"] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return self.n_gpu > 0
| 140 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
@require_tf
@require_sentencepiece
@require_tokenizers
class A__ ( unittest.TestCase ):
@slow
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[str] = TFAutoModelForSeqaSeqLM.from_pretrained("google/mt5-small" )
UpperCamelCase : Tuple = AutoTokenizer.from_pretrained("google/mt5-small" )
UpperCamelCase : Dict = tokenizer("Hello there" , return_tensors="tf" ).input_ids
UpperCamelCase : int = tokenizer("Hi I am" , return_tensors="tf" ).input_ids
UpperCamelCase : Union[str, Any] = model(A_ , labels=A_ ).loss
UpperCamelCase : List[str] = -tf.math.reduce_mean(A_ ).numpy()
UpperCamelCase : Union[str, Any] = -21.22_81_68
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2e-4 )
| 140 | 1 |
"""simple docstring"""
def lowercase_ ( __UpperCAmelCase ) -> Dict:
lowerCAmelCase__ : Optional[int] = [], []
while len(snake_case_ ) > 1:
lowerCAmelCase__ : Optional[int] = min(snake_case_ ), max(snake_case_ )
start.append(snake_case_ )
end.append(snake_case_ )
collection.remove(snake_case_ )
collection.remove(snake_case_ )
end.reverse()
return start + collection + end
if __name__ == "__main__":
_A = input("""Enter numbers separated by a comma:\n""").strip()
_A = [int(item) for item in user_input.split(""",""")]
print(*merge_sort(unsorted), sep=""",""")
| 242 |
"""simple docstring"""
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowerCAmelCase_ ( snake_case_ : Dict , snake_case_ : str , snake_case_ : str , snake_case_ : Path , snake_case_ : str = None , snake_case_ : str = None , snake_case_ : str = None , ) ->List[Any]:
if config_name_or_path is None:
lowerCamelCase__ : Dict ='facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'
if generator_tokenizer_name_or_path is None:
lowerCamelCase__ : Optional[int] =generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
lowerCamelCase__ : Optional[int] =question_encoder_name_or_path
lowerCamelCase__ : Optional[Any] =RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration
# Save model.
lowerCamelCase__ : Union[str, Any] =RagConfig.from_pretrained(snake_case_ )
lowerCamelCase__ : Optional[Any] =AutoConfig.from_pretrained(snake_case_ )
lowerCamelCase__ : Optional[Any] =AutoConfig.from_pretrained(snake_case_ )
lowerCamelCase__ : Optional[int] =gen_config
lowerCamelCase__ : str =question_encoder_config
lowerCamelCase__ : str =model_class.from_pretrained_question_encoder_generator(
snake_case_ , snake_case_ , config=snake_case_ )
rag_model.save_pretrained(snake_case_ )
# Sanity check.
model_class.from_pretrained(snake_case_ )
# Save tokenizers.
lowerCamelCase__ : str =AutoTokenizer.from_pretrained(snake_case_ )
gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/' )
lowerCamelCase__ : Optional[int] =AutoTokenizer.from_pretrained(snake_case_ )
question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/' )
if __name__ == "__main__":
lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--model_type""",
choices=["""rag_sequence""", """rag_token"""],
required=True,
type=str,
help="""RAG model type: rag_sequence, rag_token""",
)
parser.add_argument("""--dest""", type=str, required=True, help="""Path to the output checkpoint directory.""")
parser.add_argument("""--generator_name_or_path""", type=str, required=True, help="""Generator model identifier""")
parser.add_argument(
"""--question_encoder_name_or_path""", type=str, required=True, help="""Question encoder model identifier"""
)
parser.add_argument(
"""--generator_tokenizer_name_or_path""",
type=str,
help="""Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``""",
)
parser.add_argument(
"""--question_encoder_tokenizer_name_or_path""",
type=str,
help="""Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``""",
)
parser.add_argument(
"""--config_name_or_path""",
type=str,
help=(
"""Identifier of the model config to use, if not provided, resolves to a base config for a given"""
""" ``model_type``"""
),
)
lowerCAmelCase = parser.parse_args()
lowerCAmelCase = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
) | 126 | 0 |
def lowerCamelCase_ ( UpperCamelCase__ : int ) -> "list[int]":
"""simple docstring"""
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
__lowerCamelCase = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
__lowerCamelCase = 1
if upper_limit > 0:
__lowerCamelCase = 1
# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
for i in range(2 , upper_limit + 1 ):
for j in range(UpperCamelCase__ ):
catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]
return catalan_list
if __name__ == "__main__":
print("\n********* Catalan Numbers Using Dynamic Programming ************\n")
print("\n*** Enter -1 at any time to quit ***")
print("\nEnter the upper limit (≥ 0) for the Catalan number sequence: ", end="")
try:
while True:
__A = int(input().strip())
if N < 0:
print("\n********* Goodbye!! ************")
break
else:
print(f'''The Catalan numbers from 0 through {N} are:''')
print(catalan_numbers(N))
print("Try another upper limit for the sequence: ", end="")
except (NameError, ValueError):
print("\n********* Invalid input, goodbye! ************\n")
import doctest
doctest.testmod()
| 364 |
# 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 lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = [False] * len(UpperCamelCase__ )
__lowerCamelCase = [-1] * len(UpperCamelCase__ )
def dfs(UpperCamelCase__ : int , UpperCamelCase__ : int ):
__lowerCamelCase = True
__lowerCamelCase = c
for u in graph[v]:
if not visited[u]:
dfs(UpperCamelCase__ , 1 - c )
for i in range(len(UpperCamelCase__ ) ):
if not visited[i]:
dfs(UpperCamelCase__ , 0 )
for i in range(len(UpperCamelCase__ ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
__A = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 348 | 0 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
SCREAMING_SNAKE_CASE__ : Any = models.Sequential()
# Step 1 - Convolution
# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
# (3,3) is the kernel size (filter matrix)
classifier.add(
layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation='relu')
)
# Step 2 - Pooling
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Adding a second convolutional layer
classifier.add(layers.ConvaD(32, (3, 3), activation='relu'))
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Step 3 - Flattening
classifier.add(layers.Flatten())
# Step 4 - Full connection
classifier.add(layers.Dense(units=128, activation='relu'))
classifier.add(layers.Dense(units=1, activation='sigmoid'))
# Compiling the CNN
classifier.compile(
optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']
)
# Part 2 - Fitting the CNN to the images
# Load Trained model weights
# from keras.models import load_model
# regressor=load_model('cnn.h5')
SCREAMING_SNAKE_CASE__ : int = tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
)
SCREAMING_SNAKE_CASE__ : List[Any] = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)
SCREAMING_SNAKE_CASE__ : List[str] = train_datagen.flow_from_directory(
'dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary'
)
SCREAMING_SNAKE_CASE__ : List[Any] = test_datagen.flow_from_directory(
'dataset/test_set', target_size=(64, 64), batch_size=32, class_mode='binary'
)
classifier.fit_generator(
training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
)
classifier.save('cnn.h5')
# Part 3 - Making new predictions
SCREAMING_SNAKE_CASE__ : Optional[Any] = tf.keras.preprocessing.image.load_img(
'dataset/single_prediction/image.png', target_size=(64, 64)
)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tf.keras.preprocessing.image.img_to_array(test_image)
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.expand_dims(test_image, axis=0)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = classifier.predict(test_image)
# training_set.class_indices
if result[0][0] == 0:
SCREAMING_SNAKE_CASE__ : Dict = 'Normal'
if result[0][0] == 1:
SCREAMING_SNAKE_CASE__ : Tuple = 'Abnormality detected'
| 48 | """simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ : Optional[int] = logging.get_logger(__name__)
class snake_case ( __UpperCAmelCase ):
"""simple docstring"""
snake_case__ = ["input_ids", "attention_mask"]
def __init__( self : Optional[int] ,lowerCamelCase__ : int="</s>" ,lowerCamelCase__ : str="<unk>" ,lowerCamelCase__ : Union[str, Any]="<pad>" ,lowerCamelCase__ : int=125 ,lowerCamelCase__ : str=None ,**lowerCamelCase__ : Union[str, Any] ,):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
UpperCAmelCase__ = [f'''<extra_id_{i}>''' for i in range(lowerCamelCase__ )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
UpperCAmelCase__ = len(set(filter(lambda lowerCamelCase__ : bool('extra_id' in str(lowerCamelCase__ ) ) ,lowerCamelCase__ ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'
' extra_ids tokens' )
UpperCAmelCase__ = AddedToken(lowerCamelCase__ ,lstrip=lowerCamelCase__ ,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else pad_token
UpperCAmelCase__ = AddedToken(lowerCamelCase__ ,lstrip=lowerCamelCase__ ,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else eos_token
UpperCAmelCase__ = AddedToken(lowerCamelCase__ ,lstrip=lowerCamelCase__ ,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else unk_token
super().__init__(
eos_token=lowerCamelCase__ ,unk_token=lowerCamelCase__ ,pad_token=lowerCamelCase__ ,extra_ids=lowerCamelCase__ ,additional_special_tokens=lowerCamelCase__ ,**lowerCamelCase__ ,)
UpperCAmelCase__ = extra_ids
UpperCAmelCase__ = 2**8 # utf is 8 bits
# define special tokens dict
UpperCAmelCase__ = {
self.pad_token: 0,
self.eos_token: 1,
self.unk_token: 2,
}
UpperCAmelCase__ = len(self.special_tokens_encoder )
UpperCAmelCase__ = len(lowerCamelCase__ )
for i, token in enumerate(lowerCamelCase__ ):
UpperCAmelCase__ = self.vocab_size + i - n
UpperCAmelCase__ = {v: k for k, v in self.special_tokens_encoder.items()}
@property
def __lowerCAmelCase ( self : Union[str, Any] ):
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def __lowerCAmelCase ( self : Optional[Any] ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ,lowerCamelCase__ : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase__ ,token_ids_a=lowerCamelCase__ ,already_has_special_tokens=lowerCamelCase__ )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(lowerCamelCase__ )) + [1]
return ([0] * len(lowerCamelCase__ )) + [1] + ([0] * len(lowerCamelCase__ )) + [1]
def __lowerCAmelCase ( self : str ,lowerCamelCase__ : List[int] ):
if len(lowerCamelCase__ ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
' eos tokens being added.' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def __lowerCAmelCase ( self : str ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ):
UpperCAmelCase__ = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def __lowerCAmelCase ( self : Dict ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ):
UpperCAmelCase__ = self._add_eos_if_not_present(lowerCamelCase__ )
if token_ids_a is None:
return token_ids_a
else:
UpperCAmelCase__ = self._add_eos_if_not_present(lowerCamelCase__ )
return token_ids_a + token_ids_a
def __lowerCAmelCase ( self : Optional[Any] ,lowerCamelCase__ : str ):
UpperCAmelCase__ = [chr(lowerCamelCase__ ) for i in text.encode('utf-8' )]
return tokens
def __lowerCAmelCase ( self : List[Any] ,lowerCamelCase__ : str ):
if token in self.special_tokens_encoder:
UpperCAmelCase__ = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
UpperCAmelCase__ = self.added_tokens_encoder[token]
elif len(lowerCamelCase__ ) != 1:
UpperCAmelCase__ = self.unk_token_id
else:
UpperCAmelCase__ = ord(lowerCamelCase__ ) + self._num_special_tokens
return token_id
def __lowerCAmelCase ( self : Union[str, Any] ,lowerCamelCase__ : Any ):
if index in self.special_tokens_decoder:
UpperCAmelCase__ = self.special_tokens_decoder[index]
else:
UpperCAmelCase__ = chr(index - self._num_special_tokens )
return token
def __lowerCAmelCase ( self : Optional[Any] ,lowerCamelCase__ : Union[str, Any] ):
UpperCAmelCase__ = b''
for token in tokens:
if token in self.special_tokens_decoder:
UpperCAmelCase__ = self.special_tokens_decoder[token].encode('utf-8' )
elif token in self.added_tokens_decoder:
UpperCAmelCase__ = self.special_tokens_decoder[token].encode('utf-8' )
elif token in self.special_tokens_encoder:
UpperCAmelCase__ = token.encode('utf-8' )
elif token in self.added_tokens_encoder:
UpperCAmelCase__ = token.encode('utf-8' )
else:
UpperCAmelCase__ = bytes([ord(lowerCamelCase__ )] )
bstring += tok_string
UpperCAmelCase__ = bstring.decode('utf-8' ,errors='ignore' )
return string
def __lowerCAmelCase ( self : str ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[str] = None ):
return ()
| 98 | 0 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
_A = logging.getLogger(__name__)
_A = """pytorch_model.bin"""
@dataclasses.dataclass
class lowerCamelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE = dataclasses.field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'} , )
@dataclasses.dataclass
class lowerCamelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE = dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase__ , metadata={'help': 'A csv or a json file containing the validation data.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase__ , metadata={'help': 'The name of the task to train on.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase__ , metadata={'help': 'The list of labels for the task.'} )
@dataclasses.dataclass
class lowerCamelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE = dataclasses.field(
metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default='accuracy' , metadata={'help': 'The evaluation metric used for the task.'} )
SCREAMING_SNAKE_CASE = dataclasses.field(
default='no' , metadata={
'help': 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=1_0 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=0.0 , metadata={
'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase__ , metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase__ , metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase__ , metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=0.0 , metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=1_0_0 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
SCREAMING_SNAKE_CASE = dataclasses.field(
default=lowerCAmelCase__ , metadata={'help': 'Random seed for initialization.'} , )
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> str:
UpperCAmelCase__ : List[str] = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
UpperCAmelCase__ : Tuple = dataset.filter(lambda lowerCAmelCase : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
UpperCAmelCase__ : str = int(eval_result * len(lowerCAmelCase ) )
print(lowerCAmelCase )
UpperCAmelCase__ : Any = dataset.sort("""probability""" , reverse=lowerCAmelCase )
UpperCAmelCase__ : Dict = dataset.select(range(lowerCAmelCase ) )
UpperCAmelCase__ : Dict = dataset.remove_columns(["""label""", """probability"""] )
UpperCAmelCase__ : Any = dataset.rename_column("""prediction""" , """label""" )
UpperCAmelCase__ : Union[str, Any] = dataset.map(lambda lowerCAmelCase : {"label": idalabel[example["label"]]} )
UpperCAmelCase__ : Optional[int] = dataset.shuffle(seed=args.seed )
UpperCAmelCase__ : List[Any] = os.path.join(lowerCAmelCase , F"""train_pseudo.{args.data_file_extension}""" )
if args.data_file_extension == "csv":
dataset.to_csv(lowerCAmelCase , index=lowerCAmelCase )
else:
dataset.to_json(lowerCAmelCase )
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase ) -> Optional[Any]:
UpperCAmelCase__ : str = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
UpperCAmelCase__ : int = STModelArguments(model_name_or_path=lowerCAmelCase )
UpperCAmelCase__ : Optional[int] = STDataArguments(train_file=lowerCAmelCase , infer_file=lowerCAmelCase )
UpperCAmelCase__ : Optional[int] = STTrainingArguments(output_dir=lowerCAmelCase )
UpperCAmelCase__ : Tuple = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(lowerCAmelCase ).items():
setattr(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
for key, value in kwargs.items():
if hasattr(lowerCAmelCase , lowerCAmelCase ):
setattr(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# Sanity checks
UpperCAmelCase__ : Dict = {}
UpperCAmelCase__ : int = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
UpperCAmelCase__ : Union[str, Any] = args.train_file
UpperCAmelCase__ : int = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
UpperCAmelCase__ : int = args.eval_file
for key in data_files:
UpperCAmelCase__ : Any = data_files[key].split(""".""" )[-1]
assert extension in ["csv", "json"], F"""`{key}_file` should be a csv or a json file."""
if args.data_file_extension is None:
UpperCAmelCase__ : List[str] = extension
else:
assert extension == args.data_file_extension, F"""`{key}_file` should be a {args.data_file_extension} file`."""
assert (
args.eval_metric in datasets.list_metrics()
), F"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info("""Creating the initial data directory for self-training...""" )
UpperCAmelCase__ : Any = F"""{args.output_dir}/self-train_iter-{{}}""".format
UpperCAmelCase__ : Dict = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=lowerCAmelCase )
os.makedirs(lowerCAmelCase , exist_ok=lowerCAmelCase )
accelerator.wait_for_everyone()
UpperCAmelCase__ : Union[str, Any] = None
UpperCAmelCase__ : Optional[int] = None
UpperCAmelCase__ : List[Any] = 0
UpperCAmelCase__ : Optional[int] = False
# Show the progress bar
UpperCAmelCase__ : Dict = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
UpperCAmelCase__ : Union[str, Any] = data_dir_format(lowerCAmelCase )
assert os.path.exists(lowerCAmelCase )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
UpperCAmelCase__ : Tuple = os.path.join(lowerCAmelCase , """stage-1""" )
UpperCAmelCase__ : str = {
"""accelerator""": accelerator,
"""model_name_or_path""": args.model_name_or_path,
"""cache_dir""": args.cache_dir,
"""do_train""": True,
"""train_file""": data_files["""train"""] if iteration == 0 else data_files["""train_pseudo"""],
"""do_eval""": True if args.eval_file is not None else False,
"""eval_file""": data_files["""eval"""],
"""do_predict""": True,
"""infer_file""": data_files["""infer"""],
"""task_name""": args.task_name,
"""label_list""": args.label_list,
"""output_dir""": current_output_dir,
"""eval_metric""": args.eval_metric,
"""evaluation_strategy""": args.evaluation_strategy,
"""early_stopping_patience""": args.early_stopping_patience,
"""early_stopping_threshold""": args.early_stopping_threshold,
"""seed""": args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(lowerCAmelCase , lowerCAmelCase ):
arguments_dict.update({key: value} )
UpperCAmelCase__ : Union[str, Any] = os.path.join(lowerCAmelCase , """best-checkpoint""" , lowerCAmelCase )
if os.path.exists(lowerCAmelCase ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.""" , lowerCAmelCase , lowerCAmelCase , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 1 *****""" , lowerCAmelCase )
finetune(**lowerCAmelCase )
accelerator.wait_for_everyone()
assert os.path.exists(lowerCAmelCase )
logger.info("""Self-training job completed: iteration: %d, stage: 1.""" , lowerCAmelCase )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
UpperCAmelCase__ : Optional[int] = os.path.join(lowerCAmelCase , """best-checkpoint""" )
UpperCAmelCase__ : Union[str, Any] = os.path.join(lowerCAmelCase , """stage-2""" )
# Update arguments_dict
UpperCAmelCase__ : Optional[Any] = model_path
UpperCAmelCase__ : List[Any] = data_files["""train"""]
UpperCAmelCase__ : str = current_output_dir
UpperCAmelCase__ : Any = os.path.join(lowerCAmelCase , """best-checkpoint""" , lowerCAmelCase )
if os.path.exists(lowerCAmelCase ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.""" , lowerCAmelCase , lowerCAmelCase , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 2 *****""" , lowerCAmelCase )
finetune(**lowerCAmelCase )
accelerator.wait_for_everyone()
assert os.path.exists(lowerCAmelCase )
logger.info("""Self-training job completed: iteration: %d, stage: 2.""" , lowerCAmelCase )
UpperCAmelCase__ : str = iteration
UpperCAmelCase__ : Dict = data_dir_format(iteration + 1 )
UpperCAmelCase__ : Union[str, Any] = AutoConfig.from_pretrained(os.path.join(lowerCAmelCase , """best-checkpoint""" ) )
UpperCAmelCase__ : Optional[int] = config.idalabel
UpperCAmelCase__ : Optional[int] = os.path.join(lowerCAmelCase , """eval_results_best-checkpoint.json""" )
UpperCAmelCase__ : List[Any] = os.path.join(lowerCAmelCase , """test_results_best-checkpoint.json""" )
assert os.path.exists(lowerCAmelCase )
with open(lowerCAmelCase , """r""" ) as f:
UpperCAmelCase__ : Optional[int] = float(json.load(lowerCAmelCase )[args.eval_metric] )
UpperCAmelCase__ : List[Any] = os.path.join(lowerCAmelCase , """infer_output_best-checkpoint.csv""" )
assert os.path.exists(lowerCAmelCase )
# Loading the dataset from local csv or json files.
UpperCAmelCase__ : str = load_dataset(args.data_file_extension , data_files={"""data""": data_files["""infer"""]} )["""data"""]
UpperCAmelCase__ : Optional[int] = load_dataset("""csv""" , data_files={"""data""": infer_output_file} )["""data"""]
if accelerator.is_main_process:
os.makedirs(lowerCAmelCase , exist_ok=lowerCAmelCase )
shutil.copy(lowerCAmelCase , os.path.join(lowerCAmelCase , F"""eval_results_iter-{iteration}.json""" ) )
if os.path.exists(lowerCAmelCase ):
shutil.copy(lowerCAmelCase , os.path.join(lowerCAmelCase , F"""test_results_iter-{iteration}.json""" ) )
create_pseudo_labeled_data(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
accelerator.wait_for_everyone()
UpperCAmelCase__ : Optional[int] = os.path.join(lowerCAmelCase , F"""train_pseudo.{args.data_file_extension}""" )
if args.evaluation_strategy != IntervalStrategy.NO.value:
UpperCAmelCase__ : List[str] = eval_result
if best_iteration is None:
UpperCAmelCase__ : Tuple = new_iteration
UpperCAmelCase__ : int = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
UpperCAmelCase__ : List[str] = new_iteration
UpperCAmelCase__ : Tuple = new_eval_result
UpperCAmelCase__ : Union[str, Any] = 0
else:
if new_eval_result == best_eval_result:
UpperCAmelCase__ : Union[str, Any] = new_iteration
UpperCAmelCase__ : List[Any] = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
UpperCAmelCase__ : List[Any] = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info("""Best iteration: %d""" , lowerCAmelCase )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , lowerCAmelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(lowerCAmelCase , F"""eval_results_iter-{iteration}.json""" ) , os.path.join(lowerCAmelCase , """eval_results_best-iteration.json""" ) , )
else:
# Assume that the last iteration is the best
logger.info("""Best iteration: %d""" , args.max_selftrain_iterations - 1 )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , lowerCAmelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(lowerCAmelCase , F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json""" ) , os.path.join(lowerCAmelCase , """eval_results_best-iteration.json""" ) , )
| 166 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import FNetTokenizer
else:
_A = None
_A = logging.get_logger(__name__)
_A = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
_A = {
"""vocab_file""": {
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
"""google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
},
"""tokenizer_file""": {
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json""",
"""google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json""",
},
}
_A = {
"""google/fnet-base""": 5_12,
"""google/fnet-large""": 5_12,
}
_A = """▁"""
class lowerCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE = ['input_ids', 'token_type_ids']
SCREAMING_SNAKE_CASE = FNetTokenizer
def __init__(self , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase=False , _lowerCamelCase=True , _lowerCamelCase=True , _lowerCamelCase="<unk>" , _lowerCamelCase="[SEP]" , _lowerCamelCase="<pad>" , _lowerCamelCase="[CLS]" , _lowerCamelCase="[MASK]" , **_lowerCamelCase , ):
"""simple docstring"""
UpperCAmelCase__ : Optional[Any] = (
AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase , normalized=_lowerCamelCase )
if isinstance(_lowerCamelCase , _lowerCamelCase )
else mask_token
)
super().__init__(
_lowerCamelCase , tokenizer_file=_lowerCamelCase , do_lower_case=_lowerCamelCase , remove_space=_lowerCamelCase , keep_accents=_lowerCamelCase , unk_token=_lowerCamelCase , sep_token=_lowerCamelCase , pad_token=_lowerCamelCase , cls_token=_lowerCamelCase , mask_token=_lowerCamelCase , **_lowerCamelCase , )
UpperCAmelCase__ : Optional[int] = do_lower_case
UpperCAmelCase__ : List[str] = remove_space
UpperCAmelCase__ : Optional[Any] = keep_accents
UpperCAmelCase__ : List[str] = vocab_file
UpperCAmelCase__ : Optional[int] = False if not self.vocab_file else True
def _a (self , _lowerCamelCase , _lowerCamelCase = None ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = [self.sep_token_id]
UpperCAmelCase__ : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def _a (self , _lowerCamelCase , _lowerCamelCase = None ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = [self.sep_token_id]
UpperCAmelCase__ : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _a (self , _lowerCamelCase , _lowerCamelCase = None ):
"""simple docstring"""
if not os.path.isdir(_lowerCamelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase__ : List[str] = os.path.join(
_lowerCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ):
copyfile(self.vocab_file , _lowerCamelCase )
return (out_vocab_file,)
| 166 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"post_extract_proj": "feature_projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.upsample.0": "encoder.upsample.projection",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "layer_norm",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
for attribute in key.split(""".""" ):
__SCREAMING_SNAKE_CASE = getattr(UpperCamelCase_ , UpperCamelCase_ )
if weight_type is not None:
__SCREAMING_SNAKE_CASE = getattr(UpperCamelCase_ , UpperCamelCase_ ).shape
else:
__SCREAMING_SNAKE_CASE = hf_pointer.shape
assert hf_shape == value.shape, (
f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
f" {value.shape} for {full_name}"
)
if weight_type == "weight":
__SCREAMING_SNAKE_CASE = value
elif weight_type == "weight_g":
__SCREAMING_SNAKE_CASE = value
elif weight_type == "weight_v":
__SCREAMING_SNAKE_CASE = value
elif weight_type == "bias":
__SCREAMING_SNAKE_CASE = value
else:
__SCREAMING_SNAKE_CASE = value
logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = fairseq_model.state_dict()
__SCREAMING_SNAKE_CASE = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
__SCREAMING_SNAKE_CASE = False
if "conv_layers" in name:
load_conv_layer(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , hf_model.config.feat_extract_norm == """group""" , )
__SCREAMING_SNAKE_CASE = True
else:
for key, mapped_key in MAPPING.items():
__SCREAMING_SNAKE_CASE = """sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
__SCREAMING_SNAKE_CASE = True
if "*" in mapped_key:
__SCREAMING_SNAKE_CASE = name.split(UpperCamelCase_ )[0].split(""".""" )[-2]
__SCREAMING_SNAKE_CASE = mapped_key.replace("""*""" , UpperCamelCase_ )
if "weight_g" in name:
__SCREAMING_SNAKE_CASE = """weight_g"""
elif "weight_v" in name:
__SCREAMING_SNAKE_CASE = """weight_v"""
elif "weight" in name:
__SCREAMING_SNAKE_CASE = """weight"""
elif "bias" in name:
__SCREAMING_SNAKE_CASE = """bias"""
else:
__SCREAMING_SNAKE_CASE = None
set_recursively(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
continue
if not is_used:
unused_weights.append(UpperCamelCase_ )
logger.warning(f"Unused weights: {unused_weights}" )
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = full_name.split("""conv_layers.""" )[-1]
__SCREAMING_SNAKE_CASE = name.split(""".""" )
__SCREAMING_SNAKE_CASE = int(items[0] )
__SCREAMING_SNAKE_CASE = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
)
__SCREAMING_SNAKE_CASE = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
)
__SCREAMING_SNAKE_CASE = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
" found."
)
__SCREAMING_SNAKE_CASE = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
)
__SCREAMING_SNAKE_CASE = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(UpperCamelCase_ )
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = SEWConfig()
if is_finetuned:
__SCREAMING_SNAKE_CASE = model.wav_encoder.wav_model.cfg
else:
__SCREAMING_SNAKE_CASE = model.cfg
__SCREAMING_SNAKE_CASE = fs_config.conv_bias
__SCREAMING_SNAKE_CASE = eval(fs_config.conv_feature_layers )
__SCREAMING_SNAKE_CASE = [x[0] for x in conv_layers]
__SCREAMING_SNAKE_CASE = [x[1] for x in conv_layers]
__SCREAMING_SNAKE_CASE = [x[2] for x in conv_layers]
__SCREAMING_SNAKE_CASE = """gelu"""
__SCREAMING_SNAKE_CASE = """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
__SCREAMING_SNAKE_CASE = 0.0
__SCREAMING_SNAKE_CASE = fs_config.activation_fn.name
__SCREAMING_SNAKE_CASE = fs_config.encoder_embed_dim
__SCREAMING_SNAKE_CASE = 0.02
__SCREAMING_SNAKE_CASE = fs_config.encoder_ffn_embed_dim
__SCREAMING_SNAKE_CASE = 1e-5
__SCREAMING_SNAKE_CASE = fs_config.encoder_layerdrop
__SCREAMING_SNAKE_CASE = fs_config.encoder_attention_heads
__SCREAMING_SNAKE_CASE = fs_config.conv_pos_groups
__SCREAMING_SNAKE_CASE = fs_config.conv_pos
__SCREAMING_SNAKE_CASE = len(UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = fs_config.encoder_layers
__SCREAMING_SNAKE_CASE = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
__SCREAMING_SNAKE_CASE = model.cfg
__SCREAMING_SNAKE_CASE = fs_config.final_dropout
__SCREAMING_SNAKE_CASE = fs_config.layerdrop
__SCREAMING_SNAKE_CASE = fs_config.activation_dropout
__SCREAMING_SNAKE_CASE = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
__SCREAMING_SNAKE_CASE = fs_config.attention_dropout
__SCREAMING_SNAKE_CASE = fs_config.dropout_input
__SCREAMING_SNAKE_CASE = fs_config.dropout
__SCREAMING_SNAKE_CASE = fs_config.mask_channel_length
__SCREAMING_SNAKE_CASE = fs_config.mask_channel_prob
__SCREAMING_SNAKE_CASE = fs_config.mask_length
__SCREAMING_SNAKE_CASE = fs_config.mask_prob
__SCREAMING_SNAKE_CASE = """Wav2Vec2FeatureExtractor"""
__SCREAMING_SNAKE_CASE = """Wav2Vec2CTCTokenizer"""
return config
@torch.no_grad()
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_=True ):
if is_finetuned:
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
__SCREAMING_SNAKE_CASE = SEWConfig.from_pretrained(UpperCamelCase_ )
else:
__SCREAMING_SNAKE_CASE = convert_config(model[0] , UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = model[0].eval()
__SCREAMING_SNAKE_CASE = True if config.feat_extract_norm == """layer""" else False
__SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , )
if is_finetuned:
if dict_path:
__SCREAMING_SNAKE_CASE = Dictionary.load(UpperCamelCase_ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__SCREAMING_SNAKE_CASE = target_dict.pad_index
__SCREAMING_SNAKE_CASE = target_dict.bos_index
__SCREAMING_SNAKE_CASE = target_dict.pad_index
__SCREAMING_SNAKE_CASE = target_dict.bos_index
__SCREAMING_SNAKE_CASE = target_dict.eos_index
__SCREAMING_SNAKE_CASE = len(target_dict.symbols )
__SCREAMING_SNAKE_CASE = os.path.join(UpperCamelCase_ , """vocab.json""" )
if not os.path.isdir(UpperCamelCase_ ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(UpperCamelCase_ ) )
return
os.makedirs(UpperCamelCase_ , exist_ok=UpperCamelCase_ )
with open(UpperCamelCase_ , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(target_dict.indices , UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = WavaVecaCTCTokenizer(
UpperCamelCase_ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=UpperCamelCase_ , )
__SCREAMING_SNAKE_CASE = WavaVecaProcessor(feature_extractor=UpperCamelCase_ , tokenizer=UpperCamelCase_ )
processor.save_pretrained(UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = SEWForCTC(UpperCamelCase_ )
else:
__SCREAMING_SNAKE_CASE = SEWModel(UpperCamelCase_ )
feature_extractor.save_pretrained(UpperCamelCase_ )
recursively_load_weights(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
hf_model.save_pretrained(UpperCamelCase_ )
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
__magic_name__ = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 100 | import random
from .binary_exp_mod import bin_exp_mod
def UpperCamelCase__ ( A__ , A__=1000 ) -> Optional[int]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
snake_case__ : List[Any] = n - 1
snake_case__ : Optional[int] = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
snake_case__ : Union[str, Any] = 0
while count < prec:
snake_case__ : Dict = random.randint(2 , n - 1 )
snake_case__ : Dict = bin_exp_mod(A__ , A__ , A__ )
if b != 1:
snake_case__ : Tuple = True
for _ in range(A__ ):
if b == n - 1:
snake_case__ : List[str] = False
break
snake_case__ : Dict = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
lowerCAmelCase__ : str = abs(int(input('''Enter bound : ''').strip()))
print('''Here\'s the list of primes:''')
print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 143 | 0 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowerCamelCase_ :
def __init__( self : Tuple , _A : Tuple , _A : Any=13 , _A : int=3 , _A : int=True , _A : Union[str, Any]=True , _A : Optional[Any]=0.1 , _A : List[Any]=0.1 , _A : List[Any]=224 , _A : int=1_000 , _A : Any=[3, 3, 6, 4] , _A : str=[48, 56, 112, 220] , ):
'''simple docstring'''
UpperCAmelCase__ : int = parent
UpperCAmelCase__ : Optional[Any] = batch_size
UpperCAmelCase__ : Any = num_channels
UpperCAmelCase__ : Dict = is_training
UpperCAmelCase__ : Any = use_labels
UpperCAmelCase__ : Any = hidden_dropout_prob
UpperCAmelCase__ : int = attention_probs_dropout_prob
UpperCAmelCase__ : List[str] = num_labels
UpperCAmelCase__ : Any = image_size
UpperCAmelCase__ : Optional[Any] = layer_depths
UpperCAmelCase__ : List[str] = embed_dims
def lowercase_ ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : str = None
if self.use_labels:
UpperCAmelCase__ : str = ids_tensor([self.batch_size] , self.num_labels )
UpperCAmelCase__ : Optional[Any] = self.get_config()
return config, pixel_values, labels
def lowercase_ ( self : Tuple ):
'''simple docstring'''
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act='''gelu''' , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=_A , layer_scale_init_value=1e-5 , )
def lowercase_ ( self : List[Any] , _A : Optional[Any] , _A : Optional[int] , _A : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Any = SwiftFormerModel(config=_A )
model.to(_A )
model.eval()
UpperCAmelCase__ : List[str] = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def lowercase_ ( self : Dict , _A : Any , _A : Tuple , _A : int ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.num_labels
UpperCAmelCase__ : Any = SwiftFormerForImageClassification(_A )
model.to(_A )
model.eval()
UpperCAmelCase__ : List[Any] = model(_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
UpperCAmelCase__ : str = SwiftFormerForImageClassification(_A )
model.to(_A )
model.eval()
UpperCAmelCase__ : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : Dict = model(_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase_ ( self : int ):
'''simple docstring'''
(UpperCAmelCase__) : List[Any] = self.prepare_config_and_inputs()
UpperCAmelCase__ : int = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_ ( __a , __a , unittest.TestCase ):
lowerCAmelCase__ = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
lowerCAmelCase__ = (
{'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = SwiftFormerModelTester(self )
UpperCAmelCase__ : Dict = ConfigTester(
self , config_class=_A , has_text_modality=_A , hidden_size=37 , num_attention_heads=12 , num_hidden_layers=12 , )
def lowercase_ ( self : List[str] ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''SwiftFormer does not use inputs_embeds''' )
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
pass
def lowercase_ ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : str = model_class(_A )
UpperCAmelCase__ : Dict = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_A , nn.Linear ) )
def lowercase_ ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[Any] = model_class(_A )
UpperCAmelCase__ : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : Optional[Any] = [*signature.parameters.keys()]
UpperCAmelCase__ : Union[str, Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _A )
def lowercase_ ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def lowercase_ ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_A )
@slow
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ : List[str] = SwiftFormerModel.from_pretrained(_A )
self.assertIsNotNone(_A )
@unittest.skip(reason='''SwiftFormer does not output attentions''' )
def lowercase_ ( self : Tuple ):
'''simple docstring'''
pass
def lowercase_ ( self : int ):
'''simple docstring'''
def check_hidden_states_output(_A : Tuple , _A : Optional[Any] , _A : Union[str, Any] ):
UpperCAmelCase__ : List[Any] = model_class(_A )
model.to(_A )
model.eval()
with torch.no_grad():
UpperCAmelCase__ : str = model(**self._prepare_for_class(_A , _A ) )
UpperCAmelCase__ : Union[str, Any] = outputs.hidden_states
UpperCAmelCase__ : List[Any] = 8
self.assertEqual(len(_A ) , _A ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(_A ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Dict = True
check_hidden_states_output(_A , _A , _A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase__ : int = True
check_hidden_states_output(_A , _A , _A )
def lowercase_ ( self : Dict ):
'''simple docstring'''
def _config_zero_init(_A : int ):
UpperCAmelCase__ : Union[str, Any] = copy.deepcopy(_A )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(_A , _A , 1e-10 )
if isinstance(getattr(_A , _A , _A ) , _A ):
UpperCAmelCase__ : Any = _config_zero_init(getattr(_A , _A ) )
setattr(_A , _A , _A )
return configs_no_init
UpperCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : int = _config_zero_init(_A )
for model_class in self.all_model_classes:
UpperCAmelCase__ : Any = model_class(config=_A )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9) / 1e9).round().item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def lowercase_ ( self : str ):
'''simple docstring'''
pass
def a__ ( ) -> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
@cached_property
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs''' ) if is_vision_available() else None
@slow
def lowercase_ ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Any = SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''' ).to(_A )
UpperCAmelCase__ : Tuple = self.default_image_processor
UpperCAmelCase__ : Any = prepare_img()
UpperCAmelCase__ : str = image_processor(images=_A , return_tensors='''pt''' ).to(_A )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : Optional[int] = model(**_A )
# verify the logits
UpperCAmelCase__ : Optional[Any] = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , _A )
UpperCAmelCase__ : Optional[int] = torch.tensor([[-2.17_03e00, 2.11_07e00, -2.08_11e00]] ).to(_A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _A , atol=1e-4 ) )
| 363 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = ['''MMBTForClassification''', '''MMBTModel''', '''ModalEmbeddings''']
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 299 | 0 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class __lowerCAmelCase ( unittest.TestCase):
@slow
def _lowercase ( self ) -> str:
'''simple docstring'''
a__ : Union[str, Any] =FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base" )
a__ : List[str] =AutoTokenizer.from_pretrained("xlm-roberta-base" )
a__ : List[str] ="The dog is cute and lives in the garden house"
a__ : str =jnp.array([tokenizer.encode(lowerCAmelCase__ )] )
a__ : List[Any] =(1, 1_2, 7_6_8) # batch_size, sequence_length, embedding_vector_dim
a__ : Optional[int] =jnp.array(
[[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]] )
a__ : Dict =model(lowerCAmelCase__ )["last_hidden_state"]
self.assertEqual(output.shape , lowerCAmelCase__ )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] , lowerCAmelCase__ , atol=1E-3 ) )
| 95 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
UpperCAmelCase : Any = 16
UpperCAmelCase : str = 32
def _A ( SCREAMING_SNAKE_CASE : Accelerator , SCREAMING_SNAKE_CASE : int = 16 ):
"""simple docstring"""
a__ : int =AutoTokenizer.from_pretrained("bert-base-cased" )
a__ : List[str] =load_dataset("glue" , "mrpc" )
def tokenize_function(SCREAMING_SNAKE_CASE : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
a__ : int =tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=SCREAMING_SNAKE_CASE , max_length=SCREAMING_SNAKE_CASE )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
a__ : Dict =datasets.map(
SCREAMING_SNAKE_CASE , batched=SCREAMING_SNAKE_CASE , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
a__ : Dict =tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(SCREAMING_SNAKE_CASE : str ):
# On TPU it's best to pad everything to the same length or training will be very slow.
a__ : Optional[Any] =128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
a__ : str =16
elif accelerator.mixed_precision != "no":
a__ : Union[str, Any] =8
else:
a__ : List[str] =None
return tokenizer.pad(
SCREAMING_SNAKE_CASE , padding="longest" , max_length=SCREAMING_SNAKE_CASE , pad_to_multiple_of=SCREAMING_SNAKE_CASE , return_tensors="pt" , )
# Instantiate dataloaders.
a__ : Any =DataLoader(
tokenized_datasets["train"] , shuffle=SCREAMING_SNAKE_CASE , collate_fn=SCREAMING_SNAKE_CASE , batch_size=SCREAMING_SNAKE_CASE )
a__ : int =DataLoader(
tokenized_datasets["validation"] , shuffle=SCREAMING_SNAKE_CASE , collate_fn=SCREAMING_SNAKE_CASE , batch_size=SCREAMING_SNAKE_CASE )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
UpperCAmelCase : str = mocked_dataloaders # noqa: F811
def _A ( SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" , SCREAMING_SNAKE_CASE ) == "1":
a__ : Tuple =2
# Initialize accelerator
a__ : int =Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
a__ : Optional[int] =config["lr"]
a__ : Union[str, Any] =int(config["num_epochs"] )
a__ : Any =int(config["seed"] )
a__ : Dict =int(config["batch_size"] )
a__ : int =evaluate.load("glue" , "mrpc" )
# If the batch size is too big we use gradient accumulation
a__ : int =1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
a__ : Dict =batch_size // MAX_GPU_BATCH_SIZE
a__ : Tuple =MAX_GPU_BATCH_SIZE
set_seed(SCREAMING_SNAKE_CASE )
a__ , a__ : Optional[int] =get_dataloaders(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
a__ : List[str] =AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=SCREAMING_SNAKE_CASE )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
a__ : List[str] =model.to(accelerator.device )
# Instantiate optimizer
a__ : List[Any] =AdamW(params=model.parameters() , lr=SCREAMING_SNAKE_CASE )
# Instantiate scheduler
a__ : Optional[int] =get_linear_schedule_with_warmup(
optimizer=SCREAMING_SNAKE_CASE , num_warmup_steps=100 , num_training_steps=(len(SCREAMING_SNAKE_CASE ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
a__ , a__ , a__ , a__ , a__ : Optional[int] =accelerator.prepare(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Now we train the model
for epoch in range(SCREAMING_SNAKE_CASE ):
model.train()
for step, batch in enumerate(SCREAMING_SNAKE_CASE ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
a__ : Dict =model(**SCREAMING_SNAKE_CASE )
a__ : List[Any] =outputs.loss
a__ : List[str] =loss / gradient_accumulation_steps
accelerator.backward(SCREAMING_SNAKE_CASE )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
a__ : Optional[Any] =0
for step, batch in enumerate(SCREAMING_SNAKE_CASE ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
a__ : Any =model(**SCREAMING_SNAKE_CASE )
a__ : str =outputs.logits.argmax(dim=-1 )
a__ , a__ : List[str] =accelerator.gather((predictions, batch["labels"]) )
# New Code #
# First we check if it's a distributed system
if accelerator.use_distributed:
# Then see if we're on the last batch of our eval dataloader
if step == len(SCREAMING_SNAKE_CASE ) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
a__ : Optional[Any] =predictions[: len(eval_dataloader.dataset ) - samples_seen]
a__ : Dict =references[: len(eval_dataloader.dataset ) - samples_seen]
else:
# Otherwise we add the number of samples seen
samples_seen += references.shape[0]
# All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
# accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=SCREAMING_SNAKE_CASE , references=SCREAMING_SNAKE_CASE , )
a__ : Tuple =metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}:''' , SCREAMING_SNAKE_CASE )
def _A ( ):
"""simple docstring"""
a__ : List[str] =argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=SCREAMING_SNAKE_CASE , default=SCREAMING_SNAKE_CASE , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
a__ : str =parser.parse_args()
a__ : Optional[int] ={"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 95 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {
"configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
"processing_vision_text_dual_encoder": ["VisionTextDualEncoderProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = ["VisionTextDualEncoderModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = ["FlaxVisionTextDualEncoderModel"]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = ["TFVisionTextDualEncoderModel"]
if TYPE_CHECKING:
from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
from .processing_vision_text_dual_encoder import VisionTextDualEncoderProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_text_dual_encoder import TFVisionTextDualEncoderModel
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 3 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _lowerCAmelCase ( A__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = KandinskyVaaControlnetImgaImgPipeline
snake_case_ = ["image_embeds", "negative_image_embeds", "image", "hint"]
snake_case_ = ["image_embeds", "negative_image_embeds", "image", "hint"]
snake_case_ = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
snake_case_ = False
@property
def lowerCAmelCase ( self : Dict )-> str:
return 32
@property
def lowerCAmelCase ( self : int )-> List[str]:
return 32
@property
def lowerCAmelCase ( self : List[Any] )-> str:
return self.time_input_dim
@property
def lowerCAmelCase ( self : Optional[Any] )-> Any:
return self.time_input_dim * 4
@property
def lowerCAmelCase ( self : str )-> Union[str, Any]:
return 1_00
@property
def lowerCAmelCase ( self : Tuple )-> Optional[Any]:
torch.manual_seed(0 )
snake_case = {
"""in_channels""": 8,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """image_hint""",
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""encoder_hid_dim""": self.text_embedder_hidden_size,
"""encoder_hid_dim_type""": """image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
snake_case = UNetaDConditionModel(**__snake_case )
return model
@property
def lowerCAmelCase ( self : List[Any] )-> str:
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def lowerCAmelCase ( self : str )-> List[str]:
torch.manual_seed(0 )
snake_case = VQModel(**self.dummy_movq_kwargs )
return model
def lowerCAmelCase ( self : int )-> Dict:
snake_case = self.dummy_unet
snake_case = self.dummy_movq
snake_case = {
"""num_train_timesteps""": 10_00,
"""beta_schedule""": """linear""",
"""beta_start""": 0.0_00_85,
"""beta_end""": 0.0_12,
"""clip_sample""": False,
"""set_alpha_to_one""": False,
"""steps_offset""": 0,
"""prediction_type""": """epsilon""",
"""thresholding""": False,
}
snake_case = DDIMScheduler(**__snake_case )
snake_case = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def lowerCAmelCase ( self : Union[str, Any] , __snake_case : str , __snake_case : Tuple=0 )-> List[Any]:
snake_case = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__snake_case ) ).to(__snake_case )
snake_case = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__snake_case )
# create init_image
snake_case = floats_tensor((1, 3, 64, 64) , rng=random.Random(__snake_case ) ).to(__snake_case )
snake_case = image.cpu().permute(0 , 2 , 3 , 1 )[0]
snake_case = Image.fromarray(np.uinta(__snake_case ) ).convert("""RGB""" ).resize((2_56, 2_56) )
# create hint
snake_case = floats_tensor((1, 3, 64, 64) , rng=random.Random(__snake_case ) ).to(__snake_case )
if str(__snake_case ).startswith("""mps""" ):
snake_case = torch.manual_seed(__snake_case )
else:
snake_case = torch.Generator(device=__snake_case ).manual_seed(__snake_case )
snake_case = {
"""image""": init_image,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""hint""": hint,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 10,
"""guidance_scale""": 7.0,
"""strength""": 0.2,
"""output_type""": """np""",
}
return inputs
def lowerCAmelCase ( self : Dict )-> Optional[int]:
snake_case = """cpu"""
snake_case = self.get_dummy_components()
snake_case = self.pipeline_class(**__snake_case )
snake_case = pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
snake_case = pipe(**self.get_dummy_inputs(__snake_case ) )
snake_case = output.images
snake_case = pipe(
**self.get_dummy_inputs(__snake_case ) , return_dict=__snake_case , )[0]
snake_case = image[0, -3:, -3:, -1]
snake_case = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case = np.array(
[0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase ( self : List[str] )-> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase ( self : List[Any] )-> Optional[int]:
snake_case = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy""" )
snake_case = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
snake_case = init_image.resize((5_12, 5_12) )
snake_case = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/hint_image_cat.png""" )
snake_case = torch.from_numpy(np.array(__snake_case ) ).float() / 2_55.0
snake_case = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
snake_case = """A robot, 4k photo"""
snake_case = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(__snake_case )
snake_case = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-controlnet-depth""" , torch_dtype=torch.floataa )
snake_case = pipeline.to(__snake_case )
pipeline.set_progress_bar_config(disable=__snake_case )
snake_case = torch.Generator(device="""cpu""" ).manual_seed(0 )
snake_case , snake_case = pipe_prior(
__snake_case , image=__snake_case , strength=0.85 , generator=__snake_case , negative_prompt="""""" , ).to_tuple()
snake_case = pipeline(
image=__snake_case , image_embeds=__snake_case , negative_image_embeds=__snake_case , hint=__snake_case , generator=__snake_case , num_inference_steps=1_00 , height=5_12 , width=5_12 , strength=0.5 , output_type="""np""" , )
snake_case = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert_mean_pixel_difference(__snake_case , __snake_case )
| 3 | 1 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class UpperCamelCase :
def __init__( self : List[Any] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[str]=99 , UpperCAmelCase__ : Any=13 , UpperCAmelCase__ : Tuple=7 , UpperCAmelCase__ : Union[str, Any]=9 , UpperCAmelCase__ : List[str]=True , UpperCAmelCase__ : int=True , UpperCAmelCase__ : Any=False , UpperCAmelCase__ : Dict=32 , UpperCAmelCase__ : List[Any]=5 , UpperCAmelCase__ : Any=4 , UpperCAmelCase__ : Optional[int]=37 , UpperCAmelCase__ : str=8 , UpperCAmelCase__ : Tuple=0.1 , UpperCAmelCase__ : List[str]=0.0_0_2 , UpperCAmelCase__ : Tuple=1 , UpperCAmelCase__ : Union[str, Any]=0 , UpperCAmelCase__ : List[str]=0 , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : List[Any]=None , ) -> List[str]:
_a : Optional[Any] = parent
_a : Tuple = batch_size
_a : Dict = encoder_seq_length
_a : Tuple = decoder_seq_length
# For common tests
_a : Dict = self.decoder_seq_length
_a : Optional[int] = is_training
_a : Dict = use_attention_mask
_a : Dict = use_labels
_a : Tuple = vocab_size
_a : Union[str, Any] = hidden_size
_a : Optional[int] = num_hidden_layers
_a : List[str] = num_attention_heads
_a : Any = d_ff
_a : str = relative_attention_num_buckets
_a : Dict = dropout_rate
_a : Union[str, Any] = initializer_factor
_a : int = eos_token_id
_a : Tuple = pad_token_id
_a : Tuple = decoder_start_token_id
_a : Any = None
_a : Optional[int] = decoder_layers
def _lowercase ( self : List[Any] ) -> str:
return TaConfig.from_pretrained("""google/umt5-base""" )
def _lowercase ( self : Any , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Any , UpperCAmelCase__ : Union[str, Any]=None , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : Union[str, Any]=None , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Union[str, Any]=None , ) -> int:
if attention_mask is None:
_a : Any = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
_a : str = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
_a : Any = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=UpperCAmelCase__ )
if decoder_head_mask is None:
_a : Tuple = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=UpperCAmelCase__ )
if cross_attn_head_mask is None:
_a : List[Any] = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=UpperCAmelCase__ )
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 _lowercase ( self : Optional[int] ) -> List[Any]:
_a : int = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
_a : Optional[int] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
_a : Optional[Any] = input_ids.clamp(self.pad_token_id + 1 )
_a : List[str] = decoder_input_ids.clamp(self.pad_token_id + 1 )
_a : Union[str, Any] = self.get_config()
_a : Optional[int] = config.num_attention_heads
_a : int = self.prepare_inputs_dict(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
return config, input_dict
def _lowercase ( self : List[Any] ) -> Optional[int]:
_a , _a : Optional[Any] = self.prepare_config_and_inputs()
return config, inputs_dict
def _lowercase ( self : Dict ) -> Any:
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 _lowercase ( self : List[Any] ) -> Any:
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 _lowercase ( self : Dict , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Any , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : str , ) -> str:
_a : Any = UMTaModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_a : List[Any] = model(
input_ids=UpperCAmelCase__ , decoder_input_ids=UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , decoder_attention_mask=UpperCAmelCase__ , )
_a : List[Any] = model(input_ids=UpperCAmelCase__ , decoder_input_ids=UpperCAmelCase__ )
_a : str = result.last_hidden_state
_a : str = result.past_key_values
_a : str = 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(UpperCAmelCase__ ) , 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 _lowercase ( self : List[Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : Any , UpperCAmelCase__ : str , UpperCAmelCase__ : str , ) -> Optional[Any]:
_a : Union[str, Any] = UMTaModel(config=UpperCAmelCase__ ).get_decoder().to(UpperCAmelCase__ ).eval()
# first forward pass
_a : Any = model(UpperCAmelCase__ , use_cache=UpperCAmelCase__ )
_a : Optional[int] = model(UpperCAmelCase__ )
_a : Dict = model(UpperCAmelCase__ , use_cache=UpperCAmelCase__ )
self.parent.assertTrue(len(UpperCAmelCase__ ) == len(UpperCAmelCase__ ) )
self.parent.assertTrue(len(UpperCAmelCase__ ) == len(UpperCAmelCase__ ) + 1 )
_a , _a : Any = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_a : List[str] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
_a : Tuple = torch.cat([input_ids, next_tokens] , dim=-1 )
_a : int = model(UpperCAmelCase__ )["""last_hidden_state"""]
_a : Union[str, Any] = model(UpperCAmelCase__ , past_key_values=UpperCAmelCase__ )["""last_hidden_state"""]
# select random slice
_a : str = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_a : Union[str, Any] = output_from_no_past[:, -1, random_slice_idx].detach()
_a : Dict = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(UpperCAmelCase__ , UpperCAmelCase__ , atol=1E-3 ) )
def _lowercase ( self : List[str] , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[Any] , ) -> Union[str, Any]:
_a : Any = UMTaModel(config=UpperCAmelCase__ ).to(UpperCAmelCase__ ).half().eval()
_a : List[str] = model(**UpperCAmelCase__ )["""last_hidden_state"""]
self.parent.assertFalse(torch.isnan(UpperCAmelCase__ ).any().item() )
@require_torch
class UpperCamelCase ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
UpperCamelCase : Dict = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
UpperCamelCase : int = (UMTaForConditionalGeneration,) if is_torch_available() else ()
UpperCamelCase : Tuple = (
{
'''conversational''': UMTaForConditionalGeneration,
'''feature-extraction''': UMTaModel,
'''summarization''': UMTaForConditionalGeneration,
'''text2text-generation''': UMTaForConditionalGeneration,
'''translation''': UMTaForConditionalGeneration,
'''question-answering''': UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
UpperCamelCase : str = True
UpperCamelCase : int = False
UpperCamelCase : str = False
UpperCamelCase : Dict = True
UpperCamelCase : int = True
# The small UMT5 model needs higher percentages for CPU/MP tests
UpperCamelCase : Dict = [0.8, 0.9]
def _lowercase ( self : str ) -> Tuple:
_a : List[Any] = UMTaModelTester(self )
@unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
def _lowercase ( self : str ) -> Dict:
_a : List[str] = self.model_tester.prepare_config_and_inputs()
_a : List[str] = UMTaModel(config_and_inputs[0] ).to(UpperCAmelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
UpperCAmelCase__ , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f"""{tmpdirname}/t5_test.onnx""" , export_params=UpperCAmelCase__ , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def _lowercase ( self : Optional[int] ) -> List[str]:
_a : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*UpperCAmelCase__ )
def _lowercase ( self : Optional[int] ) -> str:
_a : List[str] = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
_a : Tuple = self.model_tester.prepare_config_and_inputs()
_a : List[Any] = config_and_inputs[0]
_a : str = UMTaForConditionalGeneration(UpperCAmelCase__ ).eval()
model.to(UpperCAmelCase__ )
_a : Dict = {
"""head_mask""": torch.zeros(config.num_layers , config.num_heads , device=UpperCAmelCase__ ),
"""decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=UpperCAmelCase__ ),
"""cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=UpperCAmelCase__ ),
}
for attn_name, (name, mask) in zip(UpperCAmelCase__ , head_masking.items() ):
_a : Tuple = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
_a : Tuple = torch.ones(
config.num_decoder_layers , config.num_heads , device=UpperCAmelCase__ )
_a : Union[str, Any] = model.generate(
config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=UpperCAmelCase__ , return_dict_in_generate=UpperCAmelCase__ , **UpperCAmelCase__ , )
# We check the state of decoder_attentions and cross_attentions just from the last step
_a : Any = 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 _lowercase ( self : Any ) -> int:
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCamelCase ( unittest.TestCase ):
@slow
@unittest.skip(
"""Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" )
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
_a : Dict = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=UpperCAmelCase__ ).to(UpperCAmelCase__ )
_a : Optional[Any] = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=UpperCAmelCase__ , legacy=UpperCAmelCase__ )
_a : Union[str, Any] = [
"""Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
"""No se como puedo <extra_id_0>.""",
"""This is the reason why we <extra_id_0> them.""",
"""The <extra_id_0> walks in <extra_id_1>, seats""",
"""A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
]
_a : Optional[int] = tokenizer(UpperCAmelCase__ , return_tensors="""pt""" , padding=UpperCAmelCase__ ).input_ids
# fmt: off
_a : Tuple = 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(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Optional[Any] = model.generate(input_ids.to(UpperCAmelCase__ ) )
_a : int = [
"""<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 : Tuple = tokenizer.batch_decode(UpperCAmelCase__ )
self.assertEqual(UpperCAmelCase__ , UpperCAmelCase__ )
| 294 |
"""simple docstring"""
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ):
'''simple docstring'''
_a : Optional[Any] = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError("""All input parameters must be positive""" )
if any(p > 1 for p in parameters[1:4] ):
raise ValueError("""Relative densities cannot be greater than one""" )
else:
_a : Tuple = 1 - (matter_density + radiation_density + dark_energy)
_a : int = (
radiation_density * (redshift + 1) ** 4
+ matter_density * (redshift + 1) ** 3
+ curvature * (redshift + 1) ** 2
+ dark_energy
)
_a : List[str] = hubble_constant * e_a ** (1 / 2)
return hubble
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo LCDM approximation
_snake_case = 0.3
print(
hubble_parameter(
hubble_constant=68.3,
radiation_density=1e-4,
matter_density=matter_density,
dark_energy=1 - matter_density,
redshift=0,
)
)
| 294 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : List[Any] = {
'''configuration_blenderbot_small''': [
'''BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlenderbotSmallConfig''',
'''BlenderbotSmallOnnxConfig''',
],
'''tokenization_blenderbot_small''': ['''BlenderbotSmallTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Union[str, Any] = ['''BlenderbotSmallTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Optional[int] = [
'''BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlenderbotSmallForCausalLM''',
'''BlenderbotSmallForConditionalGeneration''',
'''BlenderbotSmallModel''',
'''BlenderbotSmallPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Optional[int] = [
'''TFBlenderbotSmallForConditionalGeneration''',
'''TFBlenderbotSmallModel''',
'''TFBlenderbotSmallPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : int = [
'''FlaxBlenderbotSmallForConditionalGeneration''',
'''FlaxBlenderbotSmallModel''',
'''FlaxBlenderbotSmallPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotSmallConfig,
BlenderbotSmallOnnxConfig,
)
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotSmallForCausalLM,
BlenderbotSmallForConditionalGeneration,
BlenderbotSmallModel,
BlenderbotSmallPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot_small import (
TFBlenderbotSmallForConditionalGeneration,
TFBlenderbotSmallModel,
TFBlenderbotSmallPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallPreTrainedModel,
)
else:
import sys
a__ : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 19 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_mbart import MBartTokenizer
else:
a__ : List[str] = None
a__ : Any = logging.get_logger(__name__)
a__ : Optional[int] = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
a__ : Dict = {
'''vocab_file''': {
'''facebook/mbart-large-en-ro''': (
'''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
),
'''facebook/mbart-large-cc25''': (
'''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
),
},
'''tokenizer_file''': {
'''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
'''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
},
}
a__ : str = {
'''facebook/mbart-large-en-ro''': 1_024,
'''facebook/mbart-large-cc25''': 1_024,
}
# fmt: off
a__ : List[str] = ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']
class a_ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE : Any = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE : Any = ['input_ids', 'attention_mask']
__SCREAMING_SNAKE_CASE : Tuple = MBartTokenizer
__SCREAMING_SNAKE_CASE : List[int] = []
__SCREAMING_SNAKE_CASE : List[int] = []
def __init__( self , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase="<s>" , _lowerCamelCase="</s>" , _lowerCamelCase="</s>" , _lowerCamelCase="<s>" , _lowerCamelCase="<unk>" , _lowerCamelCase="<pad>" , _lowerCamelCase="<mask>" , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase=None , **_lowerCamelCase , ) ->List[Any]:
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE : List[str] = AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase , _lowerCamelCase ) else mask_token
super().__init__(
vocab_file=_lowerCamelCase , tokenizer_file=_lowerCamelCase , bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , sep_token=_lowerCamelCase , cls_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , mask_token=_lowerCamelCase , src_lang=_lowerCamelCase , tgt_lang=_lowerCamelCase , additional_special_tokens=_lowerCamelCase , **_lowerCamelCase , )
SCREAMING_SNAKE_CASE : Any = vocab_file
SCREAMING_SNAKE_CASE : List[Any] = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE : Any = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens} )
SCREAMING_SNAKE_CASE : int = {
lang_code: self.convert_tokens_to_ids(_lowerCamelCase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
SCREAMING_SNAKE_CASE : List[str] = src_lang if src_lang is not None else '''en_XX'''
SCREAMING_SNAKE_CASE : int = self.convert_tokens_to_ids(self._src_lang )
SCREAMING_SNAKE_CASE : List[Any] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def __lowerCAmelCase ( self ) ->str:
return self._src_lang
@src_lang.setter
def __lowerCAmelCase ( self , _lowerCamelCase ) ->None:
SCREAMING_SNAKE_CASE : Optional[int] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase = None ) ->List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase = None ) ->List[int]:
SCREAMING_SNAKE_CASE : str = [self.sep_token_id]
SCREAMING_SNAKE_CASE : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ) ->Optional[Any]:
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = src_lang
SCREAMING_SNAKE_CASE : List[str] = self(_lowerCamelCase , add_special_tokens=_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase )
SCREAMING_SNAKE_CASE : int = self.convert_tokens_to_ids(_lowerCamelCase )
SCREAMING_SNAKE_CASE : List[Any] = tgt_lang_id
return inputs
def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase = "en_XX" , _lowerCamelCase = None , _lowerCamelCase = "ro_RO" , **_lowerCamelCase , ) ->BatchEncoding:
SCREAMING_SNAKE_CASE : List[str] = src_lang
SCREAMING_SNAKE_CASE : List[str] = tgt_lang
return super().prepare_seqaseq_batch(_lowerCamelCase , _lowerCamelCase , **_lowerCamelCase )
def __lowerCAmelCase ( self ) ->Dict:
return self.set_src_lang_special_tokens(self.src_lang )
def __lowerCAmelCase ( self ) ->List[Any]:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def __lowerCAmelCase ( self , _lowerCamelCase ) ->None:
SCREAMING_SNAKE_CASE : Optional[Any] = self.convert_tokens_to_ids(_lowerCamelCase )
SCREAMING_SNAKE_CASE : Optional[int] = []
SCREAMING_SNAKE_CASE : List[Any] = [self.eos_token_id, self.cur_lang_code]
SCREAMING_SNAKE_CASE : Tuple = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE : Tuple = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE : Dict = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def __lowerCAmelCase ( self , _lowerCamelCase ) ->None:
SCREAMING_SNAKE_CASE : str = self.convert_tokens_to_ids(_lowerCamelCase )
SCREAMING_SNAKE_CASE : Optional[int] = []
SCREAMING_SNAKE_CASE : Optional[Any] = [self.eos_token_id, self.cur_lang_code]
SCREAMING_SNAKE_CASE : Tuple = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE : Any = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE : Dict = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase = None ) ->Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(_lowerCamelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
return
SCREAMING_SNAKE_CASE : List[Any] = os.path.join(
_lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ):
copyfile(self.vocab_file , _lowerCamelCase )
return (out_vocab_file,)
| 19 | 1 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment
def A ( ) -> Optional[Any]:
lowerCamelCase : str = torch.nn.Linear(2 ,4 )
lowerCamelCase : Optional[int] = torch.optim.AdamW(model.parameters() ,lr=1.0 )
lowerCamelCase : Optional[int] = torch.optim.lr_scheduler.OneCycleLR(_SCREAMING_SNAKE_CASE ,max_lr=0.01 ,steps_per_epoch=2 ,epochs=1 )
lowerCamelCase : Optional[int] = DataLoader(TensorDataset(torch.tensor([1, 2, 3] ) ) )
lowerCamelCase : Union[str, Any] = DataLoader(TensorDataset(torch.tensor([4, 5, 6] ) ) )
return model, optimizer, scheduler, train_dl, valid_dl
def A ( _SCREAMING_SNAKE_CASE ) -> Optional[int]:
return (model.weight.abs().sum() + model.bias.abs().sum()).item()
def A ( _SCREAMING_SNAKE_CASE ) -> List[Any]:
lowerCamelCase : Optional[Any] = torch.nn.Linear(*tuple(model.weight.T.shape ) ).state_dict()
model.load_state_dict(_SCREAMING_SNAKE_CASE )
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
@require_cuda
def _lowercase ( self ) -> Any:
lowerCamelCase : Optional[Any] = Accelerator()
assert PartialState._shared_state["_cpu"] is False
assert PartialState._shared_state["device"].type == "cuda"
with self.assertRaises(UpperCamelCase__ ):
lowerCamelCase : List[str] = Accelerator(cpu=UpperCamelCase__ )
def _lowercase ( self ) -> int:
lowerCamelCase : Optional[Any] = Accelerator()
lowerCamelCase : Optional[Any] = GradientState()
assert state.num_steps == 1
lowerCamelCase : Any = 4
assert state.num_steps == 4
assert state.sync_gradients is True
lowerCamelCase : Dict = False
assert state.sync_gradients is False
GradientState._reset_state()
def _lowercase ( self ) -> Dict:
lowerCamelCase : Tuple = Accelerator()
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : Optional[int] = create_components()
(
(
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) ,
) : List[Any] = accelerator.prepare(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
self.assertTrue(prepared_model in accelerator._models )
self.assertTrue(prepared_optimizer in accelerator._optimizers )
self.assertTrue(prepared_scheduler in accelerator._schedulers )
self.assertTrue(prepared_train_dl in accelerator._dataloaders )
self.assertTrue(prepared_valid_dl in accelerator._dataloaders )
def _lowercase ( self ) -> Union[str, Any]:
lowerCamelCase : str = Accelerator()
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : List[str] = create_components()
accelerator.prepare(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
accelerator.free_memory()
self.assertTrue(len(accelerator._models ) == 0 )
self.assertTrue(len(accelerator._optimizers ) == 0 )
self.assertTrue(len(accelerator._schedulers ) == 0 )
self.assertTrue(len(accelerator._dataloaders ) == 0 )
def _lowercase ( self ) -> int:
PartialState._reset_state()
# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
def noop(*UpperCamelCase__ , **UpperCamelCase__ ):
pass
with patch("torch.cuda.set_device" , UpperCamelCase__ ), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64" ):
lowerCamelCase : List[Any] = Accelerator()
self.assertEqual(str(accelerator.state.device ) , "cuda:64" )
def _lowercase ( self ) -> Dict:
lowerCamelCase : Union[str, Any] = Accelerator()
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : Union[str, Any] = create_components()
accelerator.prepare(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : List[str] = get_signature(UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(UpperCamelCase__ )
# make sure random weights don't match
load_random_weights(UpperCamelCase__ )
self.assertTrue(abs(model_signature - get_signature(UpperCamelCase__ ) ) > 1e-3 )
# make sure loaded weights match
accelerator.load_state(UpperCamelCase__ )
self.assertTrue(abs(model_signature - get_signature(UpperCamelCase__ ) ) < 1e-3 )
def _lowercase ( self ) -> List[str]:
lowerCamelCase : List[str] = Accelerator()
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : List[str] = create_components()
accelerator.prepare(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : Dict = get_signature(UpperCamelCase__ )
# saving hook
def save_config(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
lowerCamelCase : Union[str, Any] = {"class_name": models[0].__class__.__name__}
with open(os.path.join(UpperCamelCase__ , "data.json" ) , "w" ) as f:
json.dump(UpperCamelCase__ , UpperCamelCase__ )
# loading hook
def load_config(UpperCamelCase__ , UpperCamelCase__ ):
with open(os.path.join(UpperCamelCase__ , "data.json" ) , "r" ) as f:
lowerCamelCase : str = json.load(UpperCamelCase__ )
lowerCamelCase : int = config["class_name"]
lowerCamelCase : Any = accelerator.register_save_state_pre_hook(UpperCamelCase__ )
lowerCamelCase : Dict = accelerator.register_load_state_pre_hook(UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(UpperCamelCase__ )
# make sure random weights don't match with hooks
load_random_weights(UpperCamelCase__ )
self.assertTrue(abs(model_signature - get_signature(UpperCamelCase__ ) ) > 1e-3 )
# random class name to verify correct one is loaded
lowerCamelCase : Optional[int] = "random"
# make sure loaded weights match with hooks
accelerator.load_state(UpperCamelCase__ )
self.assertTrue(abs(model_signature - get_signature(UpperCamelCase__ ) ) < 1e-3 )
# mode.class_name is loaded from config
self.assertTrue(model.class_name == model.__class__.__name__ )
# remove hooks
save_hook.remove()
load_hook.remove()
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(UpperCamelCase__ )
# make sure random weights don't match with hooks removed
load_random_weights(UpperCamelCase__ )
self.assertTrue(abs(model_signature - get_signature(UpperCamelCase__ ) ) > 1e-3 )
# random class name to verify correct one is loaded
lowerCamelCase : List[Any] = "random"
# make sure loaded weights match with hooks removed
accelerator.load_state(UpperCamelCase__ )
self.assertTrue(abs(model_signature - get_signature(UpperCamelCase__ ) ) < 1e-3 )
# mode.class_name is NOT loaded from config
self.assertTrue(model.class_name != model.__class__.__name__ )
def _lowercase ( self ) -> int:
lowerCamelCase : Optional[Any] = Accelerator()
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : Dict = create_components()
lowerCamelCase : Optional[int] = None
# This should work
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : Union[str, Any] = accelerator.prepare(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
self.assertTrue(dummy_obj is None )
def _lowercase ( self ) -> List[Any]:
lowerCamelCase : List[str] = Accelerator()
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : Dict = create_components()
lowerCamelCase : Optional[int] = [1, 2, 3]
# This should work
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : Any = accelerator.prepare(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
self.assertEqual(
getattr(UpperCamelCase__ , "_is_accelerate_prepared" , UpperCamelCase__ ) , UpperCamelCase__ , "Dummy object should have `_is_accelerate_prepared` set to `True`" , )
self.assertEqual(
getattr(UpperCamelCase__ , "_is_accelerate_prepared" , UpperCamelCase__ ) , UpperCamelCase__ , "Model is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(UpperCamelCase__ , "_is_accelerate_prepared" , UpperCamelCase__ ) , UpperCamelCase__ , "Optimizer is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(UpperCamelCase__ , "_is_accelerate_prepared" , UpperCamelCase__ ) , UpperCamelCase__ , "Scheduler is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(UpperCamelCase__ , "_is_accelerate_prepared" , UpperCamelCase__ ) , UpperCamelCase__ , "Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(UpperCamelCase__ , "_is_accelerate_prepared" , UpperCamelCase__ ) , UpperCamelCase__ , "Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`" , )
@slow
@require_bnb
def _lowercase ( self ) -> int:
from transformers import AutoModelForCausalLM
lowerCamelCase : Optional[int] = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , load_in_abit=UpperCamelCase__ , device_map={"": 0} , )
lowerCamelCase : Any = Accelerator()
# This should work
lowerCamelCase : Tuple = accelerator.prepare(UpperCamelCase__ )
@slow
@require_bnb
def _lowercase ( self ) -> str:
from transformers import AutoModelForCausalLM
lowerCamelCase : Tuple = Accelerator()
with init_empty_weights():
lowerCamelCase : Tuple = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , )
model.tie_weights()
lowerCamelCase : List[Any] = infer_auto_device_map(UpperCamelCase__ )
lowerCamelCase : Optional[int] = "cpu"
lowerCamelCase : Tuple = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , device_map=UpperCamelCase__ , load_in_abit=UpperCamelCase__ , llm_inta_enable_fpaa_cpu_offload=UpperCamelCase__ )
# This should not work and get value error
with self.assertRaises(UpperCamelCase__ ):
lowerCamelCase : List[str] = accelerator.prepare(UpperCamelCase__ )
@slow
@require_bnb
@require_multi_gpu
def _lowercase ( self ) -> int:
from transformers import AutoModelForCausalLM
lowerCamelCase : Union[str, Any] = {"distributed_type": DistributedType.MULTI_GPU}
with init_empty_weights():
lowerCamelCase : str = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , )
model.tie_weights()
lowerCamelCase : Union[str, Any] = infer_auto_device_map(UpperCamelCase__ )
lowerCamelCase : Dict = 1
lowerCamelCase : List[Any] = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , load_in_abit=UpperCamelCase__ , device_map=UpperCamelCase__ , )
lowerCamelCase : Optional[Any] = Accelerator()
# This should not work and get value error
with self.assertRaises(UpperCamelCase__ ):
lowerCamelCase : Union[str, Any] = accelerator.prepare(UpperCamelCase__ )
PartialState._reset_state()
@slow
@require_bnb
@require_multi_gpu
def _lowercase ( self ) -> str:
from transformers import AutoModelForCausalLM
with init_empty_weights():
lowerCamelCase : Union[str, Any] = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , )
lowerCamelCase : int = infer_auto_device_map(UpperCamelCase__ )
lowerCamelCase : Any = 1
lowerCamelCase : Tuple = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , load_in_abit=UpperCamelCase__ , device_map=UpperCamelCase__ , )
lowerCamelCase : str = Accelerator()
# This should work
lowerCamelCase : Union[str, Any] = accelerator.prepare(UpperCamelCase__ )
@require_cuda
def _lowercase ( self ) -> Dict:
lowerCamelCase : str = torch.nn.Linear(10 , 10 )
lowerCamelCase : int = torch.optim.SGD(model.parameters() , lr=0.01 )
lowerCamelCase : int = Accelerator(cpu=UpperCamelCase__ )
lowerCamelCase : Union[str, Any] = accelerator.prepare(UpperCamelCase__ )
| 48 |
"""simple docstring"""
import os
def UpperCAmelCase__ ( ):
"""simple docstring"""
A_ : Any = os.path.join(os.path.dirname(_UpperCAmelCase ) , 'num.txt' )
with open(_UpperCAmelCase ) as file_hand:
return str(sum(int(_UpperCAmelCase ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution()) | 286 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase : int = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Union[str, Any] = ['VisionEncoderDecoderModel']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : List[Any] = ['TFVisionEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : List[Any] = ['FlaxVisionEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
UpperCAmelCase : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 331 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase : int = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def a__ ( ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = os.path.dirname(os.path.realpath(a__ ) )
__SCREAMING_SNAKE_CASE = os.path.join(a__ , """words.txt""" )
__SCREAMING_SNAKE_CASE = """"""
with open(a__ ) as f:
__SCREAMING_SNAKE_CASE = f.readline()
__SCREAMING_SNAKE_CASE = [word.strip("""\"""" ) for word in words.strip("""\r\n""" ).split(""",""" )]
__SCREAMING_SNAKE_CASE = [
word
for word in [sum(ord(a__ ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(a__ )
if __name__ == "__main__":
print(solution())
| 331 | 1 |
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