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 |
|---|---|---|---|---|
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 lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase=0.9_99 , _UpperCAmelCase="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(_UpperCAmelCase):
return math.cos((t + 0.0_08) / 1.0_08 * 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}''')
SCREAMING_SNAKE_CASE = []
for i in range(lowercase__):
SCREAMING_SNAKE_CASE = i / num_diffusion_timesteps
SCREAMING_SNAKE_CASE = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(lowercase__) / alpha_bar_fn(lowercase__) , lowercase__))
return torch.tensor(lowercase__ , dtype=torch.floataa)
class _snake_case ( A__ , A__ ):
_lowercase : Optional[int] = [e.name for e in KarrasDiffusionSchedulers]
_lowercase : Optional[Any] = 2
@register_to_config
def __init__( self , a = 1000 , a = 0.0_00_85 , a = 0.0_12 , a = "linear" , a = None , a = "epsilon" , a = "linspace" , a = 0 , ) -> Tuple:
if trained_betas is not None:
SCREAMING_SNAKE_CASE = torch.tensor(__A , dtype=torch.floataa)
elif beta_schedule == "linear":
SCREAMING_SNAKE_CASE = torch.linspace(__A , __A , __A , dtype=torch.floataa)
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
SCREAMING_SNAKE_CASE = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , __A , dtype=torch.floataa) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
SCREAMING_SNAKE_CASE = betas_for_alpha_bar(__A)
else:
raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''')
SCREAMING_SNAKE_CASE = 1.0 - self.betas
SCREAMING_SNAKE_CASE = torch.cumprod(self.alphas , dim=0)
# set all values
self.set_timesteps(__A , __A , __A)
def SCREAMING_SNAKE_CASE__ ( self , a , a=None) -> List[str]:
if schedule_timesteps is None:
SCREAMING_SNAKE_CASE = self.timesteps
SCREAMING_SNAKE_CASE = (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:
SCREAMING_SNAKE_CASE = 1 if len(__A) > 1 else 0
else:
SCREAMING_SNAKE_CASE = timestep.cpu().item() if torch.is_tensor(__A) else timestep
SCREAMING_SNAKE_CASE = self._index_counter[timestep_int]
return indices[pos].item()
@property
def SCREAMING_SNAKE_CASE__ ( self) -> str:
# 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 SCREAMING_SNAKE_CASE__ ( self , a , a , ) -> torch.FloatTensor:
SCREAMING_SNAKE_CASE = self.index_for_timestep(__A)
if self.state_in_first_order:
SCREAMING_SNAKE_CASE = self.sigmas[step_index]
else:
SCREAMING_SNAKE_CASE = self.sigmas_interpol[step_index]
SCREAMING_SNAKE_CASE = sample / ((sigma**2 + 1) ** 0.5)
return sample
def SCREAMING_SNAKE_CASE__ ( self , a , a = None , a = None , ) -> Tuple:
SCREAMING_SNAKE_CASE = num_inference_steps
SCREAMING_SNAKE_CASE = 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":
SCREAMING_SNAKE_CASE = np.linspace(0 , num_train_timesteps - 1 , __A , dtype=__A)[::-1].copy()
elif self.config.timestep_spacing == "leading":
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE = (np.arange(0 , __A) * step_ratio).round()[::-1].copy().astype(__A)
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE = (np.arange(__A , 0 , -step_ratio)).round().copy().astype(__A)
timesteps -= 1
else:
raise ValueError(
f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''')
SCREAMING_SNAKE_CASE = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
SCREAMING_SNAKE_CASE = torch.from_numpy(np.log(__A)).to(__A)
SCREAMING_SNAKE_CASE = np.interp(__A , np.arange(0 , len(__A)) , __A)
SCREAMING_SNAKE_CASE = np.concatenate([sigmas, [0.0]]).astype(np.floataa)
SCREAMING_SNAKE_CASE = torch.from_numpy(__A).to(device=__A)
# interpolate sigmas
SCREAMING_SNAKE_CASE = sigmas.log().lerp(sigmas.roll(1).log() , 0.5).exp()
SCREAMING_SNAKE_CASE = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
SCREAMING_SNAKE_CASE = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])
if str(__A).startswith('mps'):
# mps does not support float64
SCREAMING_SNAKE_CASE = torch.from_numpy(__A).to(__A , dtype=torch.floataa)
else:
SCREAMING_SNAKE_CASE = torch.from_numpy(__A).to(__A)
# interpolate timesteps
SCREAMING_SNAKE_CASE = self.sigma_to_t(__A).to(__A , dtype=timesteps.dtype)
SCREAMING_SNAKE_CASE = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) , dim=-1).flatten()
SCREAMING_SNAKE_CASE = torch.cat([timesteps[:1], interleaved_timesteps])
SCREAMING_SNAKE_CASE = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
SCREAMING_SNAKE_CASE = defaultdict(__A)
def SCREAMING_SNAKE_CASE__ ( self , a) -> Union[str, Any]:
# get log sigma
SCREAMING_SNAKE_CASE = sigma.log()
# get distribution
SCREAMING_SNAKE_CASE = log_sigma - self.log_sigmas[:, None]
# get sigmas range
SCREAMING_SNAKE_CASE = dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
SCREAMING_SNAKE_CASE = low_idx + 1
SCREAMING_SNAKE_CASE = self.log_sigmas[low_idx]
SCREAMING_SNAKE_CASE = self.log_sigmas[high_idx]
# interpolate sigmas
SCREAMING_SNAKE_CASE = (low - log_sigma) / (low - high)
SCREAMING_SNAKE_CASE = w.clamp(0 , 1)
# transform interpolation to time range
SCREAMING_SNAKE_CASE = (1 - w) * low_idx + w * high_idx
SCREAMING_SNAKE_CASE = t.view(sigma.shape)
return t
@property
def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
return self.sample is None
def SCREAMING_SNAKE_CASE__ ( self , a , a , a , a = True , ) -> Union[SchedulerOutput, Tuple]:
SCREAMING_SNAKE_CASE = self.index_for_timestep(__A)
# advance index counter by 1
SCREAMING_SNAKE_CASE = timestep.cpu().item() if torch.is_tensor(__A) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
SCREAMING_SNAKE_CASE = self.sigmas[step_index]
SCREAMING_SNAKE_CASE = self.sigmas_interpol[step_index + 1]
SCREAMING_SNAKE_CASE = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
SCREAMING_SNAKE_CASE = self.sigmas[step_index - 1]
SCREAMING_SNAKE_CASE = self.sigmas_interpol[step_index]
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 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":
SCREAMING_SNAKE_CASE = sigma_hat if self.state_in_first_order else sigma_interpol
SCREAMING_SNAKE_CASE = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
SCREAMING_SNAKE_CASE = sigma_hat if self.state_in_first_order else sigma_interpol
SCREAMING_SNAKE_CASE = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError('prediction_type not implemented yet: sample')
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''')
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
SCREAMING_SNAKE_CASE = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
SCREAMING_SNAKE_CASE = sigma_interpol - sigma_hat
# store for 2nd order step
SCREAMING_SNAKE_CASE = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
SCREAMING_SNAKE_CASE = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
SCREAMING_SNAKE_CASE = sigma_next - sigma_hat
SCREAMING_SNAKE_CASE = self.sample
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__A)
def SCREAMING_SNAKE_CASE__ ( self , a , a , a , ) -> torch.FloatTensor:
# Make sure sigmas and timesteps have the same device and dtype as original_samples
SCREAMING_SNAKE_CASE = self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype)
if original_samples.device.type == "mps" and torch.is_floating_point(__A):
# mps does not support float64
SCREAMING_SNAKE_CASE = self.timesteps.to(original_samples.device , dtype=torch.floataa)
SCREAMING_SNAKE_CASE = timesteps.to(original_samples.device , dtype=torch.floataa)
else:
SCREAMING_SNAKE_CASE = self.timesteps.to(original_samples.device)
SCREAMING_SNAKE_CASE = timesteps.to(original_samples.device)
SCREAMING_SNAKE_CASE = [self.index_for_timestep(__A , __A) for t in timesteps]
SCREAMING_SNAKE_CASE = sigmas[step_indices].flatten()
while len(sigma.shape) < len(original_samples.shape):
SCREAMING_SNAKE_CASE = sigma.unsqueeze(-1)
SCREAMING_SNAKE_CASE = original_samples + noise * sigma
return noisy_samples
def __len__( self) -> List[str]:
return self.config.num_train_timesteps
| 137 |
"""simple docstring"""
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__UpperCAmelCase = 1.054571817e-34 # unit of ℏ : J * s
__UpperCAmelCase = 3e8 # unit of c : m * s^-1
def _snake_case ( lowercase__ : float , lowercase__ : float , lowercase__ : float ) -> dict[str, 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:
lowerCAmelCase_ :Union[str, Any] = (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
2_4_0 * (distance) ** 4
)
return {"force": force}
elif area == 0:
lowerCAmelCase_ :Optional[Any] = (2_4_0 * force * (distance) ** 4) / (
REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
)
return {"area": area}
elif distance == 0:
lowerCAmelCase_ :Any = (
(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_4_0 * force)
) ** (1 / 4)
return {"distance": distance}
raise ValueError("""One and only one argument must be 0""" )
# Run doctest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 | 0 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
from ..pytorch_utils import ConvaD
if is_accelerate_available():
from accelerate import init_empty_weights
from accelerate.utils import find_tied_parameters
UpperCAmelCase__ = logging.get_logger(__name__)
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ,lowercase=None ,lowercase=None ):
"""simple docstring"""
# Recurse if needed
if "." in tensor_name:
_UpperCAmelCase = tensor_name.split(""".""" )
for split in splits[:-1]:
_UpperCAmelCase = getattr(lowercase ,lowercase )
if new_module is None:
raise ValueError(f'''{module} has no attribute {split}.''' )
_UpperCAmelCase = new_module
_UpperCAmelCase = splits[-1]
if tensor_name not in module._parameters and tensor_name not in module._buffers:
raise ValueError(f'''{module} does not have a parameter or a buffer named {tensor_name}.''' )
_UpperCAmelCase = tensor_name in module._buffers
_UpperCAmelCase = getattr(lowercase ,lowercase )
if old_value.device == torch.device("""meta""" ) and device not in ["meta", torch.device("""meta""" )] and value is None:
raise ValueError(f'''{tensor_name} is on the meta device, we need a `value` to put in on {device}.''' )
_UpperCAmelCase = False
_UpperCAmelCase = False
if is_buffer or not is_bitsandbytes_available():
_UpperCAmelCase = False
_UpperCAmelCase = False
else:
_UpperCAmelCase = hasattr(bnb.nn ,"""Params4bit""" ) and isinstance(module._parameters[tensor_name] ,bnb.nn.Paramsabit )
_UpperCAmelCase = isinstance(module._parameters[tensor_name] ,bnb.nn.IntaParams )
if is_abit or is_abit:
_UpperCAmelCase = module._parameters[tensor_name]
if param.device.type != "cuda":
if value is None:
_UpperCAmelCase = old_value.to(lowercase )
elif isinstance(lowercase ,torch.Tensor ):
_UpperCAmelCase = value.to("""cpu""" )
if value.dtype == torch.inta:
_UpperCAmelCase = version.parse(importlib.metadata.version("""bitsandbytes""" ) ) > version.parse(
"""0.37.2""" )
if not is_abit_serializable:
raise ValueError(
"""Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. """
"""Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.""" )
else:
_UpperCAmelCase = torch.tensor(lowercase ,device="""cpu""" )
# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
# Since weights are saved in the correct "orientation", we skip transposing when loading.
if issubclass(module.source_cls ,lowercase ) and fpaa_statistics is None:
_UpperCAmelCase = new_value.T
_UpperCAmelCase = old_value.__dict__
if is_abit:
_UpperCAmelCase = bnb.nn.IntaParams(lowercase ,requires_grad=lowercase ,**lowercase ).to(lowercase )
elif is_abit:
_UpperCAmelCase = bnb.nn.Paramsabit(lowercase ,requires_grad=lowercase ,**lowercase ).to(lowercase )
_UpperCAmelCase = new_value
if fpaa_statistics is not None:
setattr(module.weight ,"""SCB""" ,fpaa_statistics.to(lowercase ) )
else:
if value is None:
_UpperCAmelCase = old_value.to(lowercase )
elif isinstance(lowercase ,torch.Tensor ):
_UpperCAmelCase = value.to(lowercase )
else:
_UpperCAmelCase = torch.tensor(lowercase ,device=lowercase )
if is_buffer:
_UpperCAmelCase = new_value
else:
_UpperCAmelCase = nn.Parameter(lowercase ,requires_grad=old_value.requires_grad )
_UpperCAmelCase = new_value
def __UpperCAmelCase ( lowercase ,lowercase=None ,lowercase=None ,lowercase=None ,lowercase=False ):
"""simple docstring"""
for name, module in model.named_children():
if current_key_name is None:
_UpperCAmelCase = []
current_key_name.append(lowercase )
if (isinstance(lowercase ,nn.Linear ) or isinstance(lowercase ,lowercase )) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
if not any(key in """.""".join(lowercase ) for key in modules_to_not_convert ):
with init_empty_weights():
if isinstance(lowercase ,lowercase ):
_UpperCAmelCase , _UpperCAmelCase = module.weight.shape
else:
_UpperCAmelCase = module.in_features
_UpperCAmelCase = module.out_features
if quantization_config.quantization_method() == "llm_int8":
_UpperCAmelCase = bnb.nn.LinearabitLt(
lowercase ,lowercase ,module.bias is not None ,has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight ,threshold=quantization_config.llm_inta_threshold ,)
_UpperCAmelCase = True
else:
if (
quantization_config.llm_inta_skip_modules is not None
and name in quantization_config.llm_inta_skip_modules
):
pass
else:
_UpperCAmelCase = bnb.nn.Linearabit(
lowercase ,lowercase ,module.bias is not None ,quantization_config.bnb_abit_compute_dtype ,compress_statistics=quantization_config.bnb_abit_use_double_quant ,quant_type=quantization_config.bnb_abit_quant_type ,)
_UpperCAmelCase = True
# Store the module class in case we need to transpose the weight later
_UpperCAmelCase = type(lowercase )
# Force requires grad to False to avoid unexpected errors
model._modules[name].requires_grad_(lowercase )
if len(list(module.children() ) ) > 0:
_UpperCAmelCase , _UpperCAmelCase = _replace_with_bnb_linear(
lowercase ,lowercase ,lowercase ,lowercase ,has_been_replaced=lowercase ,)
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def __UpperCAmelCase ( lowercase ,lowercase=None ,lowercase=None ,lowercase=None ):
"""simple docstring"""
_UpperCAmelCase = ["""lm_head"""] if modules_to_not_convert is None else modules_to_not_convert
_UpperCAmelCase , _UpperCAmelCase = _replace_with_bnb_linear(
lowercase ,lowercase ,lowercase ,lowercase )
if not has_been_replaced:
logger.warning(
"""You are loading your model in 8bit or 4bit but no linear modules were found in your model."""
""" Please double check your model architecture, or submit an issue on github if you think this is"""
""" a bug.""" )
return model
def __UpperCAmelCase ( *lowercase ,**lowercase ):
"""simple docstring"""
warnings.warn(
"""`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead""" ,lowercase ,)
return replace_with_bnb_linear(*lowercase ,**lowercase )
def __UpperCAmelCase ( *lowercase ,**lowercase ):
"""simple docstring"""
warnings.warn(
"""`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead""" ,lowercase ,)
return set_module_quantized_tensor_to_device(*lowercase ,**lowercase )
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = deepcopy(lowercase ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
tied_model.tie_weights()
_UpperCAmelCase = find_tied_parameters(lowercase )
# For compatibility with Accelerate < 0.18
if isinstance(lowercase ,lowercase ):
_UpperCAmelCase = sum(list(tied_params.values() ) ,[] ) + list(tied_params.keys() )
else:
_UpperCAmelCase = sum(lowercase ,[] )
_UpperCAmelCase = len(lowercase ) > 0
# Check if it is a base model
_UpperCAmelCase = not hasattr(lowercase ,model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
_UpperCAmelCase = list(model.named_children() )
_UpperCAmelCase = [list_modules[-1][0]]
# add last module together with tied weights
_UpperCAmelCase = set(lowercase ) - set(lowercase )
_UpperCAmelCase = list(set(lowercase ) ) + list(lowercase )
# remove ".weight" from the keys
_UpperCAmelCase = [""".weight""", """.bias"""]
_UpperCAmelCase = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
_UpperCAmelCase = name.replace(lowercase ,"""""" )
filtered_module_names.append(lowercase )
return filtered_module_names
| 363 | """simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
UpperCAmelCase__ = logging.get_logger(__name__)
class a ( lowerCAmelCase_ ):
_snake_case : List[str] = 'upernet'
def __init__( self : Tuple , __lowerCAmelCase : int=None , __lowerCAmelCase : Tuple=512 , __lowerCAmelCase : Union[str, Any]=0.02 , __lowerCAmelCase : Tuple=[1, 2, 3, 6] , __lowerCAmelCase : Any=True , __lowerCAmelCase : Any=0.4 , __lowerCAmelCase : Union[str, Any]=384 , __lowerCAmelCase : Optional[int]=256 , __lowerCAmelCase : List[str]=1 , __lowerCAmelCase : Optional[int]=False , __lowerCAmelCase : Optional[int]=255 , **__lowerCAmelCase : Union[str, Any] , ):
super().__init__(**__lowerCAmelCase )
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
_UpperCAmelCase = CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
_UpperCAmelCase = backbone_config.get("""model_type""" )
_UpperCAmelCase = CONFIG_MAPPING[backbone_model_type]
_UpperCAmelCase = config_class.from_dict(__lowerCAmelCase )
_UpperCAmelCase = backbone_config
_UpperCAmelCase = hidden_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = pool_scales
_UpperCAmelCase = use_auxiliary_head
_UpperCAmelCase = auxiliary_loss_weight
_UpperCAmelCase = auxiliary_in_channels
_UpperCAmelCase = auxiliary_channels
_UpperCAmelCase = auxiliary_num_convs
_UpperCAmelCase = auxiliary_concat_input
_UpperCAmelCase = loss_ignore_index
def lowerCAmelCase_ ( self : List[Any] ):
_UpperCAmelCase = copy.deepcopy(self.__dict__ )
_UpperCAmelCase = self.backbone_config.to_dict()
_UpperCAmelCase = self.__class__.model_type
return output
| 30 | 0 |
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 UpperCAmelCase ( __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase_ = BarthezTokenizer
lowerCamelCase_ = BarthezTokenizerFast
lowerCamelCase_ = True
lowerCamelCase_ = True
def lowerCAmelCase_ ( self ):
"""simple docstring"""
super().setUp()
A_ : Union[str, Any] = BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=lowercase )
A_ : int = tokenizer
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Dict = '<pad>'
A_ : List[str] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase ) , lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase ) , lowercase )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : str = 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(lowercase ) , 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"""
A_ : Dict = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
A_ : List[Any] = [0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]
A_ : str = self.tokenizer(
lowercase , max_length=len(lowercase ) , padding=lowercase , truncation=lowercase , return_tensors='pt' )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
A_ : Optional[Any] = batch.input_ids.tolist()[0]
self.assertListEqual(lowercase , lowercase )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
A_ : int = self.get_tokenizer()
A_ : str = self.get_rust_tokenizer()
A_ : Any = 'I was born in 92000, and this is falsé.'
A_ : Any = tokenizer.tokenize(lowercase )
A_ : Union[str, Any] = rust_tokenizer.tokenize(lowercase )
self.assertListEqual(lowercase , lowercase )
A_ : Optional[Any] = tokenizer.encode(lowercase , add_special_tokens=lowercase )
A_ : List[Any] = rust_tokenizer.encode(lowercase , add_special_tokens=lowercase )
self.assertListEqual(lowercase , lowercase )
A_ : Optional[Any] = self.get_rust_tokenizer()
A_ : int = tokenizer.encode(lowercase )
A_ : int = rust_tokenizer.encode(lowercase )
self.assertListEqual(lowercase , lowercase )
@slow
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : List[Any] = {'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.
A_ : Optional[int] = [
'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=lowercase , model_name='moussaKam/mbarthez' , revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6' , sequences=lowercase , )
| 140 | 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)
_UpperCAmelCase = 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')
_UpperCAmelCase = tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
)
_UpperCAmelCase = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)
_UpperCAmelCase = train_datagen.flow_from_directory(
"""dataset/training_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
)
_UpperCAmelCase = 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
_UpperCAmelCase = tf.keras.preprocessing.image.load_img(
"""dataset/single_prediction/image.png""", target_size=(64, 64)
)
_UpperCAmelCase = tf.keras.preprocessing.image.img_to_array(test_image)
_UpperCAmelCase = np.expand_dims(test_image, axis=0)
_UpperCAmelCase = classifier.predict(test_image)
# training_set.class_indices
if result[0][0] == 0:
_UpperCAmelCase = """Normal"""
if result[0][0] == 1:
_UpperCAmelCase = """Abnormality detected"""
| 140 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase : Any = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
"""tokenization_convbert""": ["""ConvBertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Dict = ["""ConvBertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Union[str, Any] = [
"""CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ConvBertForMaskedLM""",
"""ConvBertForMultipleChoice""",
"""ConvBertForQuestionAnswering""",
"""ConvBertForSequenceClassification""",
"""ConvBertForTokenClassification""",
"""ConvBertLayer""",
"""ConvBertModel""",
"""ConvBertPreTrainedModel""",
"""load_tf_weights_in_convbert""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[str] = [
"""TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFConvBertForMaskedLM""",
"""TFConvBertForMultipleChoice""",
"""TFConvBertForQuestionAnswering""",
"""TFConvBertForSequenceClassification""",
"""TFConvBertForTokenClassification""",
"""TFConvBertLayer""",
"""TFConvBertModel""",
"""TFConvBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertLayer,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertLayer,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
else:
import sys
UpperCamelCase : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 345 | '''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
UpperCamelCase : int = """true"""
def SCREAMING_SNAKE_CASE__ ( snake_case : List[str] , snake_case : int=82 , snake_case : Tuple=16 ) -> Union[str, Any]:
"""simple docstring"""
set_seed(42 )
a : List[str] = RegressionModel()
a : Union[str, Any] = deepcopy(snake_case )
a : Dict = RegressionDataset(length=snake_case )
a : Dict = DataLoader(snake_case , batch_size=snake_case )
model.to(accelerator.device )
a , a : Optional[int] = accelerator.prepare(snake_case , snake_case )
return model, ddp_model, dataloader
def SCREAMING_SNAKE_CASE__ ( snake_case : Accelerator , snake_case : Union[str, Any]=False ) -> Optional[int]:
"""simple docstring"""
a : List[str] = AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased' )
a : Any = load_dataset('glue' , 'mrpc' , split='validation' )
def tokenize_function(snake_case : int ):
a : Any = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=snake_case , max_length=snake_case )
return outputs
with accelerator.main_process_first():
a : Dict = dataset.map(
snake_case , batched=snake_case , remove_columns=['idx', 'sentence1', 'sentence2'] , )
a : List[str] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(snake_case : Optional[Any] ):
if use_longest:
return tokenizer.pad(snake_case , padding='longest' , return_tensors='pt' )
return tokenizer.pad(snake_case , padding='max_length' , max_length=128 , return_tensors='pt' )
return DataLoader(snake_case , shuffle=snake_case , collate_fn=snake_case , batch_size=16 )
def SCREAMING_SNAKE_CASE__ ( snake_case : Optional[Any] , snake_case : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
a : int = Accelerator(dispatch_batches=snake_case , split_batches=snake_case )
a : List[str] = get_dataloader(snake_case , not dispatch_batches )
a : Optional[Any] = AutoModelForSequenceClassification.from_pretrained(
'hf-internal-testing/mrpc-bert-base-cased' , return_dict=snake_case )
a , a : Optional[Any] = accelerator.prepare(snake_case , snake_case )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def SCREAMING_SNAKE_CASE__ ( snake_case : Optional[Any] , snake_case : Optional[Any] , snake_case : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
a : Dict = []
for batch in dataloader:
a , a : Any = batch.values()
with torch.no_grad():
a : Tuple = model(snake_case )
a , a : Dict = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
a , a : List[str] = [], []
for logit, targ in logits_and_targets:
logits.append(snake_case )
targs.append(snake_case )
a , a : Any = torch.cat(snake_case ), torch.cat(snake_case )
return logits, targs
def SCREAMING_SNAKE_CASE__ ( snake_case : Accelerator , snake_case : Dict=82 , snake_case : str=False , snake_case : List[str]=False , snake_case : List[Any]=16 ) -> Optional[int]:
"""simple docstring"""
a , a , a : int = get_basic_setup(snake_case , snake_case , snake_case )
a , a : int = generate_predictions(snake_case , snake_case , snake_case )
assert (
len(snake_case ) == num_samples
), F"""Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(snake_case )}"""
def SCREAMING_SNAKE_CASE__ ( snake_case : bool = False , snake_case : bool = False ) -> List[str]:
"""simple docstring"""
a : int = evaluate.load('glue' , 'mrpc' )
a , a : Tuple = get_mrpc_setup(snake_case , snake_case )
# First do baseline
a , a , a : Tuple = setup['no']
model.to(snake_case )
model.eval()
for batch in dataloader:
batch.to(snake_case )
with torch.inference_mode():
a : List[Any] = model(**snake_case )
a : Optional[Any] = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=snake_case , references=batch['labels'] )
a : Tuple = metric.compute()
# Then do distributed
a , a , a : Tuple = setup['ddp']
model.eval()
for batch in dataloader:
with torch.inference_mode():
a : List[str] = model(**snake_case )
a : Optional[Any] = outputs.logits.argmax(dim=-1 )
a : Optional[int] = batch['labels']
a , a : Optional[int] = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=snake_case , references=snake_case )
a : str = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), F"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""
def SCREAMING_SNAKE_CASE__ ( ) -> str:
"""simple docstring"""
a : Dict = Accelerator(split_batches=snake_case , dispatch_batches=snake_case )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('**Testing gather_for_metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`""" )
test_mrpc(snake_case , snake_case )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test torch metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
a : List[Any] = Accelerator(split_batches=snake_case , dispatch_batches=snake_case )
if accelerator.is_local_main_process:
print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99""" )
test_torch_metrics(snake_case , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test last batch is not dropped when perfectly divisible**' )
a : Optional[Any] = Accelerator()
test_torch_metrics(snake_case , 512 )
accelerator.state._reset_state()
def SCREAMING_SNAKE_CASE__ ( snake_case : Union[str, Any] ) -> int:
"""simple docstring"""
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 345 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
_UpperCAmelCase : int = logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] = {
"""Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : int = 'dpt'
def __init__(self , __lowercase=7_68 , __lowercase=12 , __lowercase=12 , __lowercase=30_72 , __lowercase="gelu" , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.0_2 , __lowercase=1e-12 , __lowercase=3_84 , __lowercase=16 , __lowercase=3 , __lowercase=False , __lowercase=True , __lowercase=[2, 5, 8, 11] , __lowercase="project" , __lowercase=[4, 2, 1, 0.5] , __lowercase=[96, 1_92, 3_84, 7_68] , __lowercase=2_56 , __lowercase=-1 , __lowercase=False , __lowercase=True , __lowercase=0.4 , __lowercase=2_55 , __lowercase=0.1 , __lowercase=[1, 10_24, 24, 24] , __lowercase=[0, 1] , __lowercase=None , **__lowercase , ):
super().__init__(**__lowercase )
__lowerCAmelCase = hidden_size
__lowerCAmelCase = is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info('''Initializing the config with a `BiT` backbone.''' )
__lowerCAmelCase = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
}
__lowerCAmelCase = BitConfig(**__lowercase )
elif isinstance(__lowercase , __lowercase ):
logger.info('''Initializing the config with a `BiT` backbone.''' )
__lowerCAmelCase = BitConfig(**__lowercase )
elif isinstance(__lowercase , __lowercase ):
__lowerCAmelCase = backbone_config
else:
raise ValueError(
F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.""" )
__lowerCAmelCase = backbone_featmap_shape
__lowerCAmelCase = neck_ignore_stages
if readout_type != "project":
raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.''' )
else:
__lowerCAmelCase = None
__lowerCAmelCase = None
__lowerCAmelCase = []
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = initializer_range
__lowerCAmelCase = layer_norm_eps
__lowerCAmelCase = image_size
__lowerCAmelCase = patch_size
__lowerCAmelCase = num_channels
__lowerCAmelCase = qkv_bias
__lowerCAmelCase = backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']''' )
__lowerCAmelCase = readout_type
__lowerCAmelCase = reassemble_factors
__lowerCAmelCase = neck_hidden_sizes
__lowerCAmelCase = fusion_hidden_size
__lowerCAmelCase = head_in_index
__lowerCAmelCase = use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
__lowerCAmelCase = use_auxiliary_head
__lowerCAmelCase = auxiliary_loss_weight
__lowerCAmelCase = semantic_loss_ignore_index
__lowerCAmelCase = semantic_classifier_dropout
def _snake_case (self ):
__lowerCAmelCase = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
__lowerCAmelCase = self.backbone_config.to_dict()
__lowerCAmelCase = self.__class__.model_type
return output
| 174 |
'''simple docstring'''
def __magic_name__( lowerCamelCase, lowerCamelCase):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
__lowerCAmelCase = (boundary[1] - boundary[0]) / steps
__lowerCAmelCase = boundary[0]
__lowerCAmelCase = boundary[1]
__lowerCAmelCase = make_points(lowerCamelCase, lowerCamelCase, lowerCamelCase)
__lowerCAmelCase = 0.0
y += (h / 2.0) * f(lowerCamelCase)
for i in x_i:
# print(i)
y += h * f(lowerCamelCase)
y += (h / 2.0) * f(lowerCamelCase)
return y
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase):
__lowerCAmelCase = a + h
while x < (b - h):
yield x
__lowerCAmelCase = x + h
def __magic_name__( lowerCamelCase): # enter your function here
__lowerCAmelCase = (x - 0) * (x - 0)
return y
def __magic_name__( ):
__lowerCAmelCase = 0.0 # Lower bound of integration
__lowerCAmelCase = 1.0 # Upper bound of integration
__lowerCAmelCase = 10.0 # define number of steps or resolution
__lowerCAmelCase = [a, b] # define boundary of integration
__lowerCAmelCase = method_a(lowerCamelCase, lowerCamelCase)
print(F"""y = {y}""")
if __name__ == "__main__":
main()
| 174 | 1 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__lowerCAmelCase = datasets.utils.logging.get_logger(__name__)
@dataclass
class UpperCAmelCase__ ( datasets.BuilderConfig ):
"""simple docstring"""
__UpperCAmelCase : int = 1_0000
__UpperCAmelCase : Optional[List[str]] = None
__UpperCAmelCase : Optional[datasets.Features] = None
class UpperCAmelCase__ ( datasets.ArrowBasedBuilder ):
"""simple docstring"""
__UpperCAmelCase : List[str] = ParquetConfig
def __lowercase ( self : int ):
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features )
def __lowercase ( self : int ,_a : List[Any] ):
'''simple docstring'''
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
_a : Tuple = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_a ,(str, list, tuple) ):
_a : int = data_files
if isinstance(_a ,_a ):
_a : Tuple = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
_a : int = [dl_manager.iter_files(_a ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,gen_kwargs={'files': files} )]
_a : Dict = []
for split_name, files in data_files.items():
if isinstance(_a ,_a ):
_a : Any = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
_a : Optional[int] = [dl_manager.iter_files(_a ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(_a ):
with open(_a ,'rb' ) as f:
_a : Dict = datasets.Features.from_arrow_schema(pq.read_schema(_a ) )
break
splits.append(datasets.SplitGenerator(name=_a ,gen_kwargs={'files': files} ) )
return splits
def __lowercase ( self : List[str] ,_a : pa.Table ):
'''simple docstring'''
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
_a : List[str] = table_cast(_a ,self.info.features.arrow_schema )
return pa_table
def __lowercase ( self : Any ,_a : Optional[int] ):
'''simple docstring'''
_a : Optional[int] = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'""" )
for file_idx, file in enumerate(itertools.chain.from_iterable(_a ) ):
with open(_a ,'rb' ) as f:
_a : int = pq.ParquetFile(_a )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size ,columns=self.config.columns ) ):
_a : Tuple = pa.Table.from_batches([record_batch] )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield F"""{file_idx}_{batch_idx}""", self._cast_table(_a )
except ValueError as e:
logger.error(F"""Failed to read file '{file}' with error {type(_a )}: {e}""" )
raise
| 5 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib
__lowerCAmelCase = threading.Lock()
__lowerCAmelCase = None
__lowerCAmelCase = {
"""debug""": logging.DEBUG,
"""info""": logging.INFO,
"""warning""": logging.WARNING,
"""error""": logging.ERROR,
"""critical""": logging.CRITICAL,
}
__lowerCAmelCase = logging.WARNING
__lowerCAmelCase = True
def UpperCAmelCase_ ():
"""simple docstring"""
_a : Dict = os.getenv('TRANSFORMERS_VERBOSITY' , __a )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
f"""Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, """
f"""has to be one of: { ', '.join(log_levels.keys() ) }""" )
return _default_log_level
def UpperCAmelCase_ ():
"""simple docstring"""
return __name__.split('.' )[0]
def UpperCAmelCase_ ():
"""simple docstring"""
return logging.getLogger(_get_library_name() )
def UpperCAmelCase_ ():
"""simple docstring"""
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
_a : str = logging.StreamHandler() # Set sys.stderr as stream.
_a : Optional[Any] = sys.stderr.flush
# Apply our default configuration to the library root logger.
_a : List[Any] = _get_library_root_logger()
library_root_logger.addHandler(_default_handler )
library_root_logger.setLevel(_get_default_logging_level() )
_a : List[str] = False
def UpperCAmelCase_ ():
"""simple docstring"""
global _default_handler
with _lock:
if not _default_handler:
return
_a : int = _get_library_root_logger()
library_root_logger.removeHandler(_default_handler )
library_root_logger.setLevel(logging.NOTSET )
_a : str = None
def UpperCAmelCase_ ():
"""simple docstring"""
return log_levels
def UpperCAmelCase_ (__a : Optional[str] = None ):
"""simple docstring"""
if name is None:
_a : List[Any] = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(__a )
def UpperCAmelCase_ ():
"""simple docstring"""
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def UpperCAmelCase_ (__a : int ):
"""simple docstring"""
_configure_library_root_logger()
_get_library_root_logger().setLevel(__a )
def UpperCAmelCase_ ():
"""simple docstring"""
return set_verbosity(__a )
def UpperCAmelCase_ ():
"""simple docstring"""
return set_verbosity(__a )
def UpperCAmelCase_ ():
"""simple docstring"""
return set_verbosity(__a )
def UpperCAmelCase_ ():
"""simple docstring"""
return set_verbosity(__a )
def UpperCAmelCase_ ():
"""simple docstring"""
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler )
def UpperCAmelCase_ ():
"""simple docstring"""
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler )
def UpperCAmelCase_ (__a : logging.Handler ):
"""simple docstring"""
_configure_library_root_logger()
assert handler is not None
_get_library_root_logger().addHandler(__a )
def UpperCAmelCase_ (__a : logging.Handler ):
"""simple docstring"""
_configure_library_root_logger()
assert handler is not None and handler not in _get_library_root_logger().handlers
_get_library_root_logger().removeHandler(__a )
def UpperCAmelCase_ ():
"""simple docstring"""
_configure_library_root_logger()
_a : Union[str, Any] = False
def UpperCAmelCase_ ():
"""simple docstring"""
_configure_library_root_logger()
_a : Dict = True
def UpperCAmelCase_ ():
"""simple docstring"""
_a : Any = _get_library_root_logger().handlers
for handler in handlers:
_a : Union[str, Any] = logging.Formatter('[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s' )
handler.setFormatter(__a )
def UpperCAmelCase_ ():
"""simple docstring"""
_a : Union[str, Any] = _get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(__a )
def UpperCAmelCase_ (self : Union[str, Any] , *__a : Union[str, Any] , **__a : Union[str, Any] ):
"""simple docstring"""
_a : Union[str, Any] = os.getenv('TRANSFORMERS_NO_ADVISORY_WARNINGS' , __a )
if no_advisory_warnings:
return
self.warning(*__a , **__a )
__lowerCAmelCase = warning_advice
@functools.lru_cache(__a )
def UpperCAmelCase_ (self : int , *__a : Optional[Any] , **__a : Any ):
"""simple docstring"""
self.warning(*__a , **__a )
__lowerCAmelCase = warning_once
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : Any ,*_a : Tuple ,**_a : int ): # pylint: disable=unused-argument
'''simple docstring'''
_a : int = args[0] if args else None
def __iter__( self : str ):
'''simple docstring'''
return iter(self._iterator )
def __getattr__( self : List[Any] ,_a : int ):
'''simple docstring'''
def empty_fn(*_a : Optional[Any] ,**_a : Any ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self : List[str] ):
'''simple docstring'''
return self
def __exit__( self : List[str] ,_a : str ,_a : List[Any] ,_a : str ):
'''simple docstring'''
return
class UpperCAmelCase__ :
"""simple docstring"""
def __call__( self : Union[str, Any] ,*_a : Tuple ,**_a : Tuple ):
'''simple docstring'''
if _tqdm_active:
return tqdm_lib.tqdm(*_a ,**_a )
else:
return EmptyTqdm(*_a ,**_a )
def __lowercase ( self : str ,*_a : List[Any] ,**_a : Any ):
'''simple docstring'''
_a : Any = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*_a ,**_a )
def __lowercase ( self : List[str] ):
'''simple docstring'''
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
__lowerCAmelCase = _tqdm_cls()
def UpperCAmelCase_ ():
"""simple docstring"""
global _tqdm_active
return bool(_tqdm_active )
def UpperCAmelCase_ ():
"""simple docstring"""
global _tqdm_active
_a : str = True
hf_hub_utils.enable_progress_bars()
def UpperCAmelCase_ ():
"""simple docstring"""
global _tqdm_active
_a : Dict = False
hf_hub_utils.disable_progress_bars()
| 5 | 1 |
import os
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = "matrix.txt" ) -> int:
with open(os.path.join(os.path.dirname(_UpperCAmelCase ) , _UpperCAmelCase ) ) as in_file:
lowerCamelCase__ : int = in_file.read()
lowerCamelCase__ : Tuple = [[int(_UpperCAmelCase ) for cell in row.split(',' )] for row in data.strip().splitlines()]
lowerCamelCase__ : Any = [[0 for cell in row] for row in grid]
lowerCamelCase__ : Optional[int] = len(grid[0] )
lowerCamelCase__ : Optional[Any] = [[0 for i in range(_UpperCAmelCase )] for j in range(_UpperCAmelCase )]
lowerCamelCase__ : Any = grid[0][0]
for i in range(1 , _UpperCAmelCase ):
lowerCamelCase__ : Any = grid[0][i] + dp[0][i - 1]
for i in range(1 , _UpperCAmelCase ):
lowerCamelCase__ : Optional[int] = grid[i][0] + dp[i - 1][0]
for i in range(1 , _UpperCAmelCase ):
for j in range(1 , _UpperCAmelCase ):
lowerCamelCase__ : List[Any] = grid[i][j] + min(dp[i - 1][j] , dp[i][j - 1] )
return dp[-1][-1]
if __name__ == "__main__":
print(F"""{solution() = }""")
| 50 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class lowerCAmelCase ( nn.Module ):
UpperCAmelCase__ = 42
UpperCAmelCase__ = jnp.floataa
def A_ ( self : Any ) -> Any:
lowerCamelCase__ : str = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : int , UpperCAmelCase : Dict ) -> Optional[Any]:
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : str = hidden_states.shape
lowerCamelCase__ : Union[str, Any] = jax.image.resize(
UpperCAmelCase , shape=(batch, height * 2, width * 2, channels) , method='nearest' , )
lowerCamelCase__ : Optional[Any] = self.conv(UpperCAmelCase )
return hidden_states
class lowerCAmelCase ( nn.Module ):
UpperCAmelCase__ = 42
UpperCAmelCase__ = jnp.floataa
def A_ ( self : List[str] ) -> int:
lowerCamelCase__ : Tuple = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : str , UpperCAmelCase : Union[str, Any] ) -> Optional[Any]:
# pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim
# hidden_states = jnp.pad(hidden_states, pad_width=pad)
lowerCamelCase__ : Optional[Any] = self.conv(UpperCAmelCase )
return hidden_states
class lowerCAmelCase ( nn.Module ):
UpperCAmelCase__ = 42
UpperCAmelCase__ = None
UpperCAmelCase__ = 0.0
UpperCAmelCase__ = None
UpperCAmelCase__ = jnp.floataa
def A_ ( self : List[str] ) -> Union[str, Any]:
lowerCamelCase__ : Optional[Any] = self.in_channels if self.out_channels is None else self.out_channels
lowerCamelCase__ : Tuple = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
lowerCamelCase__ : int = nn.Conv(
UpperCAmelCase , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
lowerCamelCase__ : Union[str, Any] = nn.Dense(UpperCAmelCase , dtype=self.dtype )
lowerCamelCase__ : Union[str, Any] = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
lowerCamelCase__ : List[Any] = nn.Dropout(self.dropout_prob )
lowerCamelCase__ : Tuple = nn.Conv(
UpperCAmelCase , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
lowerCamelCase__ : Optional[Any] = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
lowerCamelCase__ : Union[str, Any] = None
if use_nin_shortcut:
lowerCamelCase__ : Dict = nn.Conv(
UpperCAmelCase , kernel_size=(1, 1) , strides=(1, 1) , padding='VALID' , dtype=self.dtype , )
def __call__( self : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int=True ) -> Optional[int]:
lowerCamelCase__ : Union[str, Any] = hidden_states
lowerCamelCase__ : List[Any] = self.norma(UpperCAmelCase )
lowerCamelCase__ : List[Any] = nn.swish(UpperCAmelCase )
lowerCamelCase__ : Any = self.conva(UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = self.time_emb_proj(nn.swish(UpperCAmelCase ) )
lowerCamelCase__ : List[str] = jnp.expand_dims(jnp.expand_dims(UpperCAmelCase , 1 ) , 1 )
lowerCamelCase__ : List[str] = hidden_states + temb
lowerCamelCase__ : Optional[Any] = self.norma(UpperCAmelCase )
lowerCamelCase__ : List[str] = nn.swish(UpperCAmelCase )
lowerCamelCase__ : Optional[int] = self.dropout(UpperCAmelCase , UpperCAmelCase )
lowerCamelCase__ : str = self.conva(UpperCAmelCase )
if self.conv_shortcut is not None:
lowerCamelCase__ : Dict = self.conv_shortcut(UpperCAmelCase )
return hidden_states + residual
| 50 | 1 |
from __future__ import annotations
import math
from collections.abc import Callable
def __UpperCAmelCase ( __a : Callable[[int | float], int | float] ,__a : int | float ,__a : int | float ,__a : int = 100 ,) -> float:
"""simple docstring"""
_a : Tuple = x_start
_a : List[str] = fnc(__a )
_a : Optional[int] = 0.0
for _ in range(__a ):
# Approximates curve as a sequence of linear lines and sums their length
_a : Dict = (x_end - x_start) / steps + xa
_a : Dict = fnc(__a )
length += math.hypot(xa - xa ,fxa - fxa )
# Increment step
_a : Optional[Any] = xa
_a : Any = fxa
return length
if __name__ == "__main__":
def __UpperCAmelCase ( __a : Any ) -> Dict:
"""simple docstring"""
return math.sin(10 * x )
print('''f(x) = sin(10 * x)''')
print('''The length of the curve from x = -10 to x = 10 is:''')
a__ = 10
while i <= 100000:
print(f'''With {i} steps: {line_length(f, -10, 10, i)}''')
i *= 10
| 15 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class UpperCAmelCase_ ( datasets.BuilderConfig ):
"""simple docstring"""
UpperCAmelCase__ : Optional[datasets.Features] = None
class UpperCAmelCase_ ( datasets.ArrowBasedBuilder ):
"""simple docstring"""
UpperCAmelCase__ : Any = PandasConfig
def __lowercase ( self ) -> Any:
return datasets.DatasetInfo(features=self.config.features )
def __lowercase ( self , _a ) -> List[Any]:
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
_a : str = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_a , (str, list, tuple) ):
_a : Dict = data_files
if isinstance(_a , _a ):
_a : Dict = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
_a : int = [dl_manager.iter_files(_a ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
_a : Optional[Any] = []
for split_name, files in data_files.items():
if isinstance(_a , _a ):
_a : List[str] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
_a : Any = [dl_manager.iter_files(_a ) for file in files]
splits.append(datasets.SplitGenerator(name=_a , gen_kwargs={'''files''': files} ) )
return splits
def __lowercase ( self , _a ) -> pa.Table:
if self.config.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
_a : Optional[Any] = table_cast(_a , self.config.features.arrow_schema )
return pa_table
def __lowercase ( self , _a ) -> List[str]:
for i, file in enumerate(itertools.chain.from_iterable(_a ) ):
with open(_a , '''rb''' ) as f:
_a : str = pa.Table.from_pandas(pd.read_pickle(_a ) )
yield i, self._cast_table(_a )
| 15 | 1 |
'''simple docstring'''
import re
def _A ( A__ ):
"""simple docstring"""
__lowercase = re.compile(
R'''^(?:0|94|\+94|0{2}94)''' R'''7(0|1|2|4|5|6|7|8)''' R'''(-| |)''' R'''\d{7}$''' )
return bool(re.search(A__ , A__ ) )
if __name__ == "__main__":
lowerCAmelCase__ = '''0094702343221'''
print(is_sri_lankan_phone_number(phone))
| 104 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase = {
"""configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
"""tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = ["""BertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
"""BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BertForMaskedLM""",
"""BertForMultipleChoice""",
"""BertForNextSentencePrediction""",
"""BertForPreTraining""",
"""BertForQuestionAnswering""",
"""BertForSequenceClassification""",
"""BertForTokenClassification""",
"""BertLayer""",
"""BertLMHeadModel""",
"""BertModel""",
"""BertPreTrainedModel""",
"""load_tf_weights_in_bert""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
"""TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFBertEmbeddings""",
"""TFBertForMaskedLM""",
"""TFBertForMultipleChoice""",
"""TFBertForNextSentencePrediction""",
"""TFBertForPreTraining""",
"""TFBertForQuestionAnswering""",
"""TFBertForSequenceClassification""",
"""TFBertForTokenClassification""",
"""TFBertLMHeadModel""",
"""TFBertMainLayer""",
"""TFBertModel""",
"""TFBertPreTrainedModel""",
]
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = ["""TFBertTokenizer"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
"""FlaxBertForCausalLM""",
"""FlaxBertForMaskedLM""",
"""FlaxBertForMultipleChoice""",
"""FlaxBertForNextSentencePrediction""",
"""FlaxBertForPreTraining""",
"""FlaxBertForQuestionAnswering""",
"""FlaxBertForSequenceClassification""",
"""FlaxBertForTokenClassification""",
"""FlaxBertModel""",
"""FlaxBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_fast import BertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bert import (
BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
BertForMaskedLM,
BertForMultipleChoice,
BertForNextSentencePrediction,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertForTokenClassification,
BertLayer,
BertLMHeadModel,
BertModel,
BertPreTrainedModel,
load_tf_weights_in_bert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_bert import (
TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBertEmbeddings,
TFBertForMaskedLM,
TFBertForMultipleChoice,
TFBertForNextSentencePrediction,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertForTokenClassification,
TFBertLMHeadModel,
TFBertMainLayer,
TFBertModel,
TFBertPreTrainedModel,
)
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_tf import TFBertTokenizer
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_bert import (
FlaxBertForCausalLM,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
FlaxBertPreTrainedModel,
)
else:
import sys
__lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 40 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
@dataclass
class lowerCAmelCase__ ( a ):
"""simple docstring"""
lowerCAmelCase__ = 42
class lowerCAmelCase__ ( a , a ):
"""simple docstring"""
lowerCAmelCase__ = True
@register_to_config
def __init__( self : Tuple , __SCREAMING_SNAKE_CASE : int = 3 , __SCREAMING_SNAKE_CASE : int = 3 , __SCREAMING_SNAKE_CASE : Tuple[str] = ("DownEncoderBlock2D",) , __SCREAMING_SNAKE_CASE : Tuple[str] = ("UpDecoderBlock2D",) , __SCREAMING_SNAKE_CASE : Tuple[int] = (64,) , __SCREAMING_SNAKE_CASE : int = 1 , __SCREAMING_SNAKE_CASE : str = "silu" , __SCREAMING_SNAKE_CASE : int = 4 , __SCREAMING_SNAKE_CASE : int = 32 , __SCREAMING_SNAKE_CASE : int = 32 , __SCREAMING_SNAKE_CASE : float = 0.18215 , ) -> List[Any]:
"""simple docstring"""
super().__init__()
# pass init params to Encoder
__SCREAMING_SNAKE_CASE = Encoder(
in_channels=__SCREAMING_SNAKE_CASE , out_channels=__SCREAMING_SNAKE_CASE , down_block_types=__SCREAMING_SNAKE_CASE , block_out_channels=__SCREAMING_SNAKE_CASE , layers_per_block=__SCREAMING_SNAKE_CASE , act_fn=__SCREAMING_SNAKE_CASE , norm_num_groups=__SCREAMING_SNAKE_CASE , double_z=__SCREAMING_SNAKE_CASE , )
# pass init params to Decoder
__SCREAMING_SNAKE_CASE = Decoder(
in_channels=__SCREAMING_SNAKE_CASE , out_channels=__SCREAMING_SNAKE_CASE , up_block_types=__SCREAMING_SNAKE_CASE , block_out_channels=__SCREAMING_SNAKE_CASE , layers_per_block=__SCREAMING_SNAKE_CASE , norm_num_groups=__SCREAMING_SNAKE_CASE , act_fn=__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = nn.Convad(2 * latent_channels , 2 * latent_channels , 1 )
__SCREAMING_SNAKE_CASE = nn.Convad(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , 1 )
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
# only relevant if vae tiling is enabled
__SCREAMING_SNAKE_CASE = self.config.sample_size
__SCREAMING_SNAKE_CASE = (
self.config.sample_size[0]
if isinstance(self.config.sample_size , (list, tuple) )
else self.config.sample_size
)
__SCREAMING_SNAKE_CASE = int(sample_size / (2 ** (len(self.config.block_out_channels ) - 1)) )
__SCREAMING_SNAKE_CASE = 0.25
def UpperCAmelCase__ ( self : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : int=False ) -> List[Any]:
"""simple docstring"""
if isinstance(__SCREAMING_SNAKE_CASE , (Encoder, Decoder) ):
__SCREAMING_SNAKE_CASE = value
def UpperCAmelCase__ ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : bool = True ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = use_tiling
def UpperCAmelCase__ ( self : Optional[Any] ) -> int:
"""simple docstring"""
self.enable_tiling(__SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = True
def UpperCAmelCase__ ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = False
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def UpperCAmelCase__ ( self : Dict ) -> Dict[str, AttentionProcessor]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {}
def fn_recursive_add_processors(__SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : torch.nn.Module , __SCREAMING_SNAKE_CASE : Dict[str, AttentionProcessor] ):
if hasattr(__SCREAMING_SNAKE_CASE , """set_processor""" ):
__SCREAMING_SNAKE_CASE = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f'{name}.{sub_name}' , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return processors
def UpperCAmelCase__ ( self : Tuple , __SCREAMING_SNAKE_CASE : Union[AttentionProcessor, Dict[str, AttentionProcessor]] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = len(self.attn_processors.keys() )
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) and len(__SCREAMING_SNAKE_CASE ) != count:
raise ValueError(
f'A dict of processors was passed, but the number of processors {len(__SCREAMING_SNAKE_CASE )} does not match the'
f' number of attention layers: {count}. Please make sure to pass {count} processor classes.' )
def fn_recursive_attn_processor(__SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : torch.nn.Module , __SCREAMING_SNAKE_CASE : Optional[Any] ):
if hasattr(__SCREAMING_SNAKE_CASE , """set_processor""" ):
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
module.set_processor(__SCREAMING_SNAKE_CASE )
else:
module.set_processor(processor.pop(f'{name}.processor' ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f'{name}.{sub_name}' , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
for name, module in self.named_children():
fn_recursive_attn_processor(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self : Any ) -> List[Any]:
"""simple docstring"""
self.set_attn_processor(AttnProcessor() )
@apply_forward_hook
def UpperCAmelCase__ ( self : Any , __SCREAMING_SNAKE_CASE : torch.FloatTensor , __SCREAMING_SNAKE_CASE : bool = True ) -> AutoencoderKLOutput:
"""simple docstring"""
if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
return self.tiled_encode(__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE )
if self.use_slicing and x.shape[0] > 1:
__SCREAMING_SNAKE_CASE = [self.encoder(__SCREAMING_SNAKE_CASE ) for x_slice in x.split(1 )]
__SCREAMING_SNAKE_CASE = torch.cat(__SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE = self.encoder(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.quant_conv(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = DiagonalGaussianDistribution(__SCREAMING_SNAKE_CASE )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=__SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self : List[Any] , __SCREAMING_SNAKE_CASE : torch.FloatTensor , __SCREAMING_SNAKE_CASE : bool = True ) -> Union[DecoderOutput, torch.FloatTensor]:
"""simple docstring"""
if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
return self.tiled_decode(__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.post_quant_conv(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.decoder(__SCREAMING_SNAKE_CASE )
if not return_dict:
return (dec,)
return DecoderOutput(sample=__SCREAMING_SNAKE_CASE )
@apply_forward_hook
def UpperCAmelCase__ ( self : Tuple , __SCREAMING_SNAKE_CASE : torch.FloatTensor , __SCREAMING_SNAKE_CASE : bool = True ) -> Union[DecoderOutput, torch.FloatTensor]:
"""simple docstring"""
if self.use_slicing and z.shape[0] > 1:
__SCREAMING_SNAKE_CASE = [self._decode(__SCREAMING_SNAKE_CASE ).sample for z_slice in z.split(1 )]
__SCREAMING_SNAKE_CASE = torch.cat(__SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE = self._decode(__SCREAMING_SNAKE_CASE ).sample
if not return_dict:
return (decoded,)
return DecoderOutput(sample=__SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self : int , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = min(a.shape[2] , b.shape[2] , __SCREAMING_SNAKE_CASE )
for y in range(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
return b
def UpperCAmelCase__ ( self : int , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : List[str] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = min(a.shape[3] , b.shape[3] , __SCREAMING_SNAKE_CASE )
for x in range(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
return b
def UpperCAmelCase__ ( self : Tuple , __SCREAMING_SNAKE_CASE : torch.FloatTensor , __SCREAMING_SNAKE_CASE : bool = True ) -> AutoencoderKLOutput:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor) )
__SCREAMING_SNAKE_CASE = int(self.tile_latent_min_size * self.tile_overlap_factor )
__SCREAMING_SNAKE_CASE = self.tile_latent_min_size - blend_extent
# Split the image into 512x512 tiles and encode them separately.
__SCREAMING_SNAKE_CASE = []
for i in range(0 , x.shape[2] , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = []
for j in range(0 , x.shape[3] , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
__SCREAMING_SNAKE_CASE = self.encoder(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.quant_conv(__SCREAMING_SNAKE_CASE )
row.append(__SCREAMING_SNAKE_CASE )
rows.append(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = []
for i, row in enumerate(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = []
for j, tile in enumerate(__SCREAMING_SNAKE_CASE ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
__SCREAMING_SNAKE_CASE = self.blend_v(rows[i - 1][j] , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if j > 0:
__SCREAMING_SNAKE_CASE = self.blend_h(row[j - 1] , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(__SCREAMING_SNAKE_CASE , dim=3 ) )
__SCREAMING_SNAKE_CASE = torch.cat(__SCREAMING_SNAKE_CASE , dim=2 )
__SCREAMING_SNAKE_CASE = DiagonalGaussianDistribution(__SCREAMING_SNAKE_CASE )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=__SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self : List[str] , __SCREAMING_SNAKE_CASE : torch.FloatTensor , __SCREAMING_SNAKE_CASE : bool = True ) -> Union[DecoderOutput, torch.FloatTensor]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor) )
__SCREAMING_SNAKE_CASE = int(self.tile_sample_min_size * self.tile_overlap_factor )
__SCREAMING_SNAKE_CASE = self.tile_sample_min_size - blend_extent
# Split z into overlapping 64x64 tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
__SCREAMING_SNAKE_CASE = []
for i in range(0 , z.shape[2] , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = []
for j in range(0 , z.shape[3] , __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
__SCREAMING_SNAKE_CASE = self.post_quant_conv(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.decoder(__SCREAMING_SNAKE_CASE )
row.append(__SCREAMING_SNAKE_CASE )
rows.append(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = []
for i, row in enumerate(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE = []
for j, tile in enumerate(__SCREAMING_SNAKE_CASE ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
__SCREAMING_SNAKE_CASE = self.blend_v(rows[i - 1][j] , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if j > 0:
__SCREAMING_SNAKE_CASE = self.blend_h(row[j - 1] , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(__SCREAMING_SNAKE_CASE , dim=3 ) )
__SCREAMING_SNAKE_CASE = torch.cat(__SCREAMING_SNAKE_CASE , dim=2 )
if not return_dict:
return (dec,)
return DecoderOutput(sample=__SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : torch.FloatTensor , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : bool = True , __SCREAMING_SNAKE_CASE : Optional[torch.Generator] = None , ) -> Union[DecoderOutput, torch.FloatTensor]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = sample
__SCREAMING_SNAKE_CASE = self.encode(__SCREAMING_SNAKE_CASE ).latent_dist
if sample_posterior:
__SCREAMING_SNAKE_CASE = posterior.sample(generator=__SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE = posterior.mode()
__SCREAMING_SNAKE_CASE = self.decode(__SCREAMING_SNAKE_CASE ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=__SCREAMING_SNAKE_CASE )
| 331 |
'''simple docstring'''
import os
def a__ ( a__ = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(a__ ) , a__ ) ) as input_file:
__SCREAMING_SNAKE_CASE = [
[int(a__ ) for element in line.split(""",""" )]
for line in input_file.readlines()
]
__SCREAMING_SNAKE_CASE = len(a__ )
__SCREAMING_SNAKE_CASE = len(matrix[0] )
__SCREAMING_SNAKE_CASE = [[-1 for _ in range(a__ )] for _ in range(a__ )]
for i in range(a__ ):
__SCREAMING_SNAKE_CASE = matrix[i][0]
for j in range(1 , a__ ):
for i in range(a__ ):
__SCREAMING_SNAKE_CASE = minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1 , a__ ):
__SCREAMING_SNAKE_CASE = min(
minimal_path_sums[i][j] , minimal_path_sums[i - 1][j] + matrix[i][j] )
for i in range(rows - 2 , -1 , -1 ):
__SCREAMING_SNAKE_CASE = min(
minimal_path_sums[i][j] , minimal_path_sums[i + 1][j] + matrix[i][j] )
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 331 | 1 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
def __init__( self , lowercase , lowercase , lowercase=1024 , lowercase=1024 , lowercase=3.6 ):
_lowerCamelCase : Union[str, Any] = tokenizer
_lowerCamelCase : Any = tokenizer.bos_token_id
_lowerCamelCase : Optional[int] = dataset
_lowerCamelCase : Optional[int] = seq_length
_lowerCamelCase : Union[str, Any] = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
_lowerCamelCase : Optional[int] = iter(self.dataset )
_lowerCamelCase : Dict = True
while more_examples:
_lowerCamelCase, _lowerCamelCase : Dict = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(lowercase )['content'] )
buffer_len += len(buffer[-1] )
except StopIteration:
_lowerCamelCase : Optional[Any] = False
break
_lowerCamelCase : Optional[Any] = tokenizer(lowercase , truncation=lowercase )['input_ids']
_lowerCamelCase : Any = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(lowercase ) , self.seq_length ):
_lowerCamelCase : List[str] = all_token_ids[i : i + self.seq_length]
if len(lowercase ) == self.seq_length:
yield torch.tensor(lowercase )
def _snake_case ( lowercase__ ):
_lowerCamelCase : List[Any] = {'streaming': True}
_lowerCamelCase : str = load_dataset(args.dataset_name , split='train' , **lowercase__ )
_lowerCamelCase : Dict = ConstantLengthDataset(lowercase__ , lowercase__ , seq_length=args.seq_length )
_lowerCamelCase : str = DataLoader(lowercase__ , batch_size=args.batch_size )
return eval_dataloader
def _snake_case ( lowercase__ ):
model.eval()
_lowerCamelCase : Tuple = []
for step, batch in enumerate(lowercase__ ):
with torch.no_grad():
_lowerCamelCase : str = model(lowercase__ , labels=lowercase__ )
_lowerCamelCase : Optional[Any] = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(lowercase__ ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
_lowerCamelCase : str = torch.mean(torch.cat(lowercase__ ) )
try:
_lowerCamelCase : Any = torch.exp(lowercase__ )
except OverflowError:
_lowerCamelCase : int = float('inf' )
return loss.item(), perplexity.item()
# Setup Accelerator
lowercase__ = Accelerator()
# Parse configuration
lowercase__ = HfArgumentParser(EvaluationArguments)
lowercase__ = parser.parse_args()
set_seed(args.seed)
# Logging
lowercase__ = logging.getLogger(__name__)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
# Load model and tokenizer
lowercase__ = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
lowercase__ = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
lowercase__ = create_dataloader(args)
# Prepare everything with our `accelerator`.
lowercase__ , lowercase__ = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
lowercase__ , lowercase__ = evaluate(args)
logger.info(F"loss/eval: {eval_loss}, perplexity: {perplexity}") | 96 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = [
"""IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""IBertForMaskedLM""",
"""IBertForMultipleChoice""",
"""IBertForQuestionAnswering""",
"""IBertForSequenceClassification""",
"""IBertForTokenClassification""",
"""IBertModel""",
"""IBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
else:
import sys
lowercase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 96 | 1 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class lowercase__ ( __snake_case , unittest.TestCase):
UpperCamelCase_ = VideoToVideoSDPipeline
UpperCamelCase_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"""video"""}) - {'image', 'width', 'height'}
UpperCamelCase_ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""video"""}) - {'image'}
UpperCamelCase_ = PipelineTesterMixin.required_optional_params - {'latents'}
UpperCamelCase_ = False
# No `output_type`.
UpperCamelCase_ = frozenset(
[
"""num_inference_steps""",
"""generator""",
"""latents""",
"""return_dict""",
"""callback""",
"""callback_steps""",
])
def __A ( self : Optional[int] ):
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[int] = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''DownBlock3D''') , up_block_types=('''UpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''') , cross_attention_dim=32 , attention_head_dim=4 , )
SCREAMING_SNAKE_CASE : Any = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=UpperCamelCase__ , set_alpha_to_one=UpperCamelCase__ , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : int = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='''gelu''' , projection_dim=512 , )
SCREAMING_SNAKE_CASE : Optional[int] = CLIPTextModel(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : List[str] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
SCREAMING_SNAKE_CASE : int = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
}
return components
def __A ( self : List[Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optional[int]=0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = floats_tensor((1, 3, 3, 32, 32) , rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
if str(UpperCamelCase__ ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.manual_seed(UpperCamelCase__ )
else:
SCREAMING_SNAKE_CASE : List[Any] = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Dict = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''video''': video,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''pt''',
}
return inputs
def __A ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE : Dict = self.get_dummy_components()
SCREAMING_SNAKE_CASE : Any = VideoToVideoSDPipeline(**UpperCamelCase__ )
SCREAMING_SNAKE_CASE : List[Any] = sd_pipe.to(UpperCamelCase__ )
sd_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Tuple = self.get_dummy_inputs(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = '''np'''
SCREAMING_SNAKE_CASE : str = sd_pipe(**UpperCamelCase__ ).frames
SCREAMING_SNAKE_CASE : Union[str, Any] = frames[0][-3:, -3:, -1]
assert frames[0].shape == (32, 32, 3)
SCREAMING_SNAKE_CASE : Dict = np.array([106, 117, 113, 174, 137, 112, 148, 151, 131] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def __A ( self : List[str] ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=UpperCamelCase__ , expected_max_diff=5E-3 )
@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' )
def __A ( self : Any ):
'''simple docstring'''
pass
@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' )
def __A ( self : str ):
'''simple docstring'''
pass
@unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.''' )
def __A ( self : Optional[Any] ):
'''simple docstring'''
pass
def __A ( self : Union[str, Any] ):
'''simple docstring'''
return super().test_progress_bar()
@slow
@skip_mps
class lowercase__ ( unittest.TestCase):
def __A ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = VideoToVideoSDPipeline.from_pretrained('''cerspense/zeroscope_v2_XL''' , torch_dtype=torch.floataa )
pipe.enable_model_cpu_offload()
# 10 frames
SCREAMING_SNAKE_CASE : List[str] = torch.Generator(device='''cpu''' ).manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = torch.randn((1, 10, 3, 1024, 576) , generator=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = video.to('''cuda''' )
SCREAMING_SNAKE_CASE : Tuple = '''Spiderman is surfing'''
SCREAMING_SNAKE_CASE : Dict = pipe(UpperCamelCase__ , video=UpperCamelCase__ , generator=UpperCamelCase__ , num_inference_steps=3 , output_type='''pt''' ).frames
SCREAMING_SNAKE_CASE : Optional[Any] = np.array([-1.045_8984, -1.127_9297, -0.966_3086, -0.9150_3906, -0.7509_7656] )
assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array ).sum() < 1E-2
| 362 | import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
__UpperCamelCase : Dict = logging.get_logger(__name__)
def A ( _lowercase , _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : Optional[int] = UniSpeechSatForSequenceClassification.from_pretrained(_lowercase , config=_lowercase )
SCREAMING_SNAKE_CASE : Any = downstream_dict['''projector.weight''']
SCREAMING_SNAKE_CASE : Optional[int] = downstream_dict['''projector.bias''']
SCREAMING_SNAKE_CASE : Optional[Any] = downstream_dict['''model.post_net.linear.weight''']
SCREAMING_SNAKE_CASE : int = downstream_dict['''model.post_net.linear.bias''']
return model
def A ( _lowercase , _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : Optional[int] = UniSpeechSatForAudioFrameClassification.from_pretrained(_lowercase , config=_lowercase )
SCREAMING_SNAKE_CASE : Union[str, Any] = downstream_dict['''model.linear.weight''']
SCREAMING_SNAKE_CASE : str = downstream_dict['''model.linear.bias''']
return model
def A ( _lowercase , _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : str = UniSpeechSatForXVector.from_pretrained(_lowercase , config=_lowercase )
SCREAMING_SNAKE_CASE : str = downstream_dict['''connector.weight''']
SCREAMING_SNAKE_CASE : Dict = 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 : List[str] = downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]
SCREAMING_SNAKE_CASE : int = downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
SCREAMING_SNAKE_CASE : Any = downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
SCREAMING_SNAKE_CASE : Tuple = downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
SCREAMING_SNAKE_CASE : List[str] = downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
SCREAMING_SNAKE_CASE : Any = downstream_dict['''objective.W''']
return model
@torch.no_grad()
def A ( _lowercase , _lowercase , _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : List[Any] = torch.load(_lowercase , map_location='''cpu''' )
SCREAMING_SNAKE_CASE : Any = checkpoint['''Downstream''']
SCREAMING_SNAKE_CASE : List[Any] = UniSpeechSatConfig.from_pretrained(_lowercase )
SCREAMING_SNAKE_CASE : int = WavaVecaFeatureExtractor.from_pretrained(
_lowercase , return_attention_mask=_lowercase , do_normalize=_lowercase )
SCREAMING_SNAKE_CASE : Tuple = hf_config.architectures[0]
if arch.endswith('''ForSequenceClassification''' ):
SCREAMING_SNAKE_CASE : str = convert_classification(_lowercase , _lowercase , _lowercase )
elif arch.endswith('''ForAudioFrameClassification''' ):
SCREAMING_SNAKE_CASE : List[Any] = convert_diarization(_lowercase , _lowercase , _lowercase )
elif arch.endswith('''ForXVector''' ):
SCREAMING_SNAKE_CASE : int = convert_xvector(_lowercase , _lowercase , _lowercase )
else:
raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}""" )
if hf_config.use_weighted_layer_sum:
SCREAMING_SNAKE_CASE : int = checkpoint['''Featurizer''']['''weights''']
hf_feature_extractor.save_pretrained(_lowercase )
hf_model.save_pretrained(_lowercase )
if __name__ == "__main__":
__UpperCamelCase : Optional[int] = 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.')
__UpperCamelCase : Union[str, Any] = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 258 | 0 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class snake_case_ ( __A , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = BertJapaneseTokenizer
SCREAMING_SNAKE_CASE : Dict = False
SCREAMING_SNAKE_CASE : str = True
def snake_case__( self : str ) ->Tuple:
super().setUp()
snake_case_ = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''こんにちは''',
'''こん''',
'''にちは''',
'''ばんは''',
'''##こん''',
'''##にちは''',
'''##ばんは''',
'''世界''',
'''##世界''',
'''、''',
'''##、''',
'''。''',
'''##。''',
]
snake_case_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def snake_case__( self : Optional[Any] , _UpperCamelCase : List[Any] ) ->List[str]:
snake_case_ = '''こんにちは、世界。 \nこんばんは、世界。'''
snake_case_ = '''こんにちは 、 世界 。 こんばんは 、 世界 。'''
return input_text, output_text
def snake_case__( self : Optional[Any] , _UpperCamelCase : Dict ) ->Tuple:
snake_case_, snake_case_ = self.get_input_output_texts(_UpperCamelCase )
snake_case_ = tokenizer.encode(_UpperCamelCase , add_special_tokens=_UpperCamelCase )
snake_case_ = tokenizer.decode(_UpperCamelCase , clean_up_tokenization_spaces=_UpperCamelCase )
return text, ids
def snake_case__( self : Any ) ->Dict:
pass # TODO add if relevant
def snake_case__( self : Optional[Any] ) ->Optional[Any]:
pass # TODO add if relevant
def snake_case__( self : Optional[Any] ) ->Any:
pass # TODO add if relevant
def snake_case__( self : Optional[int] ) ->int:
snake_case_ = self.tokenizer_class(self.vocab_file )
snake_case_ = tokenizer.tokenize('''こんにちは、世界。\nこんばんは、世界。''' )
self.assertListEqual(_UpperCamelCase , ['''こんにちは''', '''、''', '''世界''', '''。''', '''こん''', '''##ばんは''', '''、''', '''世界''', '''。'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase ) , [3, 1_2, 1_0, 1_4, 4, 9, 1_2, 1_0, 1_4] )
def snake_case__( self : Dict ) ->Any:
snake_case_ = self.tokenizer_class(self.vocab_file , word_tokenizer_type='''mecab''' )
self.assertIsNotNone(_UpperCamelCase )
snake_case_ = '''こんにちは、世界。\nこんばんは、世界。'''
snake_case_ = tokenizer.tokenize(_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , ['''こんにちは''', '''、''', '''世界''', '''。''', '''こん''', '''##ばんは''', '''、''', '''世界''', '''。'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase ) , [3, 1_2, 1_0, 1_4, 4, 9, 1_2, 1_0, 1_4] )
snake_case_ = os.path.join(self.tmpdirname , '''tokenizer.bin''' )
with open(_UpperCamelCase , '''wb''' ) as handle:
pickle.dump(_UpperCamelCase , _UpperCamelCase )
with open(_UpperCamelCase , '''rb''' ) as handle:
snake_case_ = pickle.load(_UpperCamelCase )
snake_case_ = tokenizer_new.tokenize(_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , _UpperCamelCase )
def snake_case__( self : List[Any] ) ->Tuple:
snake_case_ = MecabTokenizer(mecab_dic='''ipadic''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップルストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
def snake_case__( self : int ) ->List[Any]:
try:
snake_case_ = MecabTokenizer(mecab_dic='''unidic_lite''' )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
def snake_case__( self : Union[str, Any] ) ->str:
try:
snake_case_ = MecabTokenizer(mecab_dic='''unidic''' )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
def snake_case__( self : List[str] ) ->Dict:
snake_case_ = MecabTokenizer(do_lower_case=_UpperCamelCase , mecab_dic='''ipadic''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップルストア''', '''で''', '''iphone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
def snake_case__( self : Optional[int] ) ->List[str]:
try:
snake_case_ = MecabTokenizer(
do_lower_case=_UpperCamelCase , normalize_text=_UpperCamelCase , mecab_option='''-d /usr/local/lib/mecab/dic/jumandic''' )
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''ｱｯﾌﾟﾙストア''', '''で''', '''iPhone''', '''８''', '''が''', '''発売''', '''さ''', '''れた''', '''\u3000''', '''。'''] , )
def snake_case__( self : Optional[int] ) ->Union[str, Any]:
snake_case_ = MecabTokenizer(normalize_text=_UpperCamelCase , mecab_dic='''ipadic''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''ｱｯﾌﾟﾙストア''', '''で''', '''iPhone''', '''８''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', ''' ''', '''。'''] , )
@require_sudachi
def snake_case__( self : Optional[Any] ) ->str:
snake_case_ = self.tokenizer_class(self.vocab_file , word_tokenizer_type='''sudachi''' )
self.assertIsNotNone(_UpperCamelCase )
snake_case_ = '''こんにちは、世界。\nこんばんは、世界。'''
snake_case_ = tokenizer.tokenize(_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , ['''こんにちは''', '''、''', '''世界''', '''。''', '''こん''', '''##ばんは''', '''、''', '''世界''', '''。'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase ) , [3, 1_2, 1_0, 1_4, 4, 9, 1_2, 1_0, 1_4] )
snake_case_ = os.path.join(self.tmpdirname , '''tokenizer.bin''' )
with open(_UpperCamelCase , '''wb''' ) as handle:
pickle.dump(_UpperCamelCase , _UpperCamelCase )
with open(_UpperCamelCase , '''rb''' ) as handle:
snake_case_ = pickle.load(_UpperCamelCase )
snake_case_ = tokenizer_new.tokenize(_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , _UpperCamelCase )
@require_sudachi
def snake_case__( self : Tuple ) ->Optional[int]:
snake_case_ = SudachiTokenizer(sudachi_dict_type='''core''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , [''' ''', '''\t''', '''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', ''' ''', '''が''', ''' ''', ''' ''', '''\n ''', '''発売''', '''さ''', '''れ''', '''た''', ''' ''', '''。''', ''' ''', ''' '''] , )
@require_sudachi
def snake_case__( self : str ) ->Tuple:
snake_case_ = SudachiTokenizer(sudachi_dict_type='''core''' , sudachi_split_mode='''A''' )
self.assertListEqual(tokenizer.tokenize('''外国人参政権''' ) , ['''外国''', '''人''', '''参政''', '''権'''] )
@require_sudachi
def snake_case__( self : Dict ) ->List[Any]:
snake_case_ = SudachiTokenizer(sudachi_dict_type='''core''' , sudachi_split_mode='''B''' )
self.assertListEqual(tokenizer.tokenize('''外国人参政権''' ) , ['''外国人''', '''参政権'''] )
@require_sudachi
def snake_case__( self : Optional[int] ) ->Tuple:
snake_case_ = SudachiTokenizer(sudachi_dict_type='''core''' , sudachi_split_mode='''C''' )
self.assertListEqual(tokenizer.tokenize('''外国人参政権''' ) , ['''外国人参政権'''] )
@require_sudachi
def snake_case__( self : Optional[Any] ) ->int:
snake_case_ = SudachiTokenizer(do_lower_case=_UpperCamelCase , sudachi_dict_type='''core''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , [''' ''', '''\t''', '''アップル''', '''ストア''', '''で''', '''iphone''', '''8''', ''' ''', '''が''', ''' ''', ''' ''', '''\n ''', '''発売''', '''さ''', '''れ''', '''た''', ''' ''', '''。''', ''' ''', ''' '''] , )
@require_sudachi
def snake_case__( self : Dict ) ->List[str]:
snake_case_ = SudachiTokenizer(normalize_text=_UpperCamelCase , sudachi_dict_type='''core''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , [''' ''', '''\t''', '''ｱｯﾌﾟﾙ''', '''ストア''', '''で''', '''iPhone''', '''８''', ''' ''', '''が''', ''' ''', ''' ''', '''\n ''', '''発売''', '''さ''', '''れ''', '''た''', '''\u3000''', '''。''', ''' ''', ''' '''] , )
@require_sudachi
def snake_case__( self : List[str] ) ->List[Any]:
snake_case_ = SudachiTokenizer(trim_whitespace=_UpperCamelCase , sudachi_dict_type='''core''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
@require_jumanpp
def snake_case__( self : int ) ->Union[str, Any]:
snake_case_ = self.tokenizer_class(self.vocab_file , word_tokenizer_type='''jumanpp''' )
self.assertIsNotNone(_UpperCamelCase )
snake_case_ = '''こんにちは、世界。\nこんばんは、世界。'''
snake_case_ = tokenizer.tokenize(_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , ['''こんにちは''', '''、''', '''世界''', '''。''', '''こん''', '''##ばんは''', '''、''', '''世界''', '''。'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase ) , [3, 1_2, 1_0, 1_4, 4, 9, 1_2, 1_0, 1_4] )
snake_case_ = os.path.join(self.tmpdirname , '''tokenizer.bin''' )
with open(_UpperCamelCase , '''wb''' ) as handle:
pickle.dump(_UpperCamelCase , _UpperCamelCase )
with open(_UpperCamelCase , '''rb''' ) as handle:
snake_case_ = pickle.load(_UpperCamelCase )
snake_case_ = tokenizer_new.tokenize(_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , _UpperCamelCase )
@require_jumanpp
def snake_case__( self : List[str] ) ->Dict:
snake_case_ = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''\u3000''', '''が''', '''\u3000''', '''\u3000''', '''\u3000''', '''発売''', '''さ''', '''れた''', '''\u3000''', '''。'''] , )
@require_jumanpp
def snake_case__( self : Any ) ->Any:
snake_case_ = JumanppTokenizer(do_lower_case=_UpperCamelCase )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iphone''', '''8''', '''\u3000''', '''が''', '''\u3000''', '''\u3000''', '''\u3000''', '''発売''', '''さ''', '''れた''', '''\u3000''', '''。'''] , )
@require_jumanpp
def snake_case__( self : int ) ->Dict:
snake_case_ = JumanppTokenizer(normalize_text=_UpperCamelCase )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''ｱ''', '''ｯ''', '''ﾌ''', '''ﾟ''', '''ﾙ''', '''ストア''', '''で''', '''iPhone''', '''８''', '''\u3000''', '''が''', '''\u3000''', '''\u3000''', '''\u3000''', '''発売''', '''さ''', '''れた''', '''\u3000''', '''。'''] , )
@require_jumanpp
def snake_case__( self : int ) ->Optional[Any]:
snake_case_ = JumanppTokenizer(trim_whitespace=_UpperCamelCase )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れた''', '''。'''] , )
@require_jumanpp
def snake_case__( self : Any ) ->Optional[int]:
snake_case_ = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize('''ありがとうございますm(_ _)ｍ見つけるのが大変です。''' ) , ['''ありがとう''', '''ございます''', '''m(_ _)m''', '''見つける''', '''の''', '''が''', '''大変です''', '''。'''] , )
def snake_case__( self : Any ) ->List[Any]:
snake_case_ = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''こんにちは''', '''こん''', '''にちは''', '''ばんは''', '''##こん''', '''##にちは''', '''##ばんは''']
snake_case_ = {}
for i, token in enumerate(_UpperCamelCase ):
snake_case_ = i
snake_case_ = WordpieceTokenizer(vocab=_UpperCamelCase , unk_token='''[UNK]''' )
self.assertListEqual(tokenizer.tokenize('''''' ) , [] )
self.assertListEqual(tokenizer.tokenize('''こんにちは''' ) , ['''こんにちは'''] )
self.assertListEqual(tokenizer.tokenize('''こんばんは''' ) , ['''こん''', '''##ばんは'''] )
self.assertListEqual(tokenizer.tokenize('''こんばんは こんばんにちは こんにちは''' ) , ['''こん''', '''##ばんは''', '''[UNK]''', '''こんにちは'''] )
def snake_case__( self : Optional[Any] ) ->Optional[int]:
snake_case_ = BertJapaneseTokenizer.from_pretrained('''nlp-waseda/roberta-base-japanese-with-auto-jumanpp''' )
snake_case_ = tokenizer.subword_tokenizer
snake_case_ = subword_tokenizer.tokenize('''国境 の 長い トンネル を 抜ける と 雪国 であった 。''' )
self.assertListEqual(_UpperCamelCase , ['''▁国境''', '''▁の''', '''▁長い''', '''▁トンネル''', '''▁を''', '''▁抜ける''', '''▁と''', '''▁雪''', '''国''', '''▁であった''', '''▁。'''] )
snake_case_ = subword_tokenizer.tokenize('''こんばんは こんばん にち は こんにちは''' )
self.assertListEqual(_UpperCamelCase , ['''▁こん''', '''ばん''', '''は''', '''▁こん''', '''ばん''', '''▁に''', '''ち''', '''▁は''', '''▁こんにちは'''] )
def snake_case__( self : str ) ->Tuple:
snake_case_ = self.tokenizer_class.from_pretrained('''cl-tohoku/bert-base-japanese''' )
snake_case_ = tokenizer.encode('''ありがとう。''' , add_special_tokens=_UpperCamelCase )
snake_case_ = tokenizer.encode('''どういたしまして。''' , add_special_tokens=_UpperCamelCase )
snake_case_ = tokenizer.build_inputs_with_special_tokens(_UpperCamelCase )
snake_case_ = tokenizer.build_inputs_with_special_tokens(_UpperCamelCase , _UpperCamelCase )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class snake_case_ ( __A , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = BertJapaneseTokenizer
SCREAMING_SNAKE_CASE : int = False
def snake_case__( self : List[str] ) ->int:
super().setUp()
snake_case_ = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''こ''', '''ん''', '''に''', '''ち''', '''は''', '''ば''', '''世''', '''界''', '''、''', '''。''']
snake_case_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def snake_case__( self : Optional[Any] , **_UpperCamelCase : Union[str, Any] ) ->int:
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname , subword_tokenizer_type='''character''' , **_UpperCamelCase )
def snake_case__( self : Any , _UpperCamelCase : Union[str, Any] ) ->List[Any]:
snake_case_ = '''こんにちは、世界。 \nこんばんは、世界。'''
snake_case_ = '''こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。'''
return input_text, output_text
def snake_case__( self : Dict ) ->Union[str, Any]:
pass # TODO add if relevant
def snake_case__( self : Any ) ->Union[str, Any]:
pass # TODO add if relevant
def snake_case__( self : Tuple ) ->Tuple:
pass # TODO add if relevant
def snake_case__( self : List[Any] ) ->int:
snake_case_ = self.tokenizer_class(self.vocab_file , subword_tokenizer_type='''character''' )
snake_case_ = tokenizer.tokenize('''こんにちは、世界。 \nこんばんは、世界。''' )
self.assertListEqual(
_UpperCamelCase , ['''こ''', '''ん''', '''に''', '''ち''', '''は''', '''、''', '''世''', '''界''', '''。''', '''こ''', '''ん''', '''ば''', '''ん''', '''は''', '''、''', '''世''', '''界''', '''。'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_UpperCamelCase ) , [3, 4, 5, 6, 7, 1_1, 9, 1_0, 1_2, 3, 4, 8, 4, 7, 1_1, 9, 1_0, 1_2] )
def snake_case__( self : List[str] ) ->List[str]:
snake_case_ = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''こ''', '''ん''', '''に''', '''ち''', '''は''', '''ば''', '''世''', '''界''', '''、''', '''。''']
snake_case_ = {}
for i, token in enumerate(_UpperCamelCase ):
snake_case_ = i
snake_case_ = CharacterTokenizer(vocab=_UpperCamelCase , unk_token='''[UNK]''' )
self.assertListEqual(tokenizer.tokenize('''''' ) , [] )
self.assertListEqual(tokenizer.tokenize('''こんにちは''' ) , ['''こ''', '''ん''', '''に''', '''ち''', '''は'''] )
self.assertListEqual(tokenizer.tokenize('''こんにちほ''' ) , ['''こ''', '''ん''', '''に''', '''ち''', '''[UNK]'''] )
def snake_case__( self : Dict ) ->Tuple:
snake_case_ = self.tokenizer_class.from_pretrained('''cl-tohoku/bert-base-japanese-char''' )
snake_case_ = tokenizer.encode('''ありがとう。''' , add_special_tokens=_UpperCamelCase )
snake_case_ = tokenizer.encode('''どういたしまして。''' , add_special_tokens=_UpperCamelCase )
snake_case_ = tokenizer.build_inputs_with_special_tokens(_UpperCamelCase )
snake_case_ = tokenizer.build_inputs_with_special_tokens(_UpperCamelCase , _UpperCamelCase )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
def snake_case__( self : str ) ->int:
snake_case_ = '''cl-tohoku/bert-base-japanese'''
snake_case_ = AutoTokenizer.from_pretrained(_UpperCamelCase )
self.assertIsInstance(_UpperCamelCase , _UpperCamelCase )
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
def snake_case__( self : Optional[int] ) ->Dict:
snake_case_ = '''cl-tohoku/bert-base-japanese'''
with self.assertLogs('''transformers''' , level='''WARNING''' ) as cm:
BertTokenizer.from_pretrained(_UpperCamelCase )
self.assertTrue(
cm.records[0].message.startswith(
'''The tokenizer class you load from this checkpoint is not the same type as the class this function'''
''' is called from.''' ) )
snake_case_ = '''bert-base-cased'''
with self.assertLogs('''transformers''' , level='''WARNING''' ) as cm:
BertJapaneseTokenizer.from_pretrained(_UpperCamelCase )
self.assertTrue(
cm.records[0].message.startswith(
'''The tokenizer class you load from this checkpoint is not the same type as the class this function'''
''' is called from.''' ) ) | 8 |
"""simple docstring"""
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ = False, False, False
@dataclass
class lowercase :
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = None
# Automatically constructed
_SCREAMING_SNAKE_CASE = "dict"
_SCREAMING_SNAKE_CASE = pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
_SCREAMING_SNAKE_CASE = field(default='Audio' , init=_UpperCAmelCase , repr=_UpperCAmelCase )
def __call__( self ) -> Union[str, Any]:
return self.pa_type
def _snake_case ( self , lowercase ) -> dict:
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError("""To support encoding audio data, please install 'soundfile'.""" ) from err
if isinstance(lowercase , lowercase ):
return {"bytes": None, "path": value}
elif isinstance(lowercase , lowercase ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
lowerCAmelCase = BytesIO()
sf.write(lowercase , value["""array"""] , value["""sampling_rate"""] , format="""wav""" )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith("""pcm""" ):
# "PCM" only has raw audio bytes
if value.get("""sampling_rate""" ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError("""To use PCM files, please specify a 'sampling_rate' in Audio object""" )
if value.get("""bytes""" ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
lowerCAmelCase = np.frombuffer(value["""bytes"""] , dtype=np.intaa ).astype(np.floataa ) / 32_767
else:
lowerCAmelCase = np.memmap(value["""path"""] , dtype="""h""" , mode="""r""" ).astype(np.floataa ) / 32_767
lowerCAmelCase = BytesIO(bytes() )
sf.write(lowercase , lowercase , value["""sampling_rate"""] , format="""wav""" )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
f'An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.' )
def _snake_case ( self , lowercase , lowercase = None ) -> dict:
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead.""" )
lowerCAmelCase , lowerCAmelCase = (value["""path"""], BytesIO(value["""bytes"""] )) if value["""bytes"""] is not None else (value["""path"""], None)
if path is None and file is None:
raise ValueError(f'An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.' )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError("""To support decoding audio files, please install 'librosa' and 'soundfile'.""" ) from err
lowerCAmelCase = xsplitext(lowercase )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
"""Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, """
"""You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """ )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
"""Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, """
"""You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """ )
if file is None:
lowerCAmelCase = token_per_repo_id or {}
lowerCAmelCase = path.split("""::""" )[-1]
try:
lowerCAmelCase = string_to_dict(lowercase , config.HUB_DATASETS_URL )["""repo_id"""]
lowerCAmelCase = token_per_repo_id[repo_id]
except (ValueError, KeyError):
lowerCAmelCase = None
with xopen(lowercase , """rb""" , use_auth_token=lowercase ) as f:
lowerCAmelCase , lowerCAmelCase = sf.read(lowercase )
else:
lowerCAmelCase , lowerCAmelCase = sf.read(lowercase )
lowerCAmelCase = array.T
if self.mono:
lowerCAmelCase = librosa.to_mono(lowercase )
if self.sampling_rate and self.sampling_rate != sampling_rate:
lowerCAmelCase = librosa.resample(lowercase , orig_sr=lowercase , target_sr=self.sampling_rate )
lowerCAmelCase = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def _snake_case ( self ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
from .features import Value
if self.decode:
raise ValueError("""Cannot flatten a decoded Audio feature.""" )
return {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
def _snake_case ( self , lowercase ) -> pa.StructArray:
if pa.types.is_string(storage.type ):
lowerCAmelCase = pa.array([None] * len(lowercase ) , type=pa.binary() )
lowerCAmelCase = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowerCAmelCase = pa.array([None] * len(lowercase ) , type=pa.string() )
lowerCAmelCase = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices("""array""" ):
lowerCAmelCase = pa.array([Audio().encode_example(lowercase ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
lowerCAmelCase = storage.field("""bytes""" )
else:
lowerCAmelCase = pa.array([None] * len(lowercase ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
lowerCAmelCase = storage.field("""path""" )
else:
lowerCAmelCase = pa.array([None] * len(lowercase ) , type=pa.string() )
lowerCAmelCase = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
return array_cast(lowercase , self.pa_type )
def _snake_case ( self , lowercase ) -> pa.StructArray:
@no_op_if_value_is_null
def path_to_bytes(lowercase ):
with xopen(lowercase , """rb""" ) as f:
lowerCAmelCase = f.read()
return bytes_
lowerCAmelCase = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
lowerCAmelCase = pa.array(
[os.path.basename(lowercase ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
lowerCAmelCase = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase , self.pa_type )
| 46 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
nightly,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class SCREAMING_SNAKE_CASE (UpperCAmelCase , unittest.TestCase ):
_UpperCamelCase : Dict = LDMTextToImagePipeline
_UpperCamelCase : Optional[int] = TEXT_TO_IMAGE_PARAMS - {
'negative_prompt',
'negative_prompt_embeds',
'cross_attention_kwargs',
'prompt_embeds',
}
_UpperCamelCase : Optional[int] = PipelineTesterMixin.required_optional_params - {
'num_images_per_prompt',
'callback',
'callback_steps',
}
_UpperCamelCase : Optional[int] = TEXT_TO_IMAGE_BATCH_PARAMS
_UpperCamelCase : Optional[Any] = False
def SCREAMING_SNAKE_CASE_ ( self : Tuple )-> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
lowercase__ = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=a , set_alpha_to_one=a , )
torch.manual_seed(0 )
lowercase__ = AutoencoderKL(
block_out_channels=(32, 64) , in_channels=3 , out_channels=3 , down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') , up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') , latent_channels=4 , )
torch.manual_seed(0 )
lowercase__ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
lowercase__ = CLIPTextModel(a )
lowercase__ = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
lowercase__ = {
'unet': unet,
'scheduler': scheduler,
'vqvae': vae,
'bert': text_encoder,
'tokenizer': tokenizer,
}
return components
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , a : Optional[int] , a : Optional[Any]=0 )-> Union[str, Any]:
"""simple docstring"""
if str(a ).startswith('mps' ):
lowercase__ = torch.manual_seed(a )
else:
lowercase__ = torch.Generator(device=a ).manual_seed(a )
lowercase__ = {
'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 SCREAMING_SNAKE_CASE_ ( self : Dict )-> Any:
"""simple docstring"""
lowercase__ = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowercase__ = self.get_dummy_components()
lowercase__ = LDMTextToImagePipeline(**a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
lowercase__ = self.get_dummy_inputs(a )
lowercase__ = pipe(**a ).images
lowercase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)
lowercase__ = np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE (unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self : Dict )-> str:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE_ ( self : str , a : str , a : Any=torch.floataa , a : Any=0 )-> Any:
"""simple docstring"""
lowercase__ = torch.manual_seed(a )
lowercase__ = np.random.RandomState(a ).standard_normal((1, 4, 32, 32) )
lowercase__ = torch.from_numpy(a ).to(device=a , dtype=a )
lowercase__ = {
'prompt': 'A painting of a squirrel eating a burger',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def SCREAMING_SNAKE_CASE_ ( self : Any )-> Dict:
"""simple docstring"""
lowercase__ = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256' ).to(a )
pipe.set_progress_bar_config(disable=a )
lowercase__ = self.get_inputs(a )
lowercase__ = pipe(**a ).images
lowercase__ = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 256, 256, 3)
lowercase__ = np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878] )
lowercase__ = np.abs(expected_slice - image_slice ).max()
assert max_diff < 1E-3
@nightly
@require_torch_gpu
class SCREAMING_SNAKE_CASE (unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self : List[Any] )-> Tuple:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , a : Any , a : Optional[int]=torch.floataa , a : List[Any]=0 )-> List[str]:
"""simple docstring"""
lowercase__ = torch.manual_seed(a )
lowercase__ = np.random.RandomState(a ).standard_normal((1, 4, 32, 32) )
lowercase__ = torch.from_numpy(a ).to(device=a , dtype=a )
lowercase__ = {
'prompt': 'A painting of a squirrel eating a burger',
'latents': latents,
'generator': generator,
'num_inference_steps': 50,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def SCREAMING_SNAKE_CASE_ ( self : List[Any] )-> List[str]:
"""simple docstring"""
lowercase__ = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256' ).to(a )
pipe.set_progress_bar_config(disable=a )
lowercase__ = self.get_inputs(a )
lowercase__ = pipe(**a ).images[0]
lowercase__ = load_numpy(
'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy' )
lowercase__ = np.abs(expected_image - image ).max()
assert max_diff < 1E-3
| 269 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
lowercase_ = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE :
_UpperCamelCase : str = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
_UpperCamelCase : Optional[str] = field(
default=UpperCAmelCase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
_UpperCamelCase : Optional[str] = field(
default='NER' , metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'} )
_UpperCamelCase : Optional[str] = field(
default=UpperCAmelCase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
_UpperCamelCase : bool = field(default=UpperCAmelCase , metadata={'help': 'Set this flag to use fast tokenization.'} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_UpperCamelCase : Optional[str] = field(
default=UpperCAmelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class SCREAMING_SNAKE_CASE :
_UpperCamelCase : str = field(
metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'} )
_UpperCamelCase : Optional[str] = field(
default=UpperCAmelCase , metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'} , )
_UpperCamelCase : int = field(
default=1_28 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
_UpperCamelCase : bool = field(
default=UpperCAmelCase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def __UpperCamelCase () -> str:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowercase__ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase__ , lowercase__ , lowercase__ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase__ , lowercase__ , lowercase__ = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
' --overwrite_output_dir to overcome.' )
lowercase__ = import_module('tasks' )
try:
lowercase__ = getattr(_SCREAMING_SNAKE_CASE , model_args.task_type )
lowercase__ = token_classification_task_clazz()
except AttributeError:
raise ValueError(
F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('Training/evaluation parameters %s' , _SCREAMING_SNAKE_CASE )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
lowercase__ = token_classification_task.get_labels(data_args.labels )
lowercase__ = dict(enumerate(_SCREAMING_SNAKE_CASE ) )
lowercase__ = len(_SCREAMING_SNAKE_CASE )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase__ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_SCREAMING_SNAKE_CASE , idalabel=_SCREAMING_SNAKE_CASE , labelaid={label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE )} , cache_dir=model_args.cache_dir , )
lowercase__ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
lowercase__ = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_SCREAMING_SNAKE_CASE , cache_dir=model_args.cache_dir , )
# Get datasets
lowercase__ = (
TokenClassificationDataset(
token_classification_task=_SCREAMING_SNAKE_CASE , data_dir=data_args.data_dir , tokenizer=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
lowercase__ = (
TokenClassificationDataset(
token_classification_task=_SCREAMING_SNAKE_CASE , data_dir=data_args.data_dir , tokenizer=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple[List[int], List[int]]:
lowercase__ = np.argmax(_SCREAMING_SNAKE_CASE , axis=2 )
lowercase__ , lowercase__ = preds.shape
lowercase__ = [[] for _ in range(_SCREAMING_SNAKE_CASE )]
lowercase__ = [[] for _ in range(_SCREAMING_SNAKE_CASE )]
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(_SCREAMING_SNAKE_CASE ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(_SCREAMING_SNAKE_CASE ) -> Dict:
lowercase__ , lowercase__ = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ),
"precision": precision_score(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ),
"recall": recall_score(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ),
"f1": fa_score(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ),
}
# Data collator
lowercase__ = DataCollatorWithPadding(_SCREAMING_SNAKE_CASE , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowercase__ = Trainer(
model=_SCREAMING_SNAKE_CASE , args=_SCREAMING_SNAKE_CASE , train_dataset=_SCREAMING_SNAKE_CASE , eval_dataset=_SCREAMING_SNAKE_CASE , compute_metrics=_SCREAMING_SNAKE_CASE , data_collator=_SCREAMING_SNAKE_CASE , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowercase__ = {}
if training_args.do_eval:
logger.info('*** Evaluate ***' )
lowercase__ = trainer.evaluate()
lowercase__ = os.path.join(training_args.output_dir , 'eval_results.txt' )
if trainer.is_world_process_zero():
with open(_SCREAMING_SNAKE_CASE , 'w' ) as writer:
logger.info('***** Eval results *****' )
for key, value in result.items():
logger.info(' %s = %s' , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
writer.write('%s = %s\n' % (key, value) )
results.update(_SCREAMING_SNAKE_CASE )
# Predict
if training_args.do_predict:
lowercase__ = TokenClassificationDataset(
token_classification_task=_SCREAMING_SNAKE_CASE , data_dir=data_args.data_dir , tokenizer=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
lowercase__ , lowercase__ , lowercase__ = trainer.predict(_SCREAMING_SNAKE_CASE )
lowercase__ , lowercase__ = align_predictions(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowercase__ = os.path.join(training_args.output_dir , 'test_results.txt' )
if trainer.is_world_process_zero():
with open(_SCREAMING_SNAKE_CASE , 'w' ) as writer:
for key, value in metrics.items():
logger.info(' %s = %s' , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
writer.write('%s = %s\n' % (key, value) )
# Save predictions
lowercase__ = os.path.join(training_args.output_dir , 'test_predictions.txt' )
if trainer.is_world_process_zero():
with open(_SCREAMING_SNAKE_CASE , 'w' ) as writer:
with open(os.path.join(data_args.data_dir , 'test.txt' ) , 'r' ) as f:
token_classification_task.write_predictions_to_file(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return results
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> Any:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 269 | 1 |
"""simple docstring"""
def UpperCAmelCase__ ( lowerCAmelCase__ :float ) -> float:
'''simple docstring'''
return 1_0 - x * x
def UpperCAmelCase__ ( lowerCAmelCase__ :float , lowerCAmelCase__ :float ) -> float:
'''simple docstring'''
if equation(lowerCAmelCase__ ) * equation(lowerCAmelCase__ ) >= 0:
raise ValueError("""Wrong space!""" )
lowercase = a
while (b - a) >= 0.01:
# Find middle point
lowercase = (a + b) / 2
# Check if middle point is root
if equation(lowerCAmelCase__ ) == 0.0:
break
# Decide the side to repeat the steps
if equation(lowerCAmelCase__ ) * equation(lowerCAmelCase__ ) < 0:
lowercase = c
else:
lowercase = c
return c
if __name__ == "__main__":
import doctest
doctest.testmod()
print(bisection(-2, 5))
print(bisection(0, 6))
| 197 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : List[Any] =logging.get_logger(__name__)
__lowerCAmelCase : Union[str, Any] ={
"""s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}
class _A ( lowerCAmelCase ):
snake_case__ : Union[str, Any] = 'open-llama'
def __init__( self , __lowerCAmelCase=10_0000 , __lowerCAmelCase=4096 , __lowerCAmelCase=1_1008 , __lowerCAmelCase=32 , __lowerCAmelCase=32 , __lowerCAmelCase="silu" , __lowerCAmelCase=2048 , __lowerCAmelCase=0.0_2 , __lowerCAmelCase=1E-6 , __lowerCAmelCase=True , __lowerCAmelCase=0 , __lowerCAmelCase=1 , __lowerCAmelCase=2 , __lowerCAmelCase=False , __lowerCAmelCase=True , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.1 , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase=None , **__lowerCAmelCase , ):
"""simple docstring"""
lowercase = vocab_size
lowercase = max_position_embeddings
lowercase = hidden_size
lowercase = intermediate_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = hidden_act
lowercase = initializer_range
lowercase = rms_norm_eps
lowercase = use_cache
lowercase = kwargs.pop(
"""use_memorry_efficient_attention""" , __lowerCAmelCase )
lowercase = hidden_dropout_prob
lowercase = attention_dropout_prob
lowercase = use_stable_embedding
lowercase = shared_input_output_embedding
lowercase = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , tie_word_embeddings=__lowerCAmelCase , **__lowerCAmelCase , )
def A__ ( self ):
"""simple docstring"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __lowerCAmelCase ) 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}' )
lowercase = self.rope_scaling.get("""type""" , __lowerCAmelCase )
lowercase = self.rope_scaling.get("""factor""" , __lowerCAmelCase )
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(__lowerCAmelCase , __lowerCAmelCase ) or rope_scaling_factor <= 1.0:
raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )
| 197 | 1 |
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
lowercase :Optional[int] = len(lowerCamelCase )
lowercase :int = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
lowercase :Union[str, Any] = True
# sum is not zero and set is empty then false
for i in range(1, required_sum + 1 ):
lowercase :Optional[int] = False
for i in range(1, arr_len + 1 ):
for j in range(1, required_sum + 1 ):
if arr[i - 1] > j:
lowercase :Optional[int] = subset[i - 1][j]
if arr[i - 1] <= j:
lowercase :List[str] = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 158 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( lowerCAmelCase):
_a = (DDIMParallelScheduler,)
_a = (('''eta''', 0.0), ('''num_inference_steps''', 50))
def SCREAMING_SNAKE_CASE ( self: Any , **_lowerCAmelCase: Optional[Any] ):
lowercase :List[Any] = {
"num_train_timesteps": 10_00,
"beta_start": 0.00_01,
"beta_end": 0.02,
"beta_schedule": "linear",
"clip_sample": True,
}
config.update(**_lowerCAmelCase )
return config
def SCREAMING_SNAKE_CASE ( self: str , **_lowerCAmelCase: Any ):
lowercase :Optional[int] = self.scheduler_classes[0]
lowercase :Dict = self.get_scheduler_config(**_lowerCAmelCase )
lowercase :List[str] = scheduler_class(**_lowerCAmelCase )
lowercase , lowercase :str = 10, 0.0
lowercase :List[Any] = self.dummy_model()
lowercase :int = self.dummy_sample_deter
scheduler.set_timesteps(_lowerCAmelCase )
for t in scheduler.timesteps:
lowercase :Optional[int] = model(_lowerCAmelCase , _lowerCAmelCase )
lowercase :Dict = scheduler.step(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ).prev_sample
return sample
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
for timesteps in [1_00, 5_00, 10_00]:
self.check_over_configs(num_train_timesteps=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: int ):
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=_lowerCAmelCase )
lowercase :Optional[Any] = self.scheduler_classes[0]
lowercase :List[str] = self.get_scheduler_config(steps_offset=1 )
lowercase :Optional[int] = scheduler_class(**_lowerCAmelCase )
scheduler.set_timesteps(5 )
assert torch.equal(scheduler.timesteps , torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )
def SCREAMING_SNAKE_CASE ( self: Tuple ):
for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1] , [0.0_02, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=_lowerCAmelCase , beta_end=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: int ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
for timestep_spacing in ["trailing", "leading"]:
self.check_over_configs(timestep_spacing=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Optional[int] ):
for rescale_betas_zero_snr in [True, False]:
self.check_over_configs(rescale_betas_zero_snr=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Dict ):
self.check_over_configs(thresholding=_lowerCAmelCase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(
thresholding=_lowerCAmelCase , prediction_type=_lowerCAmelCase , sample_max_value=_lowerCAmelCase , )
def SCREAMING_SNAKE_CASE ( self: str ):
for t in [1, 10, 49]:
self.check_over_forward(time_step=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: int ):
for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 5_00] ):
self.check_over_forward(time_step=_lowerCAmelCase , num_inference_steps=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: str ):
for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0] ):
self.check_over_forward(time_step=_lowerCAmelCase , eta=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: str ):
lowercase :Dict = self.scheduler_classes[0]
lowercase :Tuple = self.get_scheduler_config()
lowercase :Optional[Any] = scheduler_class(**_lowerCAmelCase )
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(4_20 , 4_00 ) - 0.1_47_71 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(9_80 , 9_60 ) - 0.3_24_60 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(4_87 , 4_86 ) - 0.0_09_79 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(9_99 , 9_98 ) - 0.02 ) ) < 1e-5
def SCREAMING_SNAKE_CASE ( self: List[str] ):
lowercase :Union[str, Any] = self.scheduler_classes[0]
lowercase :Union[str, Any] = self.get_scheduler_config()
lowercase :Union[str, Any] = scheduler_class(**_lowerCAmelCase )
lowercase , lowercase :Union[str, Any] = 10, 0.0
scheduler.set_timesteps(_lowerCAmelCase )
lowercase :Dict = self.dummy_model()
lowercase :Dict = self.dummy_sample_deter
lowercase :Union[str, Any] = self.dummy_sample_deter + 0.1
lowercase :int = self.dummy_sample_deter - 0.1
lowercase :Dict = samplea.shape[0]
lowercase :Tuple = torch.stack([samplea, samplea, samplea] , dim=0 )
lowercase :Optional[Any] = torch.arange(_lowerCAmelCase )[0:3, None].repeat(1 , _lowerCAmelCase )
lowercase :Union[str, Any] = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
lowercase :Optional[int] = scheduler.batch_step_no_noise(_lowerCAmelCase , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) , _lowerCAmelCase )
lowercase :int = torch.sum(torch.abs(_lowerCAmelCase ) )
lowercase :Optional[int] = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 11_47.79_04 ) < 1e-2
assert abs(result_mean.item() - 0.49_82 ) < 1e-3
def SCREAMING_SNAKE_CASE ( self: List[Any] ):
lowercase :int = self.full_loop()
lowercase :Optional[int] = torch.sum(torch.abs(_lowerCAmelCase ) )
lowercase :Any = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 1_72.00_67 ) < 1e-2
assert abs(result_mean.item() - 0.22_39_67 ) < 1e-3
def SCREAMING_SNAKE_CASE ( self: Any ):
lowercase :Dict = self.full_loop(prediction_type="v_prediction" )
lowercase :int = torch.sum(torch.abs(_lowerCAmelCase ) )
lowercase :Union[str, Any] = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 52.53_02 ) < 1e-2
assert abs(result_mean.item() - 0.06_84 ) < 1e-3
def SCREAMING_SNAKE_CASE ( self: Optional[int] ):
# We specify different beta, so that the first alpha is 0.99
lowercase :List[Any] = self.full_loop(set_alpha_to_one=_lowerCAmelCase , beta_start=0.01 )
lowercase :List[Any] = torch.sum(torch.abs(_lowerCAmelCase ) )
lowercase :Union[str, Any] = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 1_49.82_95 ) < 1e-2
assert abs(result_mean.item() - 0.19_51 ) < 1e-3
def SCREAMING_SNAKE_CASE ( self: Any ):
# We specify different beta, so that the first alpha is 0.99
lowercase :Tuple = self.full_loop(set_alpha_to_one=_lowerCAmelCase , beta_start=0.01 )
lowercase :str = torch.sum(torch.abs(_lowerCAmelCase ) )
lowercase :List[str] = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 1_49.07_84 ) < 1e-2
assert abs(result_mean.item() - 0.19_41 ) < 1e-3
| 158 | 1 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
with open(lowerCAmelCase_ ) as metadata_file:
__SCREAMING_SNAKE_CASE = json.load(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = LukeConfig(use_entity_aware_attention=lowerCAmelCase_ , **metadata["model_config"] )
# Load in the weights from the checkpoint_path
__SCREAMING_SNAKE_CASE = torch.load(lowerCAmelCase_ , map_location="cpu" )
# Load the entity vocab file
__SCREAMING_SNAKE_CASE = load_entity_vocab(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"] )
# Add special tokens to the token vocabulary for downstream tasks
__SCREAMING_SNAKE_CASE = AddedToken("<ent>" , lstrip=lowerCAmelCase_ , rstrip=lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = AddedToken("<ent2>" , lstrip=lowerCAmelCase_ , rstrip=lowerCAmelCase_ )
tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f"""Saving tokenizer to {pytorch_dump_folder_path}""" )
tokenizer.save_pretrained(lowerCAmelCase_ )
with open(os.path.join(lowerCAmelCase_ , LukeTokenizer.vocab_files_names["entity_vocab_file"] ) , "w" ) as f:
json.dump(lowerCAmelCase_ , lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = LukeTokenizer.from_pretrained(lowerCAmelCase_ )
# Initialize the embeddings of the special tokens
__SCREAMING_SNAKE_CASE = state_dict["embeddings.word_embeddings.weight"]
__SCREAMING_SNAKE_CASE = word_emb[tokenizer.convert_tokens_to_ids(["@"] )[0]].unsqueeze(0 )
__SCREAMING_SNAKE_CASE = word_emb[tokenizer.convert_tokens_to_ids(["#"] )[0]].unsqueeze(0 )
__SCREAMING_SNAKE_CASE = torch.cat([word_emb, ent_emb, enta_emb] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
__SCREAMING_SNAKE_CASE = f"""encoder.layer.{layer_index}.attention.self."""
__SCREAMING_SNAKE_CASE = state_dict[prefix + matrix_name]
__SCREAMING_SNAKE_CASE = state_dict[prefix + matrix_name]
__SCREAMING_SNAKE_CASE = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
__SCREAMING_SNAKE_CASE = state_dict["entity_embeddings.entity_embeddings.weight"]
__SCREAMING_SNAKE_CASE = entity_emb[entity_vocab["[MASK]"]]
__SCREAMING_SNAKE_CASE = LukeModel(config=lowerCAmelCase_ ).eval()
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = model.load_state_dict(lowerCAmelCase_ , strict=lowerCAmelCase_ )
if not (len(lowerCAmelCase_ ) == 1 and missing_keys[0] == "embeddings.position_ids"):
raise ValueError(f"""Missing keys {', '.join(lowerCAmelCase_ )}. Expected only missing embeddings.position_ids""" )
if not (all(key.startswith("entity_predictions" ) or key.startswith("lm_head" ) for key in unexpected_keys )):
raise ValueError(
"Unexpected keys"
f""" {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions' ) or key.startswith('lm_head' ))] )}""" )
# Check outputs
__SCREAMING_SNAKE_CASE = LukeTokenizer.from_pretrained(lowerCAmelCase_ , task="entity_classification" )
__SCREAMING_SNAKE_CASE = (
"Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
" new world number one avoid a humiliating second- round exit at Wimbledon ."
)
__SCREAMING_SNAKE_CASE = (39, 42)
__SCREAMING_SNAKE_CASE = tokenizer(lowerCAmelCase_ , entity_spans=[span] , add_prefix_space=lowerCAmelCase_ , return_tensors="pt" )
__SCREAMING_SNAKE_CASE = model(**lowerCAmelCase_ )
# Verify word hidden states
if model_size == "large":
__SCREAMING_SNAKE_CASE = torch.Size((1, 42, 1024) )
__SCREAMING_SNAKE_CASE = torch.tensor(
[[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]] )
else: # base
__SCREAMING_SNAKE_CASE = torch.Size((1, 42, 768) )
__SCREAMING_SNAKE_CASE = torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}""" )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , lowerCAmelCase_ , atol=1E-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
__SCREAMING_SNAKE_CASE = torch.Size((1, 1, 1024) )
__SCREAMING_SNAKE_CASE = torch.tensor([[0.0466, -0.0106, -0.0179]] )
else: # base
__SCREAMING_SNAKE_CASE = torch.Size((1, 1, 768) )
__SCREAMING_SNAKE_CASE = torch.tensor([[0.1457, 0.1044, 0.0174]] )
if not (outputs.entity_last_hidden_state.shape != expected_shape):
raise ValueError(
f"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
f""" {expected_shape}""" )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , lowerCAmelCase_ , atol=1E-4 ):
raise ValueError
# Finally, save our PyTorch model and tokenizer
print("Saving PyTorch model to {}".format(lowerCAmelCase_ ) )
model.save_pretrained(lowerCAmelCase_ )
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = {}
with open(lowerCAmelCase_ , "r" , encoding="utf-8" ) as f:
for index, line in enumerate(lowerCAmelCase_ ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = line.rstrip().split("\t" )
__SCREAMING_SNAKE_CASE = index
return entity_vocab
if __name__ == "__main__":
a__ : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
parser.add_argument(
'''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
)
parser.add_argument(
'''--entity_vocab_path''',
default=None,
type=str,
help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
)
parser.add_argument(
'''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
)
a__ : int = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 54 |
"""simple docstring"""
class A_ :
"""simple docstring"""
def __init__( self :List[Any] , lowercase_ :int ) -> None:
UpperCAmelCase = size
UpperCAmelCase = [0] * size
UpperCAmelCase = [0] * size
@staticmethod
def UpperCAmelCase__ ( lowercase_ :int ) -> int:
return index | (index + 1)
@staticmethod
def UpperCAmelCase__ ( lowercase_ :int ) -> int:
return (index & (index + 1)) - 1
def UpperCAmelCase__ ( self :Any , lowercase_ :int , lowercase_ :int ) -> None:
UpperCAmelCase = value
while index < self.size:
UpperCAmelCase = self.get_prev(lowercase_ ) + 1
if current_left_border == index:
UpperCAmelCase = value
else:
UpperCAmelCase = max(lowercase_ , lowercase_ , lowercase_ )
UpperCAmelCase = self.get_next(lowercase_ )
def UpperCAmelCase__ ( self :List[str] , lowercase_ :int , lowercase_ :int ) -> int:
right -= 1 # Because of right is exclusive
UpperCAmelCase = 0
while left <= right:
UpperCAmelCase = self.get_prev(lowercase_ )
if left <= current_left:
UpperCAmelCase = max(lowercase_ , self.tree[right] )
UpperCAmelCase = current_left
else:
UpperCAmelCase = max(lowercase_ , self.arr[right] )
right -= 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 78 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import 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
a_ : Tuple = False
@skip_mps
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : Union[str, Any] =StableDiffusionAttendAndExcitePipeline
lowercase : Tuple =False
lowercase : Optional[Any] =TEXT_TO_IMAGE_PARAMS
lowercase : Dict =TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'} )
lowercase : Optional[Any] =TEXT_TO_IMAGE_IMAGE_PARAMS
lowercase : Optional[Any] =TEXT_TO_IMAGE_IMAGE_PARAMS
@classmethod
def lowercase__ ( cls ):
"""simple docstring"""
super().setUpClass()
torch.use_deterministic_algorithms(lowerCAmelCase )
@classmethod
def lowercase__ ( cls ):
"""simple docstring"""
super().tearDownClass()
torch.use_deterministic_algorithms(lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ =UNetaDConditionModel(
block_out_channels=(32, 64), layers_per_block=1, sample_size=32, in_channels=4, out_channels=4, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''), up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''), cross_attention_dim=32, attention_head_dim=(2, 4), use_linear_projection=lowerCAmelCase, )
lowerCamelCase_ =DDIMScheduler(
beta_start=0.0_0_0_8_5, beta_end=0.0_1_2, beta_schedule='''scaled_linear''', clip_sample=lowerCAmelCase, set_alpha_to_one=lowerCAmelCase, )
torch.manual_seed(0 )
lowerCamelCase_ =AutoencoderKL(
block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''], up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''], latent_channels=4, sample_size=128, )
torch.manual_seed(0 )
lowerCamelCase_ =CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_000, hidden_act='''gelu''', projection_dim=512, )
lowerCamelCase_ =CLIPTextModel(lowerCAmelCase )
lowerCamelCase_ =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
lowerCamelCase_ ={
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=0 ):
"""simple docstring"""
if str(lowerCAmelCase ).startswith('''mps''' ):
lowerCamelCase_ =torch.manual_seed(lowerCAmelCase )
else:
lowerCamelCase_ =torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
lowerCamelCase_ =lowerCamelCase_ ={
'''prompt''': '''a cat and a frog''',
'''token_indices''': [2, 5],
'''generator''': generator,
'''num_inference_steps''': 1,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
'''max_iter_to_alter''': 2,
'''thresholds''': {0: 0.7},
}
return inputs
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu'''
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =self.pipeline_class(**lowerCAmelCase )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1]
self.assertEqual(image.shape, (1, 64, 64, 3) )
lowerCamelCase_ =np.array(
[0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6] )
lowerCamelCase_ =np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCAmelCase, 1e-3 )
def lowercase__ ( self ):
"""simple docstring"""
super().test_cpu_offload_forward_pass(expected_max_diff=5e-4 )
def lowercase__ ( self ):
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowercase__ ( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(batch_size=2, expected_max_diff=7e-4 )
def lowercase__ ( self ):
"""simple docstring"""
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def lowercase__ ( self ):
"""simple docstring"""
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4 )
def lowercase__ ( self ):
"""simple docstring"""
super().test_save_load_local(expected_max_difference=5e-4 )
def lowercase__ ( self ):
"""simple docstring"""
super().test_save_load_optional_components(expected_max_difference=4e-4 )
@require_torch_gpu
@slow
class __UpperCamelCase ( unittest.TestCase ):
@classmethod
def lowercase__ ( cls ):
"""simple docstring"""
super().setUpClass()
torch.use_deterministic_algorithms(lowerCAmelCase )
@classmethod
def lowercase__ ( cls ):
"""simple docstring"""
super().tearDownClass()
torch.use_deterministic_algorithms(lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =torch.manual_seed(51 )
lowerCamelCase_ =StableDiffusionAttendAndExcitePipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''', safety_checker=lowerCAmelCase, torch_dtype=torch.floataa )
pipe.to('''cuda''' )
lowerCamelCase_ ='''a painting of an elephant with glasses'''
lowerCamelCase_ =[5, 7]
lowerCamelCase_ =pipe(
prompt=lowerCAmelCase, token_indices=lowerCAmelCase, guidance_scale=7.5, generator=lowerCAmelCase, num_inference_steps=5, max_iter_to_alter=5, output_type='''numpy''', ).images[0]
lowerCamelCase_ =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy''' )
assert np.abs((expected_image - image).max() ) < 5e-1
| 6 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def a_ ( __snake_case : int = 150_0000 ) -> int:
"""simple docstring"""
lowerCamelCase_ =defaultdict(__snake_case )
lowerCamelCase_ =2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , __snake_case , 2 ):
if gcd(__snake_case , __snake_case ) > 1:
continue
lowerCamelCase_ =2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(__snake_case , limit + 1 , __snake_case ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 6 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',
'BridgeTower/bridgetower-base-itm-mlm': (
'https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'
),
}
class __lowerCAmelCase ( SCREAMING_SNAKE_CASE ):
_a = """bridgetower_vision_model"""
def __init__( self , lowerCAmelCase=768 , lowerCAmelCase=12 , lowerCAmelCase=3 , lowerCAmelCase=16 , lowerCAmelCase=288 , lowerCAmelCase=1 , lowerCAmelCase=1e-05 , lowerCAmelCase=False , lowerCAmelCase=True , lowerCAmelCase=False , **lowerCAmelCase , ) -> Dict:
'''simple docstring'''
super().__init__(**lowerCAmelCase )
_lowercase =hidden_size
_lowercase =num_hidden_layers
_lowercase =num_channels
_lowercase =patch_size
_lowercase =image_size
_lowercase =initializer_factor
_lowercase =layer_norm_eps
_lowercase =stop_gradient
_lowercase =share_layernorm
_lowercase =remove_last_layer
@classmethod
def A__ ( cls , lowerCAmelCase , **lowerCAmelCase ) -> "PretrainedConfig":
'''simple docstring'''
_lowercase , _lowercase =cls.get_config_dict(lowerCAmelCase , **lowerCAmelCase )
if config_dict.get('model_type' ) == "bridgetower":
_lowercase =config_dict['text_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(lowerCAmelCase , **lowerCAmelCase )
class __lowerCAmelCase ( SCREAMING_SNAKE_CASE ):
_a = """bridgetower_text_model"""
def __init__( self , lowerCAmelCase=50_265 , lowerCAmelCase=768 , lowerCAmelCase=12 , lowerCAmelCase=12 , lowerCAmelCase=1 , lowerCAmelCase=3_072 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=514 , lowerCAmelCase=1 , lowerCAmelCase=1e-05 , lowerCAmelCase=1 , lowerCAmelCase=0 , lowerCAmelCase=2 , lowerCAmelCase="absolute" , lowerCAmelCase=True , **lowerCAmelCase , ) -> Any:
'''simple docstring'''
super().__init__(**lowerCAmelCase )
_lowercase =vocab_size
_lowercase =hidden_size
_lowercase =num_hidden_layers
_lowercase =num_attention_heads
_lowercase =hidden_act
_lowercase =initializer_factor
_lowercase =intermediate_size
_lowercase =hidden_dropout_prob
_lowercase =attention_probs_dropout_prob
_lowercase =max_position_embeddings
_lowercase =type_vocab_size
_lowercase =layer_norm_eps
_lowercase =position_embedding_type
_lowercase =use_cache
_lowercase =pad_token_id
_lowercase =bos_token_id
_lowercase =eos_token_id
@classmethod
def A__ ( cls , lowerCAmelCase , **lowerCAmelCase ) -> "PretrainedConfig":
'''simple docstring'''
_lowercase , _lowercase =cls.get_config_dict(lowerCAmelCase , **lowerCAmelCase )
if config_dict.get('model_type' ) == "bridgetower":
_lowercase =config_dict['text_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(lowerCAmelCase , **lowerCAmelCase )
class __lowerCAmelCase ( SCREAMING_SNAKE_CASE ):
_a = """bridgetower"""
def __init__( self , lowerCAmelCase=True , lowerCAmelCase="gelu" , lowerCAmelCase=768 , lowerCAmelCase=1 , lowerCAmelCase=1e-05 , lowerCAmelCase=False , lowerCAmelCase="add" , lowerCAmelCase=12 , lowerCAmelCase=6 , lowerCAmelCase=False , lowerCAmelCase=False , lowerCAmelCase=None , lowerCAmelCase=None , **lowerCAmelCase , ) -> Optional[Any]:
'''simple docstring'''
_lowercase =kwargs.pop('text_config_dict' , lowerCAmelCase )
_lowercase =kwargs.pop('vision_config_dict' , lowerCAmelCase )
super().__init__(**lowerCAmelCase )
_lowercase =share_cross_modal_transformer_layers
_lowercase =hidden_act
_lowercase =hidden_size
_lowercase =initializer_factor
_lowercase =layer_norm_eps
_lowercase =share_link_tower_layers
_lowercase =link_tower_type
_lowercase =num_attention_heads
_lowercase =num_hidden_layers
_lowercase =tie_word_embeddings
_lowercase =init_layernorm_from_vision_encoder
if text_config is None:
_lowercase ={}
logger.info('`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.' )
if vision_config is None:
_lowercase ={}
logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.' )
_lowercase =BridgeTowerTextConfig(**lowerCAmelCase )
_lowercase =BridgeTowerVisionConfig(**lowerCAmelCase )
@classmethod
def A__ ( cls , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **lowerCAmelCase )
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
_lowercase =copy.deepcopy(self.__dict__ )
_lowercase =self.text_config.to_dict()
_lowercase =self.vision_config.to_dict()
_lowercase =self.__class__.model_type
return output
| 205 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def a ( A__ : Dict ) -> str:
"""simple docstring"""
_lowercase ={}
_lowercase =job['started_at']
_lowercase =job['completed_at']
_lowercase =date_parser.parse(A__ )
_lowercase =date_parser.parse(A__ )
_lowercase =round((end_datetime - start_datetime).total_seconds() / 60.0 )
_lowercase =start
_lowercase =end
_lowercase =duration_in_min
return job_info
def a ( A__ : Dict , A__ : str=None ) -> Tuple:
"""simple docstring"""
_lowercase =None
if token is not None:
_lowercase ={'Accept': 'application/vnd.github+json', 'Authorization': F'''Bearer {token}'''}
_lowercase =F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
_lowercase =requests.get(A__ , headers=A__ ).json()
_lowercase ={}
try:
job_time.update({job['name']: extract_time_from_single_job(A__ ) for job in result['jobs']} )
_lowercase =math.ceil((result['total_count'] - 100) / 100 )
for i in range(A__ ):
_lowercase =requests.get(url + F'''&page={i + 2}''' , headers=A__ ).json()
job_time.update({job['name']: extract_time_from_single_job(A__ ) for job in result['jobs']} )
return job_time
except Exception:
print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' )
return {}
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
lowercase_ = parser.parse_args()
lowercase_ = get_job_time(args.workflow_run_id)
lowercase_ = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(f"{k}: {v['duration']}")
| 205 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
snake_case_ : List[str] = logging.get_logger(__name__) # pylint: disable=invalid-name
snake_case_ : Optional[int] = '\n Examples:\n ```py\n >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n >>> pipe_prior.to("cuda")\n >>> prompt = "red cat, 4k photo"\n >>> out = pipe_prior(prompt)\n >>> image_emb = out.image_embeds\n >>> zero_image_emb = out.negative_image_embeds\n >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n >>> pipe.to("cuda")\n >>> image = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=50,\n ... ).images\n >>> image[0].save("cat.png")\n ```\n'
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_=8 ):
_UpperCamelCase : Optional[int] = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
_UpperCamelCase : List[Any] = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class lowercase__ ( lowercase ):
def __init__( self : List[str] ,lowerCamelCase__ : UNetaDConditionModel ,lowerCamelCase__ : DDPMScheduler ,lowerCamelCase__ : VQModel ,):
'''simple docstring'''
super().__init__()
self.register_modules(
unet=lowerCamelCase__ ,scheduler=lowerCamelCase__ ,movq=lowerCamelCase__ ,)
_UpperCamelCase : str = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def UpperCamelCase_ ( self : Any ,lowerCamelCase__ : Dict ,lowerCamelCase__ : Any ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Dict ,lowerCamelCase__ : Dict ):
'''simple docstring'''
if latents is None:
_UpperCamelCase : Any = randn_tensor(lowerCamelCase__ ,generator=lowerCamelCase__ ,device=lowerCamelCase__ ,dtype=lowerCamelCase__ )
else:
if latents.shape != shape:
raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}' )
_UpperCamelCase : Tuple = latents.to(lowerCamelCase__ )
_UpperCamelCase : Dict = latents * scheduler.init_noise_sigma
return latents
def UpperCamelCase_ ( self : List[Any] ,lowerCamelCase__ : str=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
_UpperCamelCase : str = torch.device(F'cuda:{gpu_id}' )
_UpperCamelCase : Dict = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowerCamelCase__ ,lowerCamelCase__ )
def UpperCamelCase_ ( self : Optional[int] ,lowerCamelCase__ : int=0 ):
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version('>=' ,'0.17.0.dev0' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' )
_UpperCamelCase : List[Any] = torch.device(F'cuda:{gpu_id}' )
if self.device.type != "cpu":
self.to('cpu' ,silence_dtype_warnings=lowerCamelCase__ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
_UpperCamelCase : Optional[int] = None
for cpu_offloaded_model in [self.unet, self.movq]:
_UpperCamelCase : int = cpu_offload_with_hook(lowerCamelCase__ ,lowerCamelCase__ ,prev_module_hook=lowerCamelCase__ )
# We'll offload the last model manually.
_UpperCamelCase : Dict = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
if not hasattr(self.unet ,'_hf_hook' ):
return self.device
for module in self.unet.modules():
if (
hasattr(lowerCamelCase__ ,'_hf_hook' )
and hasattr(module._hf_hook ,'execution_device' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(lowerCamelCase__ )
def __call__( self : int ,lowerCamelCase__ : Union[torch.FloatTensor, List[torch.FloatTensor]] ,lowerCamelCase__ : Union[torch.FloatTensor, List[torch.FloatTensor]] ,lowerCamelCase__ : int = 512 ,lowerCamelCase__ : int = 512 ,lowerCamelCase__ : int = 100 ,lowerCamelCase__ : float = 4.0 ,lowerCamelCase__ : int = 1 ,lowerCamelCase__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None ,lowerCamelCase__ : Optional[torch.FloatTensor] = None ,lowerCamelCase__ : Optional[str] = "pil" ,lowerCamelCase__ : bool = True ,):
'''simple docstring'''
_UpperCamelCase : Any = self._execution_device
_UpperCamelCase : List[str] = guidance_scale > 1.0
if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
_UpperCamelCase : Dict = torch.cat(lowerCamelCase__ ,dim=0 )
_UpperCamelCase : List[Any] = image_embeds.shape[0] * num_images_per_prompt
if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
_UpperCamelCase : str = torch.cat(lowerCamelCase__ ,dim=0 )
if do_classifier_free_guidance:
_UpperCamelCase : Union[str, Any] = image_embeds.repeat_interleave(lowerCamelCase__ ,dim=0 )
_UpperCamelCase : int = negative_image_embeds.repeat_interleave(lowerCamelCase__ ,dim=0 )
_UpperCamelCase : Dict = torch.cat([negative_image_embeds, image_embeds] ,dim=0 ).to(dtype=self.unet.dtype ,device=lowerCamelCase__ )
self.scheduler.set_timesteps(lowerCamelCase__ ,device=lowerCamelCase__ )
_UpperCamelCase : Tuple = self.scheduler.timesteps
_UpperCamelCase : Optional[Any] = self.unet.config.in_channels
_UpperCamelCase : List[Any] = downscale_height_and_width(lowerCamelCase__ ,lowerCamelCase__ ,self.movq_scale_factor )
# create initial latent
_UpperCamelCase : str = self.prepare_latents(
(batch_size, num_channels_latents, height, width) ,image_embeds.dtype ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,self.scheduler ,)
for i, t in enumerate(self.progress_bar(lowerCamelCase__ ) ):
# expand the latents if we are doing classifier free guidance
_UpperCamelCase : Dict = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_UpperCamelCase : List[str] = {'image_embeds': image_embeds}
_UpperCamelCase : Optional[Any] = self.unet(
sample=lowerCamelCase__ ,timestep=lowerCamelCase__ ,encoder_hidden_states=lowerCamelCase__ ,added_cond_kwargs=lowerCamelCase__ ,return_dict=lowerCamelCase__ ,)[0]
if do_classifier_free_guidance:
_UpperCamelCase : Union[str, Any] = noise_pred.split(latents.shape[1] ,dim=1 )
_UpperCamelCase : Union[str, Any] = noise_pred.chunk(2 )
_UpperCamelCase : int = variance_pred.chunk(2 )
_UpperCamelCase : Optional[int] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
_UpperCamelCase : Any = 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 : str = noise_pred.split(latents.shape[1] ,dim=1 )
# compute the previous noisy sample x_t -> x_t-1
_UpperCamelCase : Tuple = self.scheduler.step(
lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,generator=lowerCamelCase__ ,)[0]
# post-processing
_UpperCamelCase : int = self.movq.decode(lowerCamelCase__ ,force_not_quantize=lowerCamelCase__ )['sample']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}' )
if output_type in ["np", "pil"]:
_UpperCamelCase : List[str] = image * 0.5 + 0.5
_UpperCamelCase : List[Any] = image.clamp(0 ,1 )
_UpperCamelCase : Optional[int] = image.cpu().permute(0 ,2 ,3 ,1 ).float().numpy()
if output_type == "pil":
_UpperCamelCase : List[str] = self.numpy_to_pil(lowerCamelCase__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCamelCase__ )
| 351 |
'''simple docstring'''
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 lowercase__ ( unittest.TestCase ):
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
_UpperCamelCase : Any = 'hf-internal-testing/tiny-random-t5'
_UpperCamelCase : str = AutoTokenizer.from_pretrained(lowerCamelCase__ )
_UpperCamelCase : int = AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__ )
_UpperCamelCase : List[str] = tokenizer('This is me' ,return_tensors='pt' )
_UpperCamelCase : str = model.to_bettertransformer()
self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
_UpperCamelCase : Optional[Any] = model.generate(**lowerCamelCase__ )
_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(lowerCamelCase__ )
_UpperCamelCase : List[Any] = AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__ )
self.assertFalse(
any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
_UpperCamelCase : Optional[Any] = model_reloaded.generate(**lowerCamelCase__ )
self.assertTrue(torch.allclose(lowerCamelCase__ ,lowerCamelCase__ ) )
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
_UpperCamelCase : List[Any] = 'hf-internal-testing/tiny-random-t5'
_UpperCamelCase : List[Any] = AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__ )
_UpperCamelCase : List[str] = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(lowerCamelCase__ ):
model.save_pretrained(lowerCamelCase__ )
_UpperCamelCase : str = model.reverse_bettertransformer()
model.save_pretrained(lowerCamelCase__ )
| 236 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
UpperCAmelCase_ = [
# tf -> hf
("""/""", """."""),
("""layer_""", """layers."""),
("""kernel""", """weight"""),
("""beta""", """bias"""),
("""gamma""", """weight"""),
("""pegasus""", """model"""),
]
UpperCAmelCase_ = [
(""".output.dense""", """.fc2"""),
("""intermediate.LayerNorm""", """final_layer_norm"""),
("""intermediate.dense""", """fc1"""),
]
UpperCAmelCase_ = (
INIT_COMMON
+ [
("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
("""attention.output.dense""", """self_attn.out_proj"""),
("""attention.self""", """self_attn"""),
("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
("""attention.encdec""", """encoder_attn"""),
("""key""", """k_proj"""),
("""value""", """v_proj"""),
("""query""", """q_proj"""),
("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
]
+ END_COMMON
)
UpperCAmelCase_ = (
INIT_COMMON
+ [
("""embeddings.word_embeddings""", """shared.weight"""),
("""embeddings.position_embeddings""", """embed_positions.weight"""),
("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
("""attention.output.dense""", """self_attn.output"""),
("""attention.self""", """self_attn.self"""),
("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
]
+ END_COMMON
)
UpperCAmelCase_ = [
"""encdec/key/bias""",
"""encdec/query/bias""",
"""encdec/value/bias""",
"""self/key/bias""",
"""self/query/bias""",
"""self/value/bias""",
"""encdec_output/dense/bias""",
"""attention/output/dense/bias""",
]
def lowerCAmelCase_ ( __UpperCAmelCase: Any , __UpperCAmelCase: Dict ) -> Any:
for tf_name, hf_name in patterns:
UpperCamelCase__ : str = k.replace(lowerCAmelCase__ , lowerCAmelCase__ )
return k
def lowerCAmelCase_ ( __UpperCAmelCase: dict , __UpperCAmelCase: dict ) -> BigBirdPegasusForConditionalGeneration:
UpperCamelCase__ : str = BigBirdPegasusConfig(**lowerCAmelCase__ )
UpperCamelCase__ : List[str] = BigBirdPegasusForConditionalGeneration(lowerCAmelCase__ )
UpperCamelCase__ : str = torch_model.state_dict()
UpperCamelCase__ : str = {}
# separating decoder weights
UpperCamelCase__ : Any = {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder''' )}
UpperCamelCase__ : Optional[Any] = {k: tf_weights[k] for k in tf_weights if not k.startswith('''pegasus/decoder''' )}
for k, v in tqdm(decoder_weights.items() , '''tf -> hf conversion''' ):
UpperCamelCase__ : Optional[Any] = [k.endswith(lowerCAmelCase__ ) for ending in KEYS_TO_IGNORE]
if any(lowerCAmelCase__ ):
continue
UpperCamelCase__ : Optional[Any] = DECODER_PATTERNS
UpperCamelCase__ : Union[str, Any] = rename_state_dict_key(lowerCAmelCase__ , lowerCAmelCase__ )
if new_k not in state_dict:
raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})" )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
UpperCamelCase__ : Optional[Any] = v.T
UpperCamelCase__ : str = torch.from_numpy(lowerCAmelCase__ )
assert v.shape == state_dict[new_k].shape, f"{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"
for k, v in tqdm(remaining_weights.items() , '''tf -> hf conversion''' ):
UpperCamelCase__ : str = [k.endswith(lowerCAmelCase__ ) for ending in KEYS_TO_IGNORE]
if any(lowerCAmelCase__ ):
continue
UpperCamelCase__ : Optional[int] = REMAINING_PATTERNS
UpperCamelCase__ : Dict = rename_state_dict_key(lowerCAmelCase__ , lowerCAmelCase__ )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})" )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
UpperCamelCase__ : Optional[int] = v.T
UpperCamelCase__ : Dict = torch.from_numpy(lowerCAmelCase__ )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, f"{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"
UpperCamelCase__ : Dict = mapping['model.embed_positions.weight']
UpperCamelCase__ : Optional[int] = mapping.pop('''model.embed_positions.weight''' )
UpperCamelCase__ : Dict = torch_model.load_state_dict(lowerCAmelCase__ , strict=lowerCAmelCase__ )
UpperCamelCase__ : List[Any] = [
k
for k in missing
if k
not in [
'final_logits_bias',
'model.encoder.embed_tokens.weight',
'model.decoder.embed_tokens.weight',
'lm_head.weight',
]
]
assert unexpected_missing == [], f"no matches found for the following torch keys {unexpected_missing}"
assert extra == [], f"no matches found for the following tf keys {extra}"
return torch_model
def lowerCAmelCase_ ( __UpperCAmelCase: List[Any] ) -> Dict:
UpperCamelCase__ : Any = tf.train.list_variables(lowerCAmelCase__ )
UpperCamelCase__ : int = {}
UpperCamelCase__ : Optional[int] = ['global_step']
for name, shape in tqdm(lowerCAmelCase__ , desc='''converting tf checkpoint to dict''' ):
UpperCamelCase__ : Any = any(pat in name for pat in ignore_name )
if skip_key:
continue
UpperCamelCase__ : Union[str, Any] = tf.train.load_variable(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCamelCase__ : Any = array
return tf_weights
def lowerCAmelCase_ ( __UpperCAmelCase: str , __UpperCAmelCase: str , __UpperCAmelCase: dict ) -> List[Any]:
UpperCamelCase__ : List[str] = get_tf_weights_as_numpy(lowerCAmelCase__ )
UpperCamelCase__ : Dict = convert_bigbird_pegasus(lowerCAmelCase__ , lowerCAmelCase__ )
torch_model.save_pretrained(lowerCAmelCase__ )
if __name__ == "__main__":
UpperCAmelCase_ = argparse.ArgumentParser()
parser.add_argument('--tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
parser.add_argument('--save_dir', default=None, type=str, help='Path to the output PyTorch model.')
UpperCAmelCase_ = parser.parse_args()
UpperCAmelCase_ = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 201 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowercase__ : Dict = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ ( lowercase_, unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = XLMRobertaTokenizer
_SCREAMING_SNAKE_CASE = XLMRobertaTokenizerFast
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase_ : Any = XLMRobertaTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
lowerCAmelCase_ : Any = '<pad>'
lowerCAmelCase_ : int = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
lowerCAmelCase_ : int = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 1_0_0_2 )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_2 )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
lowerCAmelCase_ : int = XLMRobertaTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Optional[Any] = tokenizer.tokenize('This is a test' )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
lowerCAmelCase_ : int = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
lowerCAmelCase_ : Any = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
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_ : Union[str, Any] = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def SCREAMING_SNAKE_CASE__ ( self : int ):
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_ : List[str] = (self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
lowerCAmelCase_ : str = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Optional[int] = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : str = tempfile.mkdtemp()
lowerCAmelCase_ : int = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Optional[int] = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# 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_ : Optional[Any] = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
lowerCAmelCase_ : int = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Optional[int] = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=True
lowerCAmelCase_ : str = tempfile.mkdtemp()
lowerCAmelCase_ : List[str] = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : str = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
lowerCAmelCase_ : str = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Union[str, Any] = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=False
lowerCAmelCase_ : int = tempfile.mkdtemp()
lowerCAmelCase_ : Optional[Any] = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : int = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# 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_ : List[str] = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Optional[int] = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base' )
def SCREAMING_SNAKE_CASE__ ( self : int ):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(SCREAMING_SNAKE_CASE_ , f.name )
lowerCAmelCase_ : Tuple = XLMRobertaTokenizer(f.name , keep_accents=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Dict = pickle.dumps(SCREAMING_SNAKE_CASE_ )
pickle.loads(SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
if not self.test_rust_tokenizer:
return
lowerCAmelCase_ : Union[str, Any] = self.get_tokenizer()
lowerCAmelCase_ : Dict = self.get_rust_tokenizer()
lowerCAmelCase_ : Tuple = 'I was born in 92000, and this is falsé.'
lowerCAmelCase_ : Union[str, Any] = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Dict = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Any = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : List[str] = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : int = self.get_rust_tokenizer()
lowerCAmelCase_ : Any = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : List[Any] = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Any ):
lowerCAmelCase_ : Any = 'Hello World!'
lowerCAmelCase_ : Union[str, Any] = [0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(SCREAMING_SNAKE_CASE_ , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE_ ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
lowerCAmelCase_ : Tuple = (
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
)
lowerCAmelCase_ : int = [
0,
3_2_9_3,
8_3,
1_0,
4_5_5_2,
4_9_8_9,
7_9_8_6,
6_7_8,
1_0,
5_9_1_5,
1_1_1,
1_7_9_4_5_9,
1_2_4_8_5_0,
4,
6_0_4_4,
2_3_7,
1_2,
6,
5,
6,
4,
6_7_8_0,
7_0_5,
1_5,
1_3_8_8,
4_4,
3_7_8,
1_0_1_1_4,
7_1_1,
1_5_2,
2_0,
6,
5,
2_2_3_7_6,
6_4_2,
1_2_2_1,
1_5_1_9_0,
3_4_1_5_3,
4_5_0,
5_6_0_8,
9_5_9,
1_1_1_9,
5_7_7_0_2,
1_3_6,
1_8_6,
4_7,
1_0_9_8,
2_9_3_6_7,
4_7,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6_0_4_4,
2_3_7,
6_2_8_4,
5_0_9_0_1,
5_2_8,
3_1,
9_0,
3_4,
9_2_7,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(SCREAMING_SNAKE_CASE_ , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE_ ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self : int ):
# fmt: off
lowerCAmelCase_ : List[str] = {'input_ids': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=SCREAMING_SNAKE_CASE_ , model_name='xlm-roberta-base' , revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3' , )
| 224 | 0 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
if not numbers:
return 0
if not isinstance(UpperCAmelCase__ , (list, tuple) ) or not all(
isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' )
A__ = numbers[0]
for i in range(1 , len(UpperCAmelCase__ ) ):
# update the maximum and minimum subarray products
A__ = numbers[i]
if number < 0:
A__ = min_till_now, max_till_now
A__ = max(UpperCAmelCase__ , max_till_now * number )
A__ = min(UpperCAmelCase__ , min_till_now * number )
# update the maximum product found till now
A__ = max(UpperCAmelCase__ , UpperCAmelCase__ )
return max_prod
| 371 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> 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"""))
| 231 | 0 |
"""simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
__UpperCamelCase = '''CompVis/stable-diffusion-v1-1'''
__UpperCamelCase = '''CompVis/stable-diffusion-v1-2'''
__UpperCamelCase = '''CompVis/stable-diffusion-v1-3'''
__UpperCamelCase = '''CompVis/stable-diffusion-v1-4'''
class lowerCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = True , ) -> Optional[int]:
super()._init_()
SCREAMING_SNAKE_CASE = StableDiffusionPipeline.from_pretrained(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = StableDiffusionPipeline.from_pretrained(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = StableDiffusionPipeline.from_pretrained(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = StableDiffusionPipeline(
vae=lowerCAmelCase__ , text_encoder=lowerCAmelCase__ , tokenizer=lowerCAmelCase__ , unet=lowerCAmelCase__ , scheduler=lowerCAmelCase__ , safety_checker=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , requires_safety_checker=lowerCAmelCase__ , )
self.register_modules(pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea )
@property
def __A ( self ) -> Dict[str, Any]:
return {k: getattr(self , lowerCAmelCase__ ) for k in self.config.keys() if not k.startswith('_' )}
def __A ( self , lowerCAmelCase__ = "auto" ) -> List[Any]:
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
SCREAMING_SNAKE_CASE = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(lowerCAmelCase__ )
def __A ( self ) -> Dict:
self.enable_attention_slicing(lowerCAmelCase__ )
@torch.no_grad()
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__ = 512 , lowerCAmelCase__ = 512 , lowerCAmelCase__ = 50 , lowerCAmelCase__ = 7.5 , lowerCAmelCase__ = None , lowerCAmelCase__ = 1 , lowerCAmelCase__ = 0.0 , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = "pil" , lowerCAmelCase__ = True , lowerCAmelCase__ = None , lowerCAmelCase__ = 1 , **lowerCAmelCase__ , ) -> Optional[Any]:
return self.pipea(
prompt=lowerCAmelCase__ , height=lowerCAmelCase__ , width=lowerCAmelCase__ , num_inference_steps=lowerCAmelCase__ , guidance_scale=lowerCAmelCase__ , negative_prompt=lowerCAmelCase__ , num_images_per_prompt=lowerCAmelCase__ , eta=lowerCAmelCase__ , generator=lowerCAmelCase__ , latents=lowerCAmelCase__ , output_type=lowerCAmelCase__ , return_dict=lowerCAmelCase__ , callback=lowerCAmelCase__ , callback_steps=lowerCAmelCase__ , **lowerCAmelCase__ , )
@torch.no_grad()
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__ = 512 , lowerCAmelCase__ = 512 , lowerCAmelCase__ = 50 , lowerCAmelCase__ = 7.5 , lowerCAmelCase__ = None , lowerCAmelCase__ = 1 , lowerCAmelCase__ = 0.0 , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = "pil" , lowerCAmelCase__ = True , lowerCAmelCase__ = None , lowerCAmelCase__ = 1 , **lowerCAmelCase__ , ) -> Any:
return self.pipea(
prompt=lowerCAmelCase__ , height=lowerCAmelCase__ , width=lowerCAmelCase__ , num_inference_steps=lowerCAmelCase__ , guidance_scale=lowerCAmelCase__ , negative_prompt=lowerCAmelCase__ , num_images_per_prompt=lowerCAmelCase__ , eta=lowerCAmelCase__ , generator=lowerCAmelCase__ , latents=lowerCAmelCase__ , output_type=lowerCAmelCase__ , return_dict=lowerCAmelCase__ , callback=lowerCAmelCase__ , callback_steps=lowerCAmelCase__ , **lowerCAmelCase__ , )
@torch.no_grad()
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__ = 512 , lowerCAmelCase__ = 512 , lowerCAmelCase__ = 50 , lowerCAmelCase__ = 7.5 , lowerCAmelCase__ = None , lowerCAmelCase__ = 1 , lowerCAmelCase__ = 0.0 , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = "pil" , lowerCAmelCase__ = True , lowerCAmelCase__ = None , lowerCAmelCase__ = 1 , **lowerCAmelCase__ , ) -> Optional[int]:
return self.pipea(
prompt=lowerCAmelCase__ , height=lowerCAmelCase__ , width=lowerCAmelCase__ , num_inference_steps=lowerCAmelCase__ , guidance_scale=lowerCAmelCase__ , negative_prompt=lowerCAmelCase__ , num_images_per_prompt=lowerCAmelCase__ , eta=lowerCAmelCase__ , generator=lowerCAmelCase__ , latents=lowerCAmelCase__ , output_type=lowerCAmelCase__ , return_dict=lowerCAmelCase__ , callback=lowerCAmelCase__ , callback_steps=lowerCAmelCase__ , **lowerCAmelCase__ , )
@torch.no_grad()
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__ = 512 , lowerCAmelCase__ = 512 , lowerCAmelCase__ = 50 , lowerCAmelCase__ = 7.5 , lowerCAmelCase__ = None , lowerCAmelCase__ = 1 , lowerCAmelCase__ = 0.0 , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = "pil" , lowerCAmelCase__ = True , lowerCAmelCase__ = None , lowerCAmelCase__ = 1 , **lowerCAmelCase__ , ) -> int:
return self.pipea(
prompt=lowerCAmelCase__ , height=lowerCAmelCase__ , width=lowerCAmelCase__ , num_inference_steps=lowerCAmelCase__ , guidance_scale=lowerCAmelCase__ , negative_prompt=lowerCAmelCase__ , num_images_per_prompt=lowerCAmelCase__ , eta=lowerCAmelCase__ , generator=lowerCAmelCase__ , latents=lowerCAmelCase__ , output_type=lowerCAmelCase__ , return_dict=lowerCAmelCase__ , callback=lowerCAmelCase__ , callback_steps=lowerCAmelCase__ , **lowerCAmelCase__ , )
@torch.no_grad()
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__ = 512 , lowerCAmelCase__ = 512 , lowerCAmelCase__ = 50 , lowerCAmelCase__ = 7.5 , lowerCAmelCase__ = None , lowerCAmelCase__ = 1 , lowerCAmelCase__ = 0.0 , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = "pil" , lowerCAmelCase__ = True , lowerCAmelCase__ = None , lowerCAmelCase__ = 1 , **lowerCAmelCase__ , ) -> List[Any]:
SCREAMING_SNAKE_CASE = 'cuda' if torch.cuda.is_available() else 'cpu'
self.to(lowerCAmelCase__ )
# Checks if the height and width are divisible by 8 or not
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F'`height` and `width` must be divisible by 8 but are {height} and {width}.' )
# Get first result from Stable Diffusion Checkpoint v1.1
SCREAMING_SNAKE_CASE = self.textaimg_sda_a(
prompt=lowerCAmelCase__ , height=lowerCAmelCase__ , width=lowerCAmelCase__ , num_inference_steps=lowerCAmelCase__ , guidance_scale=lowerCAmelCase__ , negative_prompt=lowerCAmelCase__ , num_images_per_prompt=lowerCAmelCase__ , eta=lowerCAmelCase__ , generator=lowerCAmelCase__ , latents=lowerCAmelCase__ , output_type=lowerCAmelCase__ , return_dict=lowerCAmelCase__ , callback=lowerCAmelCase__ , callback_steps=lowerCAmelCase__ , **lowerCAmelCase__ , )
# Get first result from Stable Diffusion Checkpoint v1.2
SCREAMING_SNAKE_CASE = self.textaimg_sda_a(
prompt=lowerCAmelCase__ , height=lowerCAmelCase__ , width=lowerCAmelCase__ , num_inference_steps=lowerCAmelCase__ , guidance_scale=lowerCAmelCase__ , negative_prompt=lowerCAmelCase__ , num_images_per_prompt=lowerCAmelCase__ , eta=lowerCAmelCase__ , generator=lowerCAmelCase__ , latents=lowerCAmelCase__ , output_type=lowerCAmelCase__ , return_dict=lowerCAmelCase__ , callback=lowerCAmelCase__ , callback_steps=lowerCAmelCase__ , **lowerCAmelCase__ , )
# Get first result from Stable Diffusion Checkpoint v1.3
SCREAMING_SNAKE_CASE = self.textaimg_sda_a(
prompt=lowerCAmelCase__ , height=lowerCAmelCase__ , width=lowerCAmelCase__ , num_inference_steps=lowerCAmelCase__ , guidance_scale=lowerCAmelCase__ , negative_prompt=lowerCAmelCase__ , num_images_per_prompt=lowerCAmelCase__ , eta=lowerCAmelCase__ , generator=lowerCAmelCase__ , latents=lowerCAmelCase__ , output_type=lowerCAmelCase__ , return_dict=lowerCAmelCase__ , callback=lowerCAmelCase__ , callback_steps=lowerCAmelCase__ , **lowerCAmelCase__ , )
# Get first result from Stable Diffusion Checkpoint v1.4
SCREAMING_SNAKE_CASE = self.textaimg_sda_a(
prompt=lowerCAmelCase__ , height=lowerCAmelCase__ , width=lowerCAmelCase__ , num_inference_steps=lowerCAmelCase__ , guidance_scale=lowerCAmelCase__ , negative_prompt=lowerCAmelCase__ , num_images_per_prompt=lowerCAmelCase__ , eta=lowerCAmelCase__ , generator=lowerCAmelCase__ , latents=lowerCAmelCase__ , output_type=lowerCAmelCase__ , return_dict=lowerCAmelCase__ , callback=lowerCAmelCase__ , callback_steps=lowerCAmelCase__ , **lowerCAmelCase__ , )
# Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]] )
| 113 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class lowerCAmelCase ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def __A ( self ) -> Dict:
return datasets.DatasetInfo(
features=datasets.Features({'content': datasets.Value('string' )} ) , supervised_keys=lowerCAmelCase__ , )
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> str:
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'examples': get_test_dummy_examples()} )]
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[int]:
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(lowerCAmelCase__ )
class lowerCAmelCase ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def __A ( self ) -> int:
return datasets.DatasetInfo(
features=datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string' )} )} ) , supervised_keys=lowerCAmelCase__ , )
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Dict:
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'examples': get_test_nested_examples()} )
]
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[Any]:
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(lowerCAmelCase__ )
def lowercase () -> str:
return [(i, {"content": content}) for i, content in enumerate(['foo', 'bar', 'foobar'] )]
def lowercase () -> Optional[Any]:
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['foo', 'bar', 'foobar'] )]
class lowerCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
@require_beam
def __A ( self ) -> Optional[int]:
SCREAMING_SNAKE_CASE = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
SCREAMING_SNAKE_CASE = DummyBeamDataset(cache_dir=lowerCAmelCase__ , beam_runner='DirectRunner' )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(lowerCAmelCase__ , builder.name , 'default' , '0.0.0' , F'{builder.name}-train.arrow' ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({'content': datasets.Value('string' )} ) )
SCREAMING_SNAKE_CASE = builder.as_dataset()
self.assertEqual(dset['train'].num_rows , lowerCAmelCase__ )
self.assertEqual(dset['train'].info.splits['train'].num_examples , lowerCAmelCase__ )
self.assertDictEqual(dset['train'][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset['train'][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(lowerCAmelCase__ , builder.name , 'default' , '0.0.0' , 'dataset_info.json' ) ) )
del dset
@require_beam
def __A ( self ) -> int:
import apache_beam as beam
SCREAMING_SNAKE_CASE = beam.io.parquetio.WriteToParquet
SCREAMING_SNAKE_CASE = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
SCREAMING_SNAKE_CASE = DummyBeamDataset(cache_dir=lowerCAmelCase__ , beam_runner='DirectRunner' )
with patch('apache_beam.io.parquetio.WriteToParquet' ) as write_parquet_mock:
SCREAMING_SNAKE_CASE = partial(lowerCAmelCase__ , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
lowerCAmelCase__ , builder.name , 'default' , '0.0.0' , F'{builder.name}-train-00000-of-00002.arrow' ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
lowerCAmelCase__ , builder.name , 'default' , '0.0.0' , F'{builder.name}-train-00000-of-00002.arrow' ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({'content': datasets.Value('string' )} ) )
SCREAMING_SNAKE_CASE = builder.as_dataset()
self.assertEqual(dset['train'].num_rows , lowerCAmelCase__ )
self.assertEqual(dset['train'].info.splits['train'].num_examples , lowerCAmelCase__ )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset['train']['content'] ) , sorted(['foo', 'bar', 'foobar'] ) )
self.assertTrue(
os.path.exists(os.path.join(lowerCAmelCase__ , builder.name , 'default' , '0.0.0' , 'dataset_info.json' ) ) )
del dset
@require_beam
def __A ( self ) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmp_cache_dir:
SCREAMING_SNAKE_CASE = DummyBeamDataset(cache_dir=lowerCAmelCase__ )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def __A ( self ) -> int:
SCREAMING_SNAKE_CASE = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
SCREAMING_SNAKE_CASE = NestedBeamDataset(cache_dir=lowerCAmelCase__ , beam_runner='DirectRunner' )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(lowerCAmelCase__ , builder.name , 'default' , '0.0.0' , F'{builder.name}-train.arrow' ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string' )} )} ) )
SCREAMING_SNAKE_CASE = builder.as_dataset()
self.assertEqual(dset['train'].num_rows , lowerCAmelCase__ )
self.assertEqual(dset['train'].info.splits['train'].num_examples , lowerCAmelCase__ )
self.assertDictEqual(dset['train'][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset['train'][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(lowerCAmelCase__ , builder.name , 'default' , '0.0.0' , 'dataset_info.json' ) ) )
del dset
| 113 | 1 |
from math import pow
def snake_case_ ( snake_case , snake_case , snake_case , snake_case , snake_case , ) -> tuple[int, int]:
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solutions_count += 1
return current_sum, solutions_count
lowercase__: int = int(pow(snake_case , snake_case ) )
if current_sum + i_to_n <= needed_sum:
# If the sum of the powers is less than needed_sum, then continue adding powers.
current_sum += i_to_n
lowercase__: Any = backtrack(
snake_case , snake_case , current_number + 1 , snake_case , snake_case )
current_sum -= i_to_n
if i_to_n < needed_sum:
# If the power of i is less than needed_sum, then try with the next power.
lowercase__: Optional[int] = backtrack(
snake_case , snake_case , current_number + 1 , snake_case , snake_case )
return current_sum, solutions_count
def snake_case_ ( snake_case , snake_case ) -> int:
if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
raise ValueError(
'Invalid input\n'
'needed_sum must be between 1 and 1000, power between 2 and 10.' )
return backtrack(snake_case , snake_case , 1 , 0 , 0 )[1] # Return the solutions_count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 354 |
from __future__ import annotations
def snake_case_ ( snake_case , snake_case ) -> list[int]:
lowercase__: Tuple = 0
lowercase__: str = len(snake_case ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
lowercase__: str = i + 1
else:
lowercase__: Dict = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'''{two_pointer([2, 7, 11, 15], 9) = }''')
| 288 | 0 |
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def UpperCamelCase ( __lowerCamelCase : Tuple=None ):
if subparsers is not None:
snake_case : Tuple = subparsers.add_parser("env" )
else:
snake_case : List[Any] = argparse.ArgumentParser("Accelerate env command" )
parser.add_argument(
"--config_file" , default=UpperCamelCase_ , help="The config file to use for the default values in the launching script." )
if subparsers is not None:
parser.set_defaults(func=UpperCamelCase_ )
return parser
def UpperCamelCase ( __lowerCamelCase : Union[str, Any] ):
snake_case : str = torch.__version__
snake_case : Union[str, Any] = torch.cuda.is_available()
snake_case : Any = is_xpu_available()
snake_case : int = is_npu_available()
snake_case : str = "Not found"
# Get the default from the config file.
if args.config_file is not None or os.path.isfile(UpperCamelCase_ ):
snake_case : Any = load_config_from_file(args.config_file ).to_dict()
snake_case : Tuple = {
"`Accelerate` version": version,
"Platform": platform.platform(),
"Python version": platform.python_version(),
"Numpy version": np.__version__,
"PyTorch version (GPU?)": f"""{pt_version} ({pt_cuda_available})""",
"PyTorch XPU available": str(UpperCamelCase_ ),
"PyTorch NPU available": str(UpperCamelCase_ ),
"System RAM": f"""{psutil.virtual_memory().total / 1024 ** 3:.2f} GB""",
}
if pt_cuda_available:
snake_case : int = torch.cuda.get_device_name()
print("\nCopy-and-paste the text below in your GitHub issue\n" )
print("\n".join([f"""- {prop}: {val}""" for prop, val in info.items()] ) )
print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:" )
snake_case : Tuple = (
"\n".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()] )
if isinstance(UpperCamelCase_ , UpperCamelCase_ )
else f"""\t{accelerate_config}"""
)
print(UpperCamelCase_ )
snake_case : Union[str, Any] = accelerate_config
return info
def UpperCamelCase ( ):
snake_case : Tuple = env_command_parser()
snake_case : List[Any] = parser.parse_args()
env_command(UpperCamelCase_ )
return 0
if __name__ == "__main__":
raise SystemExit(main())
| 59 |
"""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.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class _lowerCAmelCase ( lowercase ):
"""simple docstring"""
__UpperCAmelCase : Tuple = "openai/whisper-base"
__UpperCAmelCase : Union[str, Any] = (
"This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
"transcribed text."
)
__UpperCAmelCase : List[str] = "transcriber"
__UpperCAmelCase : Optional[Any] = WhisperProcessor
__UpperCAmelCase : str = WhisperForConditionalGeneration
__UpperCAmelCase : List[str] = ["audio"]
__UpperCAmelCase : Tuple = ["text"]
def _lowercase ( self : str, UpperCAmelCase__ : int ):
return self.pre_processor(UpperCAmelCase__, return_tensors="pt" ).input_features
def _lowercase ( self : Union[str, Any], UpperCAmelCase__ : Optional[Any] ):
return self.model.generate(inputs=UpperCAmelCase__ )
def _lowercase ( self : Dict, UpperCAmelCase__ : Optional[int] ):
return self.pre_processor.batch_decode(UpperCAmelCase__, skip_special_tokens=UpperCAmelCase__ )[0]
| 17 | 0 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def A_ ( _lowerCAmelCase : Tuple ):
"""simple docstring"""
_a = os.path.join(args.tf_model_dir, '''parameters.json''' )
_a = json.loads(open(_lowerCAmelCase ).read() )
if not params:
raise ValueError(
f'It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.' )
if not args.output.endswith('''.pt''' ):
_a = args.output + '''.pt'''
_a = OrderedDict()
with tf.device('''/CPU:0''' ):
_a = tf.train.load_checkpoint(args.tf_model_dir )
_a = reader.get_variable_to_shape_map()
for key_name in shapes.keys():
_a = reader.get_tensor(_lowerCAmelCase ).astype(np.floataa )
if key_name.endswith('''/adam_m''' ) or key_name.endswith('''/adam_v''' ):
continue
if key_name.startswith('''pasts/''' ):
if key_name.startswith('''pasts/mlp''' ):
_a = int(key_name[9] )
elif key_name.startswith('''pasts/out''' ):
_a = 8
_a = '''model.sqout.%d.weight''' % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
_a = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_a = torch.tensor(_lowerCAmelCase )
elif key_name.startswith('''model/moe''' ):
_a = int(key_name[9:].split('''/''' )[0] )
if key_name.endswith('''/switch_gating/kernel''' ):
_a = '''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
_a = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_a = torch.tensor(_lowerCAmelCase )
elif key_name.endswith('''/softmlp/kernel''' ):
_a = '''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
_a = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_a = torch.tensor(_lowerCAmelCase )
elif key_name.endswith('''/wo/kernel''' ) or key_name.endswith('''/wi/kernel''' ):
_a = key_name[-9:-7]
for i in range(16 ):
_a = '''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
_a = (
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
_a = torch.tensor(_lowerCAmelCase )
elif key_name.startswith('''model/mlp''' ):
_a = int(key_name[9:].split('''/''' )[0] )
if key_name.endswith('''/p1/kernel''' ):
_a = '''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
_a = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_a = torch.tensor(_lowerCAmelCase )
elif key_name.endswith('''/p1/bias''' ):
_a = '''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
_a = vnp.copy() # same because it is one dimensional
_a = torch.tensor(_lowerCAmelCase )
elif key_name.endswith('''/p2/kernel''' ):
_a = '''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
_a = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_a = torch.tensor(_lowerCAmelCase )
elif key_name.endswith('''/p2/bias''' ):
_a = '''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
_a = vnp.copy() # same because it is one dimensional
_a = torch.tensor(_lowerCAmelCase )
elif key_name.startswith('''model/ln''' ):
_a = int(key_name[8:].split('''/''' )[0] )
if key_name.endswith('''/b''' ):
_a = '''model.blocks.%d.feed_forward.norm.bias''' % player
_a = vnp.copy() # same because it is one dimensional
_a = torch.tensor(_lowerCAmelCase )
elif key_name.endswith('''/g''' ):
_a = '''model.blocks.%d.feed_forward.norm.weight''' % player
_a = vnp.copy() # same because it is one dimensional
_a = torch.tensor(_lowerCAmelCase )
elif key_name.startswith('''model/att''' ):
_a = int(key_name[9:].split('''/''' )[0] )
if key_name.endswith('''/qkv/kernel''' ):
_a = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
_a = state[:, 0, :, :]
_a = state[:, 1, :, :]
_a = state[:, 2, :, :]
_a = (
state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_a = (
state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_a = (
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_a = '''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
_a = torch.tensor(_lowerCAmelCase )
_a = '''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
_a = torch.tensor(_lowerCAmelCase )
_a = '''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
_a = torch.tensor(_lowerCAmelCase )
elif key_name.endswith('''/o/kernel''' ):
_a = '''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
_a = (
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
_a = torch.tensor(_lowerCAmelCase )
elif key_name.startswith('''model/an''' ):
_a = int(key_name[8:].split('''/''' )[0] )
if key_name.endswith('''/b''' ):
_a = '''model.blocks.%d.self_attn.norm.bias''' % player
_a = vnp.copy() # same because it is one dimensional
_a = torch.tensor(_lowerCAmelCase )
elif key_name.endswith('''/g''' ):
_a = '''model.blocks.%d.self_attn.norm.weight''' % player
_a = vnp.copy() # same because it is one dimensional
_a = torch.tensor(_lowerCAmelCase )
elif (
key_name.startswith('''model/wte''' )
or key_name.startswith('''model/wpe''' )
or key_name.startswith('''model/ete''' )
):
_a = {'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
key_name[-3:]
]
_a = '''model.%s.weight''' % nlayer
_a = vnp.copy() # same in embedded
_a = torch.tensor(_lowerCAmelCase )
if key_name.startswith('''model/wte''' ):
_a = '''lm_head.weight'''
_a = vnp.copy() # same in embedded
_a = torch.tensor(_lowerCAmelCase )
elif key_name.startswith('''model/wob''' ):
_a = '''final_logits_bias'''
_a = vnp.copy() # same in embedded
_a = state.reshape((1, -1) )
_a = torch.tensor(_lowerCAmelCase )
elif key_name == "model/dense/kernel":
_a = '''model.last_project.weight'''
_a = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_a = torch.tensor(_lowerCAmelCase )
elif key_name == "model/dense_1/bias":
_a = '''model.last_project.bias'''
_a = vnp.copy() # same because it is one dimensional
_a = torch.tensor(_lowerCAmelCase )
torch.save(_lowerCAmelCase, args.output )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser(
description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
__snake_case = parser.parse_args()
convert_tf_gptsan_to_pt(args) | 153 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ResNetConfig''', '''ResNetOnnxConfig''']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ResNetForImageClassification''',
'''ResNetModel''',
'''ResNetPreTrainedModel''',
'''ResNetBackbone''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFResNetForImageClassification''',
'''TFResNetModel''',
'''TFResNetPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''FlaxResNetForImageClassification''',
'''FlaxResNetModel''',
'''FlaxResNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure) | 153 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class A_ (a_ , a_ , a_ , unittest.TestCase ):
UpperCAmelCase__ = StableDiffusionInpaintPipeline
UpperCAmelCase__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
UpperCAmelCase__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
UpperCAmelCase__ = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
UpperCAmelCase__ = frozenset([] )
def _lowercase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=9 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=3_2 , attention_head_dim=(2, 4) , use_linear_projection=_A , )
UpperCAmelCase = PNDMScheduler(skip_prk_steps=_A )
torch.manual_seed(0 )
UpperCAmelCase = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=1_2_8 , )
torch.manual_seed(0 )
UpperCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , hidden_act='''gelu''' , projection_dim=5_1_2 , )
UpperCAmelCase = CLIPTextModel(_A )
UpperCAmelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
UpperCAmelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def _lowercase ( self , _A , _A=0 ):
'''simple docstring'''
UpperCAmelCase = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(_A ) ).to(_A )
UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCAmelCase = Image.fromarray(np.uinta(_A ) ).convert('''RGB''' ).resize((6_4, 6_4) )
UpperCAmelCase = Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((6_4, 6_4) )
if str(_A ).startswith('''mps''' ):
UpperCAmelCase = torch.manual_seed(_A )
else:
UpperCAmelCase = torch.Generator(device=_A ).manual_seed(_A )
UpperCAmelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': init_image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = StableDiffusionInpaintPipeline(**_A )
UpperCAmelCase = sd_pipe.to(_A )
sd_pipe.set_progress_bar_config(disable=_A )
UpperCAmelCase = self.get_dummy_inputs(_A )
UpperCAmelCase = sd_pipe(**_A ).images
UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
UpperCAmelCase = np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _lowercase ( self ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class A_ (unittest.TestCase ):
def _lowercase ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
UpperCAmelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
'''/yellow_cat_sitting_on_a_park_bench.npy''' )
UpperCAmelCase = '''stabilityai/stable-diffusion-2-inpainting'''
UpperCAmelCase = StableDiffusionInpaintPipeline.from_pretrained(_A , safety_checker=_A )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
pipe.enable_attention_slicing()
UpperCAmelCase = '''Face of a yellow cat, high resolution, sitting on a park bench'''
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = pipe(
prompt=_A , image=_A , mask_image=_A , generator=_A , output_type='''np''' , )
UpperCAmelCase = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert np.abs(expected_image - image ).max() < 9E-3
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
UpperCAmelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
'''/yellow_cat_sitting_on_a_park_bench_fp16.npy''' )
UpperCAmelCase = '''stabilityai/stable-diffusion-2-inpainting'''
UpperCAmelCase = StableDiffusionInpaintPipeline.from_pretrained(
_A , torch_dtype=torch.floataa , safety_checker=_A , )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
pipe.enable_attention_slicing()
UpperCAmelCase = '''Face of a yellow cat, high resolution, sitting on a park bench'''
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = pipe(
prompt=_A , image=_A , mask_image=_A , generator=_A , output_type='''np''' , )
UpperCAmelCase = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert np.abs(expected_image - image ).max() < 5E-1
def _lowercase ( self ):
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
UpperCAmelCase = '''stabilityai/stable-diffusion-2-inpainting'''
UpperCAmelCase = PNDMScheduler.from_pretrained(_A , subfolder='''scheduler''' )
UpperCAmelCase = StableDiffusionInpaintPipeline.from_pretrained(
_A , safety_checker=_A , scheduler=_A , torch_dtype=torch.floataa , )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
UpperCAmelCase = '''Face of a yellow cat, high resolution, sitting on a park bench'''
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = pipe(
prompt=_A , image=_A , mask_image=_A , generator=_A , num_inference_steps=2 , output_type='''np''' , )
UpperCAmelCase = torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.65 * 1_0**9
| 273 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('''TEST_SAGEMAKER''' , '''False''' ) ) is not True , reason='''Skipping test because should only be run when releasing minor transformers version''' , )
@pytest.mark.usefixtures('''sm_env''' )
@parameterized_class(
[
{
'''framework''': '''pytorch''',
'''script''': '''run_glue.py''',
'''model_name_or_path''': '''distilbert-base-cased''',
'''instance_type''': '''ml.g4dn.xlarge''',
'''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
},
{
'''framework''': '''tensorflow''',
'''script''': '''run_tf.py''',
'''model_name_or_path''': '''distilbert-base-cased''',
'''instance_type''': '''ml.g4dn.xlarge''',
'''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
},
] )
class A_ (unittest.TestCase ):
def _lowercase ( self ):
'''simple docstring'''
if self.framework == "pytorch":
subprocess.run(
F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding='''utf-8''' , check=_A , )
assert hasattr(self , '''env''' )
def _lowercase ( self , _A=1 ):
'''simple docstring'''
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=F"""{self.env.base_job_name}-single""" , instance_count=_A , instance_type=self.instance_type , debugger_hook_config=_A , hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path} , metric_definitions=self.env.metric_definitions , py_version='''py36''' , )
def _lowercase ( self , _A ):
'''simple docstring'''
TrainingJobAnalytics(_A ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""" )
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = self.create_estimator()
# run training
estimator.fit()
# result dataframe
UpperCAmelCase = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
UpperCAmelCase = list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''] )
UpperCAmelCase = list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
UpperCAmelCase = (
Session().describe_training_job(estimator.latest_training_job.name ).get('''TrainingTimeInSeconds''' , 9_9_9_9_9_9 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy )
assert all(t <= self.results['''eval_loss'''] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F"""{estimator.latest_training_job.name}.json""" , '''w''' ) as outfile:
json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss} , _A )
| 273 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Union[str, Any] = {
"""configuration_time_series_transformer""": [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TimeSeriesTransformerConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TimeSeriesTransformerForPrediction""",
"""TimeSeriesTransformerModel""",
"""TimeSeriesTransformerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TimeSeriesTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimeSeriesTransformerForPrediction,
TimeSeriesTransformerModel,
TimeSeriesTransformerPreTrainedModel,
)
else:
import sys
__A : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 363 |
from string import ascii_uppercase
__A : int = {str(ord(c) - 55): c for c in ascii_uppercase}
def __UpperCamelCase ( _A : int , _A : int ) ->str:
"""simple docstring"""
if isinstance(_A , _A ):
raise TypeError("""int() can't convert non-string with explicit base""" )
if num < 0:
raise ValueError("""parameter must be positive int""" )
if isinstance(_A , _A ):
raise TypeError("""'str' object cannot be interpreted as an integer""" )
if isinstance(_A , _A ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if base in (0, 1):
raise ValueError("""base must be >= 2""" )
if base > 36:
raise ValueError("""base must be <= 36""" )
lowerCamelCase_ =""""""
lowerCamelCase_ =0
lowerCamelCase_ =0
while div != 1:
lowerCamelCase_ , lowerCamelCase_ =divmod(_A , _A )
if base >= 11 and 9 < mod < 36:
lowerCamelCase_ =ALPHABET_VALUES[str(_A )]
else:
lowerCamelCase_ =str(_A )
new_value += actual_value
lowerCamelCase_ =num // base
lowerCamelCase_ =div
if div == 0:
return str(new_value[::-1] )
elif div == 1:
new_value += str(_A )
return str(new_value[::-1] )
return new_value[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for base in range(2, 37):
for num in range(10_00):
assert int(decimal_to_any(num, base), base) == num, (
num,
base,
decimal_to_any(num, base),
int(decimal_to_any(num, base), base),
)
| 49 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : Optional[int] = logging.get_logger(__name__)
_a : Tuple = {
'sail/poolformer_s12': 'https://huggingface.co/sail/poolformer_s12/resolve/main/config.json',
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class __A ( SCREAMING_SNAKE_CASE_ ):
_UpperCamelCase : Union[str, Any] = "poolformer"
def __init__( self , a__=3 , a__=16 , a__=16 , a__=3 , a__=4.0 , a__=[2, 2, 6, 2] , a__=[64, 128, 320, 512] , a__=[7, 3, 3, 3] , a__=[4, 2, 2, 2] , a__=[2, 1, 1, 1] , a__=4 , a__=0.0 , a__="gelu" , a__=True , a__=1e-5 , a__=0.0_2 , **a__ , ):
_lowerCAmelCase : List[Any] = num_channels
_lowerCAmelCase : str = patch_size
_lowerCAmelCase : Dict = stride
_lowerCAmelCase : Optional[int] = padding
_lowerCAmelCase : Optional[int] = pool_size
_lowerCAmelCase : Dict = hidden_sizes
_lowerCAmelCase : Optional[int] = mlp_ratio
_lowerCAmelCase : Optional[int] = depths
_lowerCAmelCase : Dict = patch_sizes
_lowerCAmelCase : Tuple = strides
_lowerCAmelCase : Any = num_encoder_blocks
_lowerCAmelCase : Any = drop_path_rate
_lowerCAmelCase : Tuple = hidden_act
_lowerCAmelCase : Optional[Any] = use_layer_scale
_lowerCAmelCase : List[str] = layer_scale_init_value
_lowerCAmelCase : List[Any] = initializer_range
super().__init__(**a__ )
class __A ( SCREAMING_SNAKE_CASE_ ):
_UpperCamelCase : Optional[Any] = version.parse("1.11" )
@property
def __A ( self ):
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def __A ( self ):
return 2e-3
| 44 | """simple docstring"""
import numpy as np
import qiskit
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 8 ,_lowerCamelCase : int | None = None ) -> str:
_lowerCAmelCase : int = np.random.default_rng(seed=_lowerCamelCase )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
_lowerCAmelCase : Tuple = 6 * key_len
# Measurement basis for Alice's qubits.
_lowerCAmelCase : Dict = rng.integers(2 ,size=_lowerCamelCase )
# The set of states Alice will prepare.
_lowerCAmelCase : Tuple = rng.integers(2 ,size=_lowerCamelCase )
# Measurement basis for Bob's qubits.
_lowerCAmelCase : Union[str, Any] = rng.integers(2 ,size=_lowerCamelCase )
# Quantum Circuit to simulate BB84
_lowerCAmelCase : Dict = qiskit.QuantumCircuit(_lowerCamelCase ,name="""BB84""" )
# Alice prepares her qubits according to rules above.
for index, _ in enumerate(_lowerCamelCase ):
if alice_state[index] == 1:
bbaa_circ.x(_lowerCamelCase )
if alice_basis[index] == 1:
bbaa_circ.h(_lowerCamelCase )
bbaa_circ.barrier()
# Bob measures the received qubits according to rules above.
for index, _ in enumerate(_lowerCamelCase ):
if bob_basis[index] == 1:
bbaa_circ.h(_lowerCamelCase )
bbaa_circ.barrier()
bbaa_circ.measure_all()
# Simulate the quantum circuit.
_lowerCAmelCase : int = qiskit.Aer.get_backend("""aer_simulator""" )
# We only need to run one shot because the key is unique.
# Multiple shots will produce the same key.
_lowerCAmelCase : List[str] = qiskit.execute(_lowerCamelCase ,_lowerCamelCase ,shots=1 ,seed_simulator=_lowerCamelCase )
# Returns the result of measurement.
_lowerCAmelCase : List[Any] = job.result().get_counts(_lowerCamelCase ).most_frequent()
# Extracting the generated key from the simulation results.
# Only keep measurement results where Alice and Bob chose the same basis.
_lowerCAmelCase : str = """""".join(
[
result_bit
for alice_basis_bit, bob_basis_bit, result_bit in zip(
_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase )
if alice_basis_bit == bob_basis_bit
] )
# Get final key. Pad with 0 if too short, otherwise truncate.
_lowerCAmelCase : List[Any] = gen_key[:key_len] if len(_lowerCamelCase ) >= key_len else gen_key.ljust(_lowerCamelCase ,"""0""" )
return key
if __name__ == "__main__":
print(F"""The generated key is : {bbaa(8, seed=0)}""")
from doctest import testmod
testmod()
| 44 | 1 |
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
lowercase__ = set()
return any(
node not in visited and depth_first_search(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for node in graph )
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
visited.add(SCREAMING_SNAKE_CASE )
rec_stk.add(SCREAMING_SNAKE_CASE )
for node in graph[vertex]:
if node not in visited:
if depth_first_search(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(SCREAMING_SNAKE_CASE )
return False
if __name__ == "__main__":
from doctest import testmod
testmod()
| 93 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase = {
'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
'tokenization_luke': ['LukeTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase = [
'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
'LukeForEntityClassification',
'LukeForEntityPairClassification',
'LukeForEntitySpanClassification',
'LukeForMultipleChoice',
'LukeForQuestionAnswering',
'LukeForSequenceClassification',
'LukeForTokenClassification',
'LukeForMaskedLM',
'LukeModel',
'LukePreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
from .tokenization_luke import LukeTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_luke import (
LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
LukeForEntityClassification,
LukeForEntityPairClassification,
LukeForEntitySpanClassification,
LukeForMaskedLM,
LukeForMultipleChoice,
LukeForQuestionAnswering,
LukeForSequenceClassification,
LukeForTokenClassification,
LukeModel,
LukePreTrainedModel,
)
else:
import sys
lowerCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 93 | 1 |
def A ( a_ ,a_ ,a_ ) -> Tuple:
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(a_ ,n - 1 ,a_ ) * a) % mod
else:
__UpperCamelCase : Dict =binary_exponentiation(a_ ,n / 2 ,a_ )
return (b * b) % mod
# a prime number
A_ :str = 701
A_ :Optional[Any] = 1000000000
A_ :int = 10
# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)
print((a / b) % p == (a * b ** (p - 2)) % p)
| 71 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ = "cpu" , lowerCAmelCase_ = None )-> None:
lowerCAmelCase_ : str = torch.load(lowerCAmelCase_ , map_location=lowerCAmelCase_ )
for k, v in tqdm(state_dict.items() ):
if not isinstance(lowerCAmelCase_ , torch.Tensor ):
raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin''' )
lowerCAmelCase_ : int = v.half()
if save_path is None: # overwrite src_path
lowerCAmelCase_ : Tuple = src_path
torch.save(lowerCAmelCase_ , lowerCAmelCase_ )
if __name__ == "__main__":
fire.Fire(convert) | 262 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A__ : Any = {
'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:
A__ : Optional[int] = ['LayoutLMv2TokenizerFast']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = ['LayoutLMv2FeatureExtractor']
A__ : Dict = ['LayoutLMv2ImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict = [
'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
A__ : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 353 |
"""simple docstring"""
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def _snake_case ( lowerCamelCase__ : int , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : int ) -> List[Any]:
if isinstance(lowerCamelCase__ , torch.Tensor ):
return image
elif isinstance(lowerCamelCase__ , PIL.Image.Image ):
lowerCamelCase_ : int =[image]
if isinstance(image[0] , PIL.Image.Image ):
lowerCamelCase_ : Tuple =[np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["lanczos"] ) )[None, :] for i in image]
lowerCamelCase_ : Any =np.concatenate(lowerCamelCase__ , axis=0 )
lowerCamelCase_ : Any =np.array(lowerCamelCase__ ).astype(np.floataa ) / 255.0
lowerCamelCase_ : Any =image.transpose(0 , 3 , 1 , 2 )
lowerCamelCase_ : Dict =2.0 * image - 1.0
lowerCamelCase_ : List[Any] =torch.from_numpy(lowerCamelCase__ )
elif isinstance(image[0] , torch.Tensor ):
lowerCamelCase_ : Tuple =torch.cat(lowerCamelCase__ , dim=0 )
return image
def _snake_case ( lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Dict , lowerCamelCase__ : Dict , lowerCamelCase__ : Optional[int]=0.9995 ) -> List[str]:
if not isinstance(lowerCamelCase__ , np.ndarray ):
lowerCamelCase_ : List[Any] =True
lowerCamelCase_ : Tuple =va.device
lowerCamelCase_ : Optional[int] =va.cpu().numpy()
lowerCamelCase_ : List[Any] =va.cpu().numpy()
lowerCamelCase_ : Any =np.sum(va * va / (np.linalg.norm(lowerCamelCase__ ) * np.linalg.norm(lowerCamelCase__ )) )
if np.abs(lowerCamelCase__ ) > DOT_THRESHOLD:
lowerCamelCase_ : str =(1 - t) * va + t * va
else:
lowerCamelCase_ : Tuple =np.arccos(lowerCamelCase__ )
lowerCamelCase_ : Any =np.sin(lowerCamelCase__ )
lowerCamelCase_ : Optional[int] =theta_a * t
lowerCamelCase_ : Tuple =np.sin(lowerCamelCase__ )
lowerCamelCase_ : List[str] =np.sin(theta_a - theta_t ) / sin_theta_a
lowerCamelCase_ : Union[str, Any] =sin_theta_t / sin_theta_a
lowerCamelCase_ : str =sa * va + sa * va
if inputs_are_torch:
lowerCamelCase_ : Any =torch.from_numpy(lowerCamelCase__ ).to(lowerCamelCase__ )
return va
def _snake_case ( lowerCamelCase__ : Tuple , lowerCamelCase__ : Dict ) -> List[str]:
lowerCamelCase_ : List[Any] =F.normalize(lowerCamelCase__ , dim=-1 )
lowerCamelCase_ : Any =F.normalize(lowerCamelCase__ , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def _snake_case ( lowerCamelCase__ : str , lowerCamelCase__ : List[Any] ) -> Any:
for param in model.parameters():
lowerCamelCase_ : Any =value
class lowercase__ ( snake_case__ ):
def __init__( self : Union[str, Any] , snake_case__ : AutoencoderKL , snake_case__ : CLIPTextModel , snake_case__ : CLIPModel , snake_case__ : CLIPTokenizer , snake_case__ : UNetaDConditionModel , snake_case__ : Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler] , snake_case__ : CLIPFeatureExtractor , snake_case__ : Optional[Any]=None , snake_case__ : int=None , snake_case__ : str=None , ):
super().__init__()
self.register_modules(
vae=snake_case__ , text_encoder=snake_case__ , clip_model=snake_case__ , tokenizer=snake_case__ , unet=snake_case__ , scheduler=snake_case__ , feature_extractor=snake_case__ , coca_model=snake_case__ , coca_tokenizer=snake_case__ , coca_transform=snake_case__ , )
lowerCamelCase_ : Optional[Any] =(
feature_extractor.size
if isinstance(feature_extractor.size , snake_case__ )
else feature_extractor.size["shortest_edge"]
)
lowerCamelCase_ : Union[str, Any] =transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , snake_case__ )
set_requires_grad(self.clip_model , snake_case__ )
def UpperCAmelCase__ ( self : Optional[Any] , snake_case__ : Optional[Union[str, int]] = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowerCamelCase_ : List[Any] =self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(snake_case__ )
def UpperCAmelCase__ ( self : int ):
self.enable_attention_slicing(snake_case__ )
def UpperCAmelCase__ ( self : str ):
set_requires_grad(self.vae , snake_case__ )
def UpperCAmelCase__ ( self : List[str] ):
set_requires_grad(self.vae , snake_case__ )
def UpperCAmelCase__ ( self : List[Any] ):
set_requires_grad(self.unet , snake_case__ )
def UpperCAmelCase__ ( self : Tuple ):
set_requires_grad(self.unet , snake_case__ )
def UpperCAmelCase__ ( self : str , snake_case__ : Optional[int] , snake_case__ : List[str] , snake_case__ : Tuple ):
# get the original timestep using init_timestep
lowerCamelCase_ : Optional[int] =min(int(num_inference_steps * strength ) , snake_case__ )
lowerCamelCase_ : Dict =max(num_inference_steps - init_timestep , 0 )
lowerCamelCase_ : Union[str, Any] =self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def UpperCAmelCase__ ( self : List[Any] , snake_case__ : List[str] , snake_case__ : Union[str, Any] , snake_case__ : Optional[int] , snake_case__ : Optional[Any] , snake_case__ : List[Any] , snake_case__ : List[Any]=None ):
if not isinstance(snake_case__ , torch.Tensor ):
raise ValueError(F"""`image` has to be of type `torch.Tensor` but is {type(snake_case__ )}""" )
lowerCamelCase_ : List[str] =image.to(device=snake_case__ , dtype=snake_case__ )
if isinstance(snake_case__ , snake_case__ ):
lowerCamelCase_ : Optional[int] =[
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(snake_case__ )
]
lowerCamelCase_ : Tuple =torch.cat(snake_case__ , dim=0 )
else:
lowerCamelCase_ : Any =self.vae.encode(snake_case__ ).latent_dist.sample(snake_case__ )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
lowerCamelCase_ : Optional[Any] =0.18_215 * init_latents
lowerCamelCase_ : Optional[Any] =init_latents.repeat_interleave(snake_case__ , dim=0 )
lowerCamelCase_ : List[Any] =randn_tensor(init_latents.shape , generator=snake_case__ , device=snake_case__ , dtype=snake_case__ )
# get latents
lowerCamelCase_ : Optional[int] =self.scheduler.add_noise(snake_case__ , snake_case__ , snake_case__ )
lowerCamelCase_ : int =init_latents
return latents
def UpperCAmelCase__ ( self : Dict , snake_case__ : List[Any] ):
lowerCamelCase_ : Optional[int] =self.coca_transform(snake_case__ ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
lowerCamelCase_ : Tuple =self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
lowerCamelCase_ : str =self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split("<end_of_text>" )[0].replace("<start_of_text>" , "" ).rstrip(" .," )
def UpperCAmelCase__ ( self : str , snake_case__ : Union[str, Any] , snake_case__ : Optional[int] ):
lowerCamelCase_ : int =self.feature_extractor.preprocess(snake_case__ )
lowerCamelCase_ : Tuple =torch.from_numpy(clip_image_input["pixel_values"][0] ).unsqueeze(0 ).to(self.device ).half()
lowerCamelCase_ : Optional[int] =self.clip_model.get_image_features(snake_case__ )
lowerCamelCase_ : List[str] =image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=snake_case__ )
lowerCamelCase_ : str =image_embeddings_clip.repeat_interleave(snake_case__ , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def UpperCAmelCase__ ( self : Union[str, Any] , snake_case__ : Dict , snake_case__ : Any , snake_case__ : str , snake_case__ : Optional[int] , snake_case__ : Union[str, Any] , snake_case__ : List[Any] , snake_case__ : str , ):
lowerCamelCase_ : Any =latents.detach().requires_grad_()
lowerCamelCase_ : Union[str, Any] =self.scheduler.scale_model_input(snake_case__ , snake_case__ )
# predict the noise residual
lowerCamelCase_ : Any =self.unet(snake_case__ , snake_case__ , encoder_hidden_states=snake_case__ ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
lowerCamelCase_ : Any =self.scheduler.alphas_cumprod[timestep]
lowerCamelCase_ : str =1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
lowerCamelCase_ : Dict =(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
lowerCamelCase_ : Tuple =torch.sqrt(snake_case__ )
lowerCamelCase_ : Optional[int] =pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , snake_case__ ):
lowerCamelCase_ : Optional[Any] =self.scheduler.sigmas[index]
lowerCamelCase_ : Optional[int] =latents - sigma * noise_pred
else:
raise ValueError(F"""scheduler type {type(self.scheduler )} not supported""" )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
lowerCamelCase_ : str =1 / 0.18_215 * sample
lowerCamelCase_ : List[Any] =self.vae.decode(snake_case__ ).sample
lowerCamelCase_ : List[Any] =(image / 2 + 0.5).clamp(0 , 1 )
lowerCamelCase_ : Any =transforms.Resize(self.feature_extractor_size )(snake_case__ )
lowerCamelCase_ : Any =self.normalize(snake_case__ ).to(latents.dtype )
lowerCamelCase_ : Any =self.clip_model.get_image_features(snake_case__ )
lowerCamelCase_ : Optional[int] =image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=snake_case__ )
lowerCamelCase_ : Union[str, Any] =spherical_dist_loss(snake_case__ , snake_case__ ).mean() * clip_guidance_scale
lowerCamelCase_ : Union[str, Any] =-torch.autograd.grad(snake_case__ , snake_case__ )[0]
if isinstance(self.scheduler , snake_case__ ):
lowerCamelCase_ : int =latents.detach() + grads * (sigma**2)
lowerCamelCase_ : Tuple =noise_pred_original
else:
lowerCamelCase_ : Union[str, Any] =noise_pred_original - torch.sqrt(snake_case__ ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self : Any , snake_case__ : Union[torch.FloatTensor, PIL.Image.Image] , snake_case__ : Union[torch.FloatTensor, PIL.Image.Image] , snake_case__ : Optional[str] = None , snake_case__ : Optional[str] = None , snake_case__ : Optional[int] = 512 , snake_case__ : Optional[int] = 512 , snake_case__ : float = 0.6 , snake_case__ : Optional[int] = 50 , snake_case__ : Optional[float] = 7.5 , snake_case__ : Optional[int] = 1 , snake_case__ : float = 0.0 , snake_case__ : Optional[float] = 100 , snake_case__ : Optional[torch.Generator] = None , snake_case__ : Optional[str] = "pil" , snake_case__ : bool = True , snake_case__ : float = 0.8 , snake_case__ : float = 0.1 , snake_case__ : float = 0.1 , ):
if isinstance(snake_case__ , snake_case__ ) and len(snake_case__ ) != batch_size:
raise ValueError(F"""You have passed {batch_size} batch_size, but only {len(snake_case__ )} generators.""" )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}.""" )
if isinstance(snake_case__ , torch.Generator ) and batch_size > 1:
lowerCamelCase_ : List[str] =[generator] + [None] * (batch_size - 1)
lowerCamelCase_ : Any =[
("model", self.coca_model is None),
("tokenizer", self.coca_tokenizer is None),
("transform", self.coca_transform is None),
]
lowerCamelCase_ : Optional[Any] =[x[0] for x in coca_is_none if x[1]]
lowerCamelCase_ : Any =", ".join(snake_case__ )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(snake_case__ ):
raise ValueError(
F"""Content prompt is None and CoCa [{coca_is_none_str}] is None."""
F"""Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.""" )
lowerCamelCase_ : Union[str, Any] =self.get_image_description(snake_case__ )
if style_prompt is None:
if len(snake_case__ ):
raise ValueError(
F"""Style prompt is None and CoCa [{coca_is_none_str}] is None."""
F""" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.""" )
lowerCamelCase_ : str =self.get_image_description(snake_case__ )
# get prompt text embeddings for content and style
lowerCamelCase_ : str =self.tokenizer(
snake_case__ , padding="max_length" , max_length=self.tokenizer.model_max_length , truncation=snake_case__ , return_tensors="pt" , )
lowerCamelCase_ : List[str] =self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
lowerCamelCase_ : int =self.tokenizer(
snake_case__ , padding="max_length" , max_length=self.tokenizer.model_max_length , truncation=snake_case__ , return_tensors="pt" , )
lowerCamelCase_ : Optional[int] =self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
lowerCamelCase_ : Dict =slerp(snake_case__ , snake_case__ , snake_case__ )
# duplicate text embeddings for each generation per prompt
lowerCamelCase_ : str =text_embeddings.repeat_interleave(snake_case__ , dim=0 )
# set timesteps
lowerCamelCase_ : List[Any] ="offset" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
lowerCamelCase_ : List[Any] ={}
if accepts_offset:
lowerCamelCase_ : Optional[int] =1
self.scheduler.set_timesteps(snake_case__ , **snake_case__ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
lowerCamelCase_ , lowerCamelCase_ : Optional[int] =self.get_timesteps(snake_case__ , snake_case__ , self.device )
lowerCamelCase_ : Union[str, Any] =timesteps[:1].repeat(snake_case__ )
# Preprocess image
lowerCamelCase_ : str =preprocess(snake_case__ , snake_case__ , snake_case__ )
lowerCamelCase_ : int =self.prepare_latents(
snake_case__ , snake_case__ , snake_case__ , text_embeddings.dtype , self.device , snake_case__ )
lowerCamelCase_ : Dict =preprocess(snake_case__ , snake_case__ , snake_case__ )
lowerCamelCase_ : Tuple =self.prepare_latents(
snake_case__ , snake_case__ , snake_case__ , text_embeddings.dtype , self.device , snake_case__ )
lowerCamelCase_ : int =slerp(snake_case__ , snake_case__ , snake_case__ )
if clip_guidance_scale > 0:
lowerCamelCase_ : List[Any] =self.get_clip_image_embeddings(snake_case__ , snake_case__ )
lowerCamelCase_ : Optional[Any] =self.get_clip_image_embeddings(snake_case__ , snake_case__ )
lowerCamelCase_ : List[Any] =slerp(
snake_case__ , snake_case__ , snake_case__ )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
lowerCamelCase_ : str =guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowerCamelCase_ : List[Any] =content_text_input.input_ids.shape[-1]
lowerCamelCase_ : Union[str, Any] =self.tokenizer([""] , padding="max_length" , max_length=snake_case__ , return_tensors="pt" )
lowerCamelCase_ : List[Any] =self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
lowerCamelCase_ : List[str] =uncond_embeddings.repeat_interleave(snake_case__ , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
lowerCamelCase_ : List[str] =torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
lowerCamelCase_ : Optional[Any] =(batch_size, self.unet.config.in_channels, height // 8, width // 8)
lowerCamelCase_ : List[str] =text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
lowerCamelCase_ : Union[str, Any] =torch.randn(snake_case__ , generator=snake_case__ , device="cpu" , dtype=snake_case__ ).to(
self.device )
else:
lowerCamelCase_ : Tuple =torch.randn(snake_case__ , generator=snake_case__ , device=self.device , dtype=snake_case__ )
else:
if latents.shape != latents_shape:
raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
lowerCamelCase_ : Any =latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
lowerCamelCase_ : Optional[int] =latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
lowerCamelCase_ : List[Any] ="eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowerCamelCase_ : Dict ={}
if accepts_eta:
lowerCamelCase_ : Tuple =eta
# check if the scheduler accepts generator
lowerCamelCase_ : Tuple ="generator" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
lowerCamelCase_ : Optional[int] =generator
with self.progress_bar(total=snake_case__ ):
for i, t in enumerate(snake_case__ ):
# expand the latents if we are doing classifier free guidance
lowerCamelCase_ : List[str] =torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCamelCase_ : Optional[Any] =self.scheduler.scale_model_input(snake_case__ , snake_case__ )
# predict the noise residual
lowerCamelCase_ : Tuple =self.unet(snake_case__ , snake_case__ , encoder_hidden_states=snake_case__ ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
lowerCamelCase_ , lowerCamelCase_ : Dict =noise_pred.chunk(2 )
lowerCamelCase_ : Optional[Any] =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
lowerCamelCase_ : Optional[int] =(
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
lowerCamelCase_ , lowerCamelCase_ : Union[str, Any] =self.cond_fn(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , )
# compute the previous noisy sample x_t -> x_t-1
lowerCamelCase_ : Dict =self.scheduler.step(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
lowerCamelCase_ : str =1 / 0.18_215 * latents
lowerCamelCase_ : List[str] =self.vae.decode(snake_case__ ).sample
lowerCamelCase_ : str =(image / 2 + 0.5).clamp(0 , 1 )
lowerCamelCase_ : List[Any] =image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
lowerCamelCase_ : List[str] =self.numpy_to_pil(snake_case__ )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=snake_case__ , nsfw_content_detected=snake_case__ )
| 209 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : int = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
'''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json''',
# See all Donut models at https://huggingface.co/models?filter=donut-swin
}
class _SCREAMING_SNAKE_CASE ( _a ):
snake_case__ : str = """donut-swin"""
snake_case__ : str = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self : List[Any] , __lowerCamelCase : Union[str, Any]=224 , __lowerCamelCase : Optional[int]=4 , __lowerCamelCase : str=3 , __lowerCamelCase : Dict=96 , __lowerCamelCase : Dict=[2, 2, 6, 2] , __lowerCamelCase : List[Any]=[3, 6, 12, 24] , __lowerCamelCase : int=7 , __lowerCamelCase : int=4.0 , __lowerCamelCase : Dict=True , __lowerCamelCase : Dict=0.0 , __lowerCamelCase : Any=0.0 , __lowerCamelCase : List[Any]=0.1 , __lowerCamelCase : Optional[Any]="gelu" , __lowerCamelCase : Union[str, Any]=False , __lowerCamelCase : Dict=0.02 , __lowerCamelCase : Dict=1E-5 , **__lowerCamelCase : Dict , ):
super().__init__(**__lowerCamelCase )
UpperCamelCase :Tuple = image_size
UpperCamelCase :Optional[Any] = patch_size
UpperCamelCase :Union[str, Any] = num_channels
UpperCamelCase :Tuple = embed_dim
UpperCamelCase :str = depths
UpperCamelCase :Any = len(__lowerCamelCase )
UpperCamelCase :Optional[int] = num_heads
UpperCamelCase :Union[str, Any] = window_size
UpperCamelCase :Optional[Any] = mlp_ratio
UpperCamelCase :List[Any] = qkv_bias
UpperCamelCase :Any = hidden_dropout_prob
UpperCamelCase :Any = attention_probs_dropout_prob
UpperCamelCase :List[Any] = drop_path_rate
UpperCamelCase :int = hidden_act
UpperCamelCase :Union[str, Any] = use_absolute_embeddings
UpperCamelCase :int = layer_norm_eps
UpperCamelCase :str = initializer_range
# 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
UpperCamelCase :Any = int(embed_dim * 2 ** (len(__lowerCamelCase ) - 1) )
| 38 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase__ = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = ["YolosFeatureExtractor"]
lowercase__ = ["YolosImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = [
"YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST",
"YolosForObjectDetection",
"YolosModel",
"YolosPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
lowercase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 151 | 0 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __snake_case ( _lowercase , unittest.TestCase):
# TODO: is there an appropriate internal test set?
snake_case__ : List[str] = "ssube/stable-diffusion-x4-upscaler-onnx"
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __lowerCAmelCase : int=0 ):
"""simple docstring"""
_lowerCamelCase : Tuple = floats_tensor((1, 3, 1_2_8, 1_2_8) , rng=random.Random(__lowerCAmelCase ) )
_lowerCamelCase : Union[str, Any] = torch.manual_seed(__lowerCAmelCase )
_lowerCamelCase : Tuple = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = self.get_dummy_inputs()
_lowerCamelCase : Any = pipe(**__lowerCAmelCase ).images
_lowerCamelCase : Dict = image[0, -3:, -3:, -1].flatten()
# started as 128, should now be 512
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_lowerCamelCase : str = np.array(
[0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23] )
assert np.abs(image_slice - expected_slice ).max() < 1E-1
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
_lowerCamelCase : List[str] = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
_lowerCamelCase : int = self.get_dummy_inputs()
_lowerCamelCase : Optional[Any] = pipe(**__lowerCAmelCase ).images
_lowerCamelCase : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_lowerCamelCase : Optional[int] = np.array(
[0.6_89_88_92, 0.59_24_05_56, 0.52_49_95_27, 0.58_86_62_15, 0.52_25_82_35, 0.52_57_27_15, 0.62_41_44_73, 0.6_17_43_87, 0.6_21_49_64] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
_lowerCamelCase : Optional[int] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
_lowerCamelCase : Tuple = self.get_dummy_inputs()
_lowerCamelCase : str = pipe(**__lowerCAmelCase ).images
_lowerCamelCase : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_lowerCamelCase : str = np.array(
[0.7_65_92_78, 0.76_43_76_64, 0.75_57_91_07, 0.7_69_11_16, 0.77_66_69_86, 0.7_72_76_72, 0.7_75_86_64, 0.7_81_22_26, 0.76_94_25_15] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def SCREAMING_SNAKE_CASE ( self : Any ):
"""simple docstring"""
_lowerCamelCase : Any = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
_lowerCamelCase : Union[str, Any] = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
_lowerCamelCase : Optional[Any] = self.get_dummy_inputs()
_lowerCamelCase : Tuple = pipe(**__lowerCAmelCase ).images
_lowerCamelCase : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_lowerCamelCase : Union[str, Any] = np.array(
[0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def SCREAMING_SNAKE_CASE ( self : Any ):
"""simple docstring"""
_lowerCamelCase : Dict = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
_lowerCamelCase : int = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = self.get_dummy_inputs()
_lowerCamelCase : List[Any] = pipe(**__lowerCAmelCase ).images
_lowerCamelCase : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_lowerCamelCase : Optional[int] = np.array(
[0.77_42_44_96, 0.77_36_01, 0.7_64_52_88, 0.7_76_95_98, 0.7_77_27_39, 0.7_73_86_88, 0.78_18_72_33, 0.77_87_95_84, 0.76_70_43] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
@nightly
@require_onnxruntime
@require_torch_gpu
class __snake_case ( unittest.TestCase):
@property
def SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def SCREAMING_SNAKE_CASE ( self : Any ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = ort.SessionOptions()
_lowerCamelCase : List[str] = False
return options
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/img2img/sketch-mountains-input.jpg''' )
_lowerCamelCase : Any = init_image.resize((1_2_8, 1_2_8) )
# using the PNDM scheduler by default
_lowerCamelCase : List[str] = OnnxStableDiffusionUpscalePipeline.from_pretrained(
'''ssube/stable-diffusion-x4-upscaler-onnx''' , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
_lowerCamelCase : int = '''A fantasy landscape, trending on artstation'''
_lowerCamelCase : List[Any] = torch.manual_seed(0 )
_lowerCamelCase : List[str] = pipe(
prompt=__lowerCAmelCase , image=__lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=1_0 , generator=__lowerCAmelCase , output_type='''np''' , )
_lowerCamelCase : List[Any] = output.images
_lowerCamelCase : List[Any] = images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]
assert images.shape == (1, 5_1_2, 5_1_2, 3)
_lowerCamelCase : str = np.array([0.48_83, 0.49_47, 0.49_80, 0.49_75, 0.49_82, 0.49_80, 0.50_00, 0.50_06, 0.49_72] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
def SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/img2img/sketch-mountains-input.jpg''' )
_lowerCamelCase : int = init_image.resize((1_2_8, 1_2_8) )
_lowerCamelCase : str = LMSDiscreteScheduler.from_pretrained(
'''ssube/stable-diffusion-x4-upscaler-onnx''' , subfolder='''scheduler''' )
_lowerCamelCase : Dict = OnnxStableDiffusionUpscalePipeline.from_pretrained(
'''ssube/stable-diffusion-x4-upscaler-onnx''' , scheduler=__lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
_lowerCamelCase : Optional[int] = '''A fantasy landscape, trending on artstation'''
_lowerCamelCase : int = torch.manual_seed(0 )
_lowerCamelCase : List[str] = pipe(
prompt=__lowerCAmelCase , image=__lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=2_0 , generator=__lowerCAmelCase , output_type='''np''' , )
_lowerCamelCase : Union[str, Any] = output.images
_lowerCamelCase : Optional[Any] = images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]
assert images.shape == (1, 5_1_2, 5_1_2, 3)
_lowerCamelCase : str = np.array(
[0.50_17_37_53, 0.50_22_33_56, 0.50_20_39, 0.50_23_30_36, 0.5_02_37_25, 0.5_02_26_01, 0.5_01_87_58, 0.50_23_40_85, 0.50_24_15_66] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
| 371 |
"""simple docstring"""
import math
def snake_case_ ( A_ : int ):
'''simple docstring'''
return math.sqrt(A_ ) * math.sqrt(A_ ) == num
def snake_case_ ( A_ : int ):
'''simple docstring'''
_lowerCamelCase : Dict = 0
_lowerCamelCase : Tuple = n
while left <= right:
_lowerCamelCase : List[str] = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
_lowerCamelCase : int = mid - 1
else:
_lowerCamelCase : Optional[Any] = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 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:
_UpperCAmelCase : Any = None
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : Tuple = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
_UpperCAmelCase : Any = {
"vocab_file": {
"google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
"google/bigbird-roberta-large": (
"https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
),
"google/bigbird-base-trivia-itc": (
"https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
),
},
"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"
),
},
}
_UpperCAmelCase : Any = {
"google/bigbird-roberta-base": 4_096,
"google/bigbird-roberta-large": 4_096,
"google/bigbird-base-trivia-itc": 4_096,
}
_UpperCAmelCase : Tuple = "▁"
class lowercase ( _SCREAMING_SNAKE_CASE ):
__lowercase : List[str] = VOCAB_FILES_NAMES
__lowercase : str = PRETRAINED_VOCAB_FILES_MAP
__lowercase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowercase : Optional[int] = BigBirdTokenizer
__lowercase : Union[str, Any] = ["input_ids", "attention_mask"]
__lowercase : List[int] = []
def __init__( self , A_=None , A_=None , A_="<unk>" , A_="<s>" , A_="</s>" , A_="<pad>" , A_="[SEP]" , A_="[MASK]" , A_="[CLS]" , **A_ , ) -> List[str]:
"""simple docstring"""
UpperCamelCase = AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else bos_token
UpperCamelCase = AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else eos_token
UpperCamelCase = AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else unk_token
UpperCamelCase = AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else pad_token
UpperCamelCase = AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else cls_token
UpperCamelCase = 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
UpperCamelCase = 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_ , )
UpperCamelCase = vocab_file
UpperCamelCase = False if not self.vocab_file else True
def __UpperCamelCase ( self , A_ , A_ = None ) -> List[int]:
"""simple docstring"""
UpperCamelCase = [self.sep_token_id]
UpperCamelCase = [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 , A_ , A_ = None , A_ = False ) -> List[int]:
"""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 , A_ , A_ = None ) -> List[int]:
"""simple docstring"""
UpperCamelCase = [self.sep_token_id]
UpperCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __UpperCamelCase ( self , A_ , A_ = None ) -> Tuple[str]:
"""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
UpperCamelCase = 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,)
| 222 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def A ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase = {
'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
'content': ['a ' * 20, 'a ' * 30, 'b ' * 7],
}
UpperCamelCase = Dataset.from_dict(lowercase )
return dataset
class lowercase ( _SCREAMING_SNAKE_CASE ):
def __UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = get_dataset()
UpperCamelCase = make_duplicate_clusters(A_ , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def __UpperCamelCase ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = get_dataset()
UpperCamelCase , UpperCamelCase = deduplicate_dataset(A_ )
self.assertEqual(len(A_ ) , 2 )
print(A_ )
self.assertEqual(duplicate_clusters[0][0]['copies'] , 2 )
self.assertEqual(duplicate_clusters[0][0]['is_extreme'] , A_ )
| 222 | 1 |
'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
__lowerCAmelCase : str ="base_with_context"
def UpperCamelCase ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Dict ):
A__ = nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"] ) )
A__ = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ) , requires_grad=__lowerCAmelCase )
for lyr_num, lyr in enumerate(model.encoders ):
A__ = weights[F"layers_{lyr_num}"]
A__ = nn.Parameter(
torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"] ) )
A__ = ly_weight["""attention"""]
A__ = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
A__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"] ) )
return model
def UpperCamelCase ( _lowerCamelCase : str , _lowerCamelCase : Union[str, Any] ):
A__ = nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T ) )
A__ = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ) , requires_grad=__lowerCAmelCase )
for lyr_num, lyr in enumerate(model.encoders ):
A__ = weights[F"layers_{lyr_num}"]
A__ = ly_weight["""attention"""]
A__ = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
A__ = nn.Parameter(
torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"] ) )
A__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
A__ = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"] ) )
return model
def UpperCamelCase ( _lowerCamelCase : Any , _lowerCamelCase : List[Any] ):
A__ = nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T ) )
A__ = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ) , requires_grad=__lowerCAmelCase )
A__ = nn.Parameter(
torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T ) )
for lyr_num, lyr in enumerate(model.decoders ):
A__ = weights[F"layers_{lyr_num}"]
A__ = nn.Parameter(
torch.FloatTensor(ly_weight["pre_self_attention_layer_norm"]["scale"] ) )
A__ = nn.Parameter(
torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T ) )
A__ = ly_weight["""self_attention"""]
A__ = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
A__ = ly_weight["""MultiHeadDotProductAttention_0"""]
A__ = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
A__ = nn.Parameter(
torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"] ) )
A__ = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
A__ = nn.Parameter(
torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
A__ = nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"] ) )
A__ = nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T ) )
return model
def UpperCamelCase ( _lowerCamelCase : Tuple ):
A__ = checkpoints.load_tax_checkpoint(args.checkpoint_path )
A__ = jnp.tree_util.tree_map(onp.array , __lowerCAmelCase )
A__ = [
"""from __gin__ import dynamic_registration""",
"""from music_spectrogram_diffusion.models.diffusion import diffusion_utils""",
"""diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0""",
"""diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()""",
]
A__ = os.path.join(args.checkpoint_path , ".." , "config.gin" )
A__ = inference.parse_training_gin_file(__lowerCAmelCase , __lowerCAmelCase )
A__ = inference.InferenceModel(args.checkpoint_path , __lowerCAmelCase )
A__ = DDPMScheduler(beta_schedule="squaredcos_cap_v2" , variance_type="fixed_large" )
A__ = SpectrogramNotesEncoder(
max_length=synth_model.sequence_length["inputs"] , vocab_size=synth_model.model.module.config.vocab_size , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="gated-gelu" , )
A__ = SpectrogramContEncoder(
input_dims=synth_model.audio_codec.n_dims , targets_context_length=synth_model.sequence_length["targets_context"] , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="gated-gelu" , )
A__ = TaFilmDecoder(
input_dims=synth_model.audio_codec.n_dims , targets_length=synth_model.sequence_length["targets_context"] , max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time , d_model=synth_model.model.module.config.emb_dim , num_layers=synth_model.model.module.config.num_decoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , dropout_rate=synth_model.model.module.config.dropout_rate , )
A__ = load_notes_encoder(ta_checkpoint["target"]["token_encoder"] , __lowerCAmelCase )
A__ = load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"] , __lowerCAmelCase )
A__ = load_decoder(ta_checkpoint["target"]["decoder"] , __lowerCAmelCase )
A__ = OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder" )
A__ = SpectrogramDiffusionPipeline(
notes_encoder=__lowerCAmelCase , continuous_encoder=__lowerCAmelCase , decoder=__lowerCAmelCase , scheduler=__lowerCAmelCase , melgan=__lowerCAmelCase , )
if args.save:
pipe.save_pretrained(args.output_path )
if __name__ == "__main__":
__lowerCAmelCase : str =argparse.ArgumentParser()
parser.add_argument("--output_path", default=None, type=str, required=True, help="Path to the converted model.")
parser.add_argument(
"--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
)
parser.add_argument(
"--checkpoint_path",
default=f"""{MODEL}/checkpoint_500000""",
type=str,
required=False,
help="Path to the original jax model checkpoint.",
)
__lowerCAmelCase : List[str] =parser.parse_args()
main(args)
| 362 |
'''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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : Any =logging.get_logger(__name__)
def UpperCamelCase ( _lowerCamelCase : List[str] , _lowerCamelCase : Tuple=False ):
A__ = []
# fmt: off
# stem:
rename_keys.append(("cls_token", "vit.embeddings.cls_token") )
rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings") )
rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias") )
# backbone
rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias") )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias") )
# transformer encoder
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") )
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 "vit" from all keys that start with "vit"
A__ = [(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"),
] )
# fmt: on
return rename_keys
def UpperCamelCase ( _lowerCamelCase : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : Union[str, Any]=False ):
for i in range(config.num_hidden_layers ):
if base_model:
A__ = ""
else:
A__ = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
A__ = state_dict.pop(F"blocks.{i}.attn.qkv.weight" )
A__ = state_dict.pop(F"blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
A__ = in_proj_weight[
: config.hidden_size, :
]
A__ = in_proj_bias[: config.hidden_size]
A__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
A__ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
A__ = in_proj_weight[
-config.hidden_size :, :
]
A__ = in_proj_bias[-config.hidden_size :]
def UpperCamelCase ( _lowerCamelCase : Any ):
A__ = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(_lowerCamelCase , _lowerCamelCase )
def UpperCamelCase ( _lowerCamelCase : str , _lowerCamelCase : Optional[int] , _lowerCamelCase : Union[str, Any] ):
A__ = dct.pop(_lowerCamelCase )
A__ = val
def UpperCamelCase ( ):
A__ = "http://images.cocodataset.org/val2017/000000039769.jpg"
A__ = Image.open(requests.get(_lowerCamelCase , stream=_lowerCamelCase ).raw )
return im
@torch.no_grad()
def UpperCamelCase ( _lowerCamelCase : Dict , _lowerCamelCase : int , _lowerCamelCase : int=False ):
A__ = BitConfig(
global_padding="same" , layer_type="bottleneck" , depths=(3, 4, 9) , out_features=["stage3"] , embedding_dynamic_padding=_lowerCamelCase , )
A__ = ViTHybridConfig(backbone_config=_lowerCamelCase , image_size=3_84 , num_labels=10_00 )
A__ = False
# load original model from timm
A__ = timm.create_model(_lowerCamelCase , pretrained=_lowerCamelCase )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
A__ = timm_model.state_dict()
if base_model:
remove_classification_head_(_lowerCamelCase )
A__ = create_rename_keys(_lowerCamelCase , _lowerCamelCase )
for src, dest in rename_keys:
rename_key(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
read_in_q_k_v(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
A__ = "huggingface/label-files"
A__ = "imagenet-1k-id2label.json"
A__ = json.load(open(hf_hub_download(_lowerCamelCase , _lowerCamelCase , repo_type="dataset" ) , "r" ) )
A__ = {int(_lowerCamelCase ): v for k, v in idalabel.items()}
A__ = idalabel
A__ = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
A__ = ViTHybridModel(_lowerCamelCase ).eval()
else:
A__ = ViTHybridForImageClassification(_lowerCamelCase ).eval()
model.load_state_dict(_lowerCamelCase )
# create image processor
A__ = create_transform(**resolve_data_config({} , model=_lowerCamelCase ) )
A__ = transform.transforms
A__ = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
A__ = ViTHybridImageProcessor(
do_resize=_lowerCamelCase , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_lowerCamelCase , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=_lowerCamelCase , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
A__ = prepare_img()
A__ = transform(_lowerCamelCase ).unsqueeze(0 )
A__ = processor(_lowerCamelCase , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(_lowerCamelCase , _lowerCamelCase )
# verify logits
with torch.no_grad():
A__ = model(_lowerCamelCase )
A__ = outputs.logits
print("Predicted class:" , logits.argmax(-1 ).item() )
if base_model:
A__ = timm_model.forward_features(_lowerCamelCase )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_lowerCamelCase , outputs.pooler_output , atol=1e-3 )
else:
A__ = timm_model(_lowerCamelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_lowerCamelCase , outputs.logits , atol=1e-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
print(F"Saving model {vit_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_lowerCamelCase )
print(F"Saving processor to {pytorch_dump_folder_path}" )
processor.save_pretrained(_lowerCamelCase )
if push_to_hub:
print(F"Pushing model and processor to the hub {vit_name}" )
model.push_to_hub(F"ybelkada/{vit_name}" )
processor.push_to_hub(F"ybelkada/{vit_name}" )
if __name__ == "__main__":
__lowerCAmelCase : Dict =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--vit_name",
default="vit_base_r50_s16_384",
type=str,
help="Name of the hybrid ViT 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."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
)
__lowerCAmelCase : Optional[Any] =parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 123 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class a__( unittest.TestCase ):
@slow
def lowercase_ ( self : List[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
a : Optional[int] = AutoConfig.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
a : Dict = TFAutoModel.from_pretrained(__snake_case , from_pt=__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
a : Tuple = AutoModel.from_pretrained(__snake_case , from_tf=__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
@slow
def lowercase_ ( self : str ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
a : List[str] = AutoConfig.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
a : Union[str, Any] = TFAutoModelForPreTraining.from_pretrained(__snake_case , from_pt=__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
a : List[Any] = AutoModelForPreTraining.from_pretrained(__snake_case , from_tf=__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
@slow
def lowercase_ ( self : int ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a : Union[str, Any] = AutoConfig.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
a : List[Any] = TFAutoModelForCausalLM.from_pretrained(__snake_case , from_pt=__snake_case )
a , a : Any = TFAutoModelForCausalLM.from_pretrained(
__snake_case , output_loading_info=__snake_case , from_pt=__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
a : Dict = AutoModelForCausalLM.from_pretrained(__snake_case , from_tf=__snake_case )
a , a : Tuple = AutoModelForCausalLM.from_pretrained(
__snake_case , output_loading_info=__snake_case , from_tf=__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
@slow
def lowercase_ ( self : Any ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a : Tuple = AutoConfig.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
a : List[str] = TFAutoModelWithLMHead.from_pretrained(__snake_case , from_pt=__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
a : Dict = AutoModelWithLMHead.from_pretrained(__snake_case , from_tf=__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
@slow
def lowercase_ ( self : Optional[int] ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a : List[str] = AutoConfig.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
a : Optional[Any] = TFAutoModelForMaskedLM.from_pretrained(__snake_case , from_pt=__snake_case )
a , a : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(
__snake_case , output_loading_info=__snake_case , from_pt=__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
a : str = AutoModelForMaskedLM.from_pretrained(__snake_case , from_tf=__snake_case )
a , a : Tuple = AutoModelForMaskedLM.from_pretrained(
__snake_case , output_loading_info=__snake_case , from_tf=__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
@slow
def lowercase_ ( self : int ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a : Optional[Any] = AutoConfig.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
a : str = TFAutoModelForSeqaSeqLM.from_pretrained(__snake_case , from_pt=__snake_case )
a , a : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(
__snake_case , output_loading_info=__snake_case , from_pt=__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
a : Dict = AutoModelForSeqaSeqLM.from_pretrained(__snake_case , from_tf=__snake_case )
a , a : str = AutoModelForSeqaSeqLM.from_pretrained(
__snake_case , output_loading_info=__snake_case , from_tf=__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
@slow
def lowercase_ ( self : Optional[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
a : Tuple = AutoConfig.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
a : List[Any] = TFAutoModelForSequenceClassification.from_pretrained(__snake_case , from_pt=__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
a : Dict = AutoModelForSequenceClassification.from_pretrained(__snake_case , from_tf=__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
@slow
def lowercase_ ( self : str ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
a : Optional[Any] = AutoConfig.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
a : int = TFAutoModelForQuestionAnswering.from_pretrained(__snake_case , from_pt=__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
a : Tuple = AutoModelForQuestionAnswering.from_pretrained(__snake_case , from_tf=__snake_case )
self.assertIsNotNone(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
def lowercase_ ( self : Tuple ):
a : List[Any] = TFAutoModelWithLMHead.from_pretrained(__snake_case , from_pt=__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=__snake_case ) , 1_44_10 )
a : Optional[int] = AutoModelWithLMHead.from_pretrained(__snake_case , from_tf=__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=__snake_case ) , 1_44_10 )
def lowercase_ ( self : Any ):
a : int = TFAutoModelWithLMHead.from_pretrained(__snake_case , from_pt=__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=__snake_case ) , 1_44_10 )
a : Optional[Any] = AutoModelWithLMHead.from_pretrained(__snake_case , from_tf=__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=__snake_case ) , 1_44_10 ) | 297 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
lowerCAmelCase: List[str] = ['\nclass', '\ndef', '\n#', '\n@', '\nprint', '\nif']
class a__( lowerCamelCase__ ):
def __init__( self : Any , __snake_case : List[str] , __snake_case : Union[str, Any] , __snake_case : List[Any]=None , __snake_case : Optional[Any]=1 ):
a : Union[str, Any] = tokenizer
a : Union[str, Any] = dataset
a : Any = len(__snake_case ) if n_tasks is None else n_tasks
a : List[str] = n_copies
def __iter__( self : str ):
a : List[Any] = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['prompt'].strip() )
a : Dict = self.tokenizer(__snake_case , padding=__snake_case , return_tensors='pt' )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class a__( lowerCamelCase__ ):
def __init__( self : Union[str, Any] , __snake_case : int , __snake_case : Optional[Any] , __snake_case : str ):
a : Dict = start_length
a : Dict = eof_strings
a : str = tokenizer
def __call__( self : Optional[Any] , __snake_case : Optional[Any] , __snake_case : Any , **__snake_case : Union[str, Any] ):
a : int = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
a : Optional[int] = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(__snake_case )
def lowerCamelCase__ ( _A ):
a : Optional[Any] = re.split('(%s)' % '|'.join(_A ) , _A )
# last string should be ""
return "".join(string_list[:-2] )
def lowerCamelCase__ ( _A , _A , _A , _A , _A , _A=20 , **_A ):
a : Optional[Any] = defaultdict(_A ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(_A ) ):
with torch.no_grad():
a : Optional[Any] = batch['ids'].shape[-1]
a : Optional[Any] = accelerator.unwrap_model(_A ).generate(
input_ids=batch['ids'][:, : batch['input_len']] , num_return_sequences=_A , **_A )
# each task is generated batch_size times
a : Tuple = batch['task_id'].repeat(_A )
a : List[Any] = accelerator.pad_across_processes(
_A , dim=1 , pad_index=tokenizer.pad_token_id )
a , a : Union[str, Any] = accelerator.gather((generated_tokens, generated_tasks) )
a : List[str] = generated_tokens.cpu().numpy()
a : int = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(_A , _A ):
gen_token_dict[task].append(_A )
a : Any = [[] for _ in range(_A )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
a : Optional[int] = tokenizer.decode(_A , skip_special_tokens=_A , clean_up_tokenization_spaces=_A )
code_gens[task].append(remove_last_block(_A ) )
return code_gens
def lowerCamelCase__ ( ):
# Setup configuration
a : Dict = HfArgumentParser(_A )
a : Any = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
a : List[Any] = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
a : int = 'false'
if args.num_workers is None:
a : Dict = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
a : List[Any] = Accelerator()
set_seed(args.seed , device_specific=_A )
# Load model and tokenizer
a : Dict = AutoTokenizer.from_pretrained(args.model_ckpt )
a : str = tokenizer.eos_token
a : List[Any] = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
a : Optional[Any] = {
'do_sample': args.do_sample,
'temperature': args.temperature,
'max_new_tokens': args.max_new_tokens,
'top_p': args.top_p,
'top_k': args.top_k,
'stopping_criteria': StoppingCriteriaList([EndOfFunctionCriteria(0 , _A , _A )] ),
}
# Load evaluation dataset and metric
a : Optional[int] = load_dataset('openai_humaneval' )
a : Optional[Any] = load_metric('code_eval' )
a : Optional[int] = args.num_tasks if args.num_tasks is not None else len(human_eval['test'] )
a : Optional[Any] = args.n_samples // args.batch_size
a : Any = TokenizedDataset(_A , human_eval['test'] , n_copies=_A , n_tasks=_A )
# do not confuse args.batch_size, which is actually the num_return_sequences
a : int = DataLoader(_A , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
a : int = code_eval_metric.compute(references=[''] , predictions=[['']] )
except ValueError as exception:
print(
'Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'
' flag to enable code evaluation.' )
raise exception
a , a : int = accelerator.prepare(_A , _A )
a : int = complete_code(
_A , _A , _A , _A , n_tasks=_A , batch_size=args.batch_size , **_A , )
if accelerator.is_main_process:
a : List[str] = []
for task in tqdm(range(_A ) ):
a : int = human_eval['test'][task]['test']
a : int = f"""check({human_eval["test"][task]["entry_point"]})"""
references.append('\n' + test_func + '\n' + entry_point )
# Evaluate completions with "code_eval" metric
a , a : Tuple = code_eval_metric.compute(
references=_A , predictions=_A , num_workers=args.num_workers )
print(f"""Results: {pass_at_k}""" )
# Save results to json file
with open(args.output_file , 'w' ) as fp:
json.dump(_A , _A )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main() | 297 | 1 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
UpperCamelCase = get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
UpperCamelCase = json.load(f)
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : Tuple ) -> Optional[Any]:
'''simple docstring'''
return FSMTTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
def _snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : str ) -> str:
'''simple docstring'''
A: str = FSMTForConditionalGeneration.from_pretrained(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
['''en-ru''', 26.0],
['''ru-en''', 22.0],
['''en-de''', 22.0],
['''de-en''', 29.0],
] )
@slow
def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Tuple ) -> Optional[Any]:
'''simple docstring'''
A: List[str] = f"""facebook/wmt19-{pair}"""
A: Any = self.get_tokenizer(SCREAMING_SNAKE_CASE_ )
A: int = self.get_model(SCREAMING_SNAKE_CASE_ )
A: str = bleu_data[pair]['''src''']
A: str = bleu_data[pair]['''tgt''']
A: Dict = tokenizer(SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' , truncation=SCREAMING_SNAKE_CASE_ , padding='''longest''' ).to(SCREAMING_SNAKE_CASE_ )
A: Dict = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
A: int = tokenizer.batch_decode(
SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ )
A: Optional[int] = calculate_bleu(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
print(SCREAMING_SNAKE_CASE_ )
self.assertGreaterEqual(scores['''bleu'''] , SCREAMING_SNAKE_CASE_ )
| 334 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 334 | 1 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ,unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = VideoToVideoSDPipeline
_UpperCAmelCase : Union[str, Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"video"} ) - {"image", "width", "height"}
_UpperCAmelCase : Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"video"} ) - {"image"}
_UpperCAmelCase : Union[str, Any] = PipelineTesterMixin.required_optional_params - {"latents"}
_UpperCAmelCase : str = False
# No `output_type`.
_UpperCAmelCase : List[str] = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback",
"callback_steps",
] )
def A ( self : Optional[int] ):
'''simple docstring'''
torch.manual_seed(0 )
_snake_case = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D') , up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D') , cross_attention_dim=32 , attention_head_dim=4 , )
_snake_case = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=lowercase , set_alpha_to_one=lowercase , )
torch.manual_seed(0 )
_snake_case = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
_snake_case = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , hidden_act='gelu' , projection_dim=512 , )
_snake_case = CLIPTextModel(lowercase )
_snake_case = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
_snake_case = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
}
return components
def A ( self : str , lowercase : str , lowercase : List[str]=0 ):
'''simple docstring'''
_snake_case = floats_tensor((1, 3, 3, 32, 32) , rng=random.Random(lowercase ) ).to(lowercase )
if str(lowercase ).startswith('mps' ):
_snake_case = torch.manual_seed(lowercase )
else:
_snake_case = torch.Generator(device=lowercase ).manual_seed(lowercase )
_snake_case = {
'prompt': 'A painting of a squirrel eating a burger',
'video': video,
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'pt',
}
return inputs
def A ( self : Any ):
'''simple docstring'''
_snake_case = 'cpu' # ensure determinism for the device-dependent torch.Generator
_snake_case = self.get_dummy_components()
_snake_case = VideoToVideoSDPipeline(**lowercase )
_snake_case = sd_pipe.to(lowercase )
sd_pipe.set_progress_bar_config(disable=lowercase )
_snake_case = self.get_dummy_inputs(lowercase )
_snake_case = 'np'
_snake_case = sd_pipe(**lowercase ).frames
_snake_case = frames[0][-3:, -3:, -1]
assert frames[0].shape == (32, 32, 3)
_snake_case = np.array([106, 117, 113, 174, 137, 112, 148, 151, 131] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def A ( self : Tuple ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowercase , expected_max_diff=5E-3 )
@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.' )
def A ( self : Optional[Any] ):
'''simple docstring'''
pass
@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.' )
def A ( self : Union[str, Any] ):
'''simple docstring'''
pass
@unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.' )
def A ( self : Tuple ):
'''simple docstring'''
pass
def A ( self : Dict ):
'''simple docstring'''
return super().test_progress_bar()
@slow
@skip_mps
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
def A ( self : List[Any] ):
'''simple docstring'''
_snake_case = VideoToVideoSDPipeline.from_pretrained('cerspense/zeroscope_v2_XL' , torch_dtype=torch.floataa )
pipe.enable_model_cpu_offload()
# 10 frames
_snake_case = torch.Generator(device='cpu' ).manual_seed(0 )
_snake_case = torch.randn((1, 10, 3, 1_024, 576) , generator=lowercase )
_snake_case = video.to('cuda' )
_snake_case = 'Spiderman is surfing'
_snake_case = pipe(lowercase , video=lowercase , generator=lowercase , num_inference_steps=3 , output_type='pt' ).frames
_snake_case = np.array([-1.0458984, -1.1279297, -0.9663086, -0.91503906, -0.75097656] )
assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array ).sum() < 1E-2 | 282 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def a_ ( __lowercase : Dict , __lowercase : int , __lowercase : Optional[Any]=None ) -> Any:
# set parameter of one layer
assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
_snake_case = nn.Parameter(__lowercase )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
_snake_case = nn.Parameter(__lowercase )
def a_ ( __lowercase : Any , __lowercase : Dict , __lowercase : Union[str, Any] ) -> Optional[Any]:
# set torch weights for 1-to-1 comparison
_snake_case = np.asarray(weights[0] )
_snake_case = np.asarray(weights[1] )
_snake_case = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(__lowercase ).transpose(1 , 2 ).contiguous().view(-1 , __lowercase ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(__lowercase ).transpose(1 , 2 ).contiguous().view(-1 , __lowercase ) , )
set_param(
torch_layer.output.dense , torch.tensor(__lowercase ).view(-1 , __lowercase ).contiguous().transpose(0 , 1 ) , )
def a_ ( __lowercase : str , __lowercase : Tuple , __lowercase : Any ) -> Optional[Any]:
# set torch weights for 1-to-1 comparison
_snake_case = np.asarray(weights[0] )
_snake_case = np.asarray(weights[1] )
_snake_case = np.asarray(weights[2] )
_snake_case = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(__lowercase ).transpose(1 , 2 ).contiguous().view(-1 , __lowercase ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(__lowercase ).transpose(1 , 2 ).contiguous().view(-1 , __lowercase ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(__lowercase ).transpose(1 , 2 ).contiguous().view(-1 , __lowercase ) , )
set_param(
torch_layer.output.dense , torch.tensor(__lowercase ).view(-1 , __lowercase ).contiguous().transpose(0 , 1 ) , )
def a_ ( __lowercase : Dict , __lowercase : List[str] , __lowercase : Union[str, Any] ) -> Optional[Any]:
# layernorm 1
_snake_case = weights[0][0][0]
_snake_case = np.asarray(layer_norm_a[0] )
_snake_case = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(__lowercase ) , torch.tensor(__lowercase ) , )
# lsh weights + output
_snake_case = weights[0][1]
if len(__lowercase ) < 4:
set_layer_weights_in_torch_lsh(__lowercase , torch_block.attention , __lowercase )
else:
set_layer_weights_in_torch_local(__lowercase , torch_block.attention , __lowercase )
# intermediate weighs
_snake_case = weights[2][0][1][2]
# Chunked Feed Forward
if len(__lowercase ) == 4:
_snake_case = intermediate_weights[2]
# layernorm 2
_snake_case = np.asarray(intermediate_weights[0][0] )
_snake_case = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(__lowercase ) , torch.tensor(__lowercase ) , )
# intermediate dense
_snake_case = np.asarray(intermediate_weights[1][0] )
_snake_case = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(__lowercase ).transpose(0 , 1 ).contiguous() , torch.tensor(__lowercase ) , )
# intermediate out
_snake_case = np.asarray(intermediate_weights[4][0] )
_snake_case = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(__lowercase ).transpose(0 , 1 ).contiguous() , torch.tensor(__lowercase ) , )
def a_ ( __lowercase : Tuple , __lowercase : Tuple , __lowercase : Dict ) -> Optional[int]:
# reformer model
_snake_case = torch_model.reformer
# word embeds
_snake_case = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(__lowercase ) , )
if isinstance(weights[3] , __lowercase ):
_snake_case = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
_snake_case = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), f'''{position_embeddings[emb_idx]} emb does not match'''
_snake_case = nn.Parameter(torch.tensor(__lowercase ) )
_snake_case = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
__lowercase ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
_snake_case = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(__lowercase , __lowercase , __lowercase )
# output layer norm
_snake_case = np.asarray(weights[7][0] )
_snake_case = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(__lowercase ) , torch.tensor(__lowercase ) , )
# output embeddings
_snake_case = np.asarray(weights[9][0] )
_snake_case = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(__lowercase ).transpose(0 , 1 ).contiguous() , torch.tensor(__lowercase ) , )
def a_ ( __lowercase : Optional[Any] , __lowercase : Dict , __lowercase : List[Any] ) -> Optional[int]:
# Initialise PyTorch model
_snake_case = ReformerConfig.from_json_file(__lowercase )
print(f'''Building PyTorch model from configuration: {config}''' )
_snake_case = ReformerModelWithLMHead(__lowercase )
with open(__lowercase , 'rb' ) as f:
_snake_case = pickle.load(__lowercase )['weights']
set_model_weights_in_torch(__lowercase , __lowercase , config.hidden_size )
# Save pytorch-model
print(f'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , __lowercase )
if __name__ == "__main__":
_lowerCamelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained Reformer model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase : List[Any] = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path) | 282 | 1 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
lowerCAmelCase__ : Optional[Any] =logging.get_logger(__name__)
class __lowercase :
"""simple docstring"""
_UpperCAmelCase = 42
_UpperCAmelCase = None
@staticmethod
def UpperCamelCase__ ( ):
"""simple docstring"""
raise NotImplementedError
def UpperCamelCase__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ):
"""simple docstring"""
raise NotImplementedError
def UpperCamelCase__ ( self , lowerCAmelCase__ ):
"""simple docstring"""
raise NotImplementedError
def UpperCamelCase__ ( self ):
"""simple docstring"""
if not self.is_available():
raise RuntimeError(
F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.''' )
@classmethod
def UpperCamelCase__ ( cls ):
"""simple docstring"""
return F'''`pip install {cls.pip_package or cls.name}`'''
class __lowercase (lowerCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase = 'optuna'
@staticmethod
def UpperCamelCase__ ( ):
"""simple docstring"""
return is_optuna_available()
def UpperCamelCase__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ):
"""simple docstring"""
return run_hp_search_optuna(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
def UpperCamelCase__ ( self , lowerCAmelCase__ ):
"""simple docstring"""
return default_hp_space_optuna(__lowerCAmelCase )
class __lowercase (lowerCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase = 'ray'
_UpperCAmelCase = '\'ray[tune]\''
@staticmethod
def UpperCamelCase__ ( ):
"""simple docstring"""
return is_ray_available()
def UpperCamelCase__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ):
"""simple docstring"""
return run_hp_search_ray(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
def UpperCamelCase__ ( self , lowerCAmelCase__ ):
"""simple docstring"""
return default_hp_space_ray(__lowerCAmelCase )
class __lowercase (lowerCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase = 'sigopt'
@staticmethod
def UpperCamelCase__ ( ):
"""simple docstring"""
return is_sigopt_available()
def UpperCamelCase__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ):
"""simple docstring"""
return run_hp_search_sigopt(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
def UpperCamelCase__ ( self , lowerCAmelCase__ ):
"""simple docstring"""
return default_hp_space_sigopt(__lowerCAmelCase )
class __lowercase (lowerCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase = 'wandb'
@staticmethod
def UpperCamelCase__ ( ):
"""simple docstring"""
return is_wandb_available()
def UpperCamelCase__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ):
"""simple docstring"""
return run_hp_search_wandb(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
def UpperCamelCase__ ( self , lowerCAmelCase__ ):
"""simple docstring"""
return default_hp_space_wandb(__lowerCAmelCase )
lowerCAmelCase__ : Any ={
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def a__ ( ):
SCREAMING_SNAKE_CASE_ : int = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(A__ ) > 0:
SCREAMING_SNAKE_CASE_ : List[Any] = available_backends[0].name
if len(A__ ) > 1:
logger.info(
F'''{len(A__ )} hyperparameter search backends available. Using {name} as the default.''' )
return name
raise RuntimeError(
'No hyperparameter search backend available.\n'
+ '\n'.join(
F''' - To install {backend.name} run {backend.pip_install()}'''
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 358 |
from __future__ import annotations
def a__ ( A__ ):
return len(set(A__ ) ) == len(A__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 162 | 0 |
"""simple docstring"""
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimizer
@require_tf
class lowerCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : int , UpperCamelCase : Optional[Any] , UpperCamelCase : str , UpperCamelCase : str ):
'''simple docstring'''
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , len(SCREAMING_SNAKE_CASE_ ) )
for a, b in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertAlmostEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , delta=SCREAMING_SNAKE_CASE_ )
def lowerCamelCase__ ( self : Any ):
'''simple docstring'''
__UpperCAmelCase : int = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0] )] )
accumulator([tf.constant([-2.0, 1.0] )] )
accumulator([tf.constant([-1.0, 2.0] )] )
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
self.assertEqual(accumulator.step , 3 )
self.assertEqual(len(accumulator.gradients ) , 1 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1e-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1e-2 )
def lowerCamelCase__ ( self : int ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = None
ops.enable_eager_execution_internal()
__UpperCAmelCase : Optional[Any] = tf.config.list_physical_devices("""CPU""" )
if len(SCREAMING_SNAKE_CASE_ ) == 1:
tf.config.set_logical_device_configuration(
physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
__UpperCAmelCase : Any = tf.config.list_logical_devices(device_type="""CPU""" )
__UpperCAmelCase : int = tf.distribute.MirroredStrategy(devices=devices[:2] )
with strategy.scope():
__UpperCAmelCase : Union[str, Any] = GradientAccumulator()
__UpperCAmelCase : str = tf.Variable([4.0, 3.0] )
__UpperCAmelCase ,__UpperCAmelCase : Union[str, Any] = create_optimizer(5e-5 , 10 , 5 )
__UpperCAmelCase : str = tf.Variable([0.0, 0.0] , trainable=SCREAMING_SNAKE_CASE_ )
def accumulate_on_replica(UpperCamelCase : Optional[int] ):
accumulator([gradient] )
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients , [variable] ) ) )
@tf.function
def accumulate(UpperCamelCase : str , UpperCamelCase : Dict ):
with strategy.scope():
__UpperCAmelCase : List[Any] = strategy.experimental_local_results(SCREAMING_SNAKE_CASE_ )
local_variables[0].assign(SCREAMING_SNAKE_CASE_ )
local_variables[1].assign(SCREAMING_SNAKE_CASE_ )
strategy.run(SCREAMING_SNAKE_CASE_ , args=(gradient_placeholder,) )
@tf.function
def apply_grad():
with strategy.scope():
strategy.run(SCREAMING_SNAKE_CASE_ )
def _check_local_values(UpperCamelCase : Tuple , UpperCamelCase : Union[str, Any] ):
__UpperCAmelCase : Optional[Any] = strategy.experimental_local_results(accumulator._gradients[0] )
self.assertListAlmostEqual(values[0].value() , SCREAMING_SNAKE_CASE_ , tol=1e-2 )
self.assertListAlmostEqual(values[1].value() , SCREAMING_SNAKE_CASE_ , tol=1e-2 )
accumulate([1.0, 2.0] , [-1.0, 1.0] )
accumulate([3.0, -1.0] , [-1.0, -1.0] )
accumulate([-2.0, 2.0] , [3.0, -2.0] )
self.assertEqual(accumulator.step , 3 )
_check_local_values([2.0, 3.0] , [1.0, -2.0] )
apply_grad()
self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1e-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
_check_local_values([0.0, 0.0] , [0.0, 0.0] )
| 115 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowerCamelCase_ = data_utils.TransfoXLTokenizer
lowerCamelCase_ = data_utils.TransfoXLCorpus
lowerCamelCase_ = data_utils
lowerCamelCase_ = data_utils
def __magic_name__ ( __a : List[Any] , __a : str , __a : Optional[Any] , __a : List[str] ):
'''simple docstring'''
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(__a , """rb""" ) as fp:
UpperCamelCase__ = pickle.load(__a , encoding="""latin1""" )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
UpperCamelCase__ = pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""pretrained_vocab_file"""]
print(f"Save vocabulary to {pytorch_vocab_dump_path}" )
UpperCamelCase__ = corpus.vocab.__dict__
torch.save(__a , __a )
UpperCamelCase__ = corpus.__dict__
corpus_dict_no_vocab.pop("""vocab""" , __a )
UpperCamelCase__ = pytorch_dump_folder_path + """/""" + CORPUS_NAME
print(f"Save dataset to {pytorch_dataset_dump_path}" )
torch.save(__a , __a )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
UpperCamelCase__ = os.path.abspath(__a )
UpperCamelCase__ = os.path.abspath(__a )
print(f"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}." )
# Initialise PyTorch model
if transfo_xl_config_file == "":
UpperCamelCase__ = TransfoXLConfig()
else:
UpperCamelCase__ = TransfoXLConfig.from_json_file(__a )
print(f"Building PyTorch model from configuration: {config}" )
UpperCamelCase__ = TransfoXLLMHeadModel(__a )
UpperCamelCase__ = load_tf_weights_in_transfo_xl(__a , __a , __a )
# Save pytorch-model
UpperCamelCase__ = os.path.join(__a , __a )
UpperCamelCase__ = os.path.join(__a , __a )
print(f"Save PyTorch model to {os.path.abspath(__a )}" )
torch.save(model.state_dict() , __a )
print(f"Save configuration file to {os.path.abspath(__a )}" )
with open(__a , """w""" , encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
)
parser.add_argument(
'''--tf_checkpoint_path''',
default='''''',
type=str,
help='''An optional path to a TensorFlow checkpoint path to be converted.''',
)
parser.add_argument(
'''--transfo_xl_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--transfo_xl_dataset_file''',
default='''''',
type=str,
help='''An optional dataset file to be converted in a vocabulary.''',
)
lowerCamelCase_ = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 244 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__a = logging.get_logger(__name__)
__a = {
'post_extract_proj': 'feature_projection.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.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
for attribute in key.split('''.''' ):
UpperCAmelCase_ = getattr(_lowercase , _lowercase )
if weight_type is not None:
UpperCAmelCase_ = getattr(_lowercase , _lowercase ).shape
else:
UpperCAmelCase_ = 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":
UpperCAmelCase_ = value
elif weight_type == "weight_g":
UpperCAmelCase_ = value
elif weight_type == "weight_v":
UpperCAmelCase_ = value
elif weight_type == "bias":
UpperCAmelCase_ = value
else:
UpperCAmelCase_ = value
logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
UpperCAmelCase_ = []
UpperCAmelCase_ = fairseq_model.state_dict()
UpperCAmelCase_ = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
UpperCAmelCase_ = False
if "conv_layers" in name:
load_conv_layer(
_lowercase , _lowercase , _lowercase , _lowercase , hf_model.config.feat_extract_norm == '''group''' , )
UpperCAmelCase_ = True
else:
for key, mapped_key in MAPPING.items():
UpperCAmelCase_ = '''hubert.''' + 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] and not is_finetuned):
UpperCAmelCase_ = True
if "*" in mapped_key:
UpperCAmelCase_ = name.split(_lowercase )[0].split('''.''' )[-2]
UpperCAmelCase_ = mapped_key.replace('''*''' , _lowercase )
if "weight_g" in name:
UpperCAmelCase_ = '''weight_g'''
elif "weight_v" in name:
UpperCAmelCase_ = '''weight_v'''
elif "weight" in name:
UpperCAmelCase_ = '''weight'''
elif "bias" in name:
UpperCAmelCase_ = '''bias'''
else:
UpperCAmelCase_ = None
set_recursively(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
continue
if not is_used:
unused_weights.append(_lowercase )
logger.warning(f'''Unused weights: {unused_weights}''' )
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
UpperCAmelCase_ = full_name.split('''conv_layers.''' )[-1]
UpperCAmelCase_ = name.split('''.''' )
UpperCAmelCase_ = int(items[0] )
UpperCAmelCase_ = 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.'''
)
UpperCAmelCase_ = 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.'''
)
UpperCAmelCase_ = 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."
)
UpperCAmelCase_ = 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.'''
)
UpperCAmelCase_ = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(_lowercase )
@torch.no_grad()
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase=None , _lowercase=None , _lowercase=True ):
'''simple docstring'''
if config_path is not None:
UpperCAmelCase_ = HubertConfig.from_pretrained(_lowercase )
else:
UpperCAmelCase_ = HubertConfig()
if is_finetuned:
if dict_path:
UpperCAmelCase_ = Dictionary.load(_lowercase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
UpperCAmelCase_ = target_dict.pad_index
UpperCAmelCase_ = target_dict.bos_index
UpperCAmelCase_ = target_dict.eos_index
UpperCAmelCase_ = len(target_dict.symbols )
UpperCAmelCase_ = os.path.join(_lowercase , '''vocab.json''' )
if not os.path.isdir(_lowercase ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_lowercase ) )
return
os.makedirs(_lowercase , exist_ok=_lowercase )
with open(_lowercase , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(target_dict.indices , _lowercase )
UpperCAmelCase_ = WavaVecaCTCTokenizer(
_lowercase , 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=_lowercase , )
UpperCAmelCase_ = True if config.feat_extract_norm == '''layer''' else False
UpperCAmelCase_ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=_lowercase , return_attention_mask=_lowercase , )
UpperCAmelCase_ = WavaVecaProcessor(feature_extractor=_lowercase , tokenizer=_lowercase )
processor.save_pretrained(_lowercase )
UpperCAmelCase_ = HubertForCTC(_lowercase )
else:
UpperCAmelCase_ = HubertModel(_lowercase )
if is_finetuned:
UpperCAmelCase_ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
UpperCAmelCase_ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
UpperCAmelCase_ = model[0].eval()
recursively_load_weights(_lowercase , _lowercase , _lowercase )
hf_wavavec.save_pretrained(_lowercase )
if __name__ == "__main__":
__a = 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'
)
__a = parser.parse_args()
convert_hubert_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
) | 350 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
__a = logging.getLogger()
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = argparse.ArgumentParser()
parser.add_argument('''-f''' )
UpperCAmelCase_ : Dict = parser.parse_args()
return args.f
class __a( _a ):
"""simple docstring"""
def a__ ( self ) -> None:
UpperCAmelCase_ : int = logging.StreamHandler(sys.stdout )
logger.addHandler(_SCREAMING_SNAKE_CASE )
def a__ ( self ,_SCREAMING_SNAKE_CASE ) -> str:
UpperCAmelCase_ : int = get_gpu_count()
if n_gpu > 1:
pass
# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
# cmd = [sys.executable] + distributed_args + args
# execute_subprocess_async(cmd, env=self.get_env())
# XXX: test the results - need to save them first into .json file
else:
args.insert(0 ,'''run_glue_deebert.py''' )
with patch.object(_SCREAMING_SNAKE_CASE ,'''argv''' ,_SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ : List[str] = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(_SCREAMING_SNAKE_CASE ,0.6_66 )
@slow
@require_torch_non_multi_gpu
def a__ ( self ) -> List[str]:
UpperCAmelCase_ : List[Any] = '''
--model_type roberta
--model_name_or_path roberta-base
--task_name MRPC
--do_train
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--max_seq_length 128
--per_gpu_eval_batch_size=1
--per_gpu_train_batch_size=8
--learning_rate 2e-4
--num_train_epochs 3
--overwrite_output_dir
--seed 42
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--save_steps 0
--overwrite_cache
--eval_after_first_stage
'''.split()
self.run_and_check(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Union[str, Any] = '''
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--eval_each_highway
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
'''.split()
self.run_and_check(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Any = '''
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--early_exit_entropy 0.1
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
'''.split()
self.run_and_check(_SCREAMING_SNAKE_CASE ) | 235 | 0 |
"""simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase=7 ) -> Any:
'''simple docstring'''
lowercase_ = None
if token is not None:
lowercase_ = {"""Accept""": """application/vnd.github+json""", """Authorization""": F'''Bearer {token}'''}
# The id of a workflow (not of a workflow run)
lowercase_ = """636036"""
lowercase_ = F'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''
lowercase_ = requests.get(__lowerCAmelCase , headers=__lowerCAmelCase ).json()
return result["workflow_runs"]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = get_daily_ci_runs(__lowerCAmelCase )
lowercase_ = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
lowercase_ = workflow_run["""id"""]
break
return workflow_run_id
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
'''simple docstring'''
lowercase_ = get_last_daily_ci_runs(__lowerCAmelCase )
if workflow_run_id is not None:
lowercase_ = get_artifacts_links(worflow_run_id=__lowerCAmelCase , token=__lowerCAmelCase )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
lowercase_ = artifacts_links[artifact_name]
download_artifact(
artifact_name=__lowerCAmelCase , artifact_url=__lowerCAmelCase , output_dir=__lowerCAmelCase , token=__lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any:
'''simple docstring'''
get_last_daily_ci_artifacts(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
lowercase_ = {}
for artifact_name in artifact_names:
lowercase_ = os.path.join(__lowerCAmelCase , F'''{artifact_name}.zip''' )
if os.path.isfile(__lowerCAmelCase ):
lowercase_ = {}
with zipfile.ZipFile(__lowerCAmelCase ) as z:
for filename in z.namelist():
if not os.path.isdir(__lowerCAmelCase ):
# read the file
with z.open(__lowerCAmelCase ) as f:
lowercase_ = f.read().decode("""UTF-8""" )
return results
| 136 |
"""simple docstring"""
def A ( snake_case :int ) -> int:
__UpperCamelCase = [1]
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 0, 0, 0
__UpperCamelCase = ugly_nums[ia] * 2
__UpperCamelCase = ugly_nums[ia] * 3
__UpperCamelCase = ugly_nums[ia] * 5
for _ in range(1 , snake_case ):
__UpperCamelCase = min(snake_case , snake_case , snake_case )
ugly_nums.append(snake_case )
if next_num == next_a:
ia += 1
__UpperCamelCase = ugly_nums[ia] * 2
if next_num == next_a:
ia += 1
__UpperCamelCase = ugly_nums[ia] * 3
if next_num == next_a:
ia += 1
__UpperCamelCase = ugly_nums[ia] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(f'''{ugly_numbers(2_0_0) = }''')
| 316 | 0 |
'''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_convbert import ConvBertTokenizer
__snake_case : Union[str, Any] = logging.get_logger(__name__)
__snake_case : Optional[Any] = {'vocab_file': 'vocab.txt'}
__snake_case : Optional[int] = {
'vocab_file': {
'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
'YituTech/conv-bert-medium-small': (
'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
),
'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
}
}
__snake_case : Dict = {
'YituTech/conv-bert-base': 512,
'YituTech/conv-bert-medium-small': 512,
'YituTech/conv-bert-small': 512,
}
__snake_case : List[str] = {
'YituTech/conv-bert-base': {'do_lower_case': True},
'YituTech/conv-bert-medium-small': {'do_lower_case': True},
'YituTech/conv-bert-small': {'do_lower_case': True},
}
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
__snake_case = VOCAB_FILES_NAMES
__snake_case = PRETRAINED_VOCAB_FILES_MAP
__snake_case = PRETRAINED_INIT_CONFIGURATION
__snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case = ConvBertTokenizer
def __init__( self : str , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : List[Any]="[UNK]" , lowerCAmelCase_ : Tuple="[SEP]" , lowerCAmelCase_ : Any="[PAD]" , lowerCAmelCase_ : Optional[Any]="[CLS]" , lowerCAmelCase_ : str="[MASK]" , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : Optional[int]=None , **lowerCAmelCase_ : Any , ) -> Tuple:
'''simple docstring'''
super().__init__(
lowerCAmelCase_ , tokenizer_file=lowerCAmelCase_ , do_lower_case=lowerCAmelCase_ , unk_token=lowerCAmelCase_ , sep_token=lowerCAmelCase_ , pad_token=lowerCAmelCase_ , cls_token=lowerCAmelCase_ , mask_token=lowerCAmelCase_ , tokenize_chinese_chars=lowerCAmelCase_ , strip_accents=lowerCAmelCase_ , **lowerCAmelCase_ , )
A__ : int =json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , lowerCAmelCase_ ) != do_lower_case
or normalizer_state.get("""strip_accents""" , lowerCAmelCase_ ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , lowerCAmelCase_ ) != tokenize_chinese_chars
):
A__ : Union[str, Any] =getattr(lowerCAmelCase_ , normalizer_state.pop("""type""" ) )
A__ : List[str] =do_lower_case
A__ : List[Any] =strip_accents
A__ : List[Any] =tokenize_chinese_chars
A__ : Dict =normalizer_class(**lowerCAmelCase_ )
A__ : Union[str, Any] =do_lower_case
def lowercase__ ( self : int , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Dict=None ) -> Union[str, Any]:
'''simple docstring'''
A__ : Union[str, 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 : Optional[int] , lowerCAmelCase_ : List[int] , lowerCAmelCase_ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
A__ : Optional[Any] =[self.sep_token_id]
A__ : List[Any] =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase__ ( self : Any , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
A__ : Tuple =self._tokenizer.model.save(lowerCAmelCase_ , name=lowerCAmelCase_ )
return tuple(lowerCAmelCase_ )
| 136 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case : List[str] = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class lowerCamelCase ( lowercase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case = XLMRobertaTokenizer
__snake_case = XLMRobertaTokenizerFast
__snake_case = True
__snake_case = True
def lowercase__ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
A__ : Any =XLMRobertaTokenizer(lowerCAmelCase_ , keep_accents=lowerCAmelCase_ )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase__ ( self : Optional[Any] ) -> Tuple:
'''simple docstring'''
A__ : Union[str, Any] ="""<pad>"""
A__ : Any =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase_ ) , lowerCAmelCase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase_ ) , lowerCAmelCase_ )
def lowercase__ ( self : List[Any] ) -> Any:
'''simple docstring'''
A__ : int =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(lowerCAmelCase_ ) , 10_02 )
def lowercase__ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 10_02 )
def lowercase__ ( self : Tuple ) -> Any:
'''simple docstring'''
A__ : List[Any] =XLMRobertaTokenizer(lowerCAmelCase_ , keep_accents=lowerCAmelCase_ )
A__ : Tuple =tokenizer.tokenize("""This is a test""" )
self.assertListEqual(lowerCAmelCase_ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase_ ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
A__ : Optional[int] =tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
lowerCAmelCase_ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
A__ : Optional[int] =tokenizer.convert_tokens_to_ids(lowerCAmelCase_ )
self.assertListEqual(
lowerCAmelCase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
A__ : Union[str, Any] =tokenizer.convert_ids_to_tokens(lowerCAmelCase_ )
self.assertListEqual(
lowerCAmelCase_ , [
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 lowercase__ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
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
A__ : Dict =(self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
A__ : List[str] =self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_ , **lowerCAmelCase_ )
A__ : Union[str, Any] =self.tokenizer_class.from_pretrained(lowerCAmelCase_ , **lowerCAmelCase_ )
A__ : Optional[Any] =tempfile.mkdtemp()
A__ : Union[str, Any] =tokenizer_r.save_pretrained(lowerCAmelCase_ )
A__ : Union[str, Any] =tokenizer_p.save_pretrained(lowerCAmelCase_ )
# 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 ) )
A__ : List[str] =tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f )
self.assertSequenceEqual(lowerCAmelCase_ , lowerCAmelCase_ )
# Checks everything loads correctly in the same way
A__ : Any =tokenizer_r.from_pretrained(lowerCAmelCase_ )
A__ : Union[str, Any] =tokenizer_p.from_pretrained(lowerCAmelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCAmelCase_ , lowerCAmelCase_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowerCAmelCase_ )
# Save tokenizer rust, legacy_format=True
A__ : List[str] =tempfile.mkdtemp()
A__ : List[str] =tokenizer_r.save_pretrained(lowerCAmelCase_ , legacy_format=lowerCAmelCase_ )
A__ : List[Any] =tokenizer_p.save_pretrained(lowerCAmelCase_ )
# Checks it save with the same files
self.assertSequenceEqual(lowerCAmelCase_ , lowerCAmelCase_ )
# Checks everything loads correctly in the same way
A__ : str =tokenizer_r.from_pretrained(lowerCAmelCase_ )
A__ : List[Any] =tokenizer_p.from_pretrained(lowerCAmelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCAmelCase_ , lowerCAmelCase_ ) )
shutil.rmtree(lowerCAmelCase_ )
# Save tokenizer rust, legacy_format=False
A__ : List[str] =tempfile.mkdtemp()
A__ : Dict =tokenizer_r.save_pretrained(lowerCAmelCase_ , legacy_format=lowerCAmelCase_ )
A__ : List[Any] =tokenizer_p.save_pretrained(lowerCAmelCase_ )
# 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
A__ : Optional[int] =tokenizer_r.from_pretrained(lowerCAmelCase_ )
A__ : str =tokenizer_p.from_pretrained(lowerCAmelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCAmelCase_ , lowerCAmelCase_ ) )
shutil.rmtree(lowerCAmelCase_ )
@cached_property
def lowercase__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base""" )
def lowercase__ ( self : Tuple ) -> Tuple:
'''simple docstring'''
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowerCAmelCase_ , f.name )
A__ : Dict =XLMRobertaTokenizer(f.name , keep_accents=lowerCAmelCase_ )
A__ : Optional[Any] =pickle.dumps(lowerCAmelCase_ )
pickle.loads(lowerCAmelCase_ )
def lowercase__ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
A__ : Any =self.get_tokenizer()
A__ : Any =self.get_rust_tokenizer()
A__ : Optional[Any] ="""I was born in 92000, and this is falsé."""
A__ : List[str] =tokenizer.tokenize(lowerCAmelCase_ )
A__ : int =rust_tokenizer.tokenize(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
A__ : str =tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
A__ : Dict =rust_tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
A__ : Union[str, Any] =self.get_rust_tokenizer()
A__ : Union[str, Any] =tokenizer.encode(lowerCAmelCase_ )
A__ : Optional[Any] =rust_tokenizer.encode(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
@slow
def lowercase__ ( self : Dict ) -> int:
'''simple docstring'''
A__ : Optional[Any] ="""Hello World!"""
A__ : Optional[Any] =[0, 3_53_78, 66_61, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCAmelCase_ , self.big_tokenizer.encode(lowerCAmelCase_ ) )
@slow
def lowercase__ ( self : List[Any] ) -> int:
'''simple docstring'''
A__ : List[Any] =(
"""This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
""" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
)
A__ : Optional[Any] =[
0,
32_93,
83,
10,
45_52,
49_89,
79_86,
6_78,
10,
59_15,
1_11,
17_94_59,
12_48_50,
4,
60_44,
2_37,
12,
6,
5,
6,
4,
67_80,
7_05,
15,
13_88,
44,
3_78,
1_01_14,
7_11,
1_52,
20,
6,
5,
2_23_76,
6_42,
12_21,
1_51_90,
3_41_53,
4_50,
56_08,
9_59,
11_19,
5_77_02,
1_36,
1_86,
47,
10_98,
2_93_67,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
60_44,
2_37,
62_84,
5_09_01,
5_28,
31,
90,
34,
9_27,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCAmelCase_ , self.big_tokenizer.encode(lowerCAmelCase_ ) )
@slow
def lowercase__ ( self : int ) -> Optional[Any]:
'''simple docstring'''
# fmt: off
A__ : List[Any] ={"""input_ids""": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase_ , model_name="""xlm-roberta-base""" , revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3""" , )
| 136 | 1 |
def lowercase ( SCREAMING_SNAKE_CASE__ : int = 10**9 ) -> int:
_snake_case : Tuple = 1
_snake_case : List[Any] = 2
_snake_case : List[str] = 0
_snake_case : Optional[Any] = 0
_snake_case : Optional[Any] = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
_snake_case : Union[str, Any] = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(F'''{solution() = }''')
| 317 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE_ )
class snake_case ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case_ : str = field(default="""question-answering-extractive""" ,metadata={"""include_in_asdict_even_if_is_default""": True} )
snake_case_ : ClassVar[Features] = Features({"""question""": Value("""string""" ), """context""": Value("""string""" )} )
snake_case_ : ClassVar[Features] = Features(
{
"""answers""": Sequence(
{
"""text""": Value("""string""" ),
"""answer_start""": Value("""int32""" ),
} )
} )
snake_case_ : str = "question"
snake_case_ : str = "context"
snake_case_ : str = "answers"
@property
def UpperCamelCase_ ( self : Any) -> Dict[str, str]:
"""simple docstring"""
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 317 | 1 |
def __A ( _lowercase=2_81_23 ):
'''simple docstring'''
_A = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
_A = set()
_A = 0
for n in range(1 , limit + 1 ):
if sum_divs[n] > n:
abundants.add(lowercase__ )
if not any((n - a in abundants) for a in abundants ):
res += n
return res
if __name__ == "__main__":
print(solution())
| 362 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __A ( self: Union[str, Any] ) -> Union[str, Any]:
_A = '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
_A = dict(zip(__A , range(len(__A ) ) ) )
_A = {
'''unk_token''': '''<unk>''',
'''bos_token''': '''<s>''',
'''eos_token''': '''</s>''',
}
_A = {
'''feature_size''': 1,
'''padding_value''': 0.0,
'''sampling_rate''': 1_60_00,
'''return_attention_mask''': False,
'''do_normalize''': True,
}
_A = tempfile.mkdtemp()
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_A = os.path.join(self.tmpdirname , __A )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__A ) + '''\n''' )
with open(self.feature_extraction_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__A ) + '''\n''' )
# load decoder from hub
_A = '''hf-internal-testing/ngram-beam-search-decoder'''
def __A ( self: Tuple , **__A: str ) -> str:
_A = self.add_kwargs_tokens_map.copy()
kwargs.update(__A )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **__A )
def __A ( self: Any , **__A: List[Any] ) -> Union[str, Any]:
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **__A )
def __A ( self: List[Any] , **__A: Union[str, Any] ) -> int:
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **__A )
def __A ( self: List[str] ) -> Optional[int]:
shutil.rmtree(self.tmpdirname )
def __A ( self: List[str] ) -> Optional[Any]:
_A = self.get_tokenizer()
_A = self.get_feature_extractor()
_A = self.get_decoder()
_A = WavaVecaProcessorWithLM(tokenizer=__A , feature_extractor=__A , decoder=__A )
processor.save_pretrained(self.tmpdirname )
_A = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , __A )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __A )
# decoder
self.assertEqual(processor.decoder._alphabet.labels , decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set , decoder.model_container[decoder._model_key]._unigram_set , )
self.assertIsInstance(processor.decoder , __A )
def __A ( self: Optional[int] ) -> Union[str, Any]:
_A = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
_A = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname , alpha=5.0 , beta=3.0 , score_boundary=-7.0 , unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha , 5.0 )
self.assertEqual(processor.language_model.beta , 3.0 )
self.assertEqual(processor.language_model.score_boundary , -7.0 )
self.assertEqual(processor.language_model.unk_score_offset , 3 )
def __A ( self: str ) -> Any:
_A = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(['''xx'''] )
with self.assertRaisesRegex(__A , '''include''' ):
WavaVecaProcessorWithLM(
tokenizer=__A , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
def __A ( self: List[str] ) -> str:
_A = self.get_feature_extractor()
_A = self.get_tokenizer()
_A = self.get_decoder()
_A = WavaVecaProcessorWithLM(tokenizer=__A , feature_extractor=__A , decoder=__A )
_A = floats_list((3, 10_00) )
_A = feature_extractor(__A , return_tensors='''np''' )
_A = processor(__A , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __A ( self: Union[str, Any] ) -> Optional[Any]:
_A = self.get_feature_extractor()
_A = self.get_tokenizer()
_A = self.get_decoder()
_A = WavaVecaProcessorWithLM(tokenizer=__A , feature_extractor=__A , decoder=__A )
_A = '''This is a test string'''
_A = processor(text=__A )
_A = tokenizer(__A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __A ( self: List[str] , __A: Optional[int]=(2, 10, 16) , __A: Optional[int]=77 ) -> List[Any]:
np.random.seed(__A )
return np.random.rand(*__A )
def __A ( self: List[Any] ) -> Optional[Any]:
_A = self.get_feature_extractor()
_A = self.get_tokenizer()
_A = self.get_decoder()
_A = WavaVecaProcessorWithLM(tokenizer=__A , feature_extractor=__A , decoder=__A )
_A = self._get_dummy_logits(shape=(10, 16) , seed=13 )
_A = processor.decode(__A )
_A = decoder.decode_beams(__A )[0]
self.assertEqual(decoded_decoder[0] , decoded_processor.text )
self.assertEqual('''</s> <s> </s>''' , decoded_processor.text )
self.assertEqual(decoded_decoder[-2] , decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] , decoded_processor.lm_score )
@parameterized.expand([[None], ['''fork'''], ['''spawn''']] )
def __A ( self: str , __A: Any ) -> int:
_A = self.get_feature_extractor()
_A = self.get_tokenizer()
_A = self.get_decoder()
_A = WavaVecaProcessorWithLM(tokenizer=__A , feature_extractor=__A , decoder=__A )
_A = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
_A = processor.batch_decode(__A )
else:
with get_context(__A ).Pool() as pool:
_A = processor.batch_decode(__A , __A )
_A = list(__A )
with get_context('''fork''' ).Pool() as p:
_A = decoder.decode_beams_batch(__A , __A )
_A ,_A ,_A = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(__A , decoded_processor.text )
self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>'''] , decoded_processor.text )
self.assertListEqual(__A , decoded_processor.logit_score )
self.assertListEqual(__A , decoded_processor.lm_score )
def __A ( self: Optional[Any] ) -> int:
_A = self.get_feature_extractor()
_A = self.get_tokenizer()
_A = self.get_decoder()
_A = WavaVecaProcessorWithLM(tokenizer=__A , feature_extractor=__A , decoder=__A )
_A = self._get_dummy_logits()
_A = 15
_A = -20.0
_A = -4.0
_A = processor.batch_decode(
__A , beam_width=__A , beam_prune_logp=__A , token_min_logp=__A , )
_A = decoded_processor_out.text
_A = list(__A )
with get_context('''fork''' ).Pool() as pool:
_A = decoder.decode_beams_batch(
__A , __A , beam_width=__A , beam_prune_logp=__A , token_min_logp=__A , )
_A = [d[0][0] for d in decoded_decoder_out]
_A = [d[0][2] for d in decoded_decoder_out]
_A = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(__A , __A )
self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>'''] , __A )
self.assertTrue(np.array_equal(__A , decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-20.054, -18.447] , __A , atol=1e-3 ) )
self.assertTrue(np.array_equal(__A , decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-15.554, -13.9_474] , __A , atol=1e-3 ) )
def __A ( self: Optional[int] ) -> Dict:
_A = self.get_feature_extractor()
_A = self.get_tokenizer()
_A = self.get_decoder()
_A = WavaVecaProcessorWithLM(tokenizer=__A , feature_extractor=__A , decoder=__A )
_A = self._get_dummy_logits()
_A = 2.0
_A = 5.0
_A = -20.0
_A = True
_A = processor.batch_decode(
__A , alpha=__A , beta=__A , unk_score_offset=__A , lm_score_boundary=__A , )
_A = decoded_processor_out.text
_A = list(__A )
decoder.reset_params(
alpha=__A , beta=__A , unk_score_offset=__A , lm_score_boundary=__A , )
with get_context('''fork''' ).Pool() as pool:
_A = decoder.decode_beams_batch(
__A , __A , )
_A = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(__A , __A )
self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>'''] , __A )
_A = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha , 2.0 )
self.assertEqual(lm_model.beta , 5.0 )
self.assertEqual(lm_model.unk_score_offset , -20.0 )
self.assertEqual(lm_model.score_boundary , __A )
def __A ( self: int ) -> Optional[Any]:
_A = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_A = processor.decoder.model_container[processor.decoder._model_key]
_A = Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
_A = os.listdir(__A )
_A = ['''alphabet.json''', '''language_model''']
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(__A , __A )
def __A ( self: Tuple ) -> Any:
_A = snapshot_download('''hf-internal-testing/processor_with_lm''' )
_A = WavaVecaProcessorWithLM.from_pretrained(__A )
_A = processor.decoder.model_container[processor.decoder._model_key]
_A = Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
_A = os.listdir(__A )
_A = os.listdir(__A )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(__A , __A )
def __A ( self: List[str] ) -> Tuple:
_A = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_A = AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_A = floats_list((3, 10_00) )
_A = processor_wavaveca(__A , return_tensors='''np''' )
_A = processor_auto(__A , return_tensors='''np''' )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() , input_auto[key].sum() , delta=1e-2 )
_A = self._get_dummy_logits()
_A = processor_wavaveca.batch_decode(__A )
_A = processor_auto.batch_decode(__A )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def __A ( self: Optional[int] ) -> Any:
_A = self.get_feature_extractor()
_A = self.get_tokenizer()
_A = self.get_decoder()
_A = WavaVecaProcessorWithLM(tokenizer=__A , feature_extractor=__A , decoder=__A )
self.assertListEqual(
processor.model_input_names , feature_extractor.model_input_names , msg='''`processor` and `feature_extractor` model input names do not match''' , )
@staticmethod
def __A ( __A: int , __A: List[str] ) -> Union[str, Any]:
_A = [d[key] for d in offsets]
return retrieved_list
def __A ( self: Optional[Any] ) -> int:
_A = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_A = self._get_dummy_logits()[0]
_A = processor.decode(__A , output_word_offsets=__A )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(__A , __A ) )
self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets'''] , '''word''' ) ) , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''word''' ) , ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''start_offset''' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''end_offset''' ) , [1, 3, 5] )
def __A ( self: Optional[Any] ) -> Tuple:
_A = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_A = self._get_dummy_logits()
_A = processor.batch_decode(__A , output_word_offsets=__A )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(__A , __A ) )
self.assertListEqual(
[''' '''.join(self.get_from_offsets(__A , '''word''' ) ) for o in outputs['''word_offsets''']] , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''word''' ) , ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''start_offset''' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''end_offset''' ) , [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def __A ( self: Optional[Any] ) -> Optional[Any]:
import torch
_A = load_dataset('''common_voice''' , '''en''' , split='''train''' , streaming=__A )
_A = ds.cast_column('''audio''' , datasets.Audio(sampling_rate=1_60_00 ) )
_A = iter(__A )
_A = next(__A )
_A = AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
_A = WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
_A = processor(sample['''audio''']['''array'''] , return_tensors='''pt''' ).input_values
with torch.no_grad():
_A = model(__A ).logits.cpu().numpy()
_A = processor.decode(logits[0] , output_word_offsets=__A )
_A = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
_A = [
{
'''start_time''': d['''start_offset'''] * time_offset,
'''end_time''': d['''end_offset'''] * time_offset,
'''word''': d['''word'''],
}
for d in output['''word_offsets''']
]
_A = '''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''
# output words
self.assertEqual(''' '''.join(self.get_from_offsets(__A , '''word''' ) ) , __A )
self.assertEqual(''' '''.join(self.get_from_offsets(__A , '''word''' ) ) , output.text )
# output times
_A = torch.tensor(self.get_from_offsets(__A , '''start_time''' ) )
_A = torch.tensor(self.get_from_offsets(__A , '''end_time''' ) )
# fmt: off
_A = torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599] )
_A = torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(__A , __A , atol=0.01 ) )
self.assertTrue(torch.allclose(__A , __A , atol=0.01 ) )
| 75 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
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 ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowerCamelCase :
'''simple docstring'''
def __init__( self : Dict , lowerCAmelCase_ : Dict , lowerCAmelCase_ : str=13 , lowerCAmelCase_ : Any=30 , lowerCAmelCase_ : List[str]=2 , lowerCAmelCase_ : str=3 , lowerCAmelCase_ : Optional[int]=True , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : str=32 , lowerCAmelCase_ : Tuple=5 , lowerCAmelCase_ : List[Any]=4 , lowerCAmelCase_ : Union[str, Any]=37 , lowerCAmelCase_ : Any="gelu" , lowerCAmelCase_ : Optional[int]=0.1 , lowerCAmelCase_ : Optional[int]=0.1 , lowerCAmelCase_ : Tuple=10 , lowerCAmelCase_ : Optional[int]=0.02 , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Tuple=2 , ) -> Tuple:
'''simple docstring'''
A__ : Optional[Any] =parent
A__ : Union[str, Any] =batch_size
A__ : List[str] =image_size
A__ : int =patch_size
A__ : Optional[int] =num_channels
A__ : Optional[Any] =is_training
A__ : List[str] =use_labels
A__ : List[Any] =hidden_size
A__ : Union[str, Any] =num_hidden_layers
A__ : Any =num_attention_heads
A__ : int =intermediate_size
A__ : Tuple =hidden_act
A__ : Tuple =hidden_dropout_prob
A__ : Dict =attention_probs_dropout_prob
A__ : List[str] =type_sequence_label_size
A__ : Union[str, Any] =initializer_range
A__ : Union[str, Any] =scope
A__ : List[str] =encoder_stride
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
A__ : Tuple =(image_size // patch_size) ** 2
A__ : int =num_patches + 1
def lowercase__ ( self : List[Any] ) -> str:
'''simple docstring'''
A__ : Any =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A__ : str =None
if self.use_labels:
A__ : str =ids_tensor([self.batch_size] , self.type_sequence_label_size )
A__ : List[str] =self.get_config()
return config, pixel_values, labels
def lowercase__ ( self : int ) -> str:
'''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=lowerCamelCase__ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def lowercase__ ( self : Optional[int] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str ) -> Dict:
'''simple docstring'''
A__ : List[Any] =ViTModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
A__ : Optional[Any] =model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase__ ( self : Any , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str ) -> Optional[Any]:
'''simple docstring'''
A__ : Union[str, Any] =ViTForMaskedImageModeling(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
A__ : Union[str, Any] =model(lowerCamelCase__ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
A__ : Optional[Any] =1
A__ : List[str] =ViTForMaskedImageModeling(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
A__ : Union[str, Any] =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A__ : Optional[int] =model(lowerCamelCase__ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def lowercase__ ( self : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
A__ : Optional[int] =self.type_sequence_label_size
A__ : Union[str, Any] =ViTForImageClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
A__ : Any =model(lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
A__ : int =1
A__ : str =ViTForImageClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
A__ : Optional[Any] =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A__ : List[Any] =model(lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowercase__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
A__ : Optional[Any] =self.prepare_config_and_inputs()
(
A__
) : Any =config_and_inputs
A__ : str ={'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase ( lowercase_ , lowercase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case = (
(
ViTModel,
ViTForImageClassification,
ViTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
__snake_case = (
{"""feature-extraction""": ViTModel, """image-classification""": ViTForImageClassification}
if is_torch_available()
else {}
)
__snake_case = True
__snake_case = False
__snake_case = False
__snake_case = False
def lowercase__ ( self : List[Any] ) -> int:
'''simple docstring'''
A__ : List[str] =ViTModelTester(self )
A__ : List[str] =ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ , hidden_size=37 )
def lowercase__ ( self : int ) -> int:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def lowercase__ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
pass
def lowercase__ ( self : int ) -> Tuple:
'''simple docstring'''
A__ : int =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ : int =model_class(lowerCamelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A__ : Union[str, Any] =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase__ , nn.Linear ) )
def lowercase__ ( self : Optional[Any] ) -> Tuple:
'''simple docstring'''
A__ : Any =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ : List[str] =model_class(lowerCamelCase__ )
A__ : List[Any] =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A__ : Optional[Any] =[*signature.parameters.keys()]
A__ : str =['pixel_values']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def lowercase__ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
A__ : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def lowercase__ ( self : Tuple ) -> Any:
'''simple docstring'''
A__ : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase__ )
def lowercase__ ( self : Any ) -> List[Any]:
'''simple docstring'''
A__ : List[str] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )
@slow
def lowercase__ ( self : List[Any] ) -> List[str]:
'''simple docstring'''
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ : Union[str, Any] =ViTModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def __lowerCamelCase ( ) -> Union[str, Any]:
"""simple docstring"""
A__ : int =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowercase__ ( self : Tuple ) -> Any:
'''simple docstring'''
return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224""" ) if is_vision_available() else None
@slow
def lowercase__ ( self : Dict ) -> List[Any]:
'''simple docstring'''
A__ : int =ViTForImageClassification.from_pretrained("""google/vit-base-patch16-224""" ).to(lowerCamelCase__ )
A__ : Optional[int] =self.default_image_processor
A__ : List[str] =prepare_img()
A__ : Optional[int] =image_processor(images=lowerCamelCase__ , return_tensors="""pt""" ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
A__ : int =model(**lowerCamelCase__ )
# verify the logits
A__ : Any =torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
A__ : Optional[Any] =torch.tensor([-0.2744, 0.8215, -0.0836] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase__ , atol=1e-4 ) )
@slow
def lowercase__ ( self : List[str] ) -> Tuple:
'''simple docstring'''
A__ : int =ViTModel.from_pretrained("""facebook/dino-vits8""" ).to(lowerCamelCase__ )
A__ : Optional[Any] =ViTImageProcessor.from_pretrained("""facebook/dino-vits8""" , size=4_80 )
A__ : Optional[Any] =prepare_img()
A__ : Any =image_processor(images=lowerCamelCase__ , return_tensors="""pt""" )
A__ : int =inputs.pixel_values.to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
A__ : Any =model(lowerCamelCase__ , interpolate_pos_encoding=lowerCamelCase__ )
# verify the logits
A__ : str =torch.Size((1, 36_01, 3_84) )
self.assertEqual(outputs.last_hidden_state.shape , lowerCamelCase__ )
A__ : Union[str, Any] =torch.tensor(
[[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , lowerCamelCase__ , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def lowercase__ ( self : Dict ) -> Dict:
'''simple docstring'''
A__ : int =ViTModel.from_pretrained("""facebook/dino-vits8""" , torch_dtype=torch.floataa , device_map="""auto""" )
A__ : Optional[int] =self.default_image_processor
A__ : Dict =prepare_img()
A__ : List[Any] =image_processor(images=lowerCamelCase__ , return_tensors="""pt""" )
A__ : List[str] =inputs.pixel_values.to(lowerCamelCase__ )
# forward pass to make sure inference works in fp16
with torch.no_grad():
A__ : List[Any] =model(lowerCamelCase__ )
| 134 |
A_ :str = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 71 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
A_ : Any = logging.get_logger(__name__)
A_ : List[Any] = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
A_ : Dict = {
'vocab_file': {
'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt',
'distilbert-base-uncased-distilled-squad': (
'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'
),
'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt',
'distilbert-base-cased-distilled-squad': (
'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'
),
'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt',
'distilbert-base-multilingual-cased': (
'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json',
'distilbert-base-uncased-distilled-squad': (
'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'
),
'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json',
'distilbert-base-cased-distilled-squad': (
'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'
),
'distilbert-base-german-cased': (
'https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'
),
'distilbert-base-multilingual-cased': (
'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'
),
},
}
A_ : Any = {
'distilbert-base-uncased': 512,
'distilbert-base-uncased-distilled-squad': 512,
'distilbert-base-cased': 512,
'distilbert-base-cased-distilled-squad': 512,
'distilbert-base-german-cased': 512,
'distilbert-base-multilingual-cased': 512,
}
A_ : int = {
'distilbert-base-uncased': {'do_lower_case': True},
'distilbert-base-uncased-distilled-squad': {'do_lower_case': True},
'distilbert-base-cased': {'do_lower_case': False},
'distilbert-base-cased-distilled-squad': {'do_lower_case': False},
'distilbert-base-german-cased': {'do_lower_case': False},
'distilbert-base-multilingual-cased': {'do_lower_case': False},
}
class _a (__magic_name__ ):
'''simple docstring'''
UpperCAmelCase__: Union[str, Any] = VOCAB_FILES_NAMES
UpperCAmelCase__: Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__: str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__: Any = PRETRAINED_INIT_CONFIGURATION
UpperCAmelCase__: List[str] = ['''input_ids''', '''attention_mask''']
UpperCAmelCase__: List[str] = DistilBertTokenizer
def __init__( self , A__=None , A__=None , A__=True , A__="[UNK]" , A__="[SEP]" , A__="[PAD]" , A__="[CLS]" , A__="[MASK]" , A__=True , A__=None , **A__ , ):
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__ , tokenize_chinese_chars=A__ , strip_accents=A__ , **A__ , )
A__ : str = 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
):
A__ : Any = getattr(A__ , normalizer_state.pop("""type""" ) )
A__ : Optional[Any] = do_lower_case
A__ : List[Any] = strip_accents
A__ : Union[str, Any] = tokenize_chinese_chars
A__ : Union[str, Any] = normalizer_class(**A__ )
A__ : Tuple = do_lower_case
def __A ( self , A__ , A__=None ):
A__ : Optional[Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __A ( self , A__ , A__ = None ):
A__ : Optional[int] = [self.sep_token_id]
A__ : Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __A ( self , A__ , A__ = None ):
A__ : int = self._tokenizer.model.save(A__ , name=A__ )
return tuple(A__ )
| 141 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class _a :
'''simple docstring'''
def __init__( self , A__ , ):
A__ : Any = parent
A__ : Any = 13
A__ : Optional[Any] = 7
A__ : Union[str, Any] = 30
A__ : str = self.seq_length + self.mem_len
A__ : Dict = 15
A__ : int = True
A__ : Tuple = True
A__ : Union[str, Any] = 99
A__ : Optional[Any] = [10, 50, 80]
A__ : str = 32
A__ : Tuple = 32
A__ : Union[str, Any] = 4
A__ : Optional[Any] = 8
A__ : int = 128
A__ : List[Any] = 2
A__ : List[str] = 2
A__ : int = None
A__ : List[str] = 1
A__ : Union[str, Any] = 0
A__ : List[str] = 3
A__ : int = self.vocab_size - 1
A__ : Optional[Any] = 0.0_1
def __A ( self ):
A__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ : Optional[Any] = None
if self.use_labels:
A__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ : Any = TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def __A ( self ):
random.seed(self.seed )
tf.random.set_seed(self.seed )
def __A ( self , A__ , A__ , A__ , A__ ):
A__ : Dict = TFTransfoXLModel(A__ )
A__ , A__ : Tuple = model(A__ ).to_tuple()
A__ : List[str] = {"""input_ids""": input_ids_a, """mems""": mems_a}
A__ , A__ : str = model(A__ ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def __A ( self , A__ , A__ , A__ , A__ ):
A__ : Optional[int] = TFTransfoXLLMHeadModel(A__ )
A__ , A__ : int = model(A__ ).to_tuple()
A__ : int = {"""input_ids""": input_ids_a, """labels""": lm_labels}
A__ , A__ : Optional[Any] = model(A__ ).to_tuple()
A__ , A__ : Union[str, Any] = model([input_ids_a, mems_a] ).to_tuple()
A__ : Any = {"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}
A__ , A__ : Tuple = model(A__ ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def __A ( self , A__ , A__ , A__ , A__ ):
A__ : Any = TFTransfoXLForSequenceClassification(A__ )
A__ : Optional[Any] = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __A ( self ):
A__ : Optional[Any] = self.prepare_config_and_inputs()
((A__) , (A__) , (A__) , (A__)) : List[Any] = config_and_inputs
A__ : int = {"""input_ids""": input_ids_a}
return config, inputs_dict
@require_tf
class _a (__magic_name__ , __magic_name__ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__: List[Any] = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
UpperCAmelCase__: Optional[Any] = () if is_tf_available() else ()
UpperCAmelCase__: int = (
{
'''feature-extraction''': TFTransfoXLModel,
'''text-classification''': TFTransfoXLForSequenceClassification,
'''text-generation''': TFTransfoXLLMHeadModel,
'''zero-shot''': TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
UpperCAmelCase__: Optional[int] = False
UpperCAmelCase__: Optional[int] = False
UpperCAmelCase__: Tuple = False
UpperCAmelCase__: List[str] = False
def __A ( self , A__ , A__ , A__ , A__ , A__ ):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def __A ( self ):
A__ : Tuple = TFTransfoXLModelTester(self )
A__ : List[Any] = ConfigTester(self , config_class=A__ , d_embed=37 )
def __A ( self ):
self.config_tester.run_common_tests()
def __A ( self ):
self.model_tester.set_seed()
A__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*A__ )
def __A ( self ):
self.model_tester.set_seed()
A__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*A__ )
def __A ( self ):
A__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*A__ )
def __A ( self ):
A__ , A__ : int = self.model_tester.prepare_config_and_inputs_for_common()
A__ : Union[str, Any] = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
A__ : Any = model_class(A__ )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
A__ : Optional[Any] = model.get_output_embeddings()
assert isinstance(A__ , tf.keras.layers.Layer )
A__ : Tuple = model.get_bias()
assert name is None
else:
A__ : Dict = model.get_output_embeddings()
assert x is None
A__ : int = model.get_bias()
assert name is None
def __A ( self ):
# TODO JP: Make TransfoXL XLA compliant
pass
@slow
def __A ( self ):
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ : List[Any] = TFTransfoXLModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
@unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss.""" )
def __A ( self ):
pass
@require_tf
class _a (unittest.TestCase ):
'''simple docstring'''
@unittest.skip("""Skip test until #12651 is resolved.""" )
@slow
def __A ( self ):
A__ : List[Any] = TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103""" )
# fmt: off
A__ : Tuple = tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]] , dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
A__ : Dict = [33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
A__ : Any = model.generate(A__ , max_length=200 , do_sample=A__ )
self.assertListEqual(output_ids[0].numpy().tolist() , A__ )
| 141 | 1 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
UpperCAmelCase_ = WebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])
def lowerCamelCase__ ( A__ : Union[str, Any] ):
'''simple docstring'''
__lowerCamelCase = test_results.split(""" """ )
__lowerCamelCase = 0
__lowerCamelCase = 0
# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
# When it is too long, those signs are not present.
__lowerCamelCase = expressions[-2] if """=""" in expressions[-1] else expressions[-1]
for i, expression in enumerate(A__ ):
if "failed" in expression:
failed += int(expressions[i - 1] )
if "passed" in expression:
success += int(expressions[i - 1] )
return failed, success, time_spent
def lowerCamelCase__ ( A__ : Optional[int] ):
'''simple docstring'''
__lowerCamelCase = {}
__lowerCamelCase = None
__lowerCamelCase = False
for line in failures_short_lines.split("""\n""" ):
if re.search(R"""_ \[doctest\]""" , A__ ):
__lowerCamelCase = True
__lowerCamelCase = line.split(""" """ )[2]
elif in_error and not line.split(""" """ )[0].isdigit():
__lowerCamelCase = line
__lowerCamelCase = False
return failures
class lowerCamelCase__:
def __init__( self: int , UpperCamelCase_: str , UpperCamelCase_: Dict ):
__lowerCamelCase = title
__lowerCamelCase = doc_test_results["""time_spent"""].split(""",""" )[0]
__lowerCamelCase = doc_test_results["""success"""]
__lowerCamelCase = doc_test_results["""failures"""]
__lowerCamelCase = self.n_success + self.n_failures
# Failures and success of the modeling tests
__lowerCamelCase = doc_test_results
@property
def lowerCAmelCase__ ( self: List[str] ):
__lowerCamelCase = [self._time_spent]
__lowerCamelCase = 0
for time in time_spent:
__lowerCamelCase = time.split(""":""" )
# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
if len(UpperCamelCase_ ) == 1:
__lowerCamelCase = [0, 0, time_parts[0]]
__lowerCamelCase, __lowerCamelCase, __lowerCamelCase = int(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] )
total_secs += hours * 36_00 + minutes * 60 + seconds
__lowerCamelCase, __lowerCamelCase, __lowerCamelCase = total_secs // 36_00, (total_secs % 36_00) // 60, total_secs % 60
return F'{int(UpperCamelCase_ )}h{int(UpperCamelCase_ )}m{int(UpperCamelCase_ )}s'
@property
def lowerCAmelCase__ ( self: List[str] ):
return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
@property
def lowerCAmelCase__ ( self: Union[str, Any] ):
return {
"type": "section",
"text": {
"type": "plain_text",
"text": F'🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.',
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": F'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
},
}
@property
def lowerCAmelCase__ ( self: Optional[int] ):
return {
"type": "section",
"text": {
"type": "plain_text",
"text": (
F'There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'
F' {self.time}.'
),
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": F'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
},
}
@property
def lowerCAmelCase__ ( self: str ):
__lowerCamelCase = 40
__lowerCamelCase = {k: v["""failed"""] for k, v in doc_test_results.items() if isinstance(UpperCamelCase_ , UpperCamelCase_ )}
__lowerCamelCase = """"""
for category, failures in category_failures.items():
if len(UpperCamelCase_ ) == 0:
continue
if report != "":
report += "\n\n"
report += F'*{category} failures*:'.ljust(line_length // 2 ).rjust(line_length // 2 ) + "\n"
report += "`"
report += "`\n`".join(UpperCamelCase_ )
report += "`"
return {
"type": "section",
"text": {
"type": "mrkdwn",
"text": F'The following examples had failures:\n\n\n{report}\n',
},
}
@property
def lowerCAmelCase__ ( self: List[str] ):
__lowerCamelCase = [self.header]
if self.n_failures > 0:
blocks.append(self.failures )
if self.n_failures > 0:
blocks.extend([self.category_failures] )
if self.n_failures == 0:
blocks.append(self.no_failures )
return json.dumps(UpperCamelCase_ )
@staticmethod
def lowerCAmelCase__ ( ):
__lowerCamelCase = [
{
"""type""": """section""",
"""text""": {
"""type""": """plain_text""",
"""text""": """There was an issue running the tests.""",
},
"""accessory""": {
"""type""": """button""",
"""text""": {"""type""": """plain_text""", """text""": """Check Action results""", """emoji""": True},
"""url""": F'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
},
}
]
print("""Sending the following payload""" )
print(json.dumps({"""blocks""": json.loads(UpperCamelCase_ )} ) )
client.chat_postMessage(
channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""] , text="""There was an issue running the tests.""" , blocks=UpperCamelCase_ , )
def lowerCAmelCase__ ( self: int ):
print("""Sending the following payload""" )
print(json.dumps({"""blocks""": json.loads(self.payload )} ) )
__lowerCamelCase = F'{self.n_failures} failures out of {self.n_tests} tests,' if self.n_failures else """All tests passed."""
__lowerCamelCase = client.chat_postMessage(
channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""] , blocks=self.payload , text=UpperCamelCase_ , )
def lowerCAmelCase__ ( self: List[Any] , UpperCamelCase_: List[str] , UpperCamelCase_: Tuple , UpperCamelCase_: Optional[Any] , UpperCamelCase_: Tuple ):
__lowerCamelCase = """"""
for key, value in failures.items():
__lowerCamelCase = value[:2_00] + """ [Truncated]""" if len(UpperCamelCase_ ) > 2_50 else value
failures_text += F'*{key}*\n_{value}_\n\n'
__lowerCamelCase = job_name
__lowerCamelCase = {"""type""": """section""", """text""": {"""type""": """mrkdwn""", """text""": text}}
if job_link is not None:
__lowerCamelCase = {
"""type""": """button""",
"""text""": {"""type""": """plain_text""", """text""": """GitHub Action job""", """emoji""": True},
"""url""": job_link,
}
return [
{"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
content,
{"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
]
def lowerCAmelCase__ ( self: List[Any] ):
if self.thread_ts is None:
raise ValueError("""Can only post reply if a post has been made.""" )
__lowerCamelCase = self.doc_test_results.pop("""job_link""" )
self.doc_test_results.pop("""failures""" )
self.doc_test_results.pop("""success""" )
self.doc_test_results.pop("""time_spent""" )
__lowerCamelCase = sorted(self.doc_test_results.items() , key=lambda UpperCamelCase_ : t[0] )
for job, job_result in sorted_dict:
if len(job_result["""failures"""] ):
__lowerCamelCase = F'*Num failures* :{len(job_result["failed"] )} \n'
__lowerCamelCase = job_result["""failures"""]
__lowerCamelCase = self.get_reply_blocks(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , text=UpperCamelCase_ )
print("""Sending the following reply""" )
print(json.dumps({"""blocks""": blocks} ) )
client.chat_postMessage(
channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""] , text=F'Results for {job}' , blocks=UpperCamelCase_ , thread_ts=self.thread_ts["""ts"""] , )
time.sleep(1 )
def lowerCamelCase__ ( ):
'''simple docstring'''
__lowerCamelCase = os.environ["""GITHUB_RUN_ID"""]
__lowerCamelCase = f'https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'
__lowerCamelCase = requests.get(A__ ).json()
__lowerCamelCase = {}
try:
jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
__lowerCamelCase = math.ceil((result["""total_count"""] - 100) / 100 )
for i in range(A__ ):
__lowerCamelCase = requests.get(url + f'&page={i + 2}' ).json()
jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
return jobs
except Exception as e:
print("""Unknown error, could not fetch links.""" , A__ )
return {}
def lowerCamelCase__ ( A__ : str ):
'''simple docstring'''
__lowerCamelCase = {}
if os.path.exists(A__ ):
__lowerCamelCase = os.listdir(A__ )
for file in files:
try:
with open(os.path.join(A__ , A__ ) , encoding="""utf-8""" ) as f:
__lowerCamelCase = f.read()
except UnicodeDecodeError as e:
raise ValueError(f'Could not open {os.path.join(A__ , A__ )}.' ) from e
return _artifact
def lowerCamelCase__ ( ):
'''simple docstring'''
class lowerCamelCase__:
def __init__( self: Optional[Any] , UpperCamelCase_: str ):
__lowerCamelCase = name
__lowerCamelCase = []
def __str__( self: List[str] ):
return self.name
def lowerCAmelCase__ ( self: List[str] , UpperCamelCase_: str ):
self.paths.append({"""name""": self.name, """path""": path} )
__lowerCamelCase = {}
__lowerCamelCase = filter(os.path.isdir , os.listdir() )
for directory in directories:
__lowerCamelCase = directory
if artifact_name not in _available_artifacts:
__lowerCamelCase = Artifact(A__ )
_available_artifacts[artifact_name].add_path(A__ )
return _available_artifacts
if __name__ == "__main__":
UpperCAmelCase_ = get_job_links()
UpperCAmelCase_ = retrieve_available_artifacts()
UpperCAmelCase_ = collections.OrderedDict(
[
('*.py', 'API Examples'),
('*.md', 'MD Examples'),
]
)
# This dict will contain all the information relative to each doc test category:
# - failed: list of failed tests
# - failures: dict in the format 'test': 'error_message'
UpperCAmelCase_ = {
v: {
'failed': [],
'failures': {},
}
for v in docs.values()
}
# Link to the GitHub Action job
UpperCAmelCase_ = github_actions_job_links.get('run_doctests')
UpperCAmelCase_ = available_artifacts['doc_tests_gpu_test_reports'].paths[0]
UpperCAmelCase_ = retrieve_artifact(artifact_path['name'])
if "stats" in artifact:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = handle_test_results(artifact['stats'])
UpperCAmelCase_ = failed
UpperCAmelCase_ = success
UpperCAmelCase_ = time_spent[1:-1] + ', '
UpperCAmelCase_ = extract_first_line_failure(artifact['failures_short'])
for line in artifact["summary_short"].split('\n'):
if re.search('FAILED', line):
UpperCAmelCase_ = line.replace('FAILED ', '')
UpperCAmelCase_ = line.split()[0].replace('\n', '')
if "::" in line:
UpperCAmelCase_ , UpperCAmelCase_ = line.split('::')
else:
UpperCAmelCase_ , UpperCAmelCase_ = line, line
for file_regex in docs.keys():
if fnmatch(file_path, file_regex):
UpperCAmelCase_ = docs[file_regex]
doc_test_results[category]["failed"].append(test)
UpperCAmelCase_ = all_failures[test] if test in all_failures else 'N/A'
UpperCAmelCase_ = failure
break
UpperCAmelCase_ = Message('🤗 Results of the doc tests.', doc_test_results)
message.post()
message.post_reply()
| 12 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowerCAmelCase : Optional[int] =logging.get_logger(__name__)
__lowerCAmelCase : Optional[Any] ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
__lowerCAmelCase : List[str] ={
'tokenizer_file': {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
},
}
__lowerCAmelCase : Optional[int] ={
'gpt-neox-20b': 2_0_4_8,
}
class _lowercase ( A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ : Dict = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ : Optional[Any] = ['''input_ids''', '''attention_mask''']
def __init__( self :int , lowerCAmelCase__ :Any=None , lowerCAmelCase__ :Optional[Any]=None , lowerCAmelCase__ :List[Any]=None , lowerCAmelCase__ :str="<|endoftext|>" , lowerCAmelCase__ :str="<|endoftext|>" , lowerCAmelCase__ :Dict="<|endoftext|>" , lowerCAmelCase__ :Union[str, Any]=False , **lowerCAmelCase__ :List[str] , ) -> Any:
super().__init__(
lowerCAmelCase__ , lowerCAmelCase__ , tokenizer_file=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , **lowerCAmelCase__ , )
__SCREAMING_SNAKE_CASE : List[Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , lowerCAmelCase__ ) != add_prefix_space:
__SCREAMING_SNAKE_CASE : List[str] = getattr(lowerCAmelCase__ , pre_tok_state.pop('''type''' ) )
__SCREAMING_SNAKE_CASE : str = add_prefix_space
__SCREAMING_SNAKE_CASE : Any = pre_tok_class(**lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = add_prefix_space
def __magic_name__( self :Union[str, Any] , lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[str] = None ) -> Tuple[str]:
__SCREAMING_SNAKE_CASE : List[str] = self._tokenizer.model.save(lowerCAmelCase__ , name=lowerCAmelCase__ )
return tuple(lowerCAmelCase__ )
def __magic_name__( self :Optional[Any] , lowerCAmelCase__ :"Conversation" ) -> List[int]:
__SCREAMING_SNAKE_CASE : Optional[Any] = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ ) + [self.eos_token_id] )
if len(lowerCAmelCase__ ) > self.model_max_length:
__SCREAMING_SNAKE_CASE : List[str] = input_ids[-self.model_max_length :]
return input_ids
| 9 | 0 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
__A = pd.read_csv(
"""https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"""
"""position_salaries.csv"""
)
__A = dataset.iloc[:, 1:2].values
__A = dataset.iloc[:, 2].values
__A , __A , __A , __A = train_test_split(X, y, test_size=0.2, random_state=0)
__A = PolynomialFeatures(degree=4)
__A = poly_reg.fit_transform(X)
__A = LinearRegression()
pol_reg.fit(X_poly, y)
def __A () ->Any:
"""simple docstring"""
plt.scatter(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , color='red' )
plt.plot(_SCREAMING_SNAKE_CASE , pol_reg.predict(poly_reg.fit_transform(_SCREAMING_SNAKE_CASE ) ) , color='blue' )
plt.title('Truth or Bluff (Linear Regression)' )
plt.xlabel('Position level' )
plt.ylabel('Salary' )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 254 |
"""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 _lowerCAmelCase ( a ):
"""simple docstring"""
def __init__( self , __UpperCAmelCase = "▁" , __UpperCAmelCase = True , __UpperCAmelCase = "<unk>" , __UpperCAmelCase = "</s>" , __UpperCAmelCase = "<pad>" , ):
'''simple docstring'''
lowerCAmelCase__ :Tuple = {
'pad': {'id': 0, 'token': pad_token},
'eos': {'id': 1, 'token': eos_token},
'unk': {'id': 2, 'token': unk_token},
}
lowerCAmelCase__ :Optional[int] = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
lowerCAmelCase__ :Any = token_dict['token']
lowerCAmelCase__ :int = Tokenizer(Unigram() )
lowerCAmelCase__ :Tuple = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(' {2,}' ) , ' ' ),
normalizers.Lowercase(),
] )
lowerCAmelCase__ :Any = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase ),
pre_tokenizers.Digits(individual_digits=__UpperCAmelCase ),
pre_tokenizers.Punctuation(),
] )
lowerCAmelCase__ :List[str] = decoders.Metaspace(replacement=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase )
lowerCAmelCase__ :Tuple = TemplateProcessing(
single=F"$A {self.special_tokens['eos']['token']}" , special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])] , )
lowerCAmelCase__ :Optional[int] = {
'model': 'SentencePieceUnigram',
'replacement': replacement,
'add_prefix_space': add_prefix_space,
}
super().__init__(__UpperCAmelCase , __UpperCAmelCase )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase = 8_0_0_0 , __UpperCAmelCase = True , ):
'''simple docstring'''
lowerCAmelCase__ :int = trainers.UnigramTrainer(
vocab_size=__UpperCAmelCase , special_tokens=self.special_tokens_list , show_progress=__UpperCAmelCase , )
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ :int = [files]
self._tokenizer.train(__UpperCAmelCase , trainer=__UpperCAmelCase )
self.add_unk_id()
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase = 8_0_0_0 , __UpperCAmelCase = True , ):
'''simple docstring'''
lowerCAmelCase__ :Optional[int] = trainers.UnigramTrainer(
vocab_size=__UpperCAmelCase , special_tokens=self.special_tokens_list , show_progress=__UpperCAmelCase , )
self._tokenizer.train_from_iterator(__UpperCAmelCase , trainer=__UpperCAmelCase )
self.add_unk_id()
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[int] = json.loads(self._tokenizer.to_str() )
lowerCAmelCase__ :List[str] = self.special_tokens['unk']['id']
lowerCAmelCase__ :Union[str, Any] = Tokenizer.from_str(json.dumps(__UpperCAmelCase ) )
| 254 | 1 |
'''simple docstring'''
# Copyright 2022 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
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def _a( UpperCamelCase__ : int=None ):
'''simple docstring'''
if subparsers is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =subparsers.add_parser('''env''' )
else:
SCREAMING_SNAKE_CASE__ : str =argparse.ArgumentParser('''Accelerate env command''' )
parser.add_argument(
'''--config_file''', default=__a, help='''The config file to use for the default values in the launching script.''' )
if subparsers is not None:
parser.set_defaults(func=__a )
return parser
def _a( UpperCamelCase__ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =torch.__version__
SCREAMING_SNAKE_CASE__ : List[Any] =torch.cuda.is_available()
SCREAMING_SNAKE_CASE__ : Optional[int] =is_xpu_available()
SCREAMING_SNAKE_CASE__ : Optional[int] =is_npu_available()
SCREAMING_SNAKE_CASE__ : Dict ='''Not found'''
# Get the default from the config file.
if args.config_file is not None or os.path.isfile(__a ):
SCREAMING_SNAKE_CASE__ : Dict =load_config_from_file(args.config_file ).to_dict()
SCREAMING_SNAKE_CASE__ : List[Any] ={
'''`Accelerate` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''Numpy version''': np.__version__,
'''PyTorch version (GPU?)''': f"{pt_version} ({pt_cuda_available})",
'''PyTorch XPU available''': str(__a ),
'''PyTorch NPU available''': str(__a ),
'''System RAM''': f"{psutil.virtual_memory().total / 1_0_2_4 ** 3:.2f} GB",
}
if pt_cuda_available:
SCREAMING_SNAKE_CASE__ : Any =torch.cuda.get_device_name()
print('''\nCopy-and-paste the text below in your GitHub issue\n''' )
print('''\n'''.join([f"- {prop}: {val}" for prop, val in info.items()] ) )
print('''- `Accelerate` default config:''' if args.config_file is None else '''- `Accelerate` config passed:''' )
SCREAMING_SNAKE_CASE__ : int =(
'''\n'''.join([f"\t- {prop}: {val}" for prop, val in accelerate_config.items()] )
if isinstance(__a, __a )
else f"\t{accelerate_config}"
)
print(__a )
SCREAMING_SNAKE_CASE__ : int =accelerate_config
return info
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] =env_command_parser()
SCREAMING_SNAKE_CASE__ : Optional[int] =parser.parse_args()
env_command(__a )
return 0
if __name__ == "__main__":
raise SystemExit(main()) | 152 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class __A( nn.Module ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "geglu" , SCREAMING_SNAKE_CASE_ = None , ):
super().__init__()
UpperCamelCase__ = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=SCREAMING_SNAKE_CASE_ , attention_head_dim=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , dropout=SCREAMING_SNAKE_CASE_ , norm_num_groups=SCREAMING_SNAKE_CASE_ , cross_attention_dim=SCREAMING_SNAKE_CASE_ , attention_bias=SCREAMING_SNAKE_CASE_ , sample_size=SCREAMING_SNAKE_CASE_ , num_vector_embeds=SCREAMING_SNAKE_CASE_ , activation_fn=SCREAMING_SNAKE_CASE_ , num_embeds_ada_norm=SCREAMING_SNAKE_CASE_ , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
UpperCamelCase__ = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
UpperCamelCase__ = [77, 2_57]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
UpperCamelCase__ = [1, 0]
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_ = True , ):
UpperCamelCase__ = hidden_states
UpperCamelCase__ = []
UpperCamelCase__ = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
UpperCamelCase__ = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
UpperCamelCase__ = self.transformer_index_for_condition[i]
UpperCamelCase__ = self.transformers[transformer_index](
SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ , timestep=SCREAMING_SNAKE_CASE_ , cross_attention_kwargs=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
UpperCamelCase__ = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
UpperCamelCase__ = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE_ )
| 244 | 0 |
"""simple docstring"""
import qiskit
def __A ( a_ :int , a_ :int) -> qiskit.result.counts.Counts:
__a : Optional[Any] = qiskit.Aer.get_backend('''aer_simulator''')
# Create a Quantum Circuit acting on the q register
__a : Optional[Any] = qiskit.QuantumCircuit(a_ , a_)
# Apply X (NOT) Gate to Qubits 0 & 1
circuit.x(0)
circuit.x(1)
# Map the quantum measurement to the classical bits
circuit.measure([0, 1] , [0, 1])
# Execute the circuit on the qasm simulator
__a : Any = qiskit.execute(a_ , a_ , shots=10_00)
# Return the histogram data of the results of the experiment.
return job.result().get_counts(a_)
if __name__ == "__main__":
A = single_qubit_measure(2, 2)
print(F'Total count for various states are: {counts}') | 357 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
def __init__( self , _UpperCAmelCase , _UpperCAmelCase=13 , _UpperCAmelCase=7 , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=False , _UpperCAmelCase=True , _UpperCAmelCase=99 , _UpperCAmelCase=32 , _UpperCAmelCase=5 , _UpperCAmelCase=4 , _UpperCAmelCase=64 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=512 , _UpperCAmelCase=16 , _UpperCAmelCase=2 , _UpperCAmelCase=0.0_2 , _UpperCAmelCase=3 , _UpperCAmelCase=4 , _UpperCAmelCase=None , _UpperCAmelCase=2 , _UpperCAmelCase=2 , _UpperCAmelCase=2 , _UpperCAmelCase=2 , _UpperCAmelCase=4 , _UpperCAmelCase=1 , ):
__a : Dict = parent
__a : str = batch_size
__a : Union[str, Any] = seq_length
__a : Any = is_training
__a : int = use_input_mask
__a : Optional[int] = use_token_type_ids
__a : int = use_labels
__a : int = vocab_size
__a : int = hidden_size
__a : str = num_hidden_layers
__a : str = num_attention_heads
__a : Any = intermediate_size
__a : Union[str, Any] = hidden_act
__a : Optional[int] = hidden_dropout_prob
__a : str = attention_probs_dropout_prob
__a : int = max_position_embeddings
__a : Union[str, Any] = type_vocab_size
__a : List[str] = type_sequence_label_size
__a : List[str] = initializer_range
__a : Optional[int] = num_labels
__a : List[str] = num_choices
__a : int = scope
__a : Union[str, Any] = q_groups
__a : Dict = k_groups
__a : List[str] = v_groups
__a : Any = post_attention_groups
__a : Optional[int] = intermediate_groups
__a : List[str] = output_groups
def _lowerCamelCase ( self ):
__a : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a : Optional[Any] = None
if self.use_input_mask:
__a : int = random_attention_mask([self.batch_size, self.seq_length] )
__a : List[str] = None
__a : Union[str, Any] = None
__a : int = None
if self.use_labels:
__a : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a : int = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a : Dict = ids_tensor([self.batch_size] , self.num_choices )
__a : int = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowerCamelCase ( self ):
return SqueezeBertConfig(
embedding_size=self.hidden_size , vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a : Dict = SqueezeBertModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__a : Optional[Any] = model(_UpperCAmelCase , _UpperCAmelCase )
__a : Dict = model(_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a : Optional[Any] = SqueezeBertForMaskedLM(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__a : int = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a : int = SqueezeBertForQuestionAnswering(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__a : Optional[int] = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , start_positions=_UpperCAmelCase , end_positions=_UpperCAmelCase )
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 , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a : Any = self.num_labels
__a : List[Any] = SqueezeBertForSequenceClassification(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__a : int = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a : List[Any] = self.num_labels
__a : List[str] = SqueezeBertForTokenClassification(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__a : Any = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a : List[str] = self.num_choices
__a : Union[str, Any] = SqueezeBertForMultipleChoice(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__a : int = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : Optional[Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : Union[str, Any] = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowerCamelCase ( self ):
__a : Any = self.prepare_config_and_inputs()
((__a) , (__a) , (__a) , (__a) , (__a) , (__a)) : Optional[Any] = config_and_inputs
__a : List[Any] = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __lowercase ( _UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
__lowerCAmelCase = (
{
'''feature-extraction''': SqueezeBertModel,
'''fill-mask''': SqueezeBertForMaskedLM,
'''question-answering''': SqueezeBertForQuestionAnswering,
'''text-classification''': SqueezeBertForSequenceClassification,
'''token-classification''': SqueezeBertForTokenClassification,
'''zero-shot''': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowerCAmelCase = False
__lowerCAmelCase = True
__lowerCAmelCase = False
def _lowerCamelCase ( self ):
__a : Union[str, Any] = SqueezeBertModelTester(self )
__a : Dict = ConfigTester(self , config_class=_UpperCAmelCase , dim=37 )
def _lowerCamelCase ( self ):
self.config_tester.run_common_tests()
def _lowerCamelCase ( self ):
__a : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*_UpperCAmelCase )
def _lowerCamelCase ( self ):
__a : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*_UpperCAmelCase )
def _lowerCamelCase ( self ):
__a : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*_UpperCAmelCase )
def _lowerCamelCase ( self ):
__a : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_UpperCAmelCase )
def _lowerCamelCase ( self ):
__a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*_UpperCAmelCase )
def _lowerCamelCase ( self ):
__a : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_UpperCAmelCase )
@slow
def _lowerCamelCase ( self ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a : Any = SqueezeBertModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
@require_sentencepiece
@require_tokenizers
@require_torch
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
@slow
def _lowerCamelCase ( self ):
__a : int = SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli''' )
__a : Tuple = torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]] )
__a : List[str] = model(_UpperCAmelCase )[0]
__a : int = torch.Size((1, 3) )
self.assertEqual(output.shape , _UpperCAmelCase )
__a : int = torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]] )
self.assertTrue(torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1e-4 ) ) | 188 | 0 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
A__ : str = {
"""/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_ : Optional[Any] ) -> int:
# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
# the original model
__lowerCamelCase : Union[str, Any] = list(s_dict.keys() )
for key in keys:
__lowerCamelCase : Any = R'.*/layers_(\d+)'
__lowerCamelCase : Union[str, Any] = key
if re.match(UpperCAmelCase_ , UpperCAmelCase_ ):
__lowerCamelCase : Union[str, Any] = re.sub(R'layers_(\d+)' , R'block/\1/layer' , UpperCAmelCase_ )
__lowerCamelCase : Optional[Any] = R'(encoder|decoder)\/'
if re.match(UpperCAmelCase_ , UpperCAmelCase_ ):
__lowerCamelCase : Union[str, Any] = re.match(UpperCAmelCase_ , UpperCAmelCase_ ).groups()
if groups[0] == "encoder":
__lowerCamelCase : int = re.sub(R'/mlp/' , R'/1/mlp/' , UpperCAmelCase_ )
__lowerCamelCase : Optional[Any] = re.sub(R'/pre_mlp_layer_norm/' , R'/1/layer_norm/' , UpperCAmelCase_ )
elif groups[0] == "decoder":
__lowerCamelCase : str = re.sub(R'/mlp/' , R'/2/mlp/' , UpperCAmelCase_ )
__lowerCamelCase : 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:
__lowerCamelCase : List[Any] = new_key.replace(UpperCAmelCase_ , UpperCAmelCase_ )
print(F'{key} -> {new_key}' )
__lowerCamelCase : int = s_dict.pop(UpperCAmelCase_ )
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
__lowerCamelCase : Optional[int] = s_dict[
'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
__lowerCamelCase : List[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:
__lowerCamelCase : List[Any] = s_dict[key].shape[0]
__lowerCamelCase : Optional[int] = s_dict[key]
for idx in range(UpperCAmelCase_ ):
__lowerCamelCase : str = expert_weihts[idx]
print(F'{key} -> {key.replace("expert/" , "nested fstring" )}' )
s_dict.pop(UpperCAmelCase_ )
return s_dict
A__ : Union[str, 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_ : Tuple , UpperCAmelCase_ : List[str] ) -> str:
# Convert a google style config to the hugging face fromat
import regex as re
with open(UpperCAmelCase_ , 'r' ) as f:
__lowerCamelCase : Optional[int] = f.read()
__lowerCamelCase : Optional[Any] = re.findall(R'(.*) = ([0-9.]*)' , UpperCAmelCase_ )
__lowerCamelCase : Union[str, Any] = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
__lowerCamelCase : List[str] = float(UpperCAmelCase_ ) if '.' in value else int(UpperCAmelCase_ )
__lowerCamelCase : Optional[int] = re.findall(R'(.*activations) = \(\'(.*)\',\)' , UpperCAmelCase_ )[0]
__lowerCamelCase : Tuple = str(activation[1] )
__lowerCamelCase : Any = num_experts
__lowerCamelCase : List[Any] = SwitchTransformersConfig(**UpperCAmelCase_ )
return config
def UpperCAmelCase__ ( UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Any=None , UpperCAmelCase_ : Any="./" , UpperCAmelCase_ : Any=8 ) -> Optional[int]:
# Initialise PyTorch model
print(F'Loading flax weights from : {flax_checkpoint_path}' )
__lowerCamelCase : List[Any] = checkpoints.load_tax_checkpoint(UpperCAmelCase_ )
if gin_file is not None:
__lowerCamelCase : List[str] = convert_gin_to_config(UpperCAmelCase_ , UpperCAmelCase_ )
else:
__lowerCamelCase : List[str] = SwitchTransformersConfig.from_pretrained(UpperCAmelCase_ )
__lowerCamelCase : List[str] = SwitchTransformersForConditionalGeneration(UpperCAmelCase_ )
__lowerCamelCase : Union[str, Any] = flax_params['target']
__lowerCamelCase : str = flatten_dict(UpperCAmelCase_ , sep='/' )
__lowerCamelCase : List[str] = rename_keys(UpperCAmelCase_ )
__lowerCamelCase : Optional[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__":
A__ : Optional[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""")
A__ : List[Any] = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 185 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class UpperCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase_ ( self ) -> List[str]:
__lowerCamelCase : Any = AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip' )
__lowerCamelCase : Tuple = AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip' )
model.to(SCREAMING_SNAKE_CASE_ )
from datasets import load_dataset
__lowerCamelCase : str = load_dataset('nielsr/rvlcdip-demo' )
__lowerCamelCase : List[Any] = dataset['train'][0]['image'].convert('RGB' )
__lowerCamelCase : str = image_processor(SCREAMING_SNAKE_CASE_ , return_tensors='pt' ).to(SCREAMING_SNAKE_CASE_ )
# forward pass
with torch.no_grad():
__lowerCamelCase : str = model(**SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : int = outputs.logits
__lowerCamelCase : List[Any] = torch.Size((1, 16) )
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Optional[Any] = torch.tensor(
[-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7] , device=SCREAMING_SNAKE_CASE_ , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
| 185 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class _A ( unittest.TestCase ):
@slow
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__UpperCAmelCase : List[str] = AutoConfig.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : List[str] = TFAutoModel.from_pretrained(__UpperCAmelCase , from_pt=__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : Union[str, Any] = AutoModel.from_pretrained(__UpperCAmelCase , from_tf=__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
@slow
def __A ( self ) -> Union[str, Any]:
'''simple docstring'''
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__UpperCAmelCase : int = AutoConfig.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : Tuple = TFAutoModelForPreTraining.from_pretrained(__UpperCAmelCase , from_pt=__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : int = AutoModelForPreTraining.from_pretrained(__UpperCAmelCase , from_tf=__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
@slow
def __A ( self ) -> List[Any]:
'''simple docstring'''
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase : Dict = AutoConfig.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : Any = TFAutoModelForCausalLM.from_pretrained(__UpperCAmelCase , from_pt=__UpperCAmelCase )
__UpperCAmelCase , __UpperCAmelCase : List[str] = TFAutoModelForCausalLM.from_pretrained(
__UpperCAmelCase , output_loading_info=__UpperCAmelCase , from_pt=__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : int = AutoModelForCausalLM.from_pretrained(__UpperCAmelCase , from_tf=__UpperCAmelCase )
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = AutoModelForCausalLM.from_pretrained(
__UpperCAmelCase , output_loading_info=__UpperCAmelCase , from_tf=__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
@slow
def __A ( self ) -> Dict:
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase : Union[str, Any] = AutoConfig.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : int = TFAutoModelWithLMHead.from_pretrained(__UpperCAmelCase , from_pt=__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : Optional[int] = AutoModelWithLMHead.from_pretrained(__UpperCAmelCase , from_tf=__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
@slow
def __A ( self ) -> List[str]:
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase : Union[str, Any] = AutoConfig.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : List[Any] = TFAutoModelForMaskedLM.from_pretrained(__UpperCAmelCase , from_pt=__UpperCAmelCase )
__UpperCAmelCase , __UpperCAmelCase : List[str] = TFAutoModelForMaskedLM.from_pretrained(
__UpperCAmelCase , output_loading_info=__UpperCAmelCase , from_pt=__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : Optional[int] = AutoModelForMaskedLM.from_pretrained(__UpperCAmelCase , from_tf=__UpperCAmelCase )
__UpperCAmelCase , __UpperCAmelCase : List[Any] = AutoModelForMaskedLM.from_pretrained(
__UpperCAmelCase , output_loading_info=__UpperCAmelCase , from_tf=__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
@slow
def __A ( self ) -> Dict:
'''simple docstring'''
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase : int = AutoConfig.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase , from_pt=__UpperCAmelCase )
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = TFAutoModelForSeqaSeqLM.from_pretrained(
__UpperCAmelCase , output_loading_info=__UpperCAmelCase , from_pt=__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : Union[str, Any] = AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase , from_tf=__UpperCAmelCase )
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = AutoModelForSeqaSeqLM.from_pretrained(
__UpperCAmelCase , output_loading_info=__UpperCAmelCase , from_tf=__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
@slow
def __A ( self ) -> Union[str, Any]:
'''simple docstring'''
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__UpperCAmelCase : Dict = AutoConfig.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : List[Any] = TFAutoModelForSequenceClassification.from_pretrained(__UpperCAmelCase , from_pt=__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : Any = AutoModelForSequenceClassification.from_pretrained(__UpperCAmelCase , from_tf=__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
@slow
def __A ( self ) -> Union[str, Any]:
'''simple docstring'''
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__UpperCAmelCase : Union[str, Any] = AutoConfig.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : List[str] = TFAutoModelForQuestionAnswering.from_pretrained(__UpperCAmelCase , from_pt=__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : int = AutoModelForQuestionAnswering.from_pretrained(__UpperCAmelCase , from_tf=__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
def __A ( self ) -> Any:
'''simple docstring'''
__UpperCAmelCase : Dict = TFAutoModelWithLMHead.from_pretrained(__UpperCAmelCase , from_pt=__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
self.assertEqual(model.num_parameters() , 14_410 )
self.assertEqual(model.num_parameters(only_trainable=__UpperCAmelCase ) , 14_410 )
__UpperCAmelCase : str = AutoModelWithLMHead.from_pretrained(__UpperCAmelCase , from_tf=__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
self.assertEqual(model.num_parameters() , 14_410 )
self.assertEqual(model.num_parameters(only_trainable=__UpperCAmelCase ) , 14_410 )
def __A ( self ) -> Dict:
'''simple docstring'''
__UpperCAmelCase : List[Any] = TFAutoModelWithLMHead.from_pretrained(__UpperCAmelCase , from_pt=__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
self.assertEqual(model.num_parameters() , 14_410 )
self.assertEqual(model.num_parameters(only_trainable=__UpperCAmelCase ) , 14_410 )
__UpperCAmelCase : Dict = AutoModelWithLMHead.from_pretrained(__UpperCAmelCase , from_tf=__UpperCAmelCase )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
self.assertEqual(model.num_parameters() , 14_410 )
self.assertEqual(model.num_parameters(only_trainable=__UpperCAmelCase ) , 14_410 )
| 16 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class _A :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=32 , __UpperCAmelCase=5 , __UpperCAmelCase=4 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , ) -> Optional[Any]:
'''simple docstring'''
__UpperCAmelCase : List[str] = parent
__UpperCAmelCase : Union[str, Any] = batch_size
__UpperCAmelCase : Tuple = seq_length
__UpperCAmelCase : str = is_training
__UpperCAmelCase : Union[str, Any] = use_input_mask
__UpperCAmelCase : List[Any] = use_token_type_ids
__UpperCAmelCase : Optional[Any] = use_labels
__UpperCAmelCase : str = vocab_size
__UpperCAmelCase : Union[str, Any] = hidden_size
__UpperCAmelCase : Optional[int] = num_hidden_layers
__UpperCAmelCase : str = num_attention_heads
__UpperCAmelCase : Optional[Any] = intermediate_size
__UpperCAmelCase : Optional[int] = hidden_act
__UpperCAmelCase : List[str] = hidden_dropout_prob
__UpperCAmelCase : List[str] = attention_probs_dropout_prob
__UpperCAmelCase : Tuple = max_position_embeddings
__UpperCAmelCase : Dict = type_vocab_size
__UpperCAmelCase : List[Any] = type_sequence_label_size
__UpperCAmelCase : List[Any] = initializer_range
__UpperCAmelCase : List[str] = num_labels
__UpperCAmelCase : str = num_choices
__UpperCAmelCase : List[Any] = scope
def __A ( self ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCAmelCase : Dict = None
if self.use_input_mask:
__UpperCAmelCase : str = random_attention_mask([self.batch_size, self.seq_length] )
__UpperCAmelCase : int = None
if self.use_token_type_ids:
__UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__UpperCAmelCase : Optional[int] = None
__UpperCAmelCase : List[Any] = None
__UpperCAmelCase : Union[str, Any] = None
if self.use_labels:
__UpperCAmelCase : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCAmelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCAmelCase : Any = ids_tensor([self.batch_size] , self.num_choices )
__UpperCAmelCase : Dict = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
return LlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCAmelCase , initializer_range=self.initializer_range , )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = LlamaModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__UpperCAmelCase : Dict = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase )
__UpperCAmelCase : Union[str, Any] = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : List[str] = True
__UpperCAmelCase : List[str] = LlamaModel(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__UpperCAmelCase : List[Any] = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , encoder_attention_mask=__UpperCAmelCase , )
__UpperCAmelCase : Tuple = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , )
__UpperCAmelCase : Union[str, Any] = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ) -> Any:
'''simple docstring'''
__UpperCAmelCase : List[Any] = LlamaForCausalLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__UpperCAmelCase : int = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = True
__UpperCAmelCase : Any = True
__UpperCAmelCase : Tuple = LlamaForCausalLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
# first forward pass
__UpperCAmelCase : Optional[int] = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , encoder_attention_mask=__UpperCAmelCase , use_cache=__UpperCAmelCase , )
__UpperCAmelCase : Union[str, Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
__UpperCAmelCase : List[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
__UpperCAmelCase : List[Any] = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
__UpperCAmelCase : str = torch.cat([input_ids, next_tokens] , dim=-1 )
__UpperCAmelCase : Union[str, Any] = torch.cat([input_mask, next_mask] , dim=-1 )
__UpperCAmelCase : int = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , encoder_attention_mask=__UpperCAmelCase , output_hidden_states=__UpperCAmelCase , )["""hidden_states"""][0]
__UpperCAmelCase : Dict = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , encoder_attention_mask=__UpperCAmelCase , past_key_values=__UpperCAmelCase , output_hidden_states=__UpperCAmelCase , )["""hidden_states"""][0]
# select random slice
__UpperCAmelCase : List[str] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__UpperCAmelCase : Dict = output_from_no_past[:, -3:, random_slice_idx].detach()
__UpperCAmelCase : Tuple = 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-3 ) )
def __A ( self ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Any = self.prepare_config_and_inputs()
(
(
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) ,
) : Any = config_and_inputs
__UpperCAmelCase : Optional[Any] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class _A ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Optional[int] = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
_SCREAMING_SNAKE_CASE : Any = (LlamaForCausalLM,) if is_torch_available() else ()
_SCREAMING_SNAKE_CASE : List[str] = (
{
"feature-extraction": LlamaModel,
"text-classification": LlamaForSequenceClassification,
"text-generation": LlamaForCausalLM,
"zero-shot": LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE : Optional[int] = False
_SCREAMING_SNAKE_CASE : List[str] = False
def __A ( self ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase : Tuple = LlamaModelTester(self )
__UpperCAmelCase : Tuple = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37 )
def __A ( self ) -> List[str]:
'''simple docstring'''
self.config_tester.run_common_tests()
def __A ( self ) -> Any:
'''simple docstring'''
__UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def __A ( self ) -> Dict:
'''simple docstring'''
__UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__UpperCAmelCase : str = type
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def __A ( self ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : Any = 3
__UpperCAmelCase : Optional[Any] = input_dict["""input_ids"""]
__UpperCAmelCase : int = input_ids.ne(1 ).to(__UpperCAmelCase )
__UpperCAmelCase : Union[str, Any] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
__UpperCAmelCase : Dict = LlamaForSequenceClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__UpperCAmelCase : List[Any] = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __A ( self ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : Optional[int] = 3
__UpperCAmelCase : Optional[Any] = """single_label_classification"""
__UpperCAmelCase : int = input_dict["""input_ids"""]
__UpperCAmelCase : List[Any] = input_ids.ne(1 ).to(__UpperCAmelCase )
__UpperCAmelCase : str = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
__UpperCAmelCase : Tuple = LlamaForSequenceClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__UpperCAmelCase : Tuple = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __A ( self ) -> Any:
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : Optional[Any] = 3
__UpperCAmelCase : str = """multi_label_classification"""
__UpperCAmelCase : Union[str, Any] = input_dict["""input_ids"""]
__UpperCAmelCase : int = input_ids.ne(1 ).to(__UpperCAmelCase )
__UpperCAmelCase : str = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
__UpperCAmelCase : Dict = LlamaForSequenceClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__UpperCAmelCase : Tuple = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip("""LLaMA buffers include complex numbers, which breaks this test""" )
def __A ( self ) -> Dict:
'''simple docstring'''
pass
@parameterized.expand([("""linear""",), ("""dynamic""",)] )
def __A ( self , __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : List[Any] = ids_tensor([1, 10] , config.vocab_size )
__UpperCAmelCase : str = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
__UpperCAmelCase : Optional[Any] = LlamaModel(__UpperCAmelCase )
original_model.to(__UpperCAmelCase )
original_model.eval()
__UpperCAmelCase : int = original_model(__UpperCAmelCase ).last_hidden_state
__UpperCAmelCase : List[str] = original_model(__UpperCAmelCase ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
__UpperCAmelCase : Dict = {"""type""": scaling_type, """factor""": 10.0}
__UpperCAmelCase : Optional[Any] = LlamaModel(__UpperCAmelCase )
scaled_model.to(__UpperCAmelCase )
scaled_model.eval()
__UpperCAmelCase : Optional[Any] = scaled_model(__UpperCAmelCase ).last_hidden_state
__UpperCAmelCase : List[str] = scaled_model(__UpperCAmelCase ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-5 ) )
@require_torch
class _A ( unittest.TestCase ):
@unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!""" )
@slow
def __A ( self ) -> Any:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
__UpperCAmelCase : Optional[int] = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf""" , device_map="""auto""" )
__UpperCAmelCase : int = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
__UpperCAmelCase : str = torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]] )
torch.testing.assert_close(out.mean(-1 ) , __UpperCAmelCase , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
__UpperCAmelCase : List[Any] = torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , __UpperCAmelCase , atol=1E-5 , rtol=1E-5 )
@unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!""" )
@slow
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
__UpperCAmelCase : Any = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
__UpperCAmelCase : int = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf""" , device_map="""auto""" )
__UpperCAmelCase : str = model(torch.tensor(__UpperCAmelCase ) )
# Expected mean on dim = -1
__UpperCAmelCase : str = torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]] )
torch.testing.assert_close(out.mean(-1 ) , __UpperCAmelCase , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
__UpperCAmelCase : List[str] = torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , __UpperCAmelCase , atol=1E-5 , rtol=1E-5 )
@unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!""" )
@slow
def __A ( self ) -> Dict:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
__UpperCAmelCase : Union[str, Any] = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf""" , device_map="""auto""" )
__UpperCAmelCase : Union[str, Any] = model(torch.tensor(__UpperCAmelCase ) )
# Expected mean on dim = -1
__UpperCAmelCase : Dict = torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]] )
torch.testing.assert_close(out.mean(-1 ) , __UpperCAmelCase , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
__UpperCAmelCase : Any = torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ) , __UpperCAmelCase , atol=1E-2 , rtol=1E-2 )
@unittest.skip(
"""Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test""" )
@slow
def __A ( self ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : Any = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
__UpperCAmelCase : str = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf""" , device_map="""auto""" )
__UpperCAmelCase : List[Any] = model(torch.tensor(__UpperCAmelCase ) )
__UpperCAmelCase : Dict = torch.tensor(
[[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]] , dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ) , __UpperCAmelCase , atol=1E-2 , rtol=1E-2 )
# fmt: off
__UpperCAmelCase : List[str] = torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , __UpperCAmelCase , atol=1E-5 , rtol=1E-5 )
@unittest.skip("""Model is curently gated""" )
@slow
def __A ( self ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = """Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"""
__UpperCAmelCase : Dict = """Simply put, the theory of relativity states that """
__UpperCAmelCase : int = LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf""" )
__UpperCAmelCase : int = tokenizer.encode(__UpperCAmelCase , return_tensors="""pt""" )
__UpperCAmelCase : int = LlamaForCausalLM.from_pretrained(
"""meta-llama/Llama-2-13b-chat-hf""" , device_map="""sequential""" , use_safetensors=__UpperCAmelCase )
# greedy generation outputs
__UpperCAmelCase : Tuple = model.generate(__UpperCAmelCase , max_new_tokens=64 , top_p=__UpperCAmelCase , temperature=1 , do_sample=__UpperCAmelCase )
__UpperCAmelCase : Optional[int] = tokenizer.decode(generated_ids[0] , skip_special_tokens=__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
| 16 | 1 |
'''simple docstring'''
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case =get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
lowerCamelCase : List[str] = AlbertTokenizer
lowerCamelCase : Dict = AlbertTokenizerFast
lowerCamelCase : Optional[Any] = True
lowerCamelCase : Any = True
lowerCamelCase : str = True
def __UpperCAmelCase ( self : int ) -> int:
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase = AlbertTokenizer(UpperCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def __UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase__ : List[str] ) -> Union[str, Any]:
lowerCAmelCase = 'this is a test'
lowerCAmelCase = 'this is a test'
return input_text, output_text
def __UpperCAmelCase ( self : List[str] ) -> List[Any]:
lowerCAmelCase = '<pad>'
lowerCAmelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__ ) , UpperCAmelCase__ )
def __UpperCAmelCase ( self : Tuple ) -> int:
lowerCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<pad>' )
self.assertEqual(vocab_keys[1] , '<unk>' )
self.assertEqual(vocab_keys[-1] , '▁eloquent' )
self.assertEqual(len(UpperCAmelCase__ ) , 3_0_0_0_0 )
def __UpperCAmelCase ( self : str ) -> List[str]:
self.assertEqual(self.get_tokenizer().vocab_size , 3_0_0_0_0 )
def __UpperCAmelCase ( self : Tuple ) -> Union[str, Any]:
if not self.test_rust_tokenizer:
return
lowerCAmelCase = self.get_tokenizer()
lowerCAmelCase = self.get_rust_tokenizer()
lowerCAmelCase = 'I was born in 92000, and this is falsé.'
lowerCAmelCase = tokenizer.tokenize(UpperCAmelCase__ )
lowerCAmelCase = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
lowerCAmelCase = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = self.get_rust_tokenizer()
lowerCAmelCase = tokenizer.encode(UpperCAmelCase__ )
lowerCAmelCase = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
def __UpperCAmelCase ( self : str ) -> str:
lowerCAmelCase = AlbertTokenizer(UpperCAmelCase__ , keep_accents=UpperCAmelCase__ )
lowerCAmelCase = tokenizer.tokenize('This is a test' )
self.assertListEqual(UpperCAmelCase__ , ['▁this', '▁is', '▁a', '▁test'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__ ) , [4_8, 2_5, 2_1, 1_2_8_9] )
lowerCAmelCase = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
UpperCAmelCase__ , ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.'] )
lowerCAmelCase = tokenizer.convert_tokens_to_ids(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , [3_1, 2_3, 3_8_6, 1_9, 5_6_1, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9] )
lowerCAmelCase = tokenizer.convert_ids_to_tokens(UpperCAmelCase__ )
self.assertListEqual(
UpperCAmelCase__ , ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.'] , )
def __UpperCAmelCase ( self : str ) -> Tuple:
lowerCAmelCase = AlbertTokenizer(UpperCAmelCase__ )
lowerCAmelCase = tokenizer.encode('sequence builders' )
lowerCAmelCase = tokenizer.encode('multi-sequence build' )
lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__ )
lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__ , UpperCAmelCase__ )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
@slow
def __UpperCAmelCase ( self : Dict ) -> int:
# fmt: off
lowerCAmelCase = {'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'input_ids': [[2, 2_1_9_7_0, 1_3, 5, 6_0_9_2, 1_6_7, 2_8, 7_1_0_3, 2_1_5_3, 6_7_3, 8, 7_0_2_8, 1_2_0_5_1, 1_8, 1_7, 7_1_0_3, 2_1_5_3, 6_7_3, 8, 3_5_1_5, 1_8_6_8_4, 8, 4_4_6_1, 6, 1_9_2_7, 2_9_7, 8, 1_2_0_6_0, 2_6_0_7, 1_8, 1_3, 5, 4_4_6_1, 1_5, 1_0_5_3_8, 3_8, 8, 1_3_5, 1_5, 8_2_2, 5_8, 1_5, 9_9_3, 1_0_3_6_3, 1_5, 1_4_6_0, 8_0_0_5, 4_4_6_1, 1_5, 9_9_3, 2_5_5, 2_3_2_8, 9, 9, 9, 6, 2_6, 1_1_1_2, 8_1_6, 3_2_6_0, 1_3, 5, 1_0_3, 2_3_7_7, 6, 1_7, 1_1_1_2, 8_1_6, 2_7_8_2, 1_3, 5, 1_0_3, 1_0_6_4_1, 6, 2_9, 8_4, 2_5_1_2, 2_4_3_0, 7_8_2, 1_8_6_8_4, 2_7_6_1, 1_9, 8_0_8, 2_4_3_0, 2_5_5_6, 1_7, 8_5_5, 1_4_8_0, 9_4_7_7, 4_0_9_1, 1_2_8, 1_1_7_1_2, 1_5, 7_1_0_3, 2_1_5_3, 6_7_3, 1_7, 2_4_8_8_3, 9_9_9_0, 9, 3], [2, 1_1_5_0_2, 2_5, 1_0_0_6, 2_0, 7_8_2, 8, 1_1_8_0_9, 8_5_5, 1_7_3_2, 1_9_3_9_3, 1_8_6_6_7, 3_7, 3_6_7, 2_1_0_1_8, 6_9, 1_8_5_4, 3_4, 1_1_8_6_0, 1_9_1_2_4, 2_7, 1_5_6, 2_2_5, 1_7, 1_9_3, 4_1_4_1, 1_9, 6_5, 9_1_2_4, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 1_4, 2_2_3_1, 8_8_6, 2_3_8_5, 1_7_6_5_9, 8_4, 1_4, 1_6_7_9_2, 1_9_5_2, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase__ , model_name='albert-base-v2' , revision='6b6560eaf5ff2e250b00c50f380c5389a9c2d82e' , )
| 4 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import VivitImageProcessor
class _snake_case ( unittest.TestCase ):
def __init__( self , a , a=7 , a=3 , a=10 , a=18 , a=30 , a=400 , a=True , a=None , a=True , a=[0.5, 0.5, 0.5] , a=[0.5, 0.5, 0.5] , a=None , ) -> Dict:
SCREAMING_SNAKE_CASE = size if size is not None else {'shortest_edge': 18}
SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else {'height': 18, 'width': 18}
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = num_frames
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = min_resolution
SCREAMING_SNAKE_CASE = max_resolution
SCREAMING_SNAKE_CASE = do_resize
SCREAMING_SNAKE_CASE = size
SCREAMING_SNAKE_CASE = do_normalize
SCREAMING_SNAKE_CASE = image_mean
SCREAMING_SNAKE_CASE = image_std
SCREAMING_SNAKE_CASE = crop_size
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[int]:
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class _snake_case ( A__ , unittest.TestCase ):
_lowercase : List[str] = VivitImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE__ ( self) -> Tuple:
SCREAMING_SNAKE_CASE = VivitImageProcessingTester(self)
@property
def SCREAMING_SNAKE_CASE__ ( self) -> int:
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE__ ( self) -> Any:
SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(a , 'image_mean'))
self.assertTrue(hasattr(a , 'image_std'))
self.assertTrue(hasattr(a , 'do_normalize'))
self.assertTrue(hasattr(a , 'do_resize'))
self.assertTrue(hasattr(a , 'do_center_crop'))
self.assertTrue(hasattr(a , 'size'))
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[Any]:
SCREAMING_SNAKE_CASE = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size , {'shortest_edge': 18})
self.assertEqual(image_processor.crop_size , {'height': 18, 'width': 18})
SCREAMING_SNAKE_CASE = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84)
self.assertEqual(image_processor.size , {'shortest_edge': 42})
self.assertEqual(image_processor.crop_size , {'height': 84, 'width': 84})
def SCREAMING_SNAKE_CASE__ ( self) -> str:
# Initialize image_processing
SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict)
# create random PIL videos
SCREAMING_SNAKE_CASE = prepare_video_inputs(self.image_processor_tester , equal_resolution=a)
for video in video_inputs:
self.assertIsInstance(a , a)
self.assertIsInstance(video[0] , Image.Image)
# Test not batched input
SCREAMING_SNAKE_CASE = image_processing(video_inputs[0] , return_tensors='pt').pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
SCREAMING_SNAKE_CASE = image_processing(a , return_tensors='pt').pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def SCREAMING_SNAKE_CASE__ ( self) -> Tuple:
# Initialize image_processing
SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
SCREAMING_SNAKE_CASE = prepare_video_inputs(self.image_processor_tester , equal_resolution=a , numpify=a)
for video in video_inputs:
self.assertIsInstance(a , a)
self.assertIsInstance(video[0] , np.ndarray)
# Test not batched input
SCREAMING_SNAKE_CASE = image_processing(video_inputs[0] , return_tensors='pt').pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
SCREAMING_SNAKE_CASE = image_processing(a , return_tensors='pt').pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[int]:
# Initialize image_processing
SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
SCREAMING_SNAKE_CASE = prepare_video_inputs(self.image_processor_tester , equal_resolution=a , torchify=a)
for video in video_inputs:
self.assertIsInstance(a , a)
self.assertIsInstance(video[0] , torch.Tensor)
# Test not batched input
SCREAMING_SNAKE_CASE = image_processing(video_inputs[0] , return_tensors='pt').pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
SCREAMING_SNAKE_CASE = image_processing(a , return_tensors='pt').pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
| 137 | 0 |
import numpy as np
import datasets
_snake_case = '\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n'
_snake_case = '\\n@article{de2000mahalanobis,\n title={The mahalanobis distance},\n author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n journal={Chemometrics and intelligent laboratory systems},\n volume={50},\n number={1},\n pages={1--18},\n year={2000},\n publisher={Elsevier}\n}\n'
_snake_case = '\nArgs:\n X: List of datapoints to be compared with the `reference_distribution`.\n reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n >>> mahalanobis_metric = datasets.load_metric("mahalanobis")\n >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n >>> print(results)\n {\'mahalanobis\': array([0.5])}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase ( datasets.Metric ):
def UpperCAmelCase ( self :int ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"X": datasets.Sequence(datasets.Value("float" , id="sequence" ) , id="X" ),
} ) , )
def UpperCAmelCase ( self :str , _lowercase :List[Any] , _lowercase :Any ):
'''simple docstring'''
lowercase__ = np.array(lowercase_ )
lowercase__ = np.array(lowercase_ )
# Assert that arrays are 2D
if len(X.shape ) != 2:
raise ValueError("Expected `X` to be a 2D vector" )
if len(reference_distribution.shape ) != 2:
raise ValueError("Expected `reference_distribution` to be a 2D vector" )
if reference_distribution.shape[0] < 2:
raise ValueError(
"Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension" )
# Get mahalanobis distance for each prediction
lowercase__ = X - np.mean(lowercase_ )
lowercase__ = np.cov(reference_distribution.T )
try:
lowercase__ = np.linalg.inv(lowercase_ )
except np.linalg.LinAlgError:
lowercase__ = np.linalg.pinv(lowercase_ )
lowercase__ = np.dot(lowercase_ , lowercase_ )
lowercase__ = np.dot(lowercase_ , X_minus_mu.T ).diagonal()
return {"mahalanobis": mahal_dist}
| 356 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class lowerCAmelCase ( unittest.TestCase ):
__lowerCamelCase = ViTImageProcessor if is_vision_available() else None
@property
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = (3, 32, 1_28)
lowercase__ = tempfile.mkdtemp()
# fmt: off
lowercase__ = ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
# fmt: on
lowercase__ = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
lowercase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(_lowercase ) + "\n" )
lowercase__ = {
"do_normalize": False,
"do_resize": True,
"image_processor_type": "ViTImageProcessor",
"resample": 3,
"size": {"height": 32, "width": 1_28},
}
lowercase__ = os.path.join(self.tmpdirname , _lowercase )
with open(self.image_processor_file , "w" , encoding="utf-8" ) as fp:
json.dump(_lowercase , _lowercase )
def UpperCAmelCase ( self :Optional[Any] , **_lowercase :str ):
'''simple docstring'''
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_lowercase )
def UpperCAmelCase ( self :List[Any] , **_lowercase :List[str] ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_lowercase )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
lowercase__ = np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )
lowercase__ = Image.fromarray(np.moveaxis(_lowercase , 0 , -1 ) )
return image_input
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_image_processor()
lowercase__ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase )
processor.save_pretrained(self.tmpdirname )
lowercase__ = MgpstrProcessor.from_pretrained(self.tmpdirname , use_fast=_lowercase )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , _lowercase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowercase )
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_image_processor()
lowercase__ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase )
processor.save_pretrained(self.tmpdirname )
lowercase__ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
lowercase__ = self.get_image_processor(do_normalize=_lowercase , padding_value=1.0 )
lowercase__ = MgpstrProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=_lowercase , padding_value=1.0 )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , _lowercase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowercase )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase )
lowercase__ = self.prepare_image_inputs()
lowercase__ = image_processor(_lowercase , return_tensors="np" )
lowercase__ = processor(images=_lowercase , return_tensors="np" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase )
lowercase__ = "test"
lowercase__ = processor(text=_lowercase )
lowercase__ = tokenizer(_lowercase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase )
lowercase__ = "test"
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=_lowercase , images=_lowercase )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "labels"] )
# test if it raises when no input is passed
with pytest.raises(_lowercase ):
processor()
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase )
lowercase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]
lowercase__ = processor.char_decode(_lowercase )
lowercase__ = tokenizer.batch_decode(_lowercase )
lowercase__ = [seq.replace(" " , "" ) for seq in decoded_tok]
self.assertListEqual(_lowercase , _lowercase )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase )
lowercase__ = None
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=_lowercase , images=_lowercase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase )
lowercase__ = torch.randn(1 , 27 , 38 )
lowercase__ = torch.randn(1 , 27 , 5_02_57 )
lowercase__ = torch.randn(1 , 27 , 3_05_22 )
lowercase__ = processor.batch_decode([char_input, bpe_input, wp_input] )
self.assertListEqual(list(results.keys() ) , ["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"] )
| 201 | 0 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
__lowerCAmelCase : Tuple ="""
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
__lowerCAmelCase : Any ="""
Args:
predictions (`list` of `int`): Predicted labels.
references (`list` of `int`): Ground truth labels.
labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
- 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
- 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.
- 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
- 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
- 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
sample_weight (`list` of `float`): Sample weights Defaults to None.
Returns:
f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.
Examples:
Example 1-A simple binary example
>>> f1_metric = datasets.load_metric(\"f1\")
>>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
>>> print(results)
{'f1': 0.5}
Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
>>> f1_metric = datasets.load_metric(\"f1\")
>>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
>>> print(round(results['f1'], 2))
0.67
Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
>>> f1_metric = datasets.load_metric(\"f1\")
>>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
>>> print(round(results['f1'], 2))
0.35
Example 4-A multiclass example, with different values for the `average` input.
>>> predictions = [0, 2, 1, 0, 0, 1]
>>> references = [0, 1, 2, 0, 1, 2]
>>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")
>>> print(round(results['f1'], 2))
0.27
>>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")
>>> print(round(results['f1'], 2))
0.33
>>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")
>>> print(round(results['f1'], 2))
0.27
>>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
>>> print(results)
{'f1': array([0.8, 0. , 0. ])}
"""
__lowerCAmelCase : Dict ="""
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _A ( datasets.Metric ):
def A__ ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""int32""" ) ),
"""references""": datasets.Sequence(datasets.Value("""int32""" ) ),
}
if self.config_name == """multilabel"""
else {
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"""] , )
def A__ ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=1 , __lowerCAmelCase="binary" , __lowerCAmelCase=None ):
"""simple docstring"""
lowercase = fa_score(
__lowerCAmelCase , __lowerCAmelCase , labels=__lowerCAmelCase , pos_label=__lowerCAmelCase , average=__lowerCAmelCase , sample_weight=__lowerCAmelCase )
return {"f1": float(__lowerCAmelCase ) if score.size == 1 else score}
| 197 | """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 | 1 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = {
"""google/owlvit-base-patch32""": """https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json""",
"""google/owlvit-base-patch16""": """https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json""",
"""google/owlvit-large-patch14""": """https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json""",
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'owlvit_text_model'
def __init__( self , A=49408 , A=512 , A=2048 , A=12 , A=8 , A=16 , A="quick_gelu" , A=1e-5 , A=0.0 , A=0.0_2 , A=1.0 , A=0 , A=49406 , A=49407 , **A , ) -> Dict:
super().__init__(pad_token_id=A , bos_token_id=A , eos_token_id=A , **A )
UpperCAmelCase : str = vocab_size
UpperCAmelCase : List[Any] = hidden_size
UpperCAmelCase : Any = intermediate_size
UpperCAmelCase : List[str] = num_hidden_layers
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = max_position_embeddings
UpperCAmelCase : Dict = hidden_act
UpperCAmelCase : List[str] = layer_norm_eps
UpperCAmelCase : str = attention_dropout
UpperCAmelCase : str = initializer_range
UpperCAmelCase : Optional[int] = initializer_factor
@classmethod
def _lowercase( cls , A , **A ) -> "PretrainedConfig":
cls._set_token_in_kwargs(A )
UpperCAmelCase , UpperCAmelCase : int = cls.get_config_dict(A , **A )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
UpperCAmelCase : Dict = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(A , **A )
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'owlvit_vision_model'
def __init__( self , A=768 , A=3072 , A=12 , A=12 , A=3 , A=768 , A=32 , A="quick_gelu" , A=1e-5 , A=0.0 , A=0.0_2 , A=1.0 , **A , ) -> Optional[int]:
super().__init__(**A )
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : int = intermediate_size
UpperCAmelCase : Dict = num_hidden_layers
UpperCAmelCase : Any = num_attention_heads
UpperCAmelCase : str = num_channels
UpperCAmelCase : Any = image_size
UpperCAmelCase : Tuple = patch_size
UpperCAmelCase : Dict = hidden_act
UpperCAmelCase : Any = layer_norm_eps
UpperCAmelCase : int = attention_dropout
UpperCAmelCase : Any = initializer_range
UpperCAmelCase : List[Any] = initializer_factor
@classmethod
def _lowercase( cls , A , **A ) -> "PretrainedConfig":
cls._set_token_in_kwargs(A )
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = cls.get_config_dict(A , **A )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
UpperCAmelCase : List[Any] = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(A , **A )
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'owlvit'
lowercase = True
def __init__( self , A=None , A=None , A=512 , A=2.6_5_9_2 , A=True , **A , ) -> int:
super().__init__(**A )
if text_config is None:
UpperCAmelCase : str = {}
logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values.""" )
if vision_config is None:
UpperCAmelCase : List[Any] = {}
logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values.""" )
UpperCAmelCase : int = OwlViTTextConfig(**A )
UpperCAmelCase : Optional[Any] = OwlViTVisionConfig(**A )
UpperCAmelCase : Union[str, Any] = projection_dim
UpperCAmelCase : int = logit_scale_init_value
UpperCAmelCase : Optional[Any] = return_dict
UpperCAmelCase : Any = 1.0
@classmethod
def _lowercase( cls , A , **A ) -> "PretrainedConfig":
cls._set_token_in_kwargs(A )
UpperCAmelCase , UpperCAmelCase : List[str] = cls.get_config_dict(A , **A )
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(A , **A )
@classmethod
def _lowercase( cls , A , A , **A ) -> Optional[Any]:
UpperCAmelCase : int = {}
UpperCAmelCase : int = text_config
UpperCAmelCase : Optional[Any] = vision_config
return cls.from_dict(A , **A )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Union[str, Any] = copy.deepcopy(self.__dict__ )
UpperCAmelCase : List[str] = self.text_config.to_dict()
UpperCAmelCase : List[str] = self.vision_config.to_dict()
UpperCAmelCase : List[str] = self.__class__.model_type
return output
class UpperCamelCase_ ( __magic_name__ ):
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""attention_mask""", {0: """batch""", 1: """sequence"""}),
] )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""logits_per_image""", {0: """batch"""}),
("""logits_per_text""", {0: """batch"""}),
("""text_embeds""", {0: """batch"""}),
("""image_embeds""", {0: """batch"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-4
def _lowercase( self , A , A = -1 , A = -1 , A = None , ) -> Mapping[str, Any]:
UpperCAmelCase : Any = super().generate_dummy_inputs(
processor.tokenizer , batch_size=A , seq_length=A , framework=A )
UpperCAmelCase : Optional[int] = super().generate_dummy_inputs(
processor.image_processor , batch_size=A , framework=A )
return {**text_input_dict, **image_input_dict}
@property
def _lowercase( self ) -> int:
return 14
| 338 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" , [
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name="""my_dataset""" )} ),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_examples=4_2 )} ),
SplitDict({"""train""": SplitInfo()} ),
] , )
def __lowerCamelCase ( _lowercase ) -> List[str]:
UpperCAmelCase : Optional[int] = split_dict._to_yaml_list()
assert len(_lowercase ) == len(_lowercase )
UpperCAmelCase : List[Any] = SplitDict._from_yaml_list(_lowercase )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
UpperCAmelCase : List[str] = None
# the split name of split_dict takes over the name of the split info object
UpperCAmelCase : int = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
"""split_info""" , [SplitInfo(), SplitInfo(dataset_name=_lowercase ), SplitInfo(dataset_name="""my_dataset""" )] )
def __lowerCamelCase ( _lowercase ) -> List[str]:
# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
UpperCAmelCase : Optional[Any] = asdict(SplitDict({"""train""": split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 338 | 1 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __A :
def __init__(self : List[Any] , __a : List[Any] , __a : int=3 , __a : Optional[int]=32 , __a : Optional[Any]=3 , __a : List[Any]=10 , __a : str=[10, 20, 30, 40] , __a : Any=[1, 1, 2, 1] , __a : str=True , __a : Optional[Any]=True , __a : Optional[int]="relu" , __a : Optional[Any]=3 , __a : Union[str, Any]=None , ):
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = image_size
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = embeddings_size
UpperCAmelCase_ = hidden_sizes
UpperCAmelCase_ = depths
UpperCAmelCase_ = is_training
UpperCAmelCase_ = use_labels
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = num_labels
UpperCAmelCase_ = scope
UpperCAmelCase_ = len(__a )
def _lowercase (self : Tuple ):
UpperCAmelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase_ = None
if self.use_labels:
UpperCAmelCase_ = ids_tensor([self.batch_size] , self.num_labels )
UpperCAmelCase_ = self.get_config()
return config, pixel_values, labels
def _lowercase (self : int ):
return ResNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def _lowercase (self : str , __a : List[Any] , __a : Tuple , __a : Dict ):
UpperCAmelCase_ = TFResNetModel(config=__a )
UpperCAmelCase_ = model(__a )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _lowercase (self : List[Any] , __a : Tuple , __a : Dict , __a : Tuple ):
UpperCAmelCase_ = self.num_labels
UpperCAmelCase_ = TFResNetForImageClassification(__a )
UpperCAmelCase_ = model(__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase (self : Any ):
UpperCAmelCase_ = self.prepare_config_and_inputs()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = config_and_inputs
UpperCAmelCase_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class __A ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ):
a__ : Union[str, Any] = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
a__ : List[Any] = (
{"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
if is_tf_available()
else {}
)
a__ : int = False
a__ : str = False
a__ : Optional[Any] = False
a__ : List[str] = False
a__ : str = False
def _lowercase (self : Tuple ):
UpperCAmelCase_ = TFResNetModelTester(self )
UpperCAmelCase_ = ConfigTester(self , config_class=__a , has_text_modality=__a )
def _lowercase (self : Union[str, Any] ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _lowercase (self : Optional[int] ):
return
@unittest.skip(reason="ResNet does not use inputs_embeds" )
def _lowercase (self : Optional[Any] ):
pass
@unittest.skip(reason="ResNet does not support input and output embeddings" )
def _lowercase (self : str ):
pass
def _lowercase (self : Optional[Any] ):
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(__a )
UpperCAmelCase_ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase_ = [*signature.parameters.keys()]
UpperCAmelCase_ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __a )
def _lowercase (self : str ):
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a )
def _lowercase (self : List[Any] ):
def check_hidden_states_output(__a : str , __a : Dict , __a : Tuple ):
UpperCAmelCase_ = model_class(__a )
UpperCAmelCase_ = model(**self._prepare_for_class(__a , __a ) )
UpperCAmelCase_ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
UpperCAmelCase_ = self.model_tester.num_stages
self.assertEqual(len(__a ) , expected_num_stages + 1 )
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase_ = ["basic", "bottleneck"]
for model_class in self.all_model_classes:
for layer_type in layers_type:
UpperCAmelCase_ = layer_type
UpperCAmelCase_ = True
check_hidden_states_output(__a , __a , __a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase_ = True
check_hidden_states_output(__a , __a , __a )
def _lowercase (self : Tuple ):
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__a )
@slow
def _lowercase (self : List[str] ):
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase_ = TFResNetModel.from_pretrained(__a )
self.assertIsNotNone(__a )
def lowerCAmelCase_ ( ) -> str:
'''simple docstring'''
UpperCAmelCase_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class __A ( unittest.TestCase ):
@cached_property
def _lowercase (self : List[str] ):
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def _lowercase (self : str ):
UpperCAmelCase_ = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
UpperCAmelCase_ = self.default_image_processor
UpperCAmelCase_ = prepare_img()
UpperCAmelCase_ = image_processor(images=__a , return_tensors="tf" )
# forward pass
UpperCAmelCase_ = model(**__a )
# verify the logits
UpperCAmelCase_ = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , __a )
UpperCAmelCase_ = tf.constant([-11.10_69, -9.78_77, -8.37_77] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , __a , atol=1E-4 ) )
| 1 | '''simple docstring'''
import os
from math import logaa
def lowerCAmelCase_ ( snake_case_ : str = "base_exp.txt" ) -> int:
'''simple docstring'''
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(snake_case_ ) , snake_case_ ) ) ):
UpperCAmelCase_ , UpperCAmelCase_ = list(map(snake_case_ , line.split("," ) ) )
if x * logaa(snake_case_ ) > largest:
UpperCAmelCase_ = x * logaa(snake_case_ )
UpperCAmelCase_ = i + 1
return result
if __name__ == "__main__":
print(solution())
| 1 | 1 |
"""simple docstring"""
import functools
from typing import Any
def _snake_case ( UpperCAmelCase_ : str , UpperCAmelCase_ : list[str] ):
# Validation
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or len(UpperCAmelCase_ ) == 0:
raise ValueError("""the string should be not empty string""" )
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or not all(
isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) and len(UpperCAmelCase_ ) > 0 for item in words ):
raise ValueError("""the words should be a list of non-empty strings""" )
# Build trie
A__ = {}
A__ = """WORD_KEEPER"""
for word in words:
A__ = trie
for c in word:
if c not in trie_node:
A__ = {}
A__ = trie_node[c]
A__ = True
A__ = len(UpperCAmelCase_ )
# Dynamic programming method
@functools.cache
def is_breakable(UpperCAmelCase_ : int ) -> bool:
if index == len_string:
return True
A__ = trie
for i in range(UpperCAmelCase_ , UpperCAmelCase_ ):
A__ = trie_node.get(string[i] , UpperCAmelCase_ )
if trie_node is None:
return False
if trie_node.get(UpperCAmelCase_ , UpperCAmelCase_ ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 69 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
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 ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class a :
"""simple docstring"""
def __init__( self: Optional[int] , UpperCamelCase: List[str] , UpperCamelCase: Dict=13 , UpperCamelCase: Optional[Any]=30 , UpperCamelCase: Optional[Any]=2 , UpperCamelCase: List[str]=3 , UpperCamelCase: Tuple=True , UpperCamelCase: Dict=True , UpperCamelCase: Optional[int]=32 , UpperCamelCase: Tuple=5 , UpperCamelCase: Optional[Any]=4 , UpperCamelCase: Optional[Any]=37 , UpperCamelCase: Optional[Any]="gelu" , UpperCamelCase: Dict=0.1 , UpperCamelCase: Any=0.1 , UpperCamelCase: str=10 , UpperCamelCase: Any=0.02 , UpperCamelCase: List[Any]=None , UpperCamelCase: int=2 , ):
"""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
A__ = encoder_stride
# 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 UpperCamelCase ( self: List[Any] ):
"""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 UpperCamelCase ( self: int ):
"""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=UpperCamelCase , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def UpperCamelCase ( self: Tuple , UpperCamelCase: str , UpperCamelCase: Union[str, Any] , UpperCamelCase: Optional[int] ):
"""simple docstring"""
A__ = ViTModel(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = model(UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self: Optional[int] , UpperCamelCase: Optional[Any] , UpperCamelCase: Optional[int] , UpperCamelCase: Union[str, Any] ):
"""simple docstring"""
A__ = ViTForMaskedImageModeling(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = model(UpperCamelCase )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
A__ = 1
A__ = ViTForMaskedImageModeling(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A__ = model(UpperCamelCase )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def UpperCamelCase ( self: str , UpperCamelCase: Dict , UpperCamelCase: List[Any] , UpperCamelCase: List[Any] ):
"""simple docstring"""
A__ = self.type_sequence_label_size
A__ = ViTForImageClassification(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = model(UpperCamelCase , labels=UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
A__ = 1
A__ = ViTForImageClassification(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A__ = model(UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
A__ = self.prepare_config_and_inputs()
(
(
A__
) , (
A__
) , (
A__
) ,
) = config_and_inputs
A__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class a ( _lowerCamelCase, _lowerCamelCase, unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = (
(
ViTModel,
ViTForImageClassification,
ViTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
UpperCAmelCase = (
{"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
if is_torch_available()
else {}
)
UpperCAmelCase = True
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
def UpperCamelCase ( self: Any ):
"""simple docstring"""
A__ = ViTModelTester(self )
A__ = ConfigTester(self , config_class=UpperCamelCase , has_text_modality=UpperCamelCase , hidden_size=37 )
def UpperCamelCase ( self: str ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def UpperCamelCase ( self: str ):
"""simple docstring"""
pass
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCamelCase , nn.Linear ) )
def UpperCamelCase ( self: 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(UpperCamelCase )
A__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A__ = [*signature.parameters.keys()]
A__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , UpperCamelCase )
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase )
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase )
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase )
@slow
def UpperCamelCase ( self: Dict ):
"""simple docstring"""
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = ViTModel.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
def _snake_case ( ):
A__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class a ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224""" ) if is_vision_available() else None
@slow
def UpperCamelCase ( self: Dict ):
"""simple docstring"""
A__ = ViTForImageClassification.from_pretrained("""google/vit-base-patch16-224""" ).to(UpperCamelCase )
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(images=UpperCamelCase , return_tensors="""pt""" ).to(UpperCamelCase )
# forward pass
with torch.no_grad():
A__ = model(**UpperCamelCase )
# verify the logits
A__ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , UpperCamelCase )
A__ = torch.tensor([-0.2_744, 0.8_215, -0.0_836] ).to(UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCamelCase , atol=1e-4 ) )
@slow
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ = ViTModel.from_pretrained("""facebook/dino-vits8""" ).to(UpperCamelCase )
A__ = ViTImageProcessor.from_pretrained("""facebook/dino-vits8""" , size=4_80 )
A__ = prepare_img()
A__ = image_processor(images=UpperCamelCase , return_tensors="""pt""" )
A__ = inputs.pixel_values.to(UpperCamelCase )
# forward pass
with torch.no_grad():
A__ = model(UpperCamelCase , interpolate_pos_encoding=UpperCamelCase )
# verify the logits
A__ = torch.Size((1, 36_01, 3_84) )
self.assertEqual(outputs.last_hidden_state.shape , UpperCamelCase )
A__ = torch.tensor(
[[4.2_340, 4.3_906, -6.6_692], [4.5_463, 1.8_928, -6.7_257], [4.4_429, 0.8_496, -5.8_585]] ).to(UpperCamelCase )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCamelCase , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
A__ = ViTModel.from_pretrained("""facebook/dino-vits8""" , torch_dtype=torch.floataa , device_map="""auto""" )
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(images=UpperCamelCase , return_tensors="""pt""" )
A__ = inputs.pixel_values.to(UpperCamelCase )
# forward pass to make sure inference works in fp16
with torch.no_grad():
A__ = model(UpperCamelCase )
| 69 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowercase = {
"""configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
"""tokenization_ctrl""": ["""CTRLTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
"""CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CTRLForSequenceClassification""",
"""CTRLLMHeadModel""",
"""CTRLModel""",
"""CTRLPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
"""TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFCTRLForSequenceClassification""",
"""TFCTRLLMHeadModel""",
"""TFCTRLModel""",
"""TFCTRLPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
from .tokenization_ctrl import CTRLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ctrl import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
CTRLPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_ctrl import (
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCTRLForSequenceClassification,
TFCTRLLMHeadModel,
TFCTRLModel,
TFCTRLPreTrainedModel,
)
else:
import sys
__lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 40 |
"""simple docstring"""
from bisect import bisect
from itertools import accumulate
def lowercase ( A_ , A_ , A_ , A_ )-> Union[str, Any]:
'''simple docstring'''
a : Any = sorted(zip(A_ , A_ ) , key=lambda A_ : x[0] / x[1] , reverse=A_ )
a , a : int = [i[0] for i in r], [i[1] for i in r]
a : Union[str, Any] = list(accumulate(A_ ) )
a : Optional[Any] = bisect(A_ , A_ )
return (
0
if k == 0
else sum(vl[:k] ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k] )
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 40 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_MODEL_FOR_PRETRAINING_MAPPING,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertModel,
)
@require_tf
class __a (UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = (
(
TFMobileBertModel,
TFMobileBertForMaskedLM,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertForMultipleChoice,
)
if is_tf_available()
else ()
)
_SCREAMING_SNAKE_CASE :List[Any] = (
{
"""feature-extraction""": TFMobileBertModel,
"""fill-mask""": TFMobileBertForMaskedLM,
"""question-answering""": TFMobileBertForQuestionAnswering,
"""text-classification""": TFMobileBertForSequenceClassification,
"""token-classification""": TFMobileBertForTokenClassification,
"""zero-shot""": TFMobileBertForSequenceClassification,
}
if is_tf_available()
else {}
)
_SCREAMING_SNAKE_CASE :str = False
_SCREAMING_SNAKE_CASE :int = False
def _a ( self , _a , _a , _a=False ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = super()._prepare_for_class(_a , _a , return_labels=_a )
if return_labels:
if model_class in get_values(_a ):
SCREAMING_SNAKE_CASE__ : List[Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
return inputs_dict
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=32 , _a=2 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=3 , _a=4 , _a=None , ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = parent
SCREAMING_SNAKE_CASE__ : Any = batch_size
SCREAMING_SNAKE_CASE__ : int = seq_length
SCREAMING_SNAKE_CASE__ : Tuple = is_training
SCREAMING_SNAKE_CASE__ : Tuple = use_input_mask
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_token_type_ids
SCREAMING_SNAKE_CASE__ : List[str] = use_labels
SCREAMING_SNAKE_CASE__ : int = vocab_size
SCREAMING_SNAKE_CASE__ : List[str] = hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : List[Any] = num_attention_heads
SCREAMING_SNAKE_CASE__ : str = intermediate_size
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_act
SCREAMING_SNAKE_CASE__ : List[str] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Dict = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Tuple = type_vocab_size
SCREAMING_SNAKE_CASE__ : List[str] = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Tuple = initializer_range
SCREAMING_SNAKE_CASE__ : int = num_labels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_choices
SCREAMING_SNAKE_CASE__ : Tuple = scope
SCREAMING_SNAKE_CASE__ : Union[str, Any] = embedding_size
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Any = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : Tuple = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Optional[int] = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : Any = MobileBertConfig(
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 , embedding_size=self.embedding_size , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self , _a , _a , _a , _a , _a , _a , _a ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = TFMobileBertModel(config=_a )
SCREAMING_SNAKE_CASE__ : str = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
SCREAMING_SNAKE_CASE__ : List[str] = model(_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [input_ids, input_mask]
SCREAMING_SNAKE_CASE__ : Any = model(_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a )
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 _a ( self , _a , _a , _a , _a , _a , _a , _a ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = TFMobileBertForMaskedLM(config=_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self , _a , _a , _a , _a , _a , _a , _a ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = TFMobileBertForNextSentencePrediction(config=_a )
SCREAMING_SNAKE_CASE__ : str = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
SCREAMING_SNAKE_CASE__ : Tuple = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def _a ( self , _a , _a , _a , _a , _a , _a , _a ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = TFMobileBertForPreTraining(config=_a )
SCREAMING_SNAKE_CASE__ : List[Any] = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
SCREAMING_SNAKE_CASE__ : Dict = model(_a )
self.parent.assertEqual(
result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def _a ( self , _a , _a , _a , _a , _a , _a , _a ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.num_labels
SCREAMING_SNAKE_CASE__ : Tuple = TFMobileBertForSequenceClassification(config=_a )
SCREAMING_SNAKE_CASE__ : Dict = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self , _a , _a , _a , _a , _a , _a , _a ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.num_choices
SCREAMING_SNAKE_CASE__ : Optional[Any] = TFMobileBertForMultipleChoice(config=_a )
SCREAMING_SNAKE_CASE__ : Dict = tf.tile(tf.expand_dims(_a , 1 ) , (1, self.num_choices, 1) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = tf.tile(tf.expand_dims(_a , 1 ) , (1, self.num_choices, 1) )
SCREAMING_SNAKE_CASE__ : Optional[int] = tf.tile(tf.expand_dims(_a , 1 ) , (1, self.num_choices, 1) )
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _a ( self , _a , _a , _a , _a , _a , _a , _a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.num_labels
SCREAMING_SNAKE_CASE__ : int = TFMobileBertForTokenClassification(config=_a )
SCREAMING_SNAKE_CASE__ : List[Any] = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
SCREAMING_SNAKE_CASE__ : Tuple = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _a ( self , _a , _a , _a , _a , _a , _a , _a ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = TFMobileBertForQuestionAnswering(config=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
SCREAMING_SNAKE_CASE__ : Tuple = model(_a )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = 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__
) ,
) : Any = config_and_inputs
SCREAMING_SNAKE_CASE__ : List[str] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = TFMobileBertModelTest.TFMobileBertModelTester(self )
SCREAMING_SNAKE_CASE__ : List[Any] = ConfigTester(self , config_class=_a , hidden_size=37 )
def _a ( self ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*_a )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*_a )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*_a )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*_a )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*_a )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*_a )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*_a )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*_a )
@slow
def _a ( self ) -> int:
"""simple docstring"""
for model_name in ["google/mobilebert-uncased"]:
SCREAMING_SNAKE_CASE__ : List[Any] = TFMobileBertModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@require_tf
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = TFMobileBertForPreTraining.from_pretrained("""google/mobilebert-uncased""" )
SCREAMING_SNAKE_CASE__ : Dict = tf.constant([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE__ : Tuple = model(_a )[0]
SCREAMING_SNAKE_CASE__ : List[Any] = [1, 6, 30_522]
self.assertEqual(output.shape , _a )
SCREAMING_SNAKE_CASE__ : int = tf.constant(
[
[
[-4.5_919_547, -9.248_295, -9.645_256],
[-6.7_306_175, -6.440_284, -6.6_052_837],
[-7.2_743_506, -6.7_847_915, -6.024_673],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , _a , atol=1E-4 )
| 56 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Dict = []
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
SCREAMING_SNAKE_CASE__ : int = {
"""^""": 3,
"""*""": 2,
"""/""": 2,
"""%""": 2,
"""+""": 1,
"""-""": 1,
} # Priority of each operator
SCREAMING_SNAKE_CASE__ : List[Any] = len(__lowerCAmelCase ) if (len(__lowerCAmelCase ) > 7) else 7
# Print table header for output
print(
"""Symbol""".center(8 ) , """Stack""".center(__lowerCAmelCase ) , """Postfix""".center(__lowerCAmelCase ) , sep=""" | """ , )
print("""-""" * (print_width * 3 + 7) )
for x in infix:
if x.isalpha() or x.isdigit():
post_fix.append(__lowerCAmelCase ) # if x is Alphabet / Digit, add it to Postfix
elif x == "(":
stack.append(__lowerCAmelCase ) # if x is "(" push to Stack
elif x == ")": # if x is ")" pop stack until "(" is encountered
while stack[-1] != "(":
post_fix.append(stack.pop() ) # Pop stack & add the content to Postfix
stack.pop()
else:
if len(__lowerCAmelCase ) == 0:
stack.append(__lowerCAmelCase ) # If stack is empty, push x to stack
else: # while priority of x is not > priority of element in the stack
while len(__lowerCAmelCase ) > 0 and priority[x] <= priority[stack[-1]]:
post_fix.append(stack.pop() ) # pop stack & add to Postfix
stack.append(__lowerCAmelCase ) # push x to stack
print(
x.center(8 ) , ("""""".join(__lowerCAmelCase )).ljust(__lowerCAmelCase ) , ("""""".join(__lowerCAmelCase )).ljust(__lowerCAmelCase ) , sep=""" | """ , ) # Output in tabular format
while len(__lowerCAmelCase ) > 0: # while stack is not empty
post_fix.append(stack.pop() ) # pop stack & add to Postfix
print(
""" """.center(8 ) , ("""""".join(__lowerCAmelCase )).ljust(__lowerCAmelCase ) , ("""""".join(__lowerCAmelCase )).ljust(__lowerCAmelCase ) , sep=""" | """ , ) # Output in tabular format
return "".join(__lowerCAmelCase ) # return Postfix as str
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : List[str] = list(infix[::-1] ) # reverse the infix equation
for i in range(len(__lowerCAmelCase ) ):
if infix[i] == "(":
SCREAMING_SNAKE_CASE__ : Optional[int] = """)""" # change "(" to ")"
elif infix[i] == ")":
SCREAMING_SNAKE_CASE__ : Optional[Any] = """(""" # change ")" to "("
return (infix_2_postfix("""""".join(__lowerCAmelCase ) ))[
::-1
] # call infix_2_postfix on Infix, return reverse of Postfix
if __name__ == "__main__":
a :Optional[int] = input("\nEnter an Infix Equation = ") # Input an Infix equation
a :Dict = "".join(Infix.split()) # Remove spaces from the input
print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")
| 56 | 1 |
"""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 snake_case_:
def lowerCamelCase__ ( self : Optional[Any] ):
torch.manual_seed(0 )
lowerCAmelCase : Optional[Any] = TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
lowerCAmelCase : List[str] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
lowerCAmelCase : str = UNetaDConditionModel(
sample_size=3_2 , layers_per_block=1 , block_out_channels=[3_2, 6_4] , down_block_types=[
'''ResnetDownsampleBlock2D''',
'''SimpleCrossAttnDownBlock2D''',
] , mid_block_type='''UNetMidBlock2DSimpleCrossAttn''' , up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''] , in_channels=3 , out_channels=6 , cross_attention_dim=3_2 , encoder_hid_dim=3_2 , 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 )
lowerCAmelCase : int = DDPMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='''squaredcos_cap_v2''' , beta_start=0.0_001 , beta_end=0.02 , thresholding=UpperCamelCase_ , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type='''epsilon''' , variance_type='''learned_range''' , )
torch.manual_seed(0 )
lowerCAmelCase : Tuple = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def lowerCamelCase__ ( self : int ):
torch.manual_seed(0 )
lowerCAmelCase : str = TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
lowerCAmelCase : Any = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
lowerCAmelCase : Any = UNetaDConditionModel(
sample_size=3_2 , layers_per_block=[1, 2] , block_out_channels=[3_2, 6_4] , down_block_types=[
'''ResnetDownsampleBlock2D''',
'''SimpleCrossAttnDownBlock2D''',
] , mid_block_type='''UNetMidBlock2DSimpleCrossAttn''' , up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''] , in_channels=6 , out_channels=6 , cross_attention_dim=3_2 , encoder_hid_dim=3_2 , 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=3_2 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowerCAmelCase : Optional[Any] = DDPMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='''squaredcos_cap_v2''' , beta_start=0.0_001 , beta_end=0.02 , thresholding=UpperCamelCase_ , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type='''epsilon''' , variance_type='''learned_range''' , )
torch.manual_seed(0 )
lowerCAmelCase : Tuple = DDPMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='''squaredcos_cap_v2''' , beta_start=0.0_001 , beta_end=0.02 , )
torch.manual_seed(0 )
lowerCAmelCase : List[str] = 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 lowerCamelCase__ ( self : Tuple ):
lowerCAmelCase : Union[str, Any] = self.get_dummy_components()
lowerCAmelCase : str = self.pipeline_class(**UpperCamelCase_ )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
lowerCAmelCase : Tuple = self.get_dummy_inputs(UpperCamelCase_ )
lowerCAmelCase : Any = inputs['''prompt''']
lowerCAmelCase : Optional[Any] = inputs['''generator''']
lowerCAmelCase : Tuple = inputs['''num_inference_steps''']
lowerCAmelCase : Any = inputs['''output_type''']
if "image" in inputs:
lowerCAmelCase : str = inputs['''image''']
else:
lowerCAmelCase : List[str] = None
if "mask_image" in inputs:
lowerCAmelCase : Optional[Any] = inputs['''mask_image''']
else:
lowerCAmelCase : List[str] = None
if "original_image" in inputs:
lowerCAmelCase : Tuple = inputs['''original_image''']
else:
lowerCAmelCase : Optional[int] = None
lowerCAmelCase, lowerCAmelCase : Optional[Any] = pipe.encode_prompt(UpperCamelCase_ )
# inputs with prompt converted to embeddings
lowerCAmelCase : Dict = {
'''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:
lowerCAmelCase : Optional[Any] = image
if mask_image is not None:
lowerCAmelCase : Tuple = mask_image
if original_image is not None:
lowerCAmelCase : Optional[Any] = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
lowerCAmelCase : Dict = pipe(**UpperCamelCase_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(UpperCamelCase_ )
lowerCAmelCase : Union[str, Any] = self.pipeline_class.from_pretrained(UpperCamelCase_ )
pipe_loaded.to(UpperCamelCase_ )
pipe_loaded.set_progress_bar_config(disable=UpperCamelCase_ )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(UpperCamelCase_ , UpperCamelCase_ ) is None , F'''`{optional_component}` did not stay set to None after loading.''' , )
lowerCAmelCase : int = self.get_dummy_inputs(UpperCamelCase_ )
lowerCAmelCase : List[str] = inputs['''generator''']
lowerCAmelCase : Dict = inputs['''num_inference_steps''']
lowerCAmelCase : Optional[int] = inputs['''output_type''']
# inputs with prompt converted to embeddings
lowerCAmelCase : Union[str, Any] = {
'''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:
lowerCAmelCase : Any = image
if mask_image is not None:
lowerCAmelCase : Optional[int] = mask_image
if original_image is not None:
lowerCAmelCase : Any = original_image
lowerCAmelCase : List[str] = pipe_loaded(**UpperCamelCase_ )[0]
lowerCAmelCase : str = np.abs(to_np(UpperCamelCase_ ) - to_np(UpperCamelCase_ ) ).max()
self.assertLess(UpperCamelCase_ , 1E-4 )
def lowerCamelCase__ ( self : str ):
lowerCAmelCase : str = self.get_dummy_components()
lowerCAmelCase : int = self.pipeline_class(**UpperCamelCase_ )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
lowerCAmelCase : Optional[int] = self.get_dummy_inputs(UpperCamelCase_ )
lowerCAmelCase : List[Any] = pipe(**UpperCamelCase_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(UpperCamelCase_ )
lowerCAmelCase : Union[str, Any] = self.pipeline_class.from_pretrained(UpperCamelCase_ )
pipe_loaded.to(UpperCamelCase_ )
pipe_loaded.set_progress_bar_config(disable=UpperCamelCase_ )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
lowerCAmelCase : Union[str, Any] = self.get_dummy_inputs(UpperCamelCase_ )
lowerCAmelCase : int = pipe_loaded(**UpperCamelCase_ )[0]
lowerCAmelCase : Union[str, Any] = np.abs(to_np(UpperCamelCase_ ) - to_np(UpperCamelCase_ ) ).max()
self.assertLess(UpperCamelCase_ , 1E-4 )
| 60 |
"""simple docstring"""
import math
def _snake_case ( ):
lowerCAmelCase : Union[str, Any] = input('''Enter message: ''' )
lowerCAmelCase : Optional[int] = int(input(f'''Enter key [2-{len(_snake_case ) - 1}]: ''' ) )
lowerCAmelCase : str = input('''Encryption/Decryption [e/d]: ''' )
if mode.lower().startswith('''e''' ):
lowerCAmelCase : Any = encrypt_message(_snake_case , _snake_case )
elif mode.lower().startswith('''d''' ):
lowerCAmelCase : Union[str, Any] = decrypt_message(_snake_case , _snake_case )
# Append pipe symbol (vertical bar) to identify spaces at the end.
print(f'''Output:\n{text + "|"}''' )
def _snake_case ( _snake_case : int , _snake_case : str ):
lowerCAmelCase : Optional[Any] = [''''''] * key
for col in range(_snake_case ):
lowerCAmelCase : Optional[Any] = col
while pointer < len(_snake_case ):
cipher_text[col] += message[pointer]
pointer += key
return "".join(_snake_case )
def _snake_case ( _snake_case : int , _snake_case : str ):
lowerCAmelCase : Union[str, Any] = math.ceil(len(_snake_case ) / key )
lowerCAmelCase : str = key
lowerCAmelCase : Any = (num_cols * num_rows) - len(_snake_case )
lowerCAmelCase : Dict = [''''''] * num_cols
lowerCAmelCase : int = 0
lowerCAmelCase : int = 0
for symbol in message:
plain_text[col] += symbol
col += 1
if (
(col == num_cols)
or (col == num_cols - 1)
and (row >= num_rows - num_shaded_boxes)
):
lowerCAmelCase : int = 0
row += 1
return "".join(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 60 | 1 |
'''simple docstring'''
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class _lowerCAmelCase :
'''simple docstring'''
def __init__(self , UpperCAmelCase ) -> Optional[int]:
_snake_case = data
_snake_case = [0x67_45_23_01, 0xEF_CD_AB_89, 0x98_BA_DC_FE, 0x10_32_54_76, 0xC3_D2_E1_F0]
@staticmethod
def lowercase (UpperCAmelCase , UpperCAmelCase ) -> str:
return ((n << b) | (n >> (32 - b))) & 0xFF_FF_FF_FF
def lowercase (self ) -> Dict:
_snake_case = B"""\x80""" + B"""\x00""" * (63 - (len(self.data ) + 8) % 64)
_snake_case = self.data + padding + struct.pack(""">Q""" , 8 * len(self.data ) )
return padded_data
def lowercase (self ) -> Optional[int]:
return [
self.padded_data[i : i + 64] for i in range(0 , len(self.padded_data ) , 64 )
]
def lowercase (self , UpperCAmelCase ) -> Optional[int]:
_snake_case = list(struct.unpack(""">16L""" , UpperCAmelCase ) ) + [0] * 64
for i in range(16 , 80 ):
_snake_case = self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]) , 1 )
return w
def lowercase (self ) -> Any:
_snake_case = self.padding()
_snake_case = self.split_blocks()
for block in self.blocks:
_snake_case = self.expand_block(UpperCAmelCase )
_snake_case, _snake_case, _snake_case, _snake_case, _snake_case = self.h
for i in range(0 , 80 ):
if 0 <= i < 20:
_snake_case = (b & c) | ((~b) & d)
_snake_case = 0x5A_82_79_99
elif 20 <= i < 40:
_snake_case = b ^ c ^ d
_snake_case = 0x6E_D9_EB_A1
elif 40 <= i < 60:
_snake_case = (b & c) | (b & d) | (c & d)
_snake_case = 0x8F_1B_BC_DC
elif 60 <= i < 80:
_snake_case = b ^ c ^ d
_snake_case = 0xCA_62_C1_D6
_snake_case, _snake_case, _snake_case, _snake_case, _snake_case = (
self.rotate(UpperCAmelCase , 5 ) + f + e + k + expanded_block[i] & 0xFF_FF_FF_FF,
a,
self.rotate(UpperCAmelCase , 30 ),
c,
d,
)
_snake_case = (
self.h[0] + a & 0xFF_FF_FF_FF,
self.h[1] + b & 0xFF_FF_FF_FF,
self.h[2] + c & 0xFF_FF_FF_FF,
self.h[3] + d & 0xFF_FF_FF_FF,
self.h[4] + e & 0xFF_FF_FF_FF,
)
return ("{:08x}" * 5).format(*self.h )
def __SCREAMING_SNAKE_CASE ( ):
_snake_case = b"""Test String"""
assert SHAaHash(_SCREAMING_SNAKE_CASE ).final_hash() == hashlib.shaa(_SCREAMING_SNAKE_CASE ).hexdigest() # noqa: S324
def __SCREAMING_SNAKE_CASE ( ):
_snake_case = argparse.ArgumentParser(description="""Process some strings or files""" )
parser.add_argument(
"""--string""" , dest="""input_string""" , default="""Hello World!! Welcome to Cryptography""" , help="""Hash the string""" , )
parser.add_argument("""--file""" , dest="""input_file""" , help="""Hash contents of a file""" )
_snake_case = parser.parse_args()
_snake_case = args.input_string
# In any case hash input should be a bytestring
if args.input_file:
with open(args.input_file , """rb""" ) as f:
_snake_case = f.read()
else:
_snake_case = bytes(_SCREAMING_SNAKE_CASE , """utf-8""" )
print(SHAaHash(_SCREAMING_SNAKE_CASE ).final_hash() )
if __name__ == "__main__":
main()
import doctest
doctest.testmod() | 270 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase = {
'configuration_efficientformer': [
'EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EfficientFormerConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = ['EfficientFormerImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = [
'EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'EfficientFormerForImageClassification',
'EfficientFormerForImageClassificationWithTeacher',
'EfficientFormerModel',
'EfficientFormerPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = [
'TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFEfficientFormerForImageClassification',
'TFEfficientFormerForImageClassificationWithTeacher',
'TFEfficientFormerModel',
'TFEfficientFormerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientformer import EfficientFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientformer import (
EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientFormerForImageClassification,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerModel,
EfficientFormerPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
TFEfficientFormerPreTrainedModel,
)
else:
import sys
__lowerCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 270 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( __snake_case : str , __snake_case : str ):
'''simple docstring'''
if not (isinstance(__snake_case , __snake_case ) and isinstance(__snake_case , __snake_case )):
raise ValueError('longest_common_substring() takes two strings for inputs' )
lowercase = len(__snake_case )
lowercase = len(__snake_case )
lowercase = [[0] * (texta_length + 1) for _ in range(texta_length + 1 )]
lowercase = 0
lowercase = 0
for i in range(1 , texta_length + 1 ):
for j in range(1 , texta_length + 1 ):
if texta[i - 1] == texta[j - 1]:
lowercase = 1 + dp[i - 1][j - 1]
if dp[i][j] > ans_length:
lowercase = i
lowercase = dp[i][j]
return texta[ans_index - ans_length : ans_index]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 220 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
_UpperCamelCase : Tuple = logging.getLogger()
def _SCREAMING_SNAKE_CASE ( __snake_case : Path , __snake_case : list ):
'''simple docstring'''
lowercase = '\n'.join(__snake_case )
Path(__snake_case ).open('w' ).writelines(__snake_case )
_UpperCamelCase : Union[str, Any] = 'patrickvonplaten/t5-tiny-random'
_UpperCamelCase : Union[str, Any] = 'sshleifer/bart-tiny-random'
_UpperCamelCase : Tuple = 'sshleifer/tiny-mbart'
_UpperCamelCase : Union[str, Any] = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class a ( a_ ):
def UpperCamelCase_ ( self , _lowerCamelCase ):
lowercase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
lowercase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
lowercase = [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
_dump_articles(_lowerCamelCase , _lowerCamelCase )
lowercase = str(Path(self.get_auto_remove_tmp_dir() ) / 'scores.json' )
lowercase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
lowercase = F'\n run_eval_search.py\n {model}\n {input_file_name}\n {output_file_name}\n --score_path {score_path}\n --task {task}\n --num_beams 2\n --length_penalty 2.0\n '.split()
with patch.object(_lowerCamelCase , 'argv' , _lowerCamelCase ):
run_generate()
assert Path(_lowerCamelCase ).exists()
# os.remove(Path(output_file_name))
def UpperCamelCase_ ( self ):
self.run_eval_tester(_lowerCamelCase )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def UpperCamelCase_ ( self , _lowerCamelCase ):
self.run_eval_tester(_lowerCamelCase )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def UpperCamelCase_ ( self , _lowerCamelCase ):
lowercase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
lowercase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
lowercase = {
'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
'de': [
'Maschinelles Lernen ist großartig, oder?',
'Ich esse gerne Bananen',
'Morgen ist wieder ein toller Tag!',
],
}
lowercase = Path(self.get_auto_remove_tmp_dir() )
lowercase = str(tmp_dir / 'scores.json' )
lowercase = str(tmp_dir / 'val.target' )
_dump_articles(_lowerCamelCase , text['en'] )
_dump_articles(_lowerCamelCase , text['de'] )
lowercase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
lowercase = F'\n run_eval_search.py\n {model}\n {str(_lowerCamelCase )}\n {str(_lowerCamelCase )}\n --score_path {score_path}\n --reference_path {reference_path}\n --task {task}\n '.split()
testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'] )
with patch.object(_lowerCamelCase , 'argv' , _lowerCamelCase ):
with CaptureStdout() as cs:
run_search()
lowercase = [' num_beams | length_penalty', model, 'Best score args']
lowercase = ['Info']
if "translation" in task:
expected_strings.append('bleu' )
else:
expected_strings.extend(_lowerCamelCase )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(_lowerCamelCase ).exists()
os.remove(Path(_lowerCamelCase ) )
| 220 | 1 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Optional[Any] = logging.get_logger()
@dataclass
class UpperCamelCase_ :
'''simple docstring'''
UpperCAmelCase__ = 42
UpperCAmelCase__ = field(default_factory=UpperCAmelCase__ )
UpperCAmelCase__ = field(default_factory=UpperCAmelCase__ )
def SCREAMING_SNAKE_CASE ( self : str , UpperCAmelCase__ : int , UpperCAmelCase__ : Tensor , UpperCAmelCase__ : Tensor) ->str:
'''simple docstring'''
A__ = len(list(m.modules())) == 1 or isinstance(UpperCAmelCase__ , nn.Convad) or isinstance(UpperCAmelCase__ , nn.BatchNormad)
if has_not_submodules:
self.traced.append(UpperCAmelCase__)
def __call__( self : int , UpperCAmelCase__ : Tensor) ->List[Any]:
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook))
self.module(UpperCAmelCase__)
[x.remove() for x in self.handles]
return self
@property
def SCREAMING_SNAKE_CASE ( self : int) ->List[Any]:
'''simple docstring'''
return list(filter(lambda UpperCAmelCase__: len(list(x.state_dict().keys())) > 0 , self.traced))
@dataclass
class UpperCamelCase_ :
'''simple docstring'''
UpperCAmelCase__ = 42
UpperCAmelCase__ = 42
UpperCAmelCase__ = 1
UpperCAmelCase__ = field(default_factory=UpperCAmelCase__ )
UpperCAmelCase__ = field(default_factory=UpperCAmelCase__ )
UpperCAmelCase__ = True
def __call__( self : Optional[Any] , UpperCAmelCase__ : Tensor) ->Union[str, Any]:
'''simple docstring'''
A__ = Tracker(self.dest)(UpperCAmelCase__).parametrized
A__ = Tracker(self.src)(UpperCAmelCase__).parametrized
A__ = list(filter(lambda UpperCAmelCase__: type(UpperCAmelCase__) not in self.src_skip , UpperCAmelCase__))
A__ = list(filter(lambda UpperCAmelCase__: type(UpperCAmelCase__) not in self.dest_skip , UpperCAmelCase__))
if len(UpperCAmelCase__) != len(UpperCAmelCase__) and self.raise_if_mismatch:
raise Exception(
f"""Numbers of operations are different. Source module has {len(UpperCAmelCase__)} operations while"""
f""" destination module has {len(UpperCAmelCase__)}.""")
for dest_m, src_m in zip(UpperCAmelCase__ , UpperCAmelCase__):
dest_m.load_state_dict(src_m.state_dict())
if self.verbose == 1:
print(f"""Transfered from={src_m} to={dest_m}""")
class UpperCamelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCAmelCase__ : nn.Module) ->Optional[int]:
'''simple docstring'''
super().__init__()
A__ = []
# - get the stem
feature_blocks.append(('''conv1''', model.stem))
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith('''block'''), f"""Unexpected layer name {k}"""
A__ = len(UpperCAmelCase__) + 1
feature_blocks.append((f"""res{block_index}""", v))
A__ = nn.ModuleDict(UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : List[Any] , UpperCAmelCase__ : Tensor) ->List[Any]:
'''simple docstring'''
return get_trunk_forward_outputs(
UpperCAmelCase__ , out_feat_keys=UpperCAmelCase__ , feature_blocks=self._feature_blocks , )
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( self : Tuple , UpperCAmelCase__ : str) ->str:
'''simple docstring'''
A__ = x.split('''-''')
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:])
def __getitem__( self : Optional[Any] , UpperCAmelCase__ : str) ->Callable[[], Tuple[nn.Module, Dict]]:
'''simple docstring'''
if x not in self:
A__ = self.convert_name_to_timm(UpperCAmelCase__)
A__ = partial(lambda: (timm.create_model(UpperCAmelCase__ , pretrained=UpperCAmelCase__).eval(), None))
else:
A__ = super().__getitem__(UpperCAmelCase__)
return val
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
def __getitem__( self : Tuple , UpperCAmelCase__ : str) ->Callable[[], nn.Module]:
'''simple docstring'''
if "seer" in x and "in1k" not in x:
A__ = RegNetModel
else:
A__ = RegNetForImageClassification
return val
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> Union[str, Any]:
"""simple docstring"""
for from_key, to_key in keys:
A__ = from_state_dict[from_key].clone()
print(f"""Copied key={from_key} to={to_key}""" )
return to_state_dict
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ = True , ) -> Union[str, Any]:
"""simple docstring"""
print(f"""Converting {name}...""" )
with torch.no_grad():
A__ , A__ = from_model_func()
A__ = our_model_func(lowercase_ ).eval()
A__ = ModuleTransfer(src=lowercase_ , dest=lowercase_ , raise_if_mismatch=lowercase_ )
A__ = torch.randn((1, 3, 224, 224) )
module_transfer(lowercase_ )
if from_state_dict is not None:
A__ = []
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
A__ = [('''0.clf.0.weight''', '''classifier.1.weight'''), ('''0.clf.0.bias''', '''classifier.1.bias''')]
A__ = manually_copy_vissl_head(lowercase_ , our_model.state_dict() , lowercase_ )
our_model.load_state_dict(lowercase_ )
A__ = our_model(lowercase_ , output_hidden_states=lowercase_ )
A__ = (
our_outputs.logits if isinstance(lowercase_ , lowercase_ ) else our_outputs.last_hidden_state
)
A__ = from_model(lowercase_ )
A__ = from_output[-1] if type(lowercase_ ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
A__ = our_outputs.hidden_states[-1]
assert torch.allclose(lowercase_ , lowercase_ ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='''Add model''' , use_temp_dir=lowercase_ , )
A__ = 224 if '''seer''' not in name else 384
# we can use the convnext one
A__ = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' , size=lowercase_ )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='''Add image processor''' , use_temp_dir=lowercase_ , )
print(f"""Pushed {name}""" )
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ = None , lowercase_ = True ) -> Any:
"""simple docstring"""
A__ = '''imagenet-1k-id2label.json'''
A__ = 1_000
A__ = (1, num_labels)
A__ = '''huggingface/label-files'''
A__ = num_labels
A__ = json.load(open(cached_download(hf_hub_url(lowercase_ , lowercase_ , repo_type='''dataset''' ) ) , '''r''' ) )
A__ = {int(lowercase_ ): v for k, v in idalabel.items()}
A__ = idalabel
A__ = {v: k for k, v in idalabel.items()}
A__ = partial(lowercase_ , num_labels=lowercase_ , idalabel=lowercase_ , labelaid=lowercase_ )
A__ = {
'''regnet-x-002''': ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 152, 368] , groups_width=8 , layer_type='''x''' ),
'''regnet-x-004''': ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12] , hidden_sizes=[32, 64, 160, 384] , groups_width=16 , layer_type='''x''' ),
'''regnet-x-006''': ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7] , hidden_sizes=[48, 96, 240, 528] , groups_width=24 , layer_type='''x''' ),
'''regnet-x-008''': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5] , hidden_sizes=[64, 128, 288, 672] , groups_width=16 , layer_type='''x''' ),
'''regnet-x-016''': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2] , hidden_sizes=[72, 168, 408, 912] , groups_width=24 , layer_type='''x''' ),
'''regnet-x-032''': ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2] , hidden_sizes=[96, 192, 432, 1_008] , groups_width=48 , layer_type='''x''' ),
'''regnet-x-040''': ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2] , hidden_sizes=[80, 240, 560, 1_360] , groups_width=40 , layer_type='''x''' ),
'''regnet-x-064''': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[168, 392, 784, 1_624] , groups_width=56 , layer_type='''x''' ),
'''regnet-x-080''': ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1] , hidden_sizes=[80, 240, 720, 1_920] , groups_width=120 , layer_type='''x''' ),
'''regnet-x-120''': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[224, 448, 896, 2_240] , groups_width=112 , layer_type='''x''' ),
'''regnet-x-160''': ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1] , hidden_sizes=[256, 512, 896, 2_048] , groups_width=128 , layer_type='''x''' ),
'''regnet-x-320''': ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1] , hidden_sizes=[336, 672, 1_344, 2_520] , groups_width=168 , layer_type='''x''' ),
# y variant
'''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 152, 368] , groups_width=8 ),
'''regnet-y-004''': ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6] , hidden_sizes=[48, 104, 208, 440] , groups_width=8 ),
'''regnet-y-006''': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4] , hidden_sizes=[48, 112, 256, 608] , groups_width=16 ),
'''regnet-y-008''': ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2] , hidden_sizes=[64, 128, 320, 768] , groups_width=16 ),
'''regnet-y-016''': ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2] , hidden_sizes=[48, 120, 336, 888] , groups_width=24 ),
'''regnet-y-032''': ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1] , hidden_sizes=[72, 216, 576, 1_512] , groups_width=24 ),
'''regnet-y-040''': ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2] , hidden_sizes=[128, 192, 512, 1_088] , groups_width=64 ),
'''regnet-y-064''': ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2] , hidden_sizes=[144, 288, 576, 1_296] , groups_width=72 ),
'''regnet-y-080''': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[168, 448, 896, 2_016] , groups_width=56 ),
'''regnet-y-120''': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[224, 448, 896, 2_240] , groups_width=112 ),
'''regnet-y-160''': ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1] , hidden_sizes=[224, 448, 1_232, 3_024] , groups_width=112 ),
'''regnet-y-320''': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[232, 696, 1_392, 3_712] , groups_width=232 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
'''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[232, 696, 1_392, 3_712] , groups_width=232 ),
'''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[328, 984, 1_968, 4_920] , groups_width=328 ),
'''regnet-y-1280-seer''': RegNetConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[528, 1_056, 2_904, 7_392] , groups_width=264 ),
'''regnet-y-2560-seer''': RegNetConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[640, 1_696, 2_544, 5_088] , groups_width=640 ),
'''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[2_020, 4_040, 11_110, 28_280] , groups_width=1_010 ),
# finetuned on imagenet
'''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[232, 696, 1_392, 3_712] , groups_width=232 ),
'''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[328, 984, 1_968, 4_920] , groups_width=328 ),
'''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[528, 1_056, 2_904, 7_392] , groups_width=264 ),
'''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[640, 1_696, 2_544, 5_088] , groups_width=640 ),
'''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[2_020, 4_040, 11_110, 28_280] , groups_width=1_010 ),
}
A__ = NameToOurModelFuncMap()
A__ = NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(lowercase_ , lowercase_ ) -> Tuple[nn.Module, Dict]:
A__ = torch.hub.load_state_dict_from_url(lowercase_ , model_dir=str(lowercase_ ) , map_location='''cpu''' )
A__ = model_func()
# check if we have a head, if yes add it
A__ = files['''classy_state_dict''']['''base_model''']['''model''']
A__ = model_state_dict['''trunk''']
model.load_state_dict(lowercase_ )
return model.eval(), model_state_dict["heads"]
# pretrained
A__ = partial(
lowercase_ , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
A__ = partial(
lowercase_ , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
A__ = partial(
lowercase_ , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
A__ = partial(
lowercase_ , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch''' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=1_010 , w_a=1_744 , w_a=6_20.83 , w_m=2.52 ) ) ) , )
# IN1K finetuned
A__ = partial(
lowercase_ , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
A__ = partial(
lowercase_ , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
A__ = partial(
lowercase_ , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
A__ = partial(
lowercase_ , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch''' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=1_010 , w_a=1_744 , w_a=6_20.83 , w_m=2.52 ) ) ) , )
if model_name:
convert_weight_and_push(
lowercase_ , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , names_to_config[model_name] , lowercase_ , lowercase_ , )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
lowercase_ , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , lowercase_ , lowercase_ , lowercase_ , )
return config, expected_shape
if __name__ == "__main__":
_lowerCamelCase : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default=None,
type=str,
help=(
"""The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
""" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=Path,
required=True,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
default=True,
type=bool,
required=False,
help="""If True, push model and image processor to the hub.""",
)
_lowerCamelCase : List[str] = parser.parse_args()
_lowerCamelCase : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 231 |
_lowerCamelCase : dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.609_344,
"knot": 1.852,
}
_lowerCamelCase : dict[str, float] = {
"km/h": 1.0,
"m/s": 0.277_777_778,
"mph": 0.621_371_192,
"knot": 0.539_956_803,
}
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> float:
"""simple docstring"""
if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
A__ = (
f"""Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"""
f"""Valid values are: {", ".join(lowercase_ )}"""
)
raise ValueError(lowercase_ )
return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to] , 3 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 231 | 1 |
from collections.abc import Iterable
from typing import Generic, TypeVar
_snake_case : Optional[Any] = TypeVar('_T')
class _UpperCAmelCase ( Generic[_T] ):
"""simple docstring"""
def __init__( self : int , lowerCAmelCase_ : Iterable[_T] | None = None ) -> None:
__lowerCAmelCase = list(iterable or [] )
__lowerCAmelCase = []
def __len__( self : Optional[int] ) -> int:
return len(self._stacka ) + len(self._stacka )
def __repr__( self : List[Any] ) -> str:
return f"""Queue({tuple(self._stacka[::-1] + self._stacka )})"""
def lowercase ( self : Tuple , lowerCAmelCase_ : _T ) -> None:
self._stacka.append(lowerCAmelCase_ )
def lowercase ( self : Union[str, Any] ) -> _T:
__lowerCAmelCase = self._stacka.pop
__lowerCAmelCase = self._stacka.append
if not self._stacka:
while self._stacka:
stacka_append(stacka_pop() )
if not self._stacka:
raise IndexError('Queue is empty' )
return self._stacka.pop()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 284 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case : Dict = {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case : Optional[int] = [
'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
'MraForMaskedLM',
'MraForMultipleChoice',
'MraForQuestionAnswering',
'MraForSequenceClassification',
'MraForTokenClassification',
'MraLayer',
'MraModel',
'MraPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
_snake_case : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 284 | 1 |
'''simple docstring'''
def lowerCAmelCase_ ( ):
'''simple docstring'''
A : int = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
A : int = 6
A : List[str] = 1
A : int = 1901
A : List[Any] = 0
while year < 2001:
day += 7
if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
if day > days_per_month[month - 1] and month != 2:
month += 1
A : List[str] = day - days_per_month[month - 2]
elif day > 29 and month == 2:
month += 1
A : Union[str, Any] = day - 29
else:
if day > days_per_month[month - 1]:
month += 1
A : Dict = day - days_per_month[month - 2]
if month > 12:
year += 1
A : Tuple = 1
if year < 2001 and day == 1:
sundays += 1
return sundays
if __name__ == "__main__":
print(solution())
| 356 |
'''simple docstring'''
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class A ( __snake_case ):
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
super().__init__()
self.register_modules(unet=SCREAMING_SNAKE_CASE , scheduler=SCREAMING_SNAKE_CASE )
@torch.no_grad()
def __call__( self , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = 50 , SCREAMING_SNAKE_CASE = "pil" , SCREAMING_SNAKE_CASE = True , **SCREAMING_SNAKE_CASE , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
A : List[Any] = torch.randn(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=SCREAMING_SNAKE_CASE , )
A : Optional[Any] = image.to(self.device )
# set step values
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
A : Tuple = self.unet(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
A : List[Any] = self.scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).prev_sample
A : List[Any] = (image / 2 + 0.5).clamp(0 , 1 )
A : Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
A : List[Any] = self.numpy_to_pil(SCREAMING_SNAKE_CASE )
if not return_dict:
return (image,), "This is a local test"
return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE ), "This is a local test"
| 311 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
),
# See all Vivit models at https://huggingface.co/models?filter=vivit
}
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
_lowerCamelCase: Dict = '''vivit'''
def __init__( self : str ,A_ : List[str]=224 ,A_ : Union[str, Any]=32 ,A_ : List[str]=[2, 16, 16] ,A_ : Any=3 ,A_ : int=768 ,A_ : Optional[int]=12 ,A_ : int=12 ,A_ : Any=3072 ,A_ : Union[str, Any]="gelu_fast" ,A_ : Any=0.0 ,A_ : Dict=0.0 ,A_ : Optional[int]=0.02 ,A_ : Union[str, Any]=1e-06 ,A_ : Union[str, Any]=True ,**A_ : List[Any] ,) -> Dict:
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 = initializer_range
A = layer_norm_eps
A = image_size
A = num_frames
A = tubelet_size
A = num_channels
A = qkv_bias
super().__init__(**A_ ) | 74 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''XGLMTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''XGLMTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XGLMForCausalLM''',
'''XGLMModel''',
'''XGLMPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''FlaxXGLMForCausalLM''',
'''FlaxXGLMModel''',
'''FlaxXGLMPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXGLMForCausalLM''',
'''TFXGLMModel''',
'''TFXGLMPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm import XGLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm_fast import XGLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
TFXGLMPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure) | 8 | 0 |
def A (__A : float , __A : float , __A : float , __A : float , __A : float , ) -> float:
"""simple docstring"""
UpperCAmelCase_ = [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:
UpperCAmelCase_ = 1 - (matter_density + radiation_density + dark_energy)
UpperCAmelCase_ = (
radiation_density * (redshift + 1) ** 4
+ matter_density * (redshift + 1) ** 3
+ curvature * (redshift + 1) ** 2
+ dark_energy
)
UpperCAmelCase_ = hubble_constant * e_a ** (1 / 2)
return hubble
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo LCDM approximation
snake_case_ : Any = 0.3
print(
hubble_parameter(
hubble_constant=68.3,
radiation_density=1e-4,
matter_density=matter_density,
dark_energy=1 - matter_density,
redshift=0,
)
)
| 356 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from transformers.utils import is_sentencepiece_available
if is_sentencepiece_available():
from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
if is_sentencepiece_available():
snake_case_ : Any = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
snake_case_ : Optional[Any] = 128022
snake_case_ : Optional[int] = 128028
@require_sentencepiece
class __snake_case ( a , unittest.TestCase ):
UpperCAmelCase__ : List[str] = MaMaaaTokenizer
UpperCAmelCase__ : int = False
UpperCAmelCase__ : Dict = False
UpperCAmelCase__ : List[str] = True
def lowerCamelCase ( self : str):
"""simple docstring"""
super().setUp()
UpperCAmelCase_ = ['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
UpperCAmelCase_ = dict(zip(_snake_case , range(len(_snake_case))))
UpperCAmelCase_ = Path(self.tmpdirname)
save_json(_snake_case , save_dir / VOCAB_FILES_NAMES['''vocab_file'''])
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(_snake_case , save_dir / VOCAB_FILES_NAMES['''spm_file'''])
UpperCAmelCase_ = MaMaaaTokenizer.from_pretrained(self.tmpdirname)
tokenizer.save_pretrained(self.tmpdirname)
def lowerCamelCase ( self : str , **_snake_case : Union[str, Any]):
"""simple docstring"""
return MaMaaaTokenizer.from_pretrained(self.tmpdirname , **_snake_case)
def lowerCamelCase ( self : Optional[int] , _snake_case : List[str]):
"""simple docstring"""
return (
"This is a test",
"This is a test",
)
def lowerCamelCase ( self : Optional[int]):
"""simple docstring"""
UpperCAmelCase_ = '''</s>'''
UpperCAmelCase_ = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_snake_case) , _snake_case)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_snake_case) , _snake_case)
def lowerCamelCase ( self : Dict):
"""simple docstring"""
UpperCAmelCase_ = self.get_tokenizer()
UpperCAmelCase_ = list(tokenizer.get_vocab().keys())
self.assertEqual(vocab_keys[0] , '''</s>''')
self.assertEqual(vocab_keys[1] , '''<unk>''')
self.assertEqual(vocab_keys[-1] , '''<s>''')
self.assertEqual(len(_snake_case) , tokenizer.vocab_size + len(tokenizer.get_added_vocab()))
@unittest.skip('''Skip this test while all models are still to be uploaded.''')
def lowerCamelCase ( self : Optional[int]):
"""simple docstring"""
pass
def lowerCamelCase ( self : Union[str, Any]):
"""simple docstring"""
UpperCAmelCase_ = self.get_tokenizer()
UpperCAmelCase_ = tokenizer.tokenize('''This is a test''')
self.assertListEqual(_snake_case , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_snake_case) , [2, 3, 4, 5, 6] , )
UpperCAmelCase_ = tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6])
self.assertListEqual(_snake_case , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''])
UpperCAmelCase_ = tokenizer.convert_tokens_to_string(_snake_case)
self.assertEqual(_snake_case , '''This is a test''')
@slow
def lowerCamelCase ( self : Tuple):
"""simple docstring"""
UpperCAmelCase_ = {'''input_ids''': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_snake_case , model_name='''facebook/m2m100_418M''' , revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e''' , )
@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case ( unittest.TestCase ):
UpperCAmelCase__ : Dict = '''facebook/m2m100_418M'''
UpperCAmelCase__ : Dict = [
'''In my opinion, there are two levels of response from the French government.''',
'''NSA Affair Emphasizes Complete Lack of Debate on Intelligence''',
]
UpperCAmelCase__ : Dict = [
'''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
'''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
]
# fmt: off
UpperCAmelCase__ : Any = [EN_CODE, 5_9_3, 1_9_4_9, 1_1_5_7_8_1, 4, 7_1_5_8_6, 4_2_3_4, 6_0_6_3_3, 1_2_6_2_3_3, 4_3_2, 1_2_3_8_0_8, 1_5_5_9_2, 1_1_9_7, 1_1_7_1_3_2, 1_2_0_6_1_8, 5, 2]
@classmethod
def lowerCamelCase ( cls : Optional[Any]):
"""simple docstring"""
UpperCAmelCase_ = MaMaaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en''' , tgt_lang='''fr''')
UpperCAmelCase_ = 1
return cls
def lowerCamelCase ( self : List[Any]):
"""simple docstring"""
self.assertEqual(self.tokenizer.get_lang_id('''ar''') , 128006)
self.assertEqual(self.tokenizer.get_lang_id('''en''') , 128022)
self.assertEqual(self.tokenizer.get_lang_id('''ro''') , 128076)
self.assertEqual(self.tokenizer.get_lang_id('''mr''') , 128063)
def lowerCamelCase ( self : Union[str, Any]):
"""simple docstring"""
UpperCAmelCase_ = self.tokenizer.get_vocab()
self.assertEqual(len(_snake_case) , self.tokenizer.vocab_size)
self.assertEqual(vocab['''<unk>'''] , 3)
self.assertIn(self.tokenizer.get_lang_token('''en''') , _snake_case)
def lowerCamelCase ( self : str):
"""simple docstring"""
UpperCAmelCase_ = '''en'''
UpperCAmelCase_ = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
self.assertListEqual(self.expected_src_tokens , _snake_case)
def lowerCamelCase ( self : Any):
"""simple docstring"""
self.assertIn(_snake_case , self.tokenizer.all_special_ids)
# fmt: off
UpperCAmelCase_ = [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
# fmt: on
UpperCAmelCase_ = self.tokenizer.decode(_snake_case , skip_special_tokens=_snake_case)
UpperCAmelCase_ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=_snake_case)
self.assertEqual(_snake_case , _snake_case)
self.assertNotIn(self.tokenizer.eos_token , _snake_case)
def lowerCamelCase ( self : Dict):
"""simple docstring"""
UpperCAmelCase_ = tempfile.mkdtemp()
UpperCAmelCase_ = self.tokenizer.lang_token_to_id
self.tokenizer.save_pretrained(_snake_case)
UpperCAmelCase_ = MaMaaaTokenizer.from_pretrained(_snake_case)
self.assertDictEqual(new_tok.lang_token_to_id , _snake_case)
@require_torch
def lowerCamelCase ( self : Tuple):
"""simple docstring"""
UpperCAmelCase_ = '''en'''
UpperCAmelCase_ = '''fr'''
UpperCAmelCase_ = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=_snake_case , return_tensors='''pt''')
UpperCAmelCase_ = shift_tokens_right(
batch['''labels'''] , self.tokenizer.pad_token_id , self.tokenizer.eos_token_id)
for k in batch:
UpperCAmelCase_ = batch[k].tolist()
# batch = {k: v.tolist() for k,v in batch.items()}
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
# batch.decoder_inputs_ids[0][0] ==
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == FR_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
@require_torch
def lowerCamelCase ( self : str):
"""simple docstring"""
UpperCAmelCase_ = '''mr'''
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''mr''')])
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id])
UpperCAmelCase_ = '''zh'''
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''zh''')])
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id])
@require_torch
def lowerCamelCase ( self : Dict):
"""simple docstring"""
UpperCAmelCase_ = '''mr'''
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''mr''')])
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id])
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang)])
UpperCAmelCase_ = '''zh'''
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''zh''')])
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id])
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang)])
@require_torch
def lowerCamelCase ( self : Tuple):
"""simple docstring"""
UpperCAmelCase_ = self.tokenizer._build_translation_inputs('''A test''' , return_tensors='''pt''' , src_lang='''en''' , tgt_lang='''ar''')
self.assertEqual(
nested_simplify(_snake_case) , {
# en_XX, A, test, EOS
'''input_ids''': [[128022, 58, 4183, 2]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 128006,
} , )
| 7 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_:str = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_:Union[str, Any] = [
'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
'LiltForQuestionAnswering',
'LiltForSequenceClassification',
'LiltForTokenClassification',
'LiltModel',
'LiltPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lilt import (
LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
LiltPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_:Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 116 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a__ : int = {
'configuration_layoutlmv3': [
'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
'LayoutLMv3Config',
'LayoutLMv3OnnxConfig',
],
'processing_layoutlmv3': ['LayoutLMv3Processor'],
'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Dict = ['LayoutLMv3TokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Any = [
'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
'LayoutLMv3ForQuestionAnswering',
'LayoutLMv3ForSequenceClassification',
'LayoutLMv3ForTokenClassification',
'LayoutLMv3Model',
'LayoutLMv3PreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : str = [
'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFLayoutLMv3ForQuestionAnswering',
'TFLayoutLMv3ForSequenceClassification',
'TFLayoutLMv3ForTokenClassification',
'TFLayoutLMv3Model',
'TFLayoutLMv3PreTrainedModel',
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : List[Any] = ['LayoutLMv3FeatureExtractor']
a__ : str = ['LayoutLMv3ImageProcessor']
if TYPE_CHECKING:
from .configuration_layoutlmva import (
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
LayoutLMvaConfig,
LayoutLMvaOnnxConfig,
)
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_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlmva import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
TFLayoutLMvaPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
else:
import sys
a__ : List[str] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 349 | 0 |
'''simple docstring'''
def _snake_case ( _SCREAMING_SNAKE_CASE : list ) -> list:
"""simple docstring"""
if len(_SCREAMING_SNAKE_CASE ) < 2:
return collection
def circle_sort_util(_SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> bool:
lowerCAmelCase = False
if low == high:
return swapped
lowerCAmelCase = low
lowerCAmelCase = high
while left < right:
if collection[left] > collection[right]:
lowerCAmelCase, lowerCAmelCase = (
collection[right],
collection[left],
)
lowerCAmelCase = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
lowerCAmelCase, lowerCAmelCase = (
collection[right + 1],
collection[left],
)
lowerCAmelCase = True
lowerCAmelCase = low + int((high - low) / 2 )
lowerCAmelCase = circle_sort_util(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase = circle_sort_util(_SCREAMING_SNAKE_CASE , mid + 1 , _SCREAMING_SNAKE_CASE )
return swapped or left_swap or right_swap
lowerCAmelCase = True
while is_not_sorted is True:
lowerCAmelCase = circle_sort_util(_SCREAMING_SNAKE_CASE , 0 , len(_SCREAMING_SNAKE_CASE ) - 1 )
return collection
if __name__ == "__main__":
UpperCAmelCase = input('Enter numbers separated by a comma:\n').strip()
UpperCAmelCase = [int(item) for item in user_input.split(',')]
print(circle_sort(unsorted)) | 187 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class __snake_case( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self , A_ , A_ , A_ , A_ , A_=1 , A_=False , **A_ ) -> Optional[int]:
super().__init__(**A_ )
lowerCAmelCase = vocab_size
lowerCAmelCase = d_embed
lowerCAmelCase = d_proj
lowerCAmelCase = cutoffs + [vocab_size]
lowerCAmelCase = [0] + self.cutoffs
lowerCAmelCase = div_val
lowerCAmelCase = self.cutoffs[0]
lowerCAmelCase = len(self.cutoffs ) - 1
lowerCAmelCase = self.shortlist_size + self.n_clusters
lowerCAmelCase = keep_order
lowerCAmelCase = []
lowerCAmelCase = []
def __snake_case ( self , A_ ) -> int:
if self.n_clusters > 0:
lowerCAmelCase = self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer="""zeros""" , trainable=A_ , name="""cluster_weight""" )
lowerCAmelCase = self.add_weight(
shape=(self.n_clusters,) , initializer="""zeros""" , trainable=A_ , name="""cluster_bias""" )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
lowerCAmelCase = self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer="""zeros""" , trainable=A_ , name=f'out_projs_._{i}' , )
self.out_projs.append(A_ )
else:
self.out_projs.append(A_ )
lowerCAmelCase = self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer="""zeros""" , trainable=A_ , name=f'out_layers_._{i}_._weight' , )
lowerCAmelCase = self.add_weight(
shape=(self.vocab_size,) , initializer="""zeros""" , trainable=A_ , name=f'out_layers_._{i}_._bias' , )
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
lowerCAmelCase, lowerCAmelCase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowerCAmelCase = self.d_embed // (self.div_val**i)
lowerCAmelCase = self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer="""zeros""" , trainable=A_ , name=f'out_projs_._{i}' )
self.out_projs.append(A_ )
lowerCAmelCase = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer="""zeros""" , trainable=A_ , name=f'out_layers_._{i}_._weight' , )
lowerCAmelCase = self.add_weight(
shape=(r_idx - l_idx,) , initializer="""zeros""" , trainable=A_ , name=f'out_layers_._{i}_._bias' , )
self.out_layers.append((weight, bias) )
super().build(A_ )
@staticmethod
def __snake_case ( A_ , A_ , A_ , A_=None ) -> List[Any]:
lowerCAmelCase = x
if proj is not None:
lowerCAmelCase = tf.einsum("""ibd,ed->ibe""" , A_ , A_ )
return tf.einsum("""ibd,nd->ibn""" , A_ , A_ ) + b
@staticmethod
def __snake_case ( A_ , A_ ) -> Dict:
lowerCAmelCase = shape_list(A_ )
lowerCAmelCase = tf.range(lp_size[0] , dtype=target.dtype )
lowerCAmelCase = tf.stack([r, target] , 1 )
return tf.gather_nd(A_ , A_ )
def __snake_case ( self , A_ , A_ , A_=True , A_=False ) -> Tuple:
lowerCAmelCase = 0
if self.n_clusters == 0:
lowerCAmelCase = self._logit(A_ , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0] )
if target is not None:
lowerCAmelCase = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=A_ , logits=A_ )
lowerCAmelCase = tf.nn.log_softmax(A_ , axis=-1 )
else:
lowerCAmelCase = shape_list(A_ )
lowerCAmelCase = []
lowerCAmelCase = tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
lowerCAmelCase, lowerCAmelCase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
lowerCAmelCase = (target >= l_idx) & (target < r_idx)
lowerCAmelCase = tf.where(A_ )
lowerCAmelCase = tf.boolean_mask(A_ , A_ ) - l_idx
if self.div_val == 1:
lowerCAmelCase = self.out_layers[0][0][l_idx:r_idx]
lowerCAmelCase = self.out_layers[0][1][l_idx:r_idx]
else:
lowerCAmelCase = self.out_layers[i][0]
lowerCAmelCase = self.out_layers[i][1]
if i == 0:
lowerCAmelCase = tf.concat([cur_W, self.cluster_weight] , 0 )
lowerCAmelCase = tf.concat([cur_b, self.cluster_bias] , 0 )
lowerCAmelCase = self._logit(A_ , A_ , A_ , self.out_projs[0] )
lowerCAmelCase = tf.nn.log_softmax(A_ )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
lowerCAmelCase = tf.boolean_mask(A_ , A_ )
lowerCAmelCase = self._gather_logprob(A_ , A_ )
else:
lowerCAmelCase = self._logit(A_ , A_ , A_ , self.out_projs[i] )
lowerCAmelCase = tf.nn.log_softmax(A_ )
lowerCAmelCase = self.cutoffs[0] + i - 1 # No probability for the head cluster
lowerCAmelCase = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(A_ )
if target is not None:
lowerCAmelCase = tf.boolean_mask(A_ , A_ )
lowerCAmelCase = tf.boolean_mask(A_ , A_ )
lowerCAmelCase = self._gather_logprob(A_ , A_ )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(A_ , -cur_logprob , shape_list(A_ ) )
lowerCAmelCase = tf.concat(A_ , axis=-1 )
if target is not None:
if return_mean:
lowerCAmelCase = tf.reduce_mean(A_ )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(A_ )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(A_ , name=self.name , aggregation="""mean""" if return_mean else """""" )
return out | 187 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: Dict = logging.get_logger(__name__)
_lowercase: int = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = "lxmert"
__A = {}
def __init__(self , lowerCamelCase_=30522 , lowerCamelCase_=768 , lowerCamelCase_=12 , lowerCamelCase_=9500 , lowerCamelCase_=1600 , lowerCamelCase_=400 , lowerCamelCase_=3072 , lowerCamelCase_="gelu" , lowerCamelCase_=0.1 , lowerCamelCase_=0.1 , lowerCamelCase_=512 , lowerCamelCase_=2 , lowerCamelCase_=0.02 , lowerCamelCase_=1E-1_2 , lowerCamelCase_=9 , lowerCamelCase_=5 , lowerCamelCase_=5 , lowerCamelCase_=2048 , lowerCamelCase_=4 , lowerCamelCase_=6.67 , lowerCamelCase_=True , lowerCamelCase_=True , lowerCamelCase_=True , lowerCamelCase_=True , lowerCamelCase_=True , lowerCamelCase_=True , lowerCamelCase_=True , **lowerCamelCase_ , ):
"""simple docstring"""
a = vocab_size
a = hidden_size
a = num_attention_heads
a = hidden_act
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = initializer_range
a = layer_norm_eps
a = num_qa_labels
a = num_object_labels
a = num_attr_labels
a = l_layers
a = x_layers
a = r_layers
a = visual_feat_dim
a = visual_pos_dim
a = visual_loss_normalizer
a = task_matched
a = task_mask_lm
a = task_obj_predict
a = task_qa
a = visual_obj_loss
a = visual_attr_loss
a = visual_feat_loss
a = {"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
super().__init__(**lowerCamelCase_ )
| 227 |
_lowercase: Dict = [
(1000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def a( A : str ) -> int:
"""simple docstring"""
a = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
a = 0
a = 0
while place < len(A ):
if (place + 1 < len(A )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def a( A : int ) -> str:
"""simple docstring"""
a = []
for arabic, roman in ROMAN:
((a) , (a)) = divmod(A , A )
result.append(roman * factor )
if number == 0:
break
return "".join(A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 227 | 1 |
"""simple docstring"""
from collections import defaultdict
from math import gcd
def lowercase ( _snake_case : Union[str, Any] = 1_500_000 ) ->Optional[Any]:
"""simple docstring"""
__snake_case : defaultdict = defaultdict(_snake_case )
__snake_case : str = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , _snake_case , 2 ):
if gcd(_snake_case , _snake_case ) > 1:
continue
__snake_case : Optional[Any] = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(_snake_case , limit + 1 , _snake_case ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(F'{solution() = }')
| 370 |
"""simple docstring"""
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 lowercase ( ) ->Optional[int]:
"""simple docstring"""
__snake_case : int = torch.nn.Linear(2 , 4 )
__snake_case : Optional[Any] = torch.optim.AdamW(model.parameters() , lr=1.0 )
__snake_case : Optional[Any] = torch.optim.lr_scheduler.OneCycleLR(_snake_case , max_lr=0.01 , steps_per_epoch=2 , epochs=1 )
__snake_case : List[str] = DataLoader(TensorDataset(torch.tensor([1, 2, 3] ) ) )
__snake_case : Dict = DataLoader(TensorDataset(torch.tensor([4, 5, 6] ) ) )
return model, optimizer, scheduler, train_dl, valid_dl
def lowercase ( _snake_case : str ) ->Optional[Any]:
"""simple docstring"""
return (model.weight.abs().sum() + model.bias.abs().sum()).item()
def lowercase ( _snake_case : Union[str, Any] ) ->Tuple:
"""simple docstring"""
__snake_case : Dict = torch.nn.Linear(*tuple(model.weight.T.shape ) ).state_dict()
model.load_state_dict(_snake_case )
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
@require_cuda
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Optional[int] = Accelerator()
assert PartialState._shared_state["_cpu"] is False
assert PartialState._shared_state["device"].type == "cuda"
with self.assertRaises(a_ ):
__snake_case : Any = Accelerator(cpu=a_ )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Optional[int] = Accelerator()
__snake_case : Optional[int] = GradientState()
assert state.num_steps == 1
__snake_case : str = 4
assert state.num_steps == 4
assert state.sync_gradients is True
__snake_case : List[Any] = False
assert state.sync_gradients is False
GradientState._reset_state()
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Optional[Any] = Accelerator()
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case : Optional[Any] = create_components()
(
(
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) ,
) : Union[str, Any] = accelerator.prepare(a_ , a_ , a_ , a_ , a_ )
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 SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Tuple = Accelerator()
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case : Union[str, Any] = create_components()
accelerator.prepare(a_ , a_ , a_ , a_ , a_ )
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 SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
PartialState._reset_state()
# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
def noop(*a_ , **a_ ):
pass
with patch('''torch.cuda.set_device''' , a_ ), patch_environment(ACCELERATE_TORCH_DEVICE='''cuda:64''' ):
__snake_case : List[Any] = Accelerator()
self.assertEqual(str(accelerator.state.device ) , '''cuda:64''' )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Optional[int] = Accelerator()
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case : str = create_components()
accelerator.prepare(a_ , a_ , a_ , a_ , a_ )
__snake_case : Any = get_signature(a_ )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(a_ )
# make sure random weights don't match
load_random_weights(a_ )
self.assertTrue(abs(model_signature - get_signature(a_ ) ) > 1E-3 )
# make sure loaded weights match
accelerator.load_state(a_ )
self.assertTrue(abs(model_signature - get_signature(a_ ) ) < 1E-3 )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : int = Accelerator()
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case : str = create_components()
accelerator.prepare(a_ , a_ , a_ , a_ , a_ )
__snake_case : List[Any] = get_signature(a_ )
# saving hook
def save_config(a_ , a_ , a_ ):
__snake_case : Optional[Any] = {'''class_name''': models[0].__class__.__name__}
with open(os.path.join(a_ , '''data.json''' ) , '''w''' ) as f:
json.dump(a_ , a_ )
# loading hook
def load_config(a_ , a_ ):
with open(os.path.join(a_ , '''data.json''' ) , '''r''' ) as f:
__snake_case : Any = json.load(a_ )
__snake_case : List[str] = config['''class_name''']
__snake_case : str = accelerator.register_save_state_pre_hook(a_ )
__snake_case : Union[str, Any] = accelerator.register_load_state_pre_hook(a_ )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(a_ )
# make sure random weights don't match with hooks
load_random_weights(a_ )
self.assertTrue(abs(model_signature - get_signature(a_ ) ) > 1E-3 )
# random class name to verify correct one is loaded
__snake_case : Any = '''random'''
# make sure loaded weights match with hooks
accelerator.load_state(a_ )
self.assertTrue(abs(model_signature - get_signature(a_ ) ) < 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(a_ )
# make sure random weights don't match with hooks removed
load_random_weights(a_ )
self.assertTrue(abs(model_signature - get_signature(a_ ) ) > 1E-3 )
# random class name to verify correct one is loaded
__snake_case : Union[str, Any] = '''random'''
# make sure loaded weights match with hooks removed
accelerator.load_state(a_ )
self.assertTrue(abs(model_signature - get_signature(a_ ) ) < 1E-3 )
# mode.class_name is NOT loaded from config
self.assertTrue(model.class_name != model.__class__.__name__ )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : List[Any] = Accelerator()
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case : Tuple = create_components()
__snake_case : Union[str, Any] = None
# This should work
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case : Tuple = accelerator.prepare(
a_ , a_ , a_ , a_ , a_ , a_ )
self.assertTrue(dummy_obj is None )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : str = Accelerator()
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case : Optional[Any] = create_components()
__snake_case : Optional[int] = [1, 2, 3]
# This should work
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case : str = accelerator.prepare(
a_ , a_ , a_ , a_ , a_ , a_ )
self.assertEqual(
getattr(a_ , '''_is_accelerate_prepared''' , a_ ) , a_ , '''Dummy object should have `_is_accelerate_prepared` set to `True`''' , )
self.assertEqual(
getattr(a_ , '''_is_accelerate_prepared''' , a_ ) , a_ , '''Model is missing `_is_accelerator_prepared` or is set to `False`''' , )
self.assertEqual(
getattr(a_ , '''_is_accelerate_prepared''' , a_ ) , a_ , '''Optimizer is missing `_is_accelerator_prepared` or is set to `False`''' , )
self.assertEqual(
getattr(a_ , '''_is_accelerate_prepared''' , a_ ) , a_ , '''Scheduler is missing `_is_accelerator_prepared` or is set to `False`''' , )
self.assertEqual(
getattr(a_ , '''_is_accelerate_prepared''' , a_ ) , a_ , '''Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`''' , )
self.assertEqual(
getattr(a_ , '''_is_accelerate_prepared''' , a_ ) , a_ , '''Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`''' , )
@slow
@require_bnb
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
from transformers import AutoModelForCausalLM
__snake_case : Dict = AutoModelForCausalLM.from_pretrained(
'''EleutherAI/gpt-neo-125m''' , load_in_abit=a_ , device_map={'''''': 0} , )
__snake_case : Optional[Any] = Accelerator()
# This should work
__snake_case : Any = accelerator.prepare(a_ )
@slow
@require_bnb
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
from transformers import AutoModelForCausalLM
__snake_case : Any = Accelerator()
with init_empty_weights():
__snake_case : List[str] = AutoModelForCausalLM.from_pretrained(
'''EleutherAI/gpt-neo-125m''' , )
model.tie_weights()
__snake_case : Union[str, Any] = infer_auto_device_map(a_ )
__snake_case : str = '''cpu'''
__snake_case : Optional[int] = AutoModelForCausalLM.from_pretrained(
'''EleutherAI/gpt-neo-125m''' , device_map=a_ , load_in_abit=a_ , llm_inta_enable_fpaa_cpu_offload=a_ )
# This should not work and get value error
with self.assertRaises(a_ ):
__snake_case : Dict = accelerator.prepare(a_ )
@slow
@require_bnb
@require_multi_gpu
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
from transformers import AutoModelForCausalLM
__snake_case : str = {'''distributed_type''': DistributedType.MULTI_GPU}
with init_empty_weights():
__snake_case : Any = AutoModelForCausalLM.from_pretrained(
'''EleutherAI/gpt-neo-125m''' , )
model.tie_weights()
__snake_case : List[Any] = infer_auto_device_map(a_ )
__snake_case : Dict = 1
__snake_case : str = AutoModelForCausalLM.from_pretrained(
'''EleutherAI/gpt-neo-125m''' , load_in_abit=a_ , device_map=a_ , )
__snake_case : Any = Accelerator()
# This should not work and get value error
with self.assertRaises(a_ ):
__snake_case : Tuple = accelerator.prepare(a_ )
PartialState._reset_state()
@slow
@require_bnb
@require_multi_gpu
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
from transformers import AutoModelForCausalLM
with init_empty_weights():
__snake_case : Dict = AutoModelForCausalLM.from_pretrained(
'''EleutherAI/gpt-neo-125m''' , )
__snake_case : Tuple = infer_auto_device_map(a_ )
__snake_case : Tuple = 1
__snake_case : List[Any] = AutoModelForCausalLM.from_pretrained(
'''EleutherAI/gpt-neo-125m''' , load_in_abit=a_ , device_map=a_ , )
__snake_case : Tuple = Accelerator()
# This should work
__snake_case : Dict = accelerator.prepare(a_ )
@require_cuda
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : List[Any] = torch.nn.Linear(10 , 10 )
__snake_case : List[str] = torch.optim.SGD(model.parameters() , lr=0.01 )
__snake_case : Optional[Any] = Accelerator(cpu=a_ )
__snake_case : str = accelerator.prepare(a_ )
| 24 | 0 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def __lowercase ( a__ , a__ , a__ , a__ , a__ ) -> np.ndarray:
__SCREAMING_SNAKE_CASE = cva.getAffineTransform(a__ , a__ )
return cva.warpAffine(a__ , a__ , (rows, cols) )
if __name__ == "__main__":
# read original image
lowerCAmelCase__ : Optional[int] =cva.imread(
str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
)
# turn image in gray scale value
lowerCAmelCase__ : Any =cva.cvtColor(image, cva.COLOR_BGR2GRAY)
# get image shape
lowerCAmelCase__ , lowerCAmelCase__ : Any =gray_img.shape
# set different points to rotate image
lowerCAmelCase__ : Tuple =np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
lowerCAmelCase__ : List[Any] =np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
lowerCAmelCase__ : List[str] =np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
lowerCAmelCase__ : List[Any] =np.array([[10, 100], [80, 50], [180, 250]], np.floataa)
# add all rotated images in a list
lowerCAmelCase__ : List[str] =[
gray_img,
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
]
# plot different image rotations
lowerCAmelCase__ : Any =plt.figure(1)
lowerCAmelCase__ : str =['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
for i, image in enumerate(images):
plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
plt.title(titles[i])
plt.axis('''off''')
plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
plt.show()
| 257 |
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 UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _A ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = FlaxStableDiffusionPipeline.from_pretrained(
'stabilityai/stable-diffusion-2' , revision='bf16' , dtype=jnp.bfloataa , )
__SCREAMING_SNAKE_CASE = 'A painting of a squirrel eating a burger'
__SCREAMING_SNAKE_CASE = jax.device_count()
__SCREAMING_SNAKE_CASE = num_samples * [prompt]
__SCREAMING_SNAKE_CASE = sd_pipe.prepare_inputs(_A )
__SCREAMING_SNAKE_CASE = replicate(_A )
__SCREAMING_SNAKE_CASE = shard(_A )
__SCREAMING_SNAKE_CASE = jax.random.PRNGKey(0 )
__SCREAMING_SNAKE_CASE = jax.random.split(_A , jax.device_count() )
__SCREAMING_SNAKE_CASE = sd_pipe(_A , _A , _A , num_inference_steps=25 , jit=_A )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
__SCREAMING_SNAKE_CASE = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
__SCREAMING_SNAKE_CASE = images[0, 253:256, 253:256, -1]
__SCREAMING_SNAKE_CASE = jnp.asarray(jax.device_get(image_slice.flatten() ) )
__SCREAMING_SNAKE_CASE = jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 'stabilityai/stable-diffusion-2'
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = FlaxDPMSolverMultistepScheduler.from_pretrained(_A , subfolder='scheduler' )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = FlaxStableDiffusionPipeline.from_pretrained(
_A , scheduler=_A , revision='bf16' , dtype=jnp.bfloataa , )
__SCREAMING_SNAKE_CASE = scheduler_params
__SCREAMING_SNAKE_CASE = 'A painting of a squirrel eating a burger'
__SCREAMING_SNAKE_CASE = jax.device_count()
__SCREAMING_SNAKE_CASE = num_samples * [prompt]
__SCREAMING_SNAKE_CASE = sd_pipe.prepare_inputs(_A )
__SCREAMING_SNAKE_CASE = replicate(_A )
__SCREAMING_SNAKE_CASE = shard(_A )
__SCREAMING_SNAKE_CASE = jax.random.PRNGKey(0 )
__SCREAMING_SNAKE_CASE = jax.random.split(_A , jax.device_count() )
__SCREAMING_SNAKE_CASE = sd_pipe(_A , _A , _A , num_inference_steps=25 , jit=_A )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
__SCREAMING_SNAKE_CASE = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
__SCREAMING_SNAKE_CASE = images[0, 253:256, 253:256, -1]
__SCREAMING_SNAKE_CASE = jnp.asarray(jax.device_get(image_slice.flatten() ) )
__SCREAMING_SNAKE_CASE = jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 257 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : List[str] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = [
'''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''SEWForCTC''',
'''SEWForSequenceClassification''',
'''SEWModel''',
'''SEWPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_sew import (
SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
SEWForCTC,
SEWForSequenceClassification,
SEWModel,
SEWPreTrainedModel,
)
else:
import sys
A : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 359 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : Dict = {
'''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
# See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCamelCase : Tuple = '''pegasus'''
__lowerCamelCase : Any = ['''past_key_values''']
__lowerCamelCase : List[str] = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self : List[Any] , __lowerCAmelCase : str=5_02_65 , __lowerCAmelCase : Dict=10_24 , __lowerCAmelCase : Union[str, Any]=12 , __lowerCAmelCase : Any=40_96 , __lowerCAmelCase : Optional[Any]=16 , __lowerCAmelCase : Dict=12 , __lowerCAmelCase : Optional[int]=40_96 , __lowerCAmelCase : Dict=16 , __lowerCAmelCase : Optional[int]=0.0 , __lowerCAmelCase : Optional[Any]=0.0 , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : List[str]=True , __lowerCAmelCase : str="gelu" , __lowerCAmelCase : Union[str, Any]=10_24 , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : Dict=0.0 , __lowerCAmelCase : Union[str, Any]=0.0 , __lowerCAmelCase : Union[str, Any]=0.0_2 , __lowerCAmelCase : List[Any]=0 , __lowerCAmelCase : str=False , __lowerCAmelCase : int=0 , __lowerCAmelCase : Optional[Any]=1 , __lowerCAmelCase : Optional[int]=1 , **__lowerCAmelCase : Dict , ) -> Dict:
"""simple docstring"""
A__ = vocab_size
A__ = max_position_embeddings
A__ = d_model
A__ = encoder_ffn_dim
A__ = encoder_layers
A__ = encoder_attention_heads
A__ = decoder_ffn_dim
A__ = decoder_layers
A__ = decoder_attention_heads
A__ = dropout
A__ = attention_dropout
A__ = activation_dropout
A__ = activation_function
A__ = init_std
A__ = encoder_layerdrop
A__ = decoder_layerdrop
A__ = use_cache
A__ = encoder_layers
A__ = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , is_encoder_decoder=__lowerCAmelCase , decoder_start_token_id=__lowerCAmelCase , forced_eos_token_id=__lowerCAmelCase , **__lowerCAmelCase , )
@property
def a_ ( self : Dict ) -> int:
"""simple docstring"""
return self.encoder_attention_heads
@property
def a_ ( self : Optional[Any] ) -> int:
"""simple docstring"""
return self.d_model
| 276 | 0 |
'''simple docstring'''
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
UpperCamelCase__ = get_tests_dir('''fixtures/dummy-config.json''')
class lowerCamelCase_ ( unittest.TestCase ):
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = 0
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
self.assertIsNotNone(transformers.models.auto.__spec__ )
self.assertIsNotNone(importlib.util.find_spec('''transformers.models.auto''' ) )
def lowercase_ ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : int = AutoConfig.from_pretrained('''bert-base-uncased''' )
self.assertIsInstance(_A , _A )
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = AutoConfig.from_pretrained(_A )
self.assertIsInstance(_A , _A )
def lowercase_ ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = AutoConfig.from_pretrained(_A )
self.assertIsInstance(_A , _A )
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = AutoConfig.for_model('''roberta''' )
self.assertIsInstance(_A , _A )
def lowercase_ ( self : str ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
UpperCAmelCase__ : str = os.path.join(_A , '''fake-roberta''' )
os.makedirs(_A , exist_ok=_A )
with open(os.path.join(_A , '''config.json''' ) , '''w''' ) as f:
f.write(json.dumps({} ) )
UpperCAmelCase__ : str = AutoConfig.from_pretrained(_A )
self.assertEqual(type(_A ) , _A )
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
try:
AutoConfig.register('''custom''' , _A )
# Wrong model type will raise an error
with self.assertRaises(_A ):
AutoConfig.register('''model''' , _A )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_A ):
AutoConfig.register('''bert''' , _A )
# Now that the config is registered, it can be used as any other config with the auto-API
UpperCAmelCase__ : int = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_A )
UpperCAmelCase__ : List[str] = AutoConfig.from_pretrained(_A )
self.assertIsInstance(_A , _A )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
with self.assertRaisesRegex(
_A , '''bert-base is not a local folder and is not a valid model identifier''' ):
UpperCAmelCase__ : Tuple = AutoConfig.from_pretrained('''bert-base''' )
def lowercase_ ( self : str ):
'''simple docstring'''
with self.assertRaisesRegex(
_A , R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
UpperCAmelCase__ : int = AutoConfig.from_pretrained(_A , revision='''aaaaaa''' )
def lowercase_ ( self : Tuple ):
'''simple docstring'''
with self.assertRaisesRegex(
_A , '''hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.''' , ):
UpperCAmelCase__ : List[str] = AutoConfig.from_pretrained('''hf-internal-testing/no-config-test-repo''' )
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
with self.assertRaises(_A ):
UpperCAmelCase__ : Union[str, Any] = AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_A ):
UpperCAmelCase__ : List[Any] = AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''' , trust_remote_code=_A )
UpperCAmelCase__ : int = AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''' , trust_remote_code=_A )
self.assertEqual(config.__class__.__name__ , '''NewModelConfig''' )
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_A )
UpperCAmelCase__ : str = AutoConfig.from_pretrained(_A , trust_remote_code=_A )
self.assertEqual(reloaded_config.__class__.__name__ , '''NewModelConfig''' )
def lowercase_ ( self : List[str] ):
'''simple docstring'''
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = 'new-model'
try:
AutoConfig.register('''new-model''' , _A )
# If remote code is not set, the default is to use local
UpperCAmelCase__ : Dict = AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''' )
self.assertEqual(config.__class__.__name__ , '''NewModelConfigLocal''' )
# If remote code is disabled, we load the local one.
UpperCAmelCase__ : List[str] = AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''' , trust_remote_code=_A )
self.assertEqual(config.__class__.__name__ , '''NewModelConfigLocal''' )
# If remote is enabled, we load from the Hub
UpperCAmelCase__ : str = AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''' , trust_remote_code=_A )
self.assertEqual(config.__class__.__name__ , '''NewModelConfig''' )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| 181 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = '''▁'''
UpperCamelCase__ = {'''vocab_file''': '''sentencepiece.bpe.model'''}
UpperCamelCase__ = {
'''vocab_file''': {
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
}
}
UpperCamelCase__ = {
'''facebook/xglm-564M''': 2_0_4_8,
}
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[Any] , _A : Optional[Any]="<s>" , _A : List[str]="</s>" , _A : Optional[Any]="</s>" , _A : List[str]="<s>" , _A : Tuple="<unk>" , _A : List[str]="<pad>" , _A : Optional[Dict[str, Any]] = None , **_A : Union[str, Any] , ):
'''simple docstring'''
UpperCAmelCase__ : str = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
UpperCAmelCase__ : Optional[int] = 7
UpperCAmelCase__ : Optional[int] = [f"""<madeupword{i}>""" for i in range(self.num_madeup_words )]
UpperCAmelCase__ : Tuple = kwargs.get('''additional_special_tokens''' , [] )
kwargs["additional_special_tokens"] += [
word for word in madeup_words if word not in kwargs["additional_special_tokens"]
]
super().__init__(
bos_token=_A , eos_token=_A , unk_token=_A , sep_token=_A , cls_token=_A , pad_token=_A , sp_model_kwargs=self.sp_model_kwargs , **_A , )
UpperCAmelCase__ : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_A ) )
UpperCAmelCase__ : List[str] = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
UpperCAmelCase__ : Any = 1
# Mimic fairseq token-to-id alignment for the first 4 token
UpperCAmelCase__ : Any = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
UpperCAmelCase__ : int = len(self.sp_model )
UpperCAmelCase__ : Optional[int] = {f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
self.fairseq_tokens_to_ids.update(_A )
UpperCAmelCase__ : Tuple = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : Dict = self.__dict__.copy()
UpperCAmelCase__ : Optional[int] = None
UpperCAmelCase__ : Optional[Any] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Union[str, Any] , _A : int ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
UpperCAmelCase__ : Dict = {}
UpperCAmelCase__ : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def lowercase_ ( self : Any , _A : List[int] , _A : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
UpperCAmelCase__ : Dict = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def lowercase_ ( self : str , _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 ))
return [1] + ([0] * len(_A )) + [1, 1] + ([0] * len(_A ))
def lowercase_ ( self : Any , _A : List[int] , _A : Optional[List[int]] = None ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = [self.sep_token_id]
if token_ids_a is None:
return len(sep + token_ids_a ) * [0]
return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
@property
def lowercase_ ( self : Any ):
'''simple docstring'''
return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = {self.convert_ids_to_tokens(_A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowercase_ ( self : Optional[Any] , _A : str ):
'''simple docstring'''
return self.sp_model.encode(_A , out_type=_A )
def lowercase_ ( self : List[str] , _A : List[Any] ):
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
UpperCAmelCase__ : Union[str, Any] = self.sp_model.PieceToId(_A )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def lowercase_ ( self : List[Any] , _A : str ):
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def lowercase_ ( self : int , _A : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = ''''''.join(_A ).replace(_A , ''' ''' ).strip()
return out_string
def lowercase_ ( self : Any , _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__ : 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 ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _A )
elif not os.path.isfile(self.vocab_file ):
with open(_A , '''wb''' ) as fi:
UpperCAmelCase__ : List[Any] = self.sp_model.serialized_model_proto()
fi.write(_A )
return (out_vocab_file,)
| 181 | 1 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
@staticmethod
def __A ( *__A: Any , **__A: List[str] ) -> Optional[int]:
pass
@is_pipeline_test
@require_vision
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
A_ = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def __A ( self: int , __A: str , __A: List[Any] , __A: Union[str, Any] ) -> Dict:
_A = pipeline(
'''zero-shot-object-detection''' , model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
_A = [
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
]
return object_detector, examples
def __A ( self: Optional[Any] , __A: str , __A: Union[str, Any] ) -> Any:
_A = object_detector(examples[0] , threshold=0.0 )
_A = len(__A )
self.assertGreater(__A , 0 )
self.assertEqual(
__A , [
{
'''score''': ANY(__A ),
'''label''': ANY(__A ),
'''box''': {'''xmin''': ANY(__A ), '''ymin''': ANY(__A ), '''xmax''': ANY(__A ), '''ymax''': ANY(__A )},
}
for i in range(__A )
] , )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def __A ( self: Union[str, Any] ) -> Tuple:
pass
@require_torch
def __A ( self: Optional[int] ) -> Union[str, Any]:
_A = pipeline(
'''zero-shot-object-detection''' , model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
_A = object_detector(
'''./tests/fixtures/tests_samples/COCO/000000039769.png''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , threshold=0.64 , )
self.assertEqual(
nested_simplify(__A , decimals=4 ) , [
{'''score''': 0.7_235, '''label''': '''cat''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
{'''score''': 0.7_218, '''label''': '''remote''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
{'''score''': 0.7_184, '''label''': '''couch''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
{'''score''': 0.6_748, '''label''': '''remote''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
{'''score''': 0.6_656, '''label''': '''cat''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
{'''score''': 0.6_614, '''label''': '''couch''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
{'''score''': 0.6_456, '''label''': '''remote''', '''box''': {'''xmin''': 4_94, '''ymin''': 1_05, '''xmax''': 5_21, '''ymax''': 1_27}},
{'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 2_74, '''xmax''': 93, '''ymax''': 2_97}},
{'''score''': 0.6_419, '''label''': '''cat''', '''box''': {'''xmin''': 4_94, '''ymin''': 1_05, '''xmax''': 5_21, '''ymax''': 1_27}},
] , )
_A = object_detector(
[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
] , threshold=0.64 , )
self.assertEqual(
nested_simplify(__A , decimals=4 ) , [
[
{'''score''': 0.7_235, '''label''': '''cat''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
{'''score''': 0.7_218, '''label''': '''remote''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
{'''score''': 0.7_184, '''label''': '''couch''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
{'''score''': 0.6_748, '''label''': '''remote''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
{'''score''': 0.6_656, '''label''': '''cat''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
{'''score''': 0.6_614, '''label''': '''couch''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
{'''score''': 0.6_456, '''label''': '''remote''', '''box''': {'''xmin''': 4_94, '''ymin''': 1_05, '''xmax''': 5_21, '''ymax''': 1_27}},
{'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 2_74, '''xmax''': 93, '''ymax''': 2_97}},
{'''score''': 0.6_419, '''label''': '''cat''', '''box''': {'''xmin''': 4_94, '''ymin''': 1_05, '''xmax''': 5_21, '''ymax''': 1_27}},
]
] , )
@require_torch
@slow
def __A ( self: List[str] ) -> int:
_A = pipeline('''zero-shot-object-detection''' )
_A = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , )
self.assertEqual(
nested_simplify(__A , decimals=4 ) , [
{'''score''': 0.2_868, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
{'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
{'''score''': 0.2_537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 3_15, '''ymax''': 4_72}},
{'''score''': 0.1_474, '''label''': '''remote''', '''box''': {'''xmin''': 3_35, '''ymin''': 74, '''xmax''': 3_71, '''ymax''': 1_87}},
{'''score''': 0.1_208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_42, '''ymax''': 4_76}},
] , )
_A = object_detector(
[
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
] , )
self.assertEqual(
nested_simplify(__A , decimals=4 ) , [
[
{'''score''': 0.2_868, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
{'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
{'''score''': 0.2_537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 3_15, '''ymax''': 4_72}},
{'''score''': 0.1_474, '''label''': '''remote''', '''box''': {'''xmin''': 3_35, '''ymin''': 74, '''xmax''': 3_71, '''ymax''': 1_87}},
{'''score''': 0.1_208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_42, '''ymax''': 4_76}},
],
[
{'''score''': 0.2_868, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
{'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
{'''score''': 0.2_537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 3_15, '''ymax''': 4_72}},
{'''score''': 0.1_474, '''label''': '''remote''', '''box''': {'''xmin''': 3_35, '''ymin''': 74, '''xmax''': 3_71, '''ymax''': 1_87}},
{'''score''': 0.1_208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_42, '''ymax''': 4_76}},
],
] , )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def __A ( self: Optional[Any] ) -> int:
pass
@require_torch
@slow
def __A ( self: Dict ) -> List[Any]:
_A = 0.2
_A = pipeline('''zero-shot-object-detection''' )
_A = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , threshold=__A , )
self.assertEqual(
nested_simplify(__A , decimals=4 ) , [
{'''score''': 0.2_868, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
{'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
{'''score''': 0.2_537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 3_15, '''ymax''': 4_72}},
] , )
@require_torch
@slow
def __A ( self: int ) -> Tuple:
_A = 2
_A = pipeline('''zero-shot-object-detection''' )
_A = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , top_k=__A , )
self.assertEqual(
nested_simplify(__A , decimals=4 ) , [
{'''score''': 0.2_868, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
{'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
] , )
| 359 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import sys
import transformers
__A = '3'
print('Python version:', sys.version)
print('transformers version:', transformers.__version__)
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
print('NCCL version:', torch.cuda.nccl.version())
except ImportError:
print('Torch version:', None)
try:
import deepspeed
print('DeepSpeed version:', deepspeed.__version__)
except ImportError:
print('DeepSpeed version:', None)
try:
import tensorflow as tf
print('TensorFlow version:', tf.__version__)
print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))
print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))
except ImportError:
print('TensorFlow version:', None)
| 75 | 0 |
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
# Check if the input is valid
if not len(lowerCamelCase ) == len(lowerCamelCase ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError("Both a & b of two equations can't be zero." )
# Extract the coefficients
lowercase , lowercase , lowercase :str = equationa
lowercase , lowercase , lowercase :int = equationa
# Calculate the determinants of the matrices
lowercase :int = aa * ba - aa * ba
lowercase :int = ca * ba - ca * ba
lowercase :Union[str, Any] = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("Infinite solutions. (Consistent system)" )
else:
raise ValueError("No solution. (Inconsistent system)" )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
lowercase :Optional[int] = determinant_x / determinant
lowercase :int = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 236 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase = "cpu", lowerCamelCase = None ):
lowercase :Optional[Any] = torch.load(lowerCamelCase, map_location=lowerCamelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(lowerCamelCase, torch.Tensor ):
raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin" )
lowercase :List[Any] = v.half()
if save_path is None: # overwrite src_path
lowercase :Optional[Any] = src_path
torch.save(lowerCamelCase, lowerCamelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 236 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "git_vision_model"
def __init__( self: Dict , __A: List[str]=7_68 , __A: Tuple=30_72 , __A: Union[str, Any]=12 , __A: str=12 , __A: Tuple=3 , __A: List[str]=2_24 , __A: str=16 , __A: str="quick_gelu" , __A: Dict=1e-5 , __A: Any=0.0 , __A: Tuple=0.02 , **__A: Tuple , ) -> Optional[int]:
super().__init__(**__A )
_A = hidden_size
_A = intermediate_size
_A = num_hidden_layers
_A = num_attention_heads
_A = num_channels
_A = patch_size
_A = image_size
_A = initializer_range
_A = attention_dropout
_A = layer_norm_eps
_A = hidden_act
@classmethod
def __A ( cls: Any , __A: Union[str, os.PathLike] , **__A: List[str] ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__A )
_A ,_A = cls.get_config_dict(__A , **__A )
# get the vision config dict if we are loading from GITConfig
if config_dict.get('''model_type''' ) == "git":
_A = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__A , **__A )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = "git"
def __init__( self: int , __A: List[str]=None , __A: str=3_05_22 , __A: Any=7_68 , __A: Tuple=6 , __A: Union[str, Any]=12 , __A: Dict=30_72 , __A: int="gelu" , __A: int=0.1 , __A: List[str]=0.1 , __A: Union[str, Any]=10_24 , __A: List[Any]=0.02 , __A: str=1e-12 , __A: List[str]=0 , __A: Union[str, Any]="absolute" , __A: Tuple=True , __A: Union[str, Any]=False , __A: Tuple=1_01 , __A: Any=1_02 , __A: Optional[int]=None , **__A: str , ) -> List[Any]:
super().__init__(bos_token_id=__A , eos_token_id=__A , pad_token_id=__A , **__A )
if vision_config is None:
_A = {}
logger.info('''vision_config is None. initializing the GitVisionConfig with default values.''' )
_A = GitVisionConfig(**__A )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = initializer_range
_A = layer_norm_eps
_A = position_embedding_type
_A = use_cache
_A = tie_word_embeddings
_A = num_image_with_embedding
_A = bos_token_id
_A = eos_token_id
def __A ( self: List[Any] ) -> Any:
_A = copy.deepcopy(self.__dict__ )
_A = self.vision_config.to_dict()
_A = self.__class__.model_type
return output
| 75 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__A = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
__A = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(f'A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def __A ( _lowercase ):
'''simple docstring'''
if "://" in dataset_path:
_A = dataset_path.split('''://''' )[1]
return dataset_path
def __A ( _lowercase ):
'''simple docstring'''
if fs is not None and fs.protocol != "file":
return True
else:
return False
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = not is_remote_filesystem(_lowercase )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(_lowercase ) , fs._strip_protocol(_lowercase ) )
else:
fs.mv(_lowercase , _lowercase , recursive=_lowercase )
def __A ( ):
'''simple docstring'''
if hasattr(fsspec.asyn , '''reset_lock''' ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
_A = None
_A = None
_A = threading.Lock()
| 75 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ = {
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InformerConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
"INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"InformerForPrediction",
"InformerModel",
"InformerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 100 |
"""simple docstring"""
lowerCAmelCase__ = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = set()
# keep track of all the paths to be checked
UpperCamelCase = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
UpperCamelCase = queue.pop(0 )
# get the last node from the path
UpperCamelCase = path[-1]
if node not in explored:
UpperCamelCase = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
UpperCamelCase = list(_SCREAMING_SNAKE_CASE )
new_path.append(_SCREAMING_SNAKE_CASE )
queue.append(_SCREAMING_SNAKE_CASE )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(_SCREAMING_SNAKE_CASE )
# in case there's no path between the 2 nodes
return []
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
UpperCamelCase = [start]
UpperCamelCase = set(_SCREAMING_SNAKE_CASE )
# Keep tab on distances from `start` node.
UpperCamelCase = {start: 0, target: -1}
while queue:
UpperCamelCase = queue.pop(0 )
if node == target:
UpperCamelCase = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(_SCREAMING_SNAKE_CASE )
queue.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, '''G''', '''D''')) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, '''G''', '''D''')) # returns 4
| 153 | 0 |
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,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__UpperCAmelCase = logging.get_logger(__name__)
def __UpperCamelCase ( lowercase__ : int ) -> List[List[ImageInput]]:
'''simple docstring'''
if isinstance(lowercase__ , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowercase__ , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowercase__ ):
return [[videos]]
raise ValueError(f'Could not make batched video from {videos}' )
class __a ( __UpperCamelCase ):
__snake_case : Union[str, Any] = ["""pixel_values"""]
def __init__( self : List[Any] , UpperCAmelCase : bool = True , UpperCAmelCase : Dict[str, int] = None , UpperCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , UpperCAmelCase : bool = True , UpperCAmelCase : Dict[str, int] = None , UpperCAmelCase : bool = True , UpperCAmelCase : Union[int, float] = 1 / 2_55 , UpperCAmelCase : bool = True , UpperCAmelCase : Optional[Union[float, List[float]]] = None , UpperCAmelCase : Optional[Union[float, List[float]]] = None , **UpperCAmelCase : Tuple , ):
super().__init__(**UpperCAmelCase )
lowerCAmelCase_ : Any = size if size is not None else {"""shortest_edge""": 2_24}
lowerCAmelCase_ : Any = get_size_dict(UpperCAmelCase , default_to_square=UpperCAmelCase )
lowerCAmelCase_ : Any = crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
lowerCAmelCase_ : Optional[int] = get_size_dict(UpperCAmelCase , param_name="""crop_size""" )
lowerCAmelCase_ : Any = do_resize
lowerCAmelCase_ : Optional[int] = size
lowerCAmelCase_ : int = do_center_crop
lowerCAmelCase_ : int = crop_size
lowerCAmelCase_ : Optional[Any] = resample
lowerCAmelCase_ : List[Any] = do_rescale
lowerCAmelCase_ : int = rescale_factor
lowerCAmelCase_ : Any = do_normalize
lowerCAmelCase_ : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCAmelCase_ : Dict = image_std if image_std is not None else IMAGENET_STANDARD_STD
def A ( self : List[str] , UpperCAmelCase : np.ndarray , UpperCAmelCase : Dict[str, int] , UpperCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase : int , ):
lowerCAmelCase_ : str = get_size_dict(UpperCAmelCase , default_to_square=UpperCAmelCase )
if "shortest_edge" in size:
lowerCAmelCase_ : Tuple = get_resize_output_image_size(UpperCAmelCase , size["""shortest_edge"""] , default_to_square=UpperCAmelCase )
elif "height" in size and "width" in size:
lowerCAmelCase_ : Tuple = (size["""height"""], size["""width"""])
else:
raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}' )
return resize(UpperCAmelCase , size=UpperCAmelCase , resample=UpperCAmelCase , data_format=UpperCAmelCase , **UpperCAmelCase )
def A ( self : List[Any] , UpperCAmelCase : np.ndarray , UpperCAmelCase : Dict[str, int] , UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase : str , ):
lowerCAmelCase_ : List[str] = get_size_dict(UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}' )
return center_crop(UpperCAmelCase , size=(size["""height"""], size["""width"""]) , data_format=UpperCAmelCase , **UpperCAmelCase )
def A ( self : Dict , UpperCAmelCase : np.ndarray , UpperCAmelCase : Union[int, float] , UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase : List[str] , ):
return rescale(UpperCAmelCase , scale=UpperCAmelCase , data_format=UpperCAmelCase , **UpperCAmelCase )
def A ( self : Union[str, Any] , UpperCAmelCase : np.ndarray , UpperCAmelCase : Union[float, List[float]] , UpperCAmelCase : Union[float, List[float]] , UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase : int , ):
return normalize(UpperCAmelCase , mean=UpperCAmelCase , std=UpperCAmelCase , data_format=UpperCAmelCase , **UpperCAmelCase )
def A ( self : List[str] , UpperCAmelCase : ImageInput , UpperCAmelCase : bool = None , UpperCAmelCase : Dict[str, int] = None , UpperCAmelCase : PILImageResampling = None , UpperCAmelCase : bool = None , UpperCAmelCase : Dict[str, int] = None , UpperCAmelCase : bool = None , UpperCAmelCase : float = None , UpperCAmelCase : bool = None , UpperCAmelCase : Optional[Union[float, List[float]]] = None , UpperCAmelCase : Optional[Union[float, List[float]]] = None , UpperCAmelCase : Optional[ChannelDimension] = ChannelDimension.FIRST , ):
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
lowerCAmelCase_ : List[str] = to_numpy_array(UpperCAmelCase )
if do_resize:
lowerCAmelCase_ : str = self.resize(image=UpperCAmelCase , size=UpperCAmelCase , resample=UpperCAmelCase )
if do_center_crop:
lowerCAmelCase_ : str = self.center_crop(UpperCAmelCase , size=UpperCAmelCase )
if do_rescale:
lowerCAmelCase_ : List[Any] = self.rescale(image=UpperCAmelCase , scale=UpperCAmelCase )
if do_normalize:
lowerCAmelCase_ : str = self.normalize(image=UpperCAmelCase , mean=UpperCAmelCase , std=UpperCAmelCase )
lowerCAmelCase_ : Dict = to_channel_dimension_format(UpperCAmelCase , UpperCAmelCase )
return image
def A ( self : List[Any] , UpperCAmelCase : ImageInput , UpperCAmelCase : bool = None , UpperCAmelCase : Dict[str, int] = None , UpperCAmelCase : PILImageResampling = None , UpperCAmelCase : bool = None , UpperCAmelCase : Dict[str, int] = None , UpperCAmelCase : bool = None , UpperCAmelCase : float = None , UpperCAmelCase : bool = None , UpperCAmelCase : Optional[Union[float, List[float]]] = None , UpperCAmelCase : Optional[Union[float, List[float]]] = None , UpperCAmelCase : Optional[Union[str, TensorType]] = None , UpperCAmelCase : ChannelDimension = ChannelDimension.FIRST , **UpperCAmelCase : Dict , ):
lowerCAmelCase_ : Optional[Any] = do_resize if do_resize is not None else self.do_resize
lowerCAmelCase_ : str = resample if resample is not None else self.resample
lowerCAmelCase_ : int = do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCAmelCase_ : int = do_rescale if do_rescale is not None else self.do_rescale
lowerCAmelCase_ : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCAmelCase_ : Any = do_normalize if do_normalize is not None else self.do_normalize
lowerCAmelCase_ : List[str] = image_mean if image_mean is not None else self.image_mean
lowerCAmelCase_ : List[str] = image_std if image_std is not None else self.image_std
lowerCAmelCase_ : Union[str, Any] = size if size is not None else self.size
lowerCAmelCase_ : Union[str, Any] = get_size_dict(UpperCAmelCase , default_to_square=UpperCAmelCase )
lowerCAmelCase_ : List[Any] = crop_size if crop_size is not None else self.crop_size
lowerCAmelCase_ : Any = get_size_dict(UpperCAmelCase , param_name="""crop_size""" )
if not valid_images(UpperCAmelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
lowerCAmelCase_ : str = make_batched(UpperCAmelCase )
lowerCAmelCase_ : List[Any] = [
[
self._preprocess_image(
image=UpperCAmelCase , do_resize=UpperCAmelCase , size=UpperCAmelCase , resample=UpperCAmelCase , do_center_crop=UpperCAmelCase , crop_size=UpperCAmelCase , do_rescale=UpperCAmelCase , rescale_factor=UpperCAmelCase , do_normalize=UpperCAmelCase , image_mean=UpperCAmelCase , image_std=UpperCAmelCase , data_format=UpperCAmelCase , )
for img in video
]
for video in videos
]
lowerCAmelCase_ : Optional[int] = {"""pixel_values""": videos}
return BatchFeature(data=UpperCAmelCase , tensor_type=UpperCAmelCase )
| 28 |
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 __a ( __UpperCamelCase ):
__snake_case : Any = ["""image_processor""", """tokenizer"""]
__snake_case : Tuple = """BlipImageProcessor"""
__snake_case : int = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : int , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] ):
lowerCAmelCase_ : str = False
super().__init__(UpperCAmelCase , UpperCAmelCase )
lowerCAmelCase_ : Tuple = self.image_processor
def __call__( self : Optional[int] , UpperCAmelCase : ImageInput = None , UpperCAmelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , UpperCAmelCase : bool = True , UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , UpperCAmelCase : Union[bool, str, TruncationStrategy] = None , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : int = 0 , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : Optional[bool] = None , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = True , UpperCAmelCase : Optional[Union[str, TensorType]] = None , **UpperCAmelCase : Tuple , ):
if images is None and text is None:
raise ValueError("""You have to specify either images or text.""" )
# Get only text
if images is None:
lowerCAmelCase_ : str = self.tokenizer
lowerCAmelCase_ : List[Any] = self.tokenizer(
text=UpperCAmelCase , add_special_tokens=UpperCAmelCase , padding=UpperCAmelCase , truncation=UpperCAmelCase , max_length=UpperCAmelCase , stride=UpperCAmelCase , pad_to_multiple_of=UpperCAmelCase , return_attention_mask=UpperCAmelCase , return_overflowing_tokens=UpperCAmelCase , return_special_tokens_mask=UpperCAmelCase , return_offsets_mapping=UpperCAmelCase , return_token_type_ids=UpperCAmelCase , return_length=UpperCAmelCase , verbose=UpperCAmelCase , return_tensors=UpperCAmelCase , **UpperCAmelCase , )
return text_encoding
# add pixel_values
lowerCAmelCase_ : Union[str, Any] = self.image_processor(UpperCAmelCase , return_tensors=UpperCAmelCase )
if text is not None:
lowerCAmelCase_ : Optional[Any] = self.tokenizer(
text=UpperCAmelCase , add_special_tokens=UpperCAmelCase , padding=UpperCAmelCase , truncation=UpperCAmelCase , max_length=UpperCAmelCase , stride=UpperCAmelCase , pad_to_multiple_of=UpperCAmelCase , return_attention_mask=UpperCAmelCase , return_overflowing_tokens=UpperCAmelCase , return_special_tokens_mask=UpperCAmelCase , return_offsets_mapping=UpperCAmelCase , return_token_type_ids=UpperCAmelCase , return_length=UpperCAmelCase , verbose=UpperCAmelCase , return_tensors=UpperCAmelCase , **UpperCAmelCase , )
else:
lowerCAmelCase_ : int = None
if text_encoding is not None:
encoding_image_processor.update(UpperCAmelCase )
return encoding_image_processor
def A ( self : Optional[Any] , *UpperCAmelCase : Optional[Any] , **UpperCAmelCase : int ):
return self.tokenizer.batch_decode(*UpperCAmelCase , **UpperCAmelCase )
def A ( self : List[Any] , *UpperCAmelCase : Optional[int] , **UpperCAmelCase : Optional[Any] ):
return self.tokenizer.decode(*UpperCAmelCase , **UpperCAmelCase )
@property
def A ( self : int ):
lowerCAmelCase_ : int = self.tokenizer.model_input_names
lowerCAmelCase_ : Optional[int] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 28 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
__UpperCamelCase = {
'''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase = [
'''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ErnieForCausalLM''',
'''ErnieForMaskedLM''',
'''ErnieForMultipleChoice''',
'''ErnieForNextSentencePrediction''',
'''ErnieForPreTraining''',
'''ErnieForQuestionAnswering''',
'''ErnieForSequenceClassification''',
'''ErnieForTokenClassification''',
'''ErnieModel''',
'''ErniePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
__UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 69 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
A__: List[Any] = logging.get_logger(__name__)
A__: Any = OrderedDict(
[
# Base model mapping
('''albert''', '''FlaxAlbertModel'''),
('''bart''', '''FlaxBartModel'''),
('''beit''', '''FlaxBeitModel'''),
('''bert''', '''FlaxBertModel'''),
('''big_bird''', '''FlaxBigBirdModel'''),
('''blenderbot''', '''FlaxBlenderbotModel'''),
('''blenderbot-small''', '''FlaxBlenderbotSmallModel'''),
('''clip''', '''FlaxCLIPModel'''),
('''distilbert''', '''FlaxDistilBertModel'''),
('''electra''', '''FlaxElectraModel'''),
('''gpt-sw3''', '''FlaxGPT2Model'''),
('''gpt2''', '''FlaxGPT2Model'''),
('''gpt_neo''', '''FlaxGPTNeoModel'''),
('''gptj''', '''FlaxGPTJModel'''),
('''longt5''', '''FlaxLongT5Model'''),
('''marian''', '''FlaxMarianModel'''),
('''mbart''', '''FlaxMBartModel'''),
('''mt5''', '''FlaxMT5Model'''),
('''opt''', '''FlaxOPTModel'''),
('''pegasus''', '''FlaxPegasusModel'''),
('''regnet''', '''FlaxRegNetModel'''),
('''resnet''', '''FlaxResNetModel'''),
('''roberta''', '''FlaxRobertaModel'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormModel'''),
('''roformer''', '''FlaxRoFormerModel'''),
('''t5''', '''FlaxT5Model'''),
('''vision-text-dual-encoder''', '''FlaxVisionTextDualEncoderModel'''),
('''vit''', '''FlaxViTModel'''),
('''wav2vec2''', '''FlaxWav2Vec2Model'''),
('''whisper''', '''FlaxWhisperModel'''),
('''xglm''', '''FlaxXGLMModel'''),
('''xlm-roberta''', '''FlaxXLMRobertaModel'''),
]
)
A__: Dict = OrderedDict(
[
# Model for pre-training mapping
('''albert''', '''FlaxAlbertForPreTraining'''),
('''bart''', '''FlaxBartForConditionalGeneration'''),
('''bert''', '''FlaxBertForPreTraining'''),
('''big_bird''', '''FlaxBigBirdForPreTraining'''),
('''electra''', '''FlaxElectraForPreTraining'''),
('''longt5''', '''FlaxLongT5ForConditionalGeneration'''),
('''mbart''', '''FlaxMBartForConditionalGeneration'''),
('''mt5''', '''FlaxMT5ForConditionalGeneration'''),
('''roberta''', '''FlaxRobertaForMaskedLM'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMaskedLM'''),
('''roformer''', '''FlaxRoFormerForMaskedLM'''),
('''t5''', '''FlaxT5ForConditionalGeneration'''),
('''wav2vec2''', '''FlaxWav2Vec2ForPreTraining'''),
('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
('''xlm-roberta''', '''FlaxXLMRobertaForMaskedLM'''),
]
)
A__: Optional[int] = OrderedDict(
[
# Model for Masked LM mapping
('''albert''', '''FlaxAlbertForMaskedLM'''),
('''bart''', '''FlaxBartForConditionalGeneration'''),
('''bert''', '''FlaxBertForMaskedLM'''),
('''big_bird''', '''FlaxBigBirdForMaskedLM'''),
('''distilbert''', '''FlaxDistilBertForMaskedLM'''),
('''electra''', '''FlaxElectraForMaskedLM'''),
('''mbart''', '''FlaxMBartForConditionalGeneration'''),
('''roberta''', '''FlaxRobertaForMaskedLM'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMaskedLM'''),
('''roformer''', '''FlaxRoFormerForMaskedLM'''),
('''xlm-roberta''', '''FlaxXLMRobertaForMaskedLM'''),
]
)
A__: Optional[Any] = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
('''bart''', '''FlaxBartForConditionalGeneration'''),
('''blenderbot''', '''FlaxBlenderbotForConditionalGeneration'''),
('''blenderbot-small''', '''FlaxBlenderbotSmallForConditionalGeneration'''),
('''encoder-decoder''', '''FlaxEncoderDecoderModel'''),
('''longt5''', '''FlaxLongT5ForConditionalGeneration'''),
('''marian''', '''FlaxMarianMTModel'''),
('''mbart''', '''FlaxMBartForConditionalGeneration'''),
('''mt5''', '''FlaxMT5ForConditionalGeneration'''),
('''pegasus''', '''FlaxPegasusForConditionalGeneration'''),
('''t5''', '''FlaxT5ForConditionalGeneration'''),
]
)
A__: Optional[Any] = OrderedDict(
[
# Model for Image-classsification
('''beit''', '''FlaxBeitForImageClassification'''),
('''regnet''', '''FlaxRegNetForImageClassification'''),
('''resnet''', '''FlaxResNetForImageClassification'''),
('''vit''', '''FlaxViTForImageClassification'''),
]
)
A__: List[Any] = OrderedDict(
[
('''vision-encoder-decoder''', '''FlaxVisionEncoderDecoderModel'''),
]
)
A__: int = OrderedDict(
[
# Model for Causal LM mapping
('''bart''', '''FlaxBartForCausalLM'''),
('''bert''', '''FlaxBertForCausalLM'''),
('''big_bird''', '''FlaxBigBirdForCausalLM'''),
('''electra''', '''FlaxElectraForCausalLM'''),
('''gpt-sw3''', '''FlaxGPT2LMHeadModel'''),
('''gpt2''', '''FlaxGPT2LMHeadModel'''),
('''gpt_neo''', '''FlaxGPTNeoForCausalLM'''),
('''gptj''', '''FlaxGPTJForCausalLM'''),
('''opt''', '''FlaxOPTForCausalLM'''),
('''roberta''', '''FlaxRobertaForCausalLM'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForCausalLM'''),
('''xglm''', '''FlaxXGLMForCausalLM'''),
('''xlm-roberta''', '''FlaxXLMRobertaForCausalLM'''),
]
)
A__: Optional[Any] = OrderedDict(
[
# Model for Sequence Classification mapping
('''albert''', '''FlaxAlbertForSequenceClassification'''),
('''bart''', '''FlaxBartForSequenceClassification'''),
('''bert''', '''FlaxBertForSequenceClassification'''),
('''big_bird''', '''FlaxBigBirdForSequenceClassification'''),
('''distilbert''', '''FlaxDistilBertForSequenceClassification'''),
('''electra''', '''FlaxElectraForSequenceClassification'''),
('''mbart''', '''FlaxMBartForSequenceClassification'''),
('''roberta''', '''FlaxRobertaForSequenceClassification'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForSequenceClassification'''),
('''roformer''', '''FlaxRoFormerForSequenceClassification'''),
('''xlm-roberta''', '''FlaxXLMRobertaForSequenceClassification'''),
]
)
A__: Optional[Any] = OrderedDict(
[
# Model for Question Answering mapping
('''albert''', '''FlaxAlbertForQuestionAnswering'''),
('''bart''', '''FlaxBartForQuestionAnswering'''),
('''bert''', '''FlaxBertForQuestionAnswering'''),
('''big_bird''', '''FlaxBigBirdForQuestionAnswering'''),
('''distilbert''', '''FlaxDistilBertForQuestionAnswering'''),
('''electra''', '''FlaxElectraForQuestionAnswering'''),
('''mbart''', '''FlaxMBartForQuestionAnswering'''),
('''roberta''', '''FlaxRobertaForQuestionAnswering'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForQuestionAnswering'''),
('''roformer''', '''FlaxRoFormerForQuestionAnswering'''),
('''xlm-roberta''', '''FlaxXLMRobertaForQuestionAnswering'''),
]
)
A__: List[Any] = OrderedDict(
[
# Model for Token Classification mapping
('''albert''', '''FlaxAlbertForTokenClassification'''),
('''bert''', '''FlaxBertForTokenClassification'''),
('''big_bird''', '''FlaxBigBirdForTokenClassification'''),
('''distilbert''', '''FlaxDistilBertForTokenClassification'''),
('''electra''', '''FlaxElectraForTokenClassification'''),
('''roberta''', '''FlaxRobertaForTokenClassification'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForTokenClassification'''),
('''roformer''', '''FlaxRoFormerForTokenClassification'''),
('''xlm-roberta''', '''FlaxXLMRobertaForTokenClassification'''),
]
)
A__: Optional[int] = OrderedDict(
[
# Model for Multiple Choice mapping
('''albert''', '''FlaxAlbertForMultipleChoice'''),
('''bert''', '''FlaxBertForMultipleChoice'''),
('''big_bird''', '''FlaxBigBirdForMultipleChoice'''),
('''distilbert''', '''FlaxDistilBertForMultipleChoice'''),
('''electra''', '''FlaxElectraForMultipleChoice'''),
('''roberta''', '''FlaxRobertaForMultipleChoice'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMultipleChoice'''),
('''roformer''', '''FlaxRoFormerForMultipleChoice'''),
('''xlm-roberta''', '''FlaxXLMRobertaForMultipleChoice'''),
]
)
A__: Optional[Any] = OrderedDict(
[
('''bert''', '''FlaxBertForNextSentencePrediction'''),
]
)
A__: Dict = OrderedDict(
[
('''speech-encoder-decoder''', '''FlaxSpeechEncoderDecoderModel'''),
('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
]
)
A__: Dict = OrderedDict(
[
('''whisper''', '''FlaxWhisperForAudioClassification'''),
]
)
A__: Optional[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
A__: List[str] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
A__: str = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
A__: int = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
A__: str = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
A__: List[str] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
A__: List[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
A__: int = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
A__: str = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
A__: Any = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
A__: Optional[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
A__: Optional[int] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
A__: str = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
A__: Union[str, Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class _a ( _BaseAutoModelClass):
"""simple docstring"""
UpperCamelCase__ = FLAX_MODEL_MAPPING
A__: int = auto_class_update(FlaxAutoModel)
class _a ( _BaseAutoModelClass):
"""simple docstring"""
UpperCamelCase__ = FLAX_MODEL_FOR_PRETRAINING_MAPPING
A__: Dict = auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')
class _a ( _BaseAutoModelClass):
"""simple docstring"""
UpperCamelCase__ = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
A__: Any = auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')
class _a ( _BaseAutoModelClass):
"""simple docstring"""
UpperCamelCase__ = FLAX_MODEL_FOR_MASKED_LM_MAPPING
A__: List[Any] = auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')
class _a ( _BaseAutoModelClass):
"""simple docstring"""
UpperCamelCase__ = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
A__: int = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc='''sequence-to-sequence language modeling''', checkpoint_for_example='''t5-base'''
)
class _a ( _BaseAutoModelClass):
"""simple docstring"""
UpperCamelCase__ = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
A__: Tuple = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)
class _a ( _BaseAutoModelClass):
"""simple docstring"""
UpperCamelCase__ = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
A__: Optional[Any] = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')
class _a ( _BaseAutoModelClass):
"""simple docstring"""
UpperCamelCase__ = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
A__: str = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)
class _a ( _BaseAutoModelClass):
"""simple docstring"""
UpperCamelCase__ = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
A__: List[Any] = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')
class _a ( _BaseAutoModelClass):
"""simple docstring"""
UpperCamelCase__ = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
A__: Any = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)
class _a ( _BaseAutoModelClass):
"""simple docstring"""
UpperCamelCase__ = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
A__: Dict = auto_class_update(
FlaxAutoModelForImageClassification, head_doc='''image classification'''
)
class _a ( _BaseAutoModelClass):
"""simple docstring"""
UpperCamelCase__ = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
A__: Dict = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')
class _a ( _BaseAutoModelClass):
"""simple docstring"""
UpperCamelCase__ = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
A__: List[Any] = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc='''sequence-to-sequence speech-to-text modeling'''
)
| 149 | 0 |
"""simple docstring"""
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
a__ : Any = '''\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
and Akul Arora
and Steven Basart
and Eric Tang
and Dawn Song
and Jacob Steinhardt},
journal={arXiv preprint arXiv:2103.03874},
year={2021}
}
'''
a__ : Dict = '''\
This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
It first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.
'''
a__ : Optional[int] = r'''
Calculates accuracy after canonicalizing inputs.
Args:
predictions: list of predictions to score. Each prediction
is a string that contains natural language and LaTex.
references: list of reference for each prediction. Each
reference is a string that contains natural language
and LaTex.
Returns:
accuracy: accuracy after canonicalizing inputs
(e.g., converting "1/2" to "\\frac{1}{2}")
Examples:
>>> metric = datasets.load_metric("competition_math")
>>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])
>>> print(results)
{\'accuracy\': 1.0}
'''
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class UpperCamelCase_ ( datasets.Metric):
"""simple docstring"""
def UpperCAmelCase_ ( self : Tuple ) -> Optional[int]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" ),
"references": datasets.Value("string" ),
} ) , homepage="https://github.com/hendrycks/math" , codebase_urls=["https://github.com/hendrycks/math"] , )
def UpperCAmelCase_ ( self : Dict , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[int] ) -> Any:
__SCREAMING_SNAKE_CASE = 0.0
for i, j in zip(UpperCAmelCase__ , UpperCAmelCase__ ):
n_correct += 1.0 if math_equivalence.is_equiv(UpperCAmelCase__ , UpperCAmelCase__ ) else 0.0
__SCREAMING_SNAKE_CASE = n_correct / len(UpperCAmelCase__ )
return {
"accuracy": accuracy,
}
| 195 |
"""simple docstring"""
a__ : Optional[Any] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def UpperCAmelCase__ ():
'''simple docstring'''
__SCREAMING_SNAKE_CASE = input("Enter message: " )
__SCREAMING_SNAKE_CASE = input("Enter key [alphanumeric]: " )
__SCREAMING_SNAKE_CASE = input("Encrypt/Decrypt [e/d]: " )
if mode.lower().startswith("e" ):
__SCREAMING_SNAKE_CASE = "encrypt"
__SCREAMING_SNAKE_CASE = encrypt_message(lowerCAmelCase_ , lowerCAmelCase_ )
elif mode.lower().startswith("d" ):
__SCREAMING_SNAKE_CASE = "decrypt"
__SCREAMING_SNAKE_CASE = decrypt_message(lowerCAmelCase_ , lowerCAmelCase_ )
print(f"""\n{mode.title()}ed message:""" )
print(lowerCAmelCase_ )
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
return translate_message(lowerCAmelCase_ , lowerCAmelCase_ , "encrypt" )
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
return translate_message(lowerCAmelCase_ , lowerCAmelCase_ , "decrypt" )
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = key.upper()
for symbol in message:
__SCREAMING_SNAKE_CASE = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(lowerCAmelCase_ )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(lowerCAmelCase_ ):
__SCREAMING_SNAKE_CASE = 0
else:
translated.append(lowerCAmelCase_ )
return "".join(lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 195 | 1 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version
__A = logging.getLogger(__name__)
require_version("pytorch_lightning>=1.0.4")
__A = {
"base": AutoModel,
"sequence-classification": AutoModelForSequenceClassification,
"question-answering": AutoModelForQuestionAnswering,
"pretraining": AutoModelForPreTraining,
"token-classification": AutoModelForTokenClassification,
"language-modeling": AutoModelWithLMHead,
"summarization": AutoModelForSeqaSeqLM,
"translation": AutoModelForSeqaSeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
__A = {
"linear": get_linear_schedule_with_warmup,
"cosine": get_cosine_schedule_with_warmup,
"cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
"polynomial": get_polynomial_decay_schedule_with_warmup,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
__A = sorted(arg_to_scheduler.keys())
__A = "{" + ", ".join(arg_to_scheduler_choices) + "}"
class A ( pl.LightningModule ):
def __init__( self , lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__="base" , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , **lowerCamelCase__ , ) -> Dict:
'''simple docstring'''
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(lowerCamelCase__ )
lowercase__ = 0
lowercase__ = Path(self.hparams.output_dir )
lowercase__ = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
lowercase__ = AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path , **({"""num_labels""": num_labels} if num_labels is not None else {}) , cache_dir=lowerCamelCase__ , **lowerCamelCase__ , )
else:
lowercase__ = config
lowercase__ = ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
for p in extra_model_params:
if getattr(self.hparams , lowerCamelCase__ , lowerCamelCase__ ):
assert hasattr(self.config , lowerCamelCase__ ), F'''model config doesn\'t have a `{p}` attribute'''
setattr(self.config , lowerCamelCase__ , getattr(self.hparams , lowerCamelCase__ ) )
if tokenizer is None:
lowercase__ = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=lowerCamelCase__ , )
else:
lowercase__ = tokenizer
lowercase__ = MODEL_MODES[mode]
if model is None:
lowercase__ = self.model_type.from_pretrained(
self.hparams.model_name_or_path , from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path ) , config=self.config , cache_dir=lowerCamelCase__ , )
else:
lowercase__ = model
def A__ ( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
lowercase__ = self.model_type.from_pretrained(*lowerCamelCase__ , **lowerCamelCase__ )
def A__ ( self ) -> str:
'''simple docstring'''
lowercase__ = arg_to_scheduler[self.hparams.lr_scheduler]
lowercase__ = get_schedule_func(
self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps() )
lowercase__ = {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
return scheduler
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase__ = self.model
lowercase__ = ["""bias""", """LayerNorm.weight"""]
lowercase__ = [
{
"""params""": [
p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
], # check this named paramters
"""weight_decay""": self.hparams.weight_decay,
},
{
"""params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
"""weight_decay""": 0.0,
},
]
if self.hparams.adafactor:
lowercase__ = Adafactor(
lowerCamelCase__ , lr=self.hparams.learning_rate , scale_parameter=lowerCamelCase__ , relative_step=lowerCamelCase__ )
else:
lowercase__ = AdamW(
lowerCamelCase__ , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon )
lowercase__ = optimizer
lowercase__ = self.get_lr_scheduler()
return [optimizer], [scheduler]
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
'''simple docstring'''
return self.validation_step(lowerCamelCase__ , lowerCamelCase__ )
def A__ ( self , lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
return self.validation_end(lowerCamelCase__ )
def A__ ( self ) -> int:
'''simple docstring'''
lowercase__ = max(1 , self.hparams.gpus ) # TODO: consider num_tpu_cores
lowercase__ = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def A__ ( self , lowerCamelCase__ ) -> Dict:
'''simple docstring'''
if stage == "test":
lowercase__ = len(self.test_dataloader().dataset )
else:
lowercase__ = self.get_dataloader("""train""" , self.hparams.train_batch_size , shuffle=lowerCamelCase__ )
lowercase__ = len(self.train_dataloader().dataset )
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = False ) -> List[Any]:
'''simple docstring'''
raise NotImplementedError("""You must implement this for your task""" )
def A__ ( self ) -> str:
'''simple docstring'''
return self.train_loader
def A__ ( self ) -> Tuple:
'''simple docstring'''
return self.get_dataloader("""dev""" , self.hparams.eval_batch_size , shuffle=lowerCamelCase__ )
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
return self.get_dataloader("""test""" , self.hparams.eval_batch_size , shuffle=lowerCamelCase__ )
def A__ ( self , lowerCamelCase__ ) -> int:
'''simple docstring'''
return os.path.join(
self.hparams.data_dir , """cached_{}_{}_{}""".format(
lowerCamelCase__ , list(filter(lowerCamelCase__ , self.hparams.model_name_or_path.split("""/""" ) ) ).pop() , str(self.hparams.max_seq_length ) , ) , )
@pl.utilities.rank_zero_only
def A__ ( self , lowerCamelCase__ ) -> None:
'''simple docstring'''
lowercase__ = self.output_dir.joinpath("""best_tfmr""" )
lowercase__ = self.step_count
self.model.save_pretrained(lowerCamelCase__ )
self.tokenizer.save_pretrained(lowerCamelCase__ )
@staticmethod
def A__ ( lowerCamelCase__ , lowerCamelCase__ ) -> str:
'''simple docstring'''
parser.add_argument(
"""--model_name_or_path""" , default=lowerCamelCase__ , type=lowerCamelCase__ , required=lowerCamelCase__ , help="""Path to pretrained model or model identifier from huggingface.co/models""" , )
parser.add_argument(
"""--config_name""" , default="""""" , type=lowerCamelCase__ , help="""Pretrained config name or path if not the same as model_name""" )
parser.add_argument(
"""--tokenizer_name""" , default=lowerCamelCase__ , type=lowerCamelCase__ , help="""Pretrained tokenizer name or path if not the same as model_name""" , )
parser.add_argument(
"""--cache_dir""" , default=str(Path(lowerCamelCase__ ).parent / """test_run""" / """cache""" ) , type=lowerCamelCase__ , help="""Where do you want to store the pre-trained models downloaded from huggingface.co""" , )
parser.add_argument(
"""--encoder_layerdrop""" , type=lowerCamelCase__ , help="""Encoder layer dropout probability (Optional). Goes into model.config""" , )
parser.add_argument(
"""--decoder_layerdrop""" , type=lowerCamelCase__ , help="""Decoder layer dropout probability (Optional). Goes into model.config""" , )
parser.add_argument(
"""--dropout""" , type=lowerCamelCase__ , help="""Dropout probability (Optional). Goes into model.config""" , )
parser.add_argument(
"""--attention_dropout""" , type=lowerCamelCase__ , help="""Attention dropout probability (Optional). Goes into model.config""" , )
parser.add_argument("""--learning_rate""" , default=5e-5 , type=lowerCamelCase__ , help="""The initial learning rate for Adam.""" )
parser.add_argument(
"""--lr_scheduler""" , default="""linear""" , choices=lowerCamelCase__ , metavar=lowerCamelCase__ , type=lowerCamelCase__ , help="""Learning rate scheduler""" , )
parser.add_argument("""--weight_decay""" , default=0.0 , type=lowerCamelCase__ , help="""Weight decay if we apply some.""" )
parser.add_argument("""--adam_epsilon""" , default=1e-8 , type=lowerCamelCase__ , help="""Epsilon for Adam optimizer.""" )
parser.add_argument("""--warmup_steps""" , default=0 , type=lowerCamelCase__ , help="""Linear warmup over warmup_steps.""" )
parser.add_argument("""--num_workers""" , default=4 , type=lowerCamelCase__ , help="""kwarg passed to DataLoader""" )
parser.add_argument("""--num_train_epochs""" , dest="""max_epochs""" , default=3 , type=lowerCamelCase__ )
parser.add_argument("""--train_batch_size""" , default=32 , type=lowerCamelCase__ )
parser.add_argument("""--eval_batch_size""" , default=32 , type=lowerCamelCase__ )
parser.add_argument("""--adafactor""" , action="""store_true""" )
class A ( pl.Callback ):
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
if (
trainer.is_global_zero and trainer.global_rank == 0
): # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
pl_module.model.rag.retriever.init_retrieval() # better to use hook functions.
class A ( pl.Callback ):
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(lowerCamelCase__ )
class A ( pl.Callback ):
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
'''simple docstring'''
lowercase__ = trainer.lr_schedulers[0]["""scheduler"""]
lowercase__ = {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
pl_module.logger.log_metrics(lowerCamelCase__ )
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> int:
'''simple docstring'''
rank_zero_info("""***** Validation results *****""" )
lowercase__ = trainer.callback_metrics
# Log results
for key in sorted(lowerCamelCase__ ):
if key not in ["log", "progress_bar"]:
rank_zero_info("""{} = {}\n""".format(lowerCamelCase__ , str(metrics[key] ) ) )
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
'''simple docstring'''
rank_zero_info("""***** Test results *****""" )
lowercase__ = trainer.callback_metrics
# Log and save results to file
lowercase__ = os.path.join(pl_module.hparams.output_dir , """test_results.txt""" )
with open(lowerCamelCase__ , """w""" ) as writer:
for key in sorted(lowerCamelCase__ ):
if key not in ["log", "progress_bar"]:
rank_zero_info("""{} = {}\n""".format(lowerCamelCase__ , str(metrics[key] ) ) )
writer.write("""{} = {}\n""".format(lowerCamelCase__ , str(metrics[key] ) ) )
def _A ( lowercase__ , lowercase__ ):
# To allow all pl args uncomment the following line
# parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument(
"""--output_dir""" , default=str(Path(lowercase__ ).parent / """test_run""" / """model_checkpoints""" ) , type=lowercase__ , help="""The output directory where the model predictions and checkpoints will be written.""" , )
parser.add_argument(
"""--fp16""" , action="""store_true""" , help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit""" , )
parser.add_argument(
"""--fp16_opt_level""" , type=lowercase__ , default="""O2""" , help=(
"""For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
"""See details at https://nvidia.github.io/apex/amp.html"""
) , )
parser.add_argument("""--n_tpu_cores""" , dest="""tpu_cores""" , type=lowercase__ )
parser.add_argument("""--max_grad_norm""" , dest="""gradient_clip_val""" , default=1.0 , type=lowercase__ , help="""Max gradient norm""" )
parser.add_argument("""--do_train""" , action="""store_true""" , help="""Whether to run training.""" )
parser.add_argument("""--do_predict""" , action="""store_true""" , help="""Whether to run predictions on the test set.""" )
parser.add_argument(
"""--gradient_accumulation_steps""" , dest="""accumulate_grad_batches""" , type=lowercase__ , default=1 , help="""Number of updates steps to accumulate before performing a backward/update pass.""" , )
parser.add_argument("""--seed""" , type=lowercase__ , default=42 , help="""random seed for initialization""" )
parser.add_argument(
"""--data_dir""" , default=str(Path(lowercase__ ).parent / """test_run""" / """dummy-train-data""" ) , type=lowercase__ , help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task.""" , )
def _A ( lowercase__ , lowercase__ , lowercase__=None , lowercase__=True , lowercase__=[] , lowercase__=None , lowercase__=None , **lowercase__ , ):
pl.seed_everything(args.seed )
# init model
lowercase__ = Path(model.hparams.output_dir )
odir.mkdir(exist_ok=lowercase__ )
# add custom checkpoints
if checkpoint_callback is None:
lowercase__ = pl.callbacks.ModelCheckpoint(
filepath=args.output_dir , prefix="""checkpoint""" , monitor="""val_loss""" , mode="""min""" , save_top_k=1 )
if early_stopping_callback:
extra_callbacks.append(lowercase__ )
if logging_callback is None:
lowercase__ = LoggingCallback()
lowercase__ = {}
if args.fpaa:
lowercase__ = 16
if args.gpus > 1:
lowercase__ = """auto"""
lowercase__ = """ddp"""
lowercase__ = args.accumulate_grad_batches
lowercase__ = None
lowercase__ = """auto"""
lowercase__ = pl.Trainer.from_argparse_args(
lowercase__ , weights_summary=lowercase__ , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=lowercase__ , val_check_interval=1 , num_sanity_val_steps=2 , **lowercase__ , )
if args.do_train:
trainer.fit(lowercase__ )
else:
print("""RAG modeling tests with new set functions successfuly executed!""" )
return trainer
| 164 |
'''simple docstring'''
def _A ( ):
lowercase__ = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
lowercase__ = 6
lowercase__ = 1
lowercase__ = 1901
lowercase__ = 0
while year < 2001:
day += 7
if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
if day > days_per_month[month - 1] and month != 2:
month += 1
lowercase__ = day - days_per_month[month - 2]
elif day > 29 and month == 2:
month += 1
lowercase__ = day - 29
else:
if day > days_per_month[month - 1]:
month += 1
lowercase__ = day - days_per_month[month - 2]
if month > 12:
year += 1
lowercase__ = 1
if year < 2001 and day == 1:
sundays += 1
return sundays
if __name__ == "__main__":
print(solution())
| 164 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class UpperCamelCase__:
__magic_name__ : List[str]
__magic_name__ : Optional[str] = None
# Automatically constructed
__magic_name__ : ClassVar[str] = "dict"
__magic_name__ : ClassVar[Any] = None
__magic_name__ : str = field(default="Translation" , init=lowerCAmelCase , repr=lowerCAmelCase )
def __call__( self : Union[str, Any] )-> str:
"""simple docstring"""
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def a__( self : int )-> Union["FeatureType", Dict[str, "FeatureType"]]:
"""simple docstring"""
from .features import Value
return {k: Value('''string''' ) for k in sorted(self.languages )}
@dataclass
class UpperCamelCase__:
__magic_name__ : Optional[List] = None
__magic_name__ : Optional[int] = None
__magic_name__ : Optional[str] = None
# Automatically constructed
__magic_name__ : ClassVar[str] = "dict"
__magic_name__ : ClassVar[Any] = None
__magic_name__ : str = field(default="TranslationVariableLanguages" , init=lowerCAmelCase , repr=lowerCAmelCase )
def a__( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = sorted(set(self.languages ) ) if self.languages else None
UpperCAmelCase = len(self.languages ) if self.languages else None
def __call__( self : int )-> Optional[Any]:
"""simple docstring"""
return pa.struct({'''language''': pa.list_(pa.string() ), '''translation''': pa.list_(pa.string() )} )
def a__( self : Optional[int] , lowerCAmelCase : Dict )-> Tuple:
"""simple docstring"""
UpperCAmelCase = set(self.languages )
if self.languages and set(lowerCAmelCase ) - lang_set:
raise ValueError(
F"""Some languages in example ({", ".join(sorted(set(lowerCAmelCase ) - lang_set ) )}) are not in valid set ({", ".join(lowerCAmelCase )}).""" )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
UpperCAmelCase = []
for lang, text in translation_dict.items():
if isinstance(lowerCAmelCase , lowerCAmelCase ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
UpperCAmelCase , UpperCAmelCase = zip(*sorted(lowerCAmelCase ) )
return {"language": languages, "translation": translations}
def a__( self : Any )-> Union["FeatureType", Dict[str, "FeatureType"]]:
"""simple docstring"""
from .features import Sequence, Value
return {
"language": Sequence(Value('''string''' ) ),
"translation": Sequence(Value('''string''' ) ),
}
| 363 |
'''simple docstring'''
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_soundfile_availble():
import soundfile as sf
if is_vision_available():
from PIL import Image
def lowerCamelCase__ ( A : str="" ):
'''simple docstring'''
UpperCAmelCase = tempfile.mkdtemp()
return os.path.join(A , str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class UpperCamelCase__( unittest.TestCase ):
def a__( self : int )-> int:
"""simple docstring"""
UpperCAmelCase = torch.rand(12 , dtype=torch.floataa ) - 0.5
UpperCAmelCase = AgentAudio(lowerCAmelCase )
UpperCAmelCase = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowerCAmelCase , agent_type.to_raw() , atol=1E-4 ) )
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(lowerCAmelCase ) )
# Ensure that the file contains the same value as the original tensor
UpperCAmelCase , UpperCAmelCase = sf.read(lowerCAmelCase )
self.assertTrue(torch.allclose(lowerCAmelCase , torch.tensor(lowerCAmelCase ) , atol=1E-4 ) )
def a__( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = torch.rand(12 , dtype=torch.floataa ) - 0.5
UpperCAmelCase = get_new_path(suffix='''.wav''' )
sf.write(lowerCAmelCase , lowerCAmelCase , 16000 )
UpperCAmelCase = AgentAudio(lowerCAmelCase )
self.assertTrue(torch.allclose(lowerCAmelCase , agent_type.to_raw() , atol=1E-4 ) )
self.assertEqual(agent_type.to_string() , lowerCAmelCase )
@require_vision
@require_torch
class UpperCamelCase__( unittest.TestCase ):
def a__( self : List[Any] )-> Any:
"""simple docstring"""
UpperCAmelCase = torch.randint(0 , 256 , (64, 64, 3) )
UpperCAmelCase = AgentImage(lowerCAmelCase )
UpperCAmelCase = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowerCAmelCase , agent_type._tensor , atol=1E-4 ) )
self.assertIsInstance(agent_type.to_raw() , Image.Image )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowerCAmelCase ) )
def a__( self : List[Any] )-> List[Any]:
"""simple docstring"""
UpperCAmelCase = Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png'''
UpperCAmelCase = Image.open(lowerCAmelCase )
UpperCAmelCase = AgentImage(lowerCAmelCase )
self.assertTrue(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowerCAmelCase ) )
def a__( self : Optional[Any] )-> List[str]:
"""simple docstring"""
UpperCAmelCase = Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png'''
UpperCAmelCase = Image.open(lowerCAmelCase )
UpperCAmelCase = AgentImage(lowerCAmelCase )
self.assertFalse(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowerCAmelCase ) )
class UpperCamelCase__( unittest.TestCase ):
def a__( self : int )-> Any:
"""simple docstring"""
UpperCAmelCase = '''Hey!'''
UpperCAmelCase = AgentText(lowerCAmelCase )
self.assertEqual(lowerCAmelCase , agent_type.to_string() )
self.assertEqual(lowerCAmelCase , agent_type.to_raw() )
self.assertEqual(lowerCAmelCase , lowerCAmelCase )
| 91 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __snake_case ( UpperCAmelCase_ : Dict ):
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items() )
def __snake_case ( UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Union[str, Any] ):
lowerCamelCase_ = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
lowerCamelCase_ = key.replace("heads.cmd.mim_head.cls.predictions" , "mmm_image_head" )
lowerCamelCase_ = key.replace("heads.cmd.mlm_head.cls.predictions" , "mmm_text_head" )
lowerCamelCase_ = key.replace("heads.cmd.itm_head.cls" , "itm_head" )
lowerCamelCase_ = key.replace("heads.cmd.itm_head.pooler" , "itm_head.pooler" )
lowerCamelCase_ = key.replace("heads.cmd.clip_head.logit_scale" , "flava.logit_scale" )
lowerCamelCase_ = key.replace("heads.fairseq_mlm.cls.predictions" , "mlm_head" )
lowerCamelCase_ = key.replace("heads.imagenet.mim_head.cls.predictions" , "mim_head" )
lowerCamelCase_ = key.replace("mm_text_projection" , "flava.text_to_mm_projection" )
lowerCamelCase_ = key.replace("mm_image_projection" , "flava.image_to_mm_projection" )
lowerCamelCase_ = key.replace("image_encoder.module" , "flava.image_model" )
lowerCamelCase_ = key.replace("text_encoder.module" , "flava.text_model" )
lowerCamelCase_ = key.replace("mm_encoder.module.encoder.cls_token" , "flava.multimodal_model.cls_token" )
lowerCamelCase_ = key.replace("mm_encoder.module" , "flava.multimodal_model" )
lowerCamelCase_ = key.replace("text_projection" , "flava.text_projection" )
lowerCamelCase_ = key.replace("image_projection" , "flava.image_projection" )
lowerCamelCase_ = value.float()
for key, value in codebook_state_dict.items():
lowerCamelCase_ = value
return upgrade
@torch.no_grad()
def __snake_case ( UpperCAmelCase_ : str , UpperCAmelCase_ : int , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Dict=None ):
if config_path is not None:
lowerCamelCase_ = FlavaConfig.from_pretrained(UpperCAmelCase_ )
else:
lowerCamelCase_ = FlavaConfig()
lowerCamelCase_ = FlavaForPreTraining(UpperCAmelCase_ ).eval()
lowerCamelCase_ = convert_dalle_checkpoint(UpperCAmelCase_ , UpperCAmelCase_ , save_checkpoint=UpperCAmelCase_ )
if os.path.exists(UpperCAmelCase_ ):
lowerCamelCase_ = torch.load(UpperCAmelCase_ , map_location="cpu" )
else:
lowerCamelCase_ = torch.hub.load_state_dict_from_url(UpperCAmelCase_ , map_location="cpu" )
lowerCamelCase_ = upgrade_state_dict(UpperCAmelCase_ , UpperCAmelCase_ )
hf_model.load_state_dict(UpperCAmelCase_ )
lowerCamelCase_ = hf_model.state_dict()
lowerCamelCase_ = count_parameters(UpperCAmelCase_ )
lowerCamelCase_ = count_parameters(UpperCAmelCase_ ) + count_parameters(UpperCAmelCase_ )
assert torch.allclose(UpperCAmelCase_ , UpperCAmelCase_ , atol=1E-3 )
hf_model.save_pretrained(UpperCAmelCase_ )
if __name__ == "__main__":
a_ : Optional[int] = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
a_ : Union[str, Any] = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 55 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
A_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class _snake_case ( _a ):
def __init__( self : List[Any] ,SCREAMING_SNAKE_CASE__ : CLIPSegForImageSegmentation ,SCREAMING_SNAKE_CASE__ : CLIPSegProcessor ,SCREAMING_SNAKE_CASE__ : AutoencoderKL ,SCREAMING_SNAKE_CASE__ : CLIPTextModel ,SCREAMING_SNAKE_CASE__ : CLIPTokenizer ,SCREAMING_SNAKE_CASE__ : UNetaDConditionModel ,SCREAMING_SNAKE_CASE__ : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] ,SCREAMING_SNAKE_CASE__ : StableDiffusionSafetyChecker ,SCREAMING_SNAKE_CASE__ : CLIPImageProcessor ,):
super().__init__()
if hasattr(scheduler.config ,"steps_offset" ) and scheduler.config.steps_offset != 1:
SCREAMING_SNAKE_CASE:Union[str, Any] = (
F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1" ,"1.0.0" ,SCREAMING_SNAKE_CASE__ ,standard_warn=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Tuple = dict(scheduler.config )
SCREAMING_SNAKE_CASE:Union[str, Any] = 1
SCREAMING_SNAKE_CASE:Dict = FrozenDict(SCREAMING_SNAKE_CASE__ )
if hasattr(scheduler.config ,"skip_prk_steps" ) and scheduler.config.skip_prk_steps is False:
SCREAMING_SNAKE_CASE:List[Any] = (
F'''The configuration file of this scheduler: {scheduler} has not set the configuration'''
" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
" Hub, it would be very nice if you could open a Pull request for the"
" `scheduler/scheduler_config.json` file"
)
deprecate("skip_prk_steps not set" ,"1.0.0" ,SCREAMING_SNAKE_CASE__ ,standard_warn=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Tuple = dict(scheduler.config )
SCREAMING_SNAKE_CASE:int = True
SCREAMING_SNAKE_CASE:Optional[int] = FrozenDict(SCREAMING_SNAKE_CASE__ )
if safety_checker is None:
logger.warning(
F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." )
self.register_modules(
segmentation_model=SCREAMING_SNAKE_CASE__ ,segmentation_processor=SCREAMING_SNAKE_CASE__ ,vae=SCREAMING_SNAKE_CASE__ ,text_encoder=SCREAMING_SNAKE_CASE__ ,tokenizer=SCREAMING_SNAKE_CASE__ ,unet=SCREAMING_SNAKE_CASE__ ,scheduler=SCREAMING_SNAKE_CASE__ ,safety_checker=SCREAMING_SNAKE_CASE__ ,feature_extractor=SCREAMING_SNAKE_CASE__ ,)
def __UpperCamelCase ( self : int ,SCREAMING_SNAKE_CASE__ : Optional[Union[str, int]] = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
SCREAMING_SNAKE_CASE:Optional[Any] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : str ):
self.enable_attention_slicing(SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : List[str] ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
SCREAMING_SNAKE_CASE:str = torch.device("cuda" )
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def __UpperCamelCase ( self : Any ):
if self.device != torch.device("meta" ) or not hasattr(self.unet ,"_hf_hook" ):
return self.device
for module in self.unet.modules():
if (
hasattr(SCREAMING_SNAKE_CASE__ ,"_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()
def __call__( self : Dict ,SCREAMING_SNAKE_CASE__ : Union[str, List[str]] ,SCREAMING_SNAKE_CASE__ : Union[torch.FloatTensor, PIL.Image.Image] ,SCREAMING_SNAKE_CASE__ : str ,SCREAMING_SNAKE_CASE__ : int = 512 ,SCREAMING_SNAKE_CASE__ : int = 512 ,SCREAMING_SNAKE_CASE__ : int = 50 ,SCREAMING_SNAKE_CASE__ : float = 7.5 ,SCREAMING_SNAKE_CASE__ : Optional[Union[str, List[str]]] = None ,SCREAMING_SNAKE_CASE__ : Optional[int] = 1 ,SCREAMING_SNAKE_CASE__ : float = 0.0 ,SCREAMING_SNAKE_CASE__ : Optional[torch.Generator] = None ,SCREAMING_SNAKE_CASE__ : Optional[torch.FloatTensor] = None ,SCREAMING_SNAKE_CASE__ : Optional[str] = "pil" ,SCREAMING_SNAKE_CASE__ : bool = True ,SCREAMING_SNAKE_CASE__ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,SCREAMING_SNAKE_CASE__ : int = 1 ,**SCREAMING_SNAKE_CASE__ : Dict ,):
SCREAMING_SNAKE_CASE:str = self.segmentation_processor(
text=[text] ,images=[image] ,padding="max_length" ,return_tensors="pt" ).to(self.device )
SCREAMING_SNAKE_CASE:Union[str, Any] = self.segmentation_model(**SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Optional[int] = torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
SCREAMING_SNAKE_CASE:Optional[Any] = self.numpy_to_pil(SCREAMING_SNAKE_CASE__ )[0].resize(image.size )
# Run inpainting pipeline with the generated mask
SCREAMING_SNAKE_CASE:Any = StableDiffusionInpaintPipeline(
vae=self.vae ,text_encoder=self.text_encoder ,tokenizer=self.tokenizer ,unet=self.unet ,scheduler=self.scheduler ,safety_checker=self.safety_checker ,feature_extractor=self.feature_extractor ,)
return inpainting_pipeline(
prompt=SCREAMING_SNAKE_CASE__ ,image=SCREAMING_SNAKE_CASE__ ,mask_image=SCREAMING_SNAKE_CASE__ ,height=SCREAMING_SNAKE_CASE__ ,width=SCREAMING_SNAKE_CASE__ ,num_inference_steps=SCREAMING_SNAKE_CASE__ ,guidance_scale=SCREAMING_SNAKE_CASE__ ,negative_prompt=SCREAMING_SNAKE_CASE__ ,num_images_per_prompt=SCREAMING_SNAKE_CASE__ ,eta=SCREAMING_SNAKE_CASE__ ,generator=SCREAMING_SNAKE_CASE__ ,latents=SCREAMING_SNAKE_CASE__ ,output_type=SCREAMING_SNAKE_CASE__ ,return_dict=SCREAMING_SNAKE_CASE__ ,callback=SCREAMING_SNAKE_CASE__ ,callback_steps=SCREAMING_SNAKE_CASE__ ,)
| 139 | 0 |
from __future__ import annotations
__UpperCamelCase : str = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class lowercase__ :
def __init__( self : Dict , UpperCamelCase__ : dict[str, list[str]] , UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = graph
# mapping node to its parent in resulting breadth first tree
SCREAMING_SNAKE_CASE : dict[str, str | None] = {}
SCREAMING_SNAKE_CASE : str = source_vertex
def __A ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = {self.source_vertex}
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : List[Any] = [self.source_vertex] # first in first out queue
while queue:
SCREAMING_SNAKE_CASE : Tuple = queue.pop(0 )
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[int] = vertex
queue.append(UpperCamelCase__ )
def __A ( self : Union[str, Any] , UpperCamelCase__ : str ):
'''simple docstring'''
if target_vertex == self.source_vertex:
return self.source_vertex
SCREAMING_SNAKE_CASE : Dict = self.parent.get(UpperCamelCase__ )
if target_vertex_parent is None:
SCREAMING_SNAKE_CASE : Tuple = (
f"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
)
raise ValueError(UpperCamelCase__ )
return self.shortest_path(UpperCamelCase__ ) + f"""->{target_vertex}"""
if __name__ == "__main__":
__UpperCamelCase : List[str] = Graph(graph, 'G')
g.breath_first_search()
print(g.shortest_path('D'))
print(g.shortest_path('G'))
print(g.shortest_path('Foo'))
| 258 | import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 lowercase__ ( UpperCamelCase_ , unittest.TestCase):
UpperCamelCase_ = KandinskyInpaintPipeline
UpperCamelCase_ = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
UpperCamelCase_ = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
UpperCamelCase_ = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
UpperCamelCase_ = False
@property
def __A ( self : Tuple ):
'''simple docstring'''
return 32
@property
def __A ( self : List[str] ):
'''simple docstring'''
return 32
@property
def __A ( self : List[Any] ):
'''simple docstring'''
return self.time_input_dim
@property
def __A ( self : List[Any] ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def __A ( self : List[Any] ):
'''simple docstring'''
return 100
@property
def __A ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
return tokenizer
@property
def __A ( self : int ):
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[str] = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1005 , )
SCREAMING_SNAKE_CASE : Any = MultilingualCLIP(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = text_encoder.eval()
return text_encoder
@property
def __A ( self : Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : int = {
'''in_channels''': 9,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''text_image''',
'''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''': '''text_image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
SCREAMING_SNAKE_CASE : str = UNetaDConditionModel(**UpperCamelCase__ )
return model
@property
def __A ( self : int ):
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["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",
],
"vq_embed_dim": 4,
}
@property
def __A ( self : Any ):
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = VQModel(**self.dummy_movq_kwargs )
return model
def __A ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = self.dummy_text_encoder
SCREAMING_SNAKE_CASE : Dict = self.dummy_tokenizer
SCREAMING_SNAKE_CASE : List[str] = self.dummy_unet
SCREAMING_SNAKE_CASE : int = self.dummy_movq
SCREAMING_SNAKE_CASE : List[str] = DDIMScheduler(
num_train_timesteps=1000 , beta_schedule='''linear''' , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=UpperCamelCase__ , set_alpha_to_one=UpperCamelCase__ , steps_offset=1 , prediction_type='''epsilon''' , thresholding=UpperCamelCase__ , )
SCREAMING_SNAKE_CASE : Any = {
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __A ( self : str , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[Any]=0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : int = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(UpperCamelCase__ )
# create init_image
SCREAMING_SNAKE_CASE : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE : Optional[int] = Image.fromarray(np.uinta(UpperCamelCase__ ) ).convert('''RGB''' ).resize((256, 256) )
# create mask
SCREAMING_SNAKE_CASE : Tuple = np.ones((64, 64) , dtype=np.floataa )
SCREAMING_SNAKE_CASE : List[Any] = 0
if str(UpperCamelCase__ ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE : Dict = torch.manual_seed(UpperCamelCase__ )
else:
SCREAMING_SNAKE_CASE : Dict = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Dict = {
'''prompt''': '''horse''',
'''image''': init_image,
'''mask_image''': mask,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 2,
'''guidance_scale''': 4.0,
'''output_type''': '''np''',
}
return inputs
def __A ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = '''cpu'''
SCREAMING_SNAKE_CASE : Any = self.get_dummy_components()
SCREAMING_SNAKE_CASE : str = self.pipeline_class(**UpperCamelCase__ )
SCREAMING_SNAKE_CASE : List[Any] = pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = pipe(**self.get_dummy_inputs(UpperCamelCase__ ) )
SCREAMING_SNAKE_CASE : Union[str, Any] = output.images
SCREAMING_SNAKE_CASE : Any = pipe(
**self.get_dummy_inputs(UpperCamelCase__ ) , return_dict=UpperCamelCase__ , )[0]
SCREAMING_SNAKE_CASE : Optional[Any] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : int = image_from_tuple[0, -3:, -3:, -1]
print(f"""image.shape {image.shape}""" )
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE : str = np.array(
[0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786] )
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()}"""
def __A ( self : str ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class lowercase__ ( unittest.TestCase):
def __A ( self : str ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __A ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy''' )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
SCREAMING_SNAKE_CASE : int = np.ones((768, 768) , dtype=np.floataa )
SCREAMING_SNAKE_CASE : Optional[int] = 0
SCREAMING_SNAKE_CASE : Optional[Any] = '''a hat'''
SCREAMING_SNAKE_CASE : Dict = KandinskyPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = KandinskyInpaintPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-inpaint''' , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE : Optional[Any] = pipeline.to(UpperCamelCase__ )
pipeline.set_progress_bar_config(disable=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.Generator(device='''cpu''' ).manual_seed(0 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = pipe_prior(
UpperCamelCase__ , generator=UpperCamelCase__ , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
SCREAMING_SNAKE_CASE : Optional[Any] = pipeline(
UpperCamelCase__ , image=UpperCamelCase__ , mask_image=UpperCamelCase__ , image_embeds=UpperCamelCase__ , negative_image_embeds=UpperCamelCase__ , generator=UpperCamelCase__ , num_inference_steps=100 , height=768 , width=768 , output_type='''np''' , )
SCREAMING_SNAKE_CASE : Any = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(UpperCamelCase__ , UpperCamelCase__ )
| 258 | 1 |
"""simple docstring"""
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase , unittest.TestCase ):
__lowerCAmelCase : Union[str, Any] = BertJapaneseTokenizer
__lowerCAmelCase : Tuple = False
__lowerCAmelCase : Optional[Any] = True
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
super().setUp()
UpperCAmelCase : str = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""こんにちは""",
"""こん""",
"""にちは""",
"""ばんは""",
"""##こん""",
"""##にちは""",
"""##ばんは""",
"""世界""",
"""##世界""",
"""、""",
"""##、""",
"""。""",
"""##。""",
]
UpperCAmelCase : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
UpperCAmelCase : int = """こんにちは、世界。 \nこんばんは、世界。"""
UpperCAmelCase : str = """こんにちは 、 世界 。 こんばんは 、 世界 。"""
return input_text, output_text
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.get_input_output_texts(lowerCAmelCase_ )
UpperCAmelCase : Optional[Any] = tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
UpperCAmelCase : str = tokenizer.decode(lowerCAmelCase_ , clean_up_tokenization_spaces=lowerCAmelCase_ )
return text, ids
def SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
'''simple docstring'''
pass # TODO add if relevant
def SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
pass # TODO add if relevant
def SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
pass # TODO add if relevant
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Optional[Any] = self.tokenizer_class(self.vocab_file )
UpperCAmelCase : Optional[Any] = tokenizer.tokenize("""こんにちは、世界。\nこんばんは、世界。""" )
self.assertListEqual(lowerCAmelCase_ , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : List[str] = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""mecab""" )
self.assertIsNotNone(lowerCAmelCase_ )
UpperCAmelCase : Optional[Any] = """こんにちは、世界。\nこんばんは、世界。"""
UpperCAmelCase : Union[str, Any] = tokenizer.tokenize(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
UpperCAmelCase : Any = os.path.join(self.tmpdirname , """tokenizer.bin""" )
with open(lowerCAmelCase_ , """wb""" ) as handle:
pickle.dump(lowerCAmelCase_ , lowerCAmelCase_ )
with open(lowerCAmelCase_ , """rb""" ) as handle:
UpperCAmelCase : Optional[Any] = pickle.load(lowerCAmelCase_ )
UpperCAmelCase : List[Any] = tokenizer_new.tokenize(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Any = MecabTokenizer(mecab_dic="""ipadic""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップルストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
try:
UpperCAmelCase : Tuple = MecabTokenizer(mecab_dic="""unidic_lite""" )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
try:
UpperCAmelCase : Dict = MecabTokenizer(mecab_dic="""unidic""" )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = MecabTokenizer(do_lower_case=lowerCAmelCase_ , mecab_dic="""ipadic""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップルストア""", """で""", """iphone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
try:
UpperCAmelCase : List[str] = MecabTokenizer(
do_lower_case=lowerCAmelCase_ , normalize_text=lowerCAmelCase_ , mecab_option="""-d /usr/local/lib/mecab/dic/jumandic""" )
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
def SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = MecabTokenizer(normalize_text=lowerCAmelCase_ , mecab_dic="""ipadic""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れ""", """た""", """ """, """。"""] , )
@require_sudachi
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : List[str] = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""sudachi""" )
self.assertIsNotNone(lowerCAmelCase_ )
UpperCAmelCase : Any = """こんにちは、世界。\nこんばんは、世界。"""
UpperCAmelCase : Dict = tokenizer.tokenize(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
UpperCAmelCase : Dict = os.path.join(self.tmpdirname , """tokenizer.bin""" )
with open(lowerCAmelCase_ , """wb""" ) as handle:
pickle.dump(lowerCAmelCase_ , lowerCAmelCase_ )
with open(lowerCAmelCase_ , """rb""" ) as handle:
UpperCAmelCase : Optional[Any] = pickle.load(lowerCAmelCase_ )
UpperCAmelCase : List[str] = tokenizer_new.tokenize(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
@require_sudachi
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : List[str] = SudachiTokenizer(sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , [""" """, """\t""", """アップル""", """ストア""", """で""", """iPhone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """] , )
@require_sudachi
def SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Dict = SudachiTokenizer(sudachi_dict_type="""core""" , sudachi_split_mode="""A""" )
self.assertListEqual(tokenizer.tokenize("""外国人参政権""" ) , ["""外国""", """人""", """参政""", """権"""] )
@require_sudachi
def SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Dict = SudachiTokenizer(sudachi_dict_type="""core""" , sudachi_split_mode="""B""" )
self.assertListEqual(tokenizer.tokenize("""外国人参政権""" ) , ["""外国人""", """参政権"""] )
@require_sudachi
def SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Any = SudachiTokenizer(sudachi_dict_type="""core""" , sudachi_split_mode="""C""" )
self.assertListEqual(tokenizer.tokenize("""外国人参政権""" ) , ["""外国人参政権"""] )
@require_sudachi
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : Any = SudachiTokenizer(do_lower_case=lowerCAmelCase_ , sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , [""" """, """\t""", """アップル""", """ストア""", """で""", """iphone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """] , )
@require_sudachi
def SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : List[str] = SudachiTokenizer(normalize_text=lowerCAmelCase_ , sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , [""" """, """\t""", """ｱｯﾌﾟﾙ""", """ストア""", """で""", """iPhone""", """８""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """\u3000""", """。""", """ """, """ """] , )
@require_sudachi
def SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : Optional[int] = SudachiTokenizer(trim_whitespace=lowerCAmelCase_ , sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
@require_jumanpp
def SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Tuple = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""jumanpp""" )
self.assertIsNotNone(lowerCAmelCase_ )
UpperCAmelCase : Tuple = """こんにちは、世界。\nこんばんは、世界。"""
UpperCAmelCase : Optional[int] = tokenizer.tokenize(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
UpperCAmelCase : Any = os.path.join(self.tmpdirname , """tokenizer.bin""" )
with open(lowerCAmelCase_ , """wb""" ) as handle:
pickle.dump(lowerCAmelCase_ , lowerCAmelCase_ )
with open(lowerCAmelCase_ , """rb""" ) as handle:
UpperCAmelCase : Dict = pickle.load(lowerCAmelCase_ )
UpperCAmelCase : int = tokenizer_new.tokenize(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
@require_jumanpp
def SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Dict = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
@require_jumanpp
def SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Dict = JumanppTokenizer(do_lower_case=lowerCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iphone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
@require_jumanpp
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Any = JumanppTokenizer(normalize_text=lowerCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""ｱ""", """ｯ""", """ﾌ""", """ﾟ""", """ﾙ""", """ストア""", """で""", """iPhone""", """８""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
@require_jumanpp
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] = JumanppTokenizer(trim_whitespace=lowerCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れた""", """。"""] , )
@require_jumanpp
def SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : List[Any] = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize("""ありがとうございますm(_ _)ｍ見つけるのが大変です。""" ) , ["""ありがとう""", """ございます""", """m(_ _)m""", """見つける""", """の""", """が""", """大変です""", """。"""] , )
def SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : Optional[int] = ["""[UNK]""", """[CLS]""", """[SEP]""", """こんにちは""", """こん""", """にちは""", """ばんは""", """##こん""", """##にちは""", """##ばんは"""]
UpperCAmelCase : int = {}
for i, token in enumerate(lowerCAmelCase_ ):
UpperCAmelCase : Union[str, Any] = i
UpperCAmelCase : Any = WordpieceTokenizer(vocab=lowerCAmelCase_ , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""こんにちは""" ) , ["""こんにちは"""] )
self.assertListEqual(tokenizer.tokenize("""こんばんは""" ) , ["""こん""", """##ばんは"""] )
self.assertListEqual(tokenizer.tokenize("""こんばんは こんばんにちは こんにちは""" ) , ["""こん""", """##ばんは""", """[UNK]""", """こんにちは"""] )
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Dict = BertJapaneseTokenizer.from_pretrained("""nlp-waseda/roberta-base-japanese-with-auto-jumanpp""" )
UpperCAmelCase : Any = tokenizer.subword_tokenizer
UpperCAmelCase : List[str] = subword_tokenizer.tokenize("""国境 の 長い トンネル を 抜ける と 雪国 であった 。""" )
self.assertListEqual(lowerCAmelCase_ , ["""▁国境""", """▁の""", """▁長い""", """▁トンネル""", """▁を""", """▁抜ける""", """▁と""", """▁雪""", """国""", """▁であった""", """▁。"""] )
UpperCAmelCase : Optional[int] = subword_tokenizer.tokenize("""こんばんは こんばん にち は こんにちは""" )
self.assertListEqual(lowerCAmelCase_ , ["""▁こん""", """ばん""", """は""", """▁こん""", """ばん""", """▁に""", """ち""", """▁は""", """▁こんにちは"""] )
def SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : Dict = self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese""" )
UpperCAmelCase : Optional[int] = tokenizer.encode("""ありがとう。""" , add_special_tokens=lowerCAmelCase_ )
UpperCAmelCase : int = tokenizer.encode("""どういたしまして。""" , add_special_tokens=lowerCAmelCase_ )
UpperCAmelCase : int = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_ )
UpperCAmelCase : List[Any] = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_ , lowerCAmelCase_ )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase , unittest.TestCase ):
__lowerCAmelCase : Dict = BertJapaneseTokenizer
__lowerCAmelCase : Optional[int] = False
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
super().setUp()
UpperCAmelCase : Tuple = ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]
UpperCAmelCase : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def SCREAMING_SNAKE_CASE ( self , **_SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname , subword_tokenizer_type="""character""" , **lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[str] = """こんにちは、世界。 \nこんばんは、世界。"""
UpperCAmelCase : List[Any] = """こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"""
return input_text, output_text
def SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
pass # TODO add if relevant
def SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
pass # TODO add if relevant
def SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
pass # TODO add if relevant
def SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : Dict = self.tokenizer_class(self.vocab_file , subword_tokenizer_type="""character""" )
UpperCAmelCase : int = tokenizer.tokenize("""こんにちは、世界。 \nこんばんは、世界。""" )
self.assertListEqual(
lowerCAmelCase_ , ["""こ""", """ん""", """に""", """ち""", """は""", """、""", """世""", """界""", """。""", """こ""", """ん""", """ば""", """ん""", """は""", """、""", """世""", """界""", """。"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase_ ) , [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12] )
def SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : Optional[int] = ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]
UpperCAmelCase : Any = {}
for i, token in enumerate(lowerCAmelCase_ ):
UpperCAmelCase : Optional[Any] = i
UpperCAmelCase : Tuple = CharacterTokenizer(vocab=lowerCAmelCase_ , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""こんにちは""" ) , ["""こ""", """ん""", """に""", """ち""", """は"""] )
self.assertListEqual(tokenizer.tokenize("""こんにちほ""" ) , ["""こ""", """ん""", """に""", """ち""", """[UNK]"""] )
def SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese-char""" )
UpperCAmelCase : int = tokenizer.encode("""ありがとう。""" , add_special_tokens=lowerCAmelCase_ )
UpperCAmelCase : List[Any] = tokenizer.encode("""どういたしまして。""" , add_special_tokens=lowerCAmelCase_ )
UpperCAmelCase : Dict = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_ )
UpperCAmelCase : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_ , lowerCAmelCase_ )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Dict = """cl-tohoku/bert-base-japanese"""
UpperCAmelCase : Dict = AutoTokenizer.from_pretrained(lowerCAmelCase_ )
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_ )
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[Any] = """cl-tohoku/bert-base-japanese"""
with self.assertLogs("""transformers""" , level="""WARNING""" ) as cm:
BertTokenizer.from_pretrained(lowerCAmelCase_ )
self.assertTrue(
cm.records[0].message.startswith(
"""The tokenizer class you load from this checkpoint is not the same type as the class this function"""
""" is called from.""" ) )
UpperCAmelCase : Optional[Any] = """bert-base-cased"""
with self.assertLogs("""transformers""" , level="""WARNING""" ) as cm:
BertJapaneseTokenizer.from_pretrained(lowerCAmelCase_ )
self.assertTrue(
cm.records[0].message.startswith(
"""The tokenizer class you load from this checkpoint is not the same type as the class this function"""
""" is called from.""" ) )
| 109 |
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
_snake_case : List[Any] = logging.get_logger(__name__)
_snake_case : List[Any] = {
'microsoft/beit-base-patch16-224-pt22k': (
'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class _UpperCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
a_ = """beit"""
def __init__( self : List[Any] , lowerCAmelCase_ : Tuple=8_1_9_2 , lowerCAmelCase_ : Optional[int]=7_6_8 , lowerCAmelCase_ : int=1_2 , lowerCAmelCase_ : Optional[int]=1_2 , lowerCAmelCase_ : Any=3_0_7_2 , lowerCAmelCase_ : Optional[int]="gelu" , lowerCAmelCase_ : Any=0.0 , lowerCAmelCase_ : Any=0.0 , lowerCAmelCase_ : Any=0.02 , lowerCAmelCase_ : int=1e-12 , lowerCAmelCase_ : int=2_2_4 , lowerCAmelCase_ : str=1_6 , lowerCAmelCase_ : int=3 , lowerCAmelCase_ : Dict=False , lowerCAmelCase_ : int=False , lowerCAmelCase_ : List[Any]=False , lowerCAmelCase_ : int=False , lowerCAmelCase_ : List[str]=0.1 , lowerCAmelCase_ : Union[str, Any]=0.1 , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : List[Any]=[3, 5, 7, 1_1] , lowerCAmelCase_ : Optional[Any]=[1, 2, 3, 6] , lowerCAmelCase_ : Tuple=True , lowerCAmelCase_ : Dict=0.4 , lowerCAmelCase_ : Tuple=2_5_6 , lowerCAmelCase_ : Any=1 , lowerCAmelCase_ : Any=False , lowerCAmelCase_ : Optional[int]=2_5_5 , **lowerCAmelCase_ : Any , ) -> Dict:
super().__init__(**lowerCAmelCase_ )
__lowerCAmelCase = vocab_size
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = initializer_range
__lowerCAmelCase = layer_norm_eps
__lowerCAmelCase = image_size
__lowerCAmelCase = patch_size
__lowerCAmelCase = num_channels
__lowerCAmelCase = use_mask_token
__lowerCAmelCase = use_absolute_position_embeddings
__lowerCAmelCase = use_relative_position_bias
__lowerCAmelCase = use_shared_relative_position_bias
__lowerCAmelCase = layer_scale_init_value
__lowerCAmelCase = drop_path_rate
__lowerCAmelCase = use_mean_pooling
# decode head attributes (semantic segmentation)
__lowerCAmelCase = out_indices
__lowerCAmelCase = pool_scales
# auxiliary head attributes (semantic segmentation)
__lowerCAmelCase = use_auxiliary_head
__lowerCAmelCase = auxiliary_loss_weight
__lowerCAmelCase = auxiliary_channels
__lowerCAmelCase = auxiliary_num_convs
__lowerCAmelCase = auxiliary_concat_input
__lowerCAmelCase = semantic_loss_ignore_index
class _UpperCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
a_ = version.parse("""1.11""" )
@property
def lowercase ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowercase ( self : Optional[Any] ) -> float:
return 1e-4
| 284 | 0 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
)
from packaging import version
from requests import HTTPError
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
__UpperCAmelCase : Dict = get_logger(__name__)
__UpperCAmelCase : Optional[Any] = Path(__file__).parent / "model_card_template.md"
__UpperCAmelCase : int = uuida().hex
__UpperCAmelCase : Optional[Any] = os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
__UpperCAmelCase : Optional[Any] = os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
__UpperCAmelCase : Union[str, Any] = HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"
def A__ ( SCREAMING_SNAKE_CASE__ = None) -> str:
__snake_case: int = F'''diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'''
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += F'''; torch/{_torch_version}'''
if is_flax_available():
ua += F'''; jax/{_jax_version}'''
ua += F'''; flax/{_flax_version}'''
if is_onnx_available():
ua += F'''; onnxruntime/{_onnxruntime_version}'''
# CI will set this value to True
if os.environ.get("""DIFFUSERS_IS_CI""" , """""").upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__):
ua += "; " + "; ".join(F'''{k}/{v}''' for k, v in user_agent.items())
elif isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__):
ua += "; " + user_agent
return ua
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None) -> List[Any]:
if token is None:
__snake_case: List[Any] = HfFolder.get_token()
if organization is None:
__snake_case: List[Any] = whoami(SCREAMING_SNAKE_CASE__)["""name"""]
return F'''{username}/{model_id}'''
else:
return F'''{organization}/{model_id}'''
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> List[str]:
if not is_jinja_available():
raise ValueError(
"""Modelcard rendering is based on Jinja templates."""
""" Please make sure to have `jinja` installed before using `create_model_card`."""
""" To install it, please run `pip install Jinja2`.""")
if hasattr(SCREAMING_SNAKE_CASE__ , """local_rank""") and args.local_rank not in [-1, 0]:
return
__snake_case: Tuple = args.hub_token if hasattr(SCREAMING_SNAKE_CASE__ , """hub_token""") else None
__snake_case: Optional[Any] = get_full_repo_name(SCREAMING_SNAKE_CASE__ , token=SCREAMING_SNAKE_CASE__)
__snake_case: Tuple = ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
language="""en""" , license="""apache-2.0""" , library_name="""diffusers""" , tags=[] , datasets=args.dataset_name , metrics=[] , ) , template_path=SCREAMING_SNAKE_CASE__ , model_name=SCREAMING_SNAKE_CASE__ , repo_name=SCREAMING_SNAKE_CASE__ , dataset_name=args.dataset_name if hasattr(SCREAMING_SNAKE_CASE__ , """dataset_name""") else None , learning_rate=args.learning_rate , train_batch_size=args.train_batch_size , eval_batch_size=args.eval_batch_size , gradient_accumulation_steps=(
args.gradient_accumulation_steps if hasattr(SCREAMING_SNAKE_CASE__ , """gradient_accumulation_steps""") else None
) , adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE__ , """adam_beta1""") else None , adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE__ , """adam_beta2""") else None , adam_weight_decay=args.adam_weight_decay if hasattr(SCREAMING_SNAKE_CASE__ , """adam_weight_decay""") else None , adam_epsilon=args.adam_epsilon if hasattr(SCREAMING_SNAKE_CASE__ , """adam_epsilon""") else None , lr_scheduler=args.lr_scheduler if hasattr(SCREAMING_SNAKE_CASE__ , """lr_scheduler""") else None , lr_warmup_steps=args.lr_warmup_steps if hasattr(SCREAMING_SNAKE_CASE__ , """lr_warmup_steps""") else None , ema_inv_gamma=args.ema_inv_gamma if hasattr(SCREAMING_SNAKE_CASE__ , """ema_inv_gamma""") else None , ema_power=args.ema_power if hasattr(SCREAMING_SNAKE_CASE__ , """ema_power""") else None , ema_max_decay=args.ema_max_decay if hasattr(SCREAMING_SNAKE_CASE__ , """ema_max_decay""") else None , mixed_precision=args.mixed_precision , )
__snake_case: str = os.path.join(args.output_dir , """README.md""")
model_card.save(SCREAMING_SNAKE_CASE__)
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None) -> Optional[int]:
if resolved_file is None or commit_hash is not None:
return commit_hash
__snake_case: List[str] = str(Path(SCREAMING_SNAKE_CASE__).as_posix())
__snake_case: Optional[int] = re.search(r"""snapshots/([^/]+)/""" , SCREAMING_SNAKE_CASE__)
if search is None:
return None
__snake_case: Tuple = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(SCREAMING_SNAKE_CASE__) else None
# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
__UpperCAmelCase : Tuple = os.path.expanduser(
os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
__UpperCAmelCase : int = os.path.join(hf_cache_home, "diffusers")
def A__ ( SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None) -> None:
if new_cache_dir is None:
__snake_case: List[Any] = DIFFUSERS_CACHE
if old_cache_dir is None:
__snake_case: Dict = old_diffusers_cache
__snake_case: str = Path(SCREAMING_SNAKE_CASE__).expanduser()
__snake_case: List[str] = Path(SCREAMING_SNAKE_CASE__).expanduser()
for old_blob_path in old_cache_dir.glob("""**/blobs/*"""):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
__snake_case: Optional[Any] = new_cache_dir / old_blob_path.relative_to(SCREAMING_SNAKE_CASE__)
new_blob_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__)
os.replace(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
try:
os.symlink(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
except OSError:
logger.warning(
"""Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.""")
# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
__UpperCAmelCase : Optional[Any] = os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
__UpperCAmelCase : Tuple = 0
else:
with open(cache_version_file) as f:
try:
__UpperCAmelCase : Optional[int] = int(f.read())
except ValueError:
__UpperCAmelCase : Dict = 0
if cache_version < 1:
__UpperCAmelCase : Tuple = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
if old_cache_is_not_empty:
logger.warning(
"The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
"existing cached models. This is a one-time operation, you can interrupt it or run it "
"later by calling `diffusers.utils.hub_utils.move_cache()`."
)
try:
move_cache()
except Exception as e:
__UpperCAmelCase : List[Any] = "\n".join(traceback.format_tb(e.__traceback__))
logger.error(
f'There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '
"file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
"message and we will do our best to help."
)
if cache_version < 1:
try:
os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
with open(cache_version_file, "w") as f:
f.write("1")
except Exception:
logger.warning(
f'There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '
"the directory exists and can be written to."
)
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None) -> str:
if variant is not None:
__snake_case: List[Any] = weights_name.split(""".""")
__snake_case: str = splits[:-1] + [variant] + splits[-1:]
__snake_case: Optional[int] = """.""".join(SCREAMING_SNAKE_CASE__)
return weights_name
def A__ ( SCREAMING_SNAKE_CASE__ , *,
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None , ) -> Dict:
__snake_case: Union[str, Any] = str(SCREAMING_SNAKE_CASE__)
if os.path.isfile(SCREAMING_SNAKE_CASE__):
return pretrained_model_name_or_path
elif os.path.isdir(SCREAMING_SNAKE_CASE__):
if os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)):
# Load from a PyTorch checkpoint
__snake_case: List[Any] = os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)):
__snake_case: List[Any] = os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
return model_file
else:
raise EnvironmentError(
F'''Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.''')
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(SCREAMING_SNAKE_CASE__).base_version) >= version.parse("""0.20.0""")
):
try:
__snake_case: str = hf_hub_download(
SCREAMING_SNAKE_CASE__ , filename=_add_variant(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) , cache_dir=SCREAMING_SNAKE_CASE__ , force_download=SCREAMING_SNAKE_CASE__ , proxies=SCREAMING_SNAKE_CASE__ , resume_download=SCREAMING_SNAKE_CASE__ , local_files_only=SCREAMING_SNAKE_CASE__ , use_auth_token=SCREAMING_SNAKE_CASE__ , user_agent=SCREAMING_SNAKE_CASE__ , subfolder=SCREAMING_SNAKE_CASE__ , revision=revision or commit_hash , )
warnings.warn(
F'''Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.''' , SCREAMING_SNAKE_CASE__ , )
return model_file
except: # noqa: E722
warnings.warn(
F'''You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)}\' so that the correct variant file can be added.''' , SCREAMING_SNAKE_CASE__ , )
try:
# 2. Load model file as usual
__snake_case: str = hf_hub_download(
SCREAMING_SNAKE_CASE__ , filename=SCREAMING_SNAKE_CASE__ , cache_dir=SCREAMING_SNAKE_CASE__ , force_download=SCREAMING_SNAKE_CASE__ , proxies=SCREAMING_SNAKE_CASE__ , resume_download=SCREAMING_SNAKE_CASE__ , local_files_only=SCREAMING_SNAKE_CASE__ , use_auth_token=SCREAMING_SNAKE_CASE__ , user_agent=SCREAMING_SNAKE_CASE__ , subfolder=SCREAMING_SNAKE_CASE__ , revision=revision or commit_hash , )
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
F'''{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '''
"""listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a """
"""token having permission to this repo with `use_auth_token` or log in with `huggingface-cli """
"""login`.""")
except RevisionNotFoundError:
raise EnvironmentError(
F'''{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '''
"""this model name. Check the model page at """
F'''\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.''')
except EntryNotFoundError:
raise EnvironmentError(
F'''{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.''')
except HTTPError as err:
raise EnvironmentError(
F'''There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}''')
except ValueError:
raise EnvironmentError(
F'''We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'''
F''' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'''
F''' directory containing a file named {weights_name} or'''
""" \nCheckout your internet connection or see how to run the library in"""
""" offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'.""")
except EnvironmentError:
raise EnvironmentError(
F'''Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '''
"""'https://huggingface.co/models', make sure you don't have a local directory with the same name. """
F'''Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '''
F'''containing a file named {weights_name}''')
| 293 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class __snake_case ( __lowerCamelCase ):
'''simple docstring'''
def UpperCAmelCase__ ( self : Optional[int] ):
__snake_case: Optional[int] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(A , """hidden_sizes""" ) )
self.parent.assertTrue(hasattr(A , """neck_hidden_sizes""" ) )
self.parent.assertTrue(hasattr(A , """num_attention_heads""" ) )
class __snake_case :
'''simple docstring'''
def __init__( self : int , A : str , A : Dict=13 , A : str=32 , A : Any=2 , A : Optional[Any]=3 , A : str=640 , A : Tuple=4 , A : Dict="silu" , A : List[Any]=3 , A : Any=32 , A : Any=0.1 , A : int=0.1 , A : Dict=0.1 , A : Optional[Any]=0.02 , A : List[Any]=True , A : Tuple=True , A : Any=10 , A : Optional[int]=None , ):
__snake_case: List[Any] = parent
__snake_case: Dict = batch_size
__snake_case: int = image_size
__snake_case: Tuple = patch_size
__snake_case: Tuple = num_channels
__snake_case: str = last_hidden_size
__snake_case: Dict = num_attention_heads
__snake_case: Dict = hidden_act
__snake_case: Tuple = conv_kernel_size
__snake_case: List[str] = output_stride
__snake_case: List[str] = hidden_dropout_prob
__snake_case: Optional[Any] = attention_probs_dropout_prob
__snake_case: int = classifier_dropout_prob
__snake_case: List[Any] = use_labels
__snake_case: Union[str, Any] = is_training
__snake_case: Union[str, Any] = num_labels
__snake_case: str = initializer_range
__snake_case: List[Any] = scope
def UpperCAmelCase__ ( self : List[Any] ):
__snake_case: Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__snake_case: Tuple = None
__snake_case: Any = None
if self.use_labels:
__snake_case: Union[str, Any] = ids_tensor([self.batch_size] , self.num_labels )
__snake_case: str = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
__snake_case: Any = self.get_config()
return config, pixel_values, labels, pixel_labels
def UpperCAmelCase__ ( self : int ):
return MobileViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def UpperCAmelCase__ ( self : str , A : Optional[Any] , A : Any , A : Any , A : Union[str, Any] ):
__snake_case: List[Any] = MobileViTModel(config=A )
model.to(A )
model.eval()
__snake_case: int = model(A )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def UpperCAmelCase__ ( self : str , A : List[Any] , A : Any , A : Any , A : int ):
__snake_case: str = self.num_labels
__snake_case: Optional[int] = MobileViTForImageClassification(A )
model.to(A )
model.eval()
__snake_case: Union[str, Any] = model(A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase__ ( self : Optional[int] , A : str , A : Optional[Any] , A : int , A : str ):
__snake_case: List[Any] = self.num_labels
__snake_case: Dict = MobileViTForSemanticSegmentation(A )
model.to(A )
model.eval()
__snake_case: Union[str, Any] = model(A )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
__snake_case: Tuple = model(A , labels=A )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def UpperCAmelCase__ ( self : Dict ):
__snake_case: Tuple = self.prepare_config_and_inputs()
__snake_case , __snake_case , __snake_case , __snake_case: Any = config_and_inputs
__snake_case: Optional[int] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __snake_case ( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
lowerCAmelCase__ = (
{
"""feature-extraction""": MobileViTModel,
"""image-classification""": MobileViTForImageClassification,
"""image-segmentation""": MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def UpperCAmelCase__ ( self : List[str] ):
__snake_case: List[Any] = MobileViTModelTester(self )
__snake_case: str = MobileViTConfigTester(self , config_class=A , has_text_modality=A )
def UpperCAmelCase__ ( self : str ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""MobileViT does not use inputs_embeds""" )
def UpperCAmelCase__ ( self : List[Any] ):
pass
@unittest.skip(reason="""MobileViT does not support input and output embeddings""" )
def UpperCAmelCase__ ( self : Dict ):
pass
@unittest.skip(reason="""MobileViT does not output attentions""" )
def UpperCAmelCase__ ( self : Optional[Any] ):
pass
def UpperCAmelCase__ ( self : str ):
__snake_case , __snake_case: Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__snake_case: Optional[Any] = model_class(A )
__snake_case: int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__snake_case: Optional[int] = [*signature.parameters.keys()]
__snake_case: List[Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , A )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def UpperCAmelCase__ ( self : Optional[int] ):
pass
def UpperCAmelCase__ ( self : Dict ):
__snake_case: Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def UpperCAmelCase__ ( self : Dict ):
def check_hidden_states_output(A : List[Any] , A : int , A : Tuple ):
__snake_case: List[str] = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
__snake_case: str = model(**self._prepare_for_class(A , A ) )
__snake_case: Optional[int] = outputs.hidden_states
__snake_case: Any = 5
self.assertEqual(len(A ) , A )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
__snake_case: Union[str, Any] = 2
for i in range(len(A ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
__snake_case , __snake_case: List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__snake_case: Optional[Any] = True
check_hidden_states_output(A , A , A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__snake_case: Dict = True
check_hidden_states_output(A , A , A )
def UpperCAmelCase__ ( self : int ):
__snake_case: Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A )
def UpperCAmelCase__ ( self : Union[str, Any] ):
__snake_case: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*A )
@slow
def UpperCAmelCase__ ( self : Union[str, Any] ):
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case: List[Any] = MobileViTModel.from_pretrained(A )
self.assertIsNotNone(A )
def A__ ( ) -> Optional[int]:
__snake_case: Optional[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
return image
@require_torch
@require_vision
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCAmelCase__ ( self : Dict ):
return MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small""" ) if is_vision_available() else None
@slow
def UpperCAmelCase__ ( self : List[Any] ):
__snake_case: Tuple = MobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small""" ).to(A )
__snake_case: str = self.default_image_processor
__snake_case: Optional[Any] = prepare_img()
__snake_case: List[Any] = image_processor(images=A , return_tensors="""pt""" ).to(A )
# forward pass
with torch.no_grad():
__snake_case: Dict = model(**A )
# verify the logits
__snake_case: List[str] = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , A )
__snake_case: Union[str, Any] = torch.tensor([-1.9364, -1.2327, -0.4653] ).to(A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , A , atol=1E-4 ) )
@slow
def UpperCAmelCase__ ( self : Tuple ):
__snake_case: Tuple = MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )
__snake_case: List[str] = model.to(A )
__snake_case: Dict = MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )
__snake_case: List[Any] = prepare_img()
__snake_case: List[str] = image_processor(images=A , return_tensors="""pt""" ).to(A )
# forward pass
with torch.no_grad():
__snake_case: List[Any] = model(**A )
__snake_case: Optional[int] = outputs.logits
# verify the logits
__snake_case: Dict = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , A )
__snake_case: Optional[int] = torch.tensor(
[
[[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
[[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
[[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
] , device=A , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , A , atol=1E-4 ) )
@slow
def UpperCAmelCase__ ( self : Dict ):
__snake_case: int = MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )
__snake_case: str = model.to(A )
__snake_case: Optional[Any] = MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )
__snake_case: List[str] = prepare_img()
__snake_case: Optional[int] = image_processor(images=A , return_tensors="""pt""" ).to(A )
# forward pass
with torch.no_grad():
__snake_case: Dict = model(**A )
__snake_case: List[Any] = outputs.logits.detach().cpu()
__snake_case: List[str] = image_processor.post_process_semantic_segmentation(outputs=A , target_sizes=[(50, 60)] )
__snake_case: str = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , A )
__snake_case: int = image_processor.post_process_semantic_segmentation(outputs=A )
__snake_case: Tuple = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , A )
| 293 | 1 |
import math
import random
def A__ ( __lowerCamelCase, __lowerCamelCase = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
__UpperCAmelCase = 0.02
def A__ ( __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = float(2 * (random.randint(1, 1_00 )) - 1 )
for _ in range(__lowerCamelCase ):
# Forward propagation
SCREAMING_SNAKE_CASE_ = sigmoid_function(INITIAL_VALUE * weight )
# How much did we miss?
SCREAMING_SNAKE_CASE_ = (expected / 1_00) - layer_a
# Error delta
SCREAMING_SNAKE_CASE_ = layer_1_error * sigmoid_function(__lowerCamelCase, __lowerCamelCase )
# Update weight
weight += INITIAL_VALUE * layer_1_delta
return layer_a * 1_00
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCAmelCase = int(input("Expected value: "))
__UpperCAmelCase = int(input("Number of propagations: "))
print(forward_propagation(expected, number_propagations))
| 299 |
def A__ ( __lowerCamelCase ):
if not isinstance(__lowerCamelCase, __lowerCamelCase ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor in range(1, input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 299 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def UpperCAmelCase_ (__a : int ):
"""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(math.sqrt(__a ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
__lowerCAmelCase = [num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]
def UpperCAmelCase_ (__a : int ):
"""simple docstring"""
if not isinstance(__a , __a ):
raise ValueError('n must be an integer' )
if n <= 0:
raise ValueError('n must be >= 0' )
_a : Optional[int] = []
for num in range(len(__a ) ):
_a : List[str] = 0
while 2 * i * i <= odd_composites[num]:
_a : Optional[Any] = odd_composites[num] - 2 * i * i
if is_prime(__a ):
break
i += 1
else:
list_nums.append(odd_composites[num] )
if len(__a ) == n:
return list_nums
return []
def UpperCAmelCase_ ():
"""simple docstring"""
return compute_nums(1 )[0]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 352 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCAmelCase = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SqueezeBertConfig""",
"""SqueezeBertOnnxConfig""",
],
"""tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = ["""SqueezeBertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = [
"""SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SqueezeBertForMaskedLM""",
"""SqueezeBertForMultipleChoice""",
"""SqueezeBertForQuestionAnswering""",
"""SqueezeBertForSequenceClassification""",
"""SqueezeBertForTokenClassification""",
"""SqueezeBertModel""",
"""SqueezeBertModule""",
"""SqueezeBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_squeezebert import (
SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
SqueezeBertConfig,
SqueezeBertOnnxConfig,
)
from .tokenization_squeezebert import SqueezeBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_squeezebert import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
SqueezeBertModule,
SqueezeBertPreTrainedModel,
)
else:
import sys
__lowerCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 5 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__snake_case : List[Any] = {"""processing_wav2vec2_with_lm""": ["""Wav2Vec2ProcessorWithLM"""]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
__snake_case : str = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 248 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, 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 A__(unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def UpperCamelCase__ ( self ) -> List[Any]:
a_ , a_ : Any = FlaxControlNetModel.from_pretrained(
"""lllyasviel/sd-controlnet-canny""" , from_pt=_lowercase , dtype=jnp.bfloataa )
a_ , a_ : Any = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , controlnet=_lowercase , from_pt=_lowercase , dtype=jnp.bfloataa )
a_ : Union[str, Any] = controlnet_params
a_ : int = """bird"""
a_ : Tuple = jax.device_count()
a_ : List[Any] = pipe.prepare_text_inputs([prompts] * num_samples )
a_ : List[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" )
a_ : Optional[Any] = pipe.prepare_image_inputs([canny_image] * num_samples )
a_ : int = jax.random.PRNGKey(0 )
a_ : Union[str, Any] = jax.random.split(_lowercase , jax.device_count() )
a_ : Any = replicate(_lowercase )
a_ : Optional[int] = shard(_lowercase )
a_ : List[Any] = shard(_lowercase )
a_ : int = pipe(
prompt_ids=_lowercase , image=_lowercase , params=_lowercase , prng_seed=_lowercase , num_inference_steps=50 , jit=_lowercase , ).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
a_ : Optional[Any] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
a_ : str = images[0, 253:256, 253:256, -1]
a_ : Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
a_ : List[Any] = jnp.array(
[0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8] )
print(F'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def UpperCamelCase__ ( self ) -> str:
a_ , a_ : str = FlaxControlNetModel.from_pretrained(
"""lllyasviel/sd-controlnet-openpose""" , from_pt=_lowercase , dtype=jnp.bfloataa )
a_ , a_ : Any = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , controlnet=_lowercase , from_pt=_lowercase , dtype=jnp.bfloataa )
a_ : Tuple = controlnet_params
a_ : str = """Chef in the kitchen"""
a_ : Optional[Any] = jax.device_count()
a_ : Any = pipe.prepare_text_inputs([prompts] * num_samples )
a_ : Union[str, Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png""" )
a_ : Any = pipe.prepare_image_inputs([pose_image] * num_samples )
a_ : str = jax.random.PRNGKey(0 )
a_ : int = jax.random.split(_lowercase , jax.device_count() )
a_ : Optional[int] = replicate(_lowercase )
a_ : Tuple = shard(_lowercase )
a_ : List[Any] = shard(_lowercase )
a_ : str = pipe(
prompt_ids=_lowercase , image=_lowercase , params=_lowercase , prng_seed=_lowercase , num_inference_steps=50 , jit=_lowercase , ).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
a_ : List[str] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
a_ : List[str] = images[0, 253:256, 253:256, -1]
a_ : str = jnp.asarray(jax.device_get(image_slice.flatten() ) )
a_ : Optional[int] = jnp.array(
[[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]] )
print(F'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 248 | 1 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ : int = [
'word_embeddings_layernorm.weight',
'word_embeddings_layernorm.bias',
'input_layernorm.weight',
'input_layernorm.bias',
'post_attention_layernorm.weight',
'post_attention_layernorm.bias',
'self_attention.dense.bias',
'mlp.dense_4h_to_h.bias',
'ln_f.weight',
'ln_f.bias',
]
UpperCAmelCase__ : int = [
'mlp.dense_4h_to_h.weight',
'self_attention.dense.weight',
]
def lowerCamelCase__ ( a , a ) -> int:
_A: Union[str, Any] = {
'''word_embeddings.weight''': '''word_embeddings.weight''',
'''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
'''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
'''weight''': '''ln_f.weight''',
'''bias''': '''ln_f.bias''',
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
_A: Tuple = int(re.match(R'''.*layer_(\d*).*''' , a )[1] )
layer_number -= 3
return f"""h.{layer_number}.""" + key
def lowerCamelCase__ ( a ) -> Optional[Any]:
if dtype == torch.bool:
return 1 / 8
_A: Tuple = re.search(R'''[^\d](\d+)$''' , str(a ) )
if bit_search is None:
raise ValueError(f"""`dtype` is not a valid dtype: {dtype}.""" )
_A: Any = int(bit_search.groups()[0] )
return bit_size // 8
def lowerCamelCase__ ( a , a , a , a , a ) -> Tuple:
# Construct model
if bloom_config_file == "":
_A: Union[str, Any] = BloomConfig()
else:
_A: Optional[Any] = BloomConfig.from_json_file(a )
if shard_model:
_A: Dict = os.listdir(a )
_A: Optional[Any] = sorted(filter(lambda a : s.startswith('''layer''' ) and "model_00" in s , a ) )
_A: Tuple = {'''weight_map''': {}, '''metadata''': {}}
_A: Optional[Any] = 0
_A: int = None
_A: str = BloomConfig()
for j, file in enumerate(a ):
print('''Processing file: {}'''.format(a ) )
_A: Tuple = None
for i in range(a ):
# load all TP files
_A: str = file.replace('''model_00''' , f"""model_0{i}""" )
_A: str = torch.load(os.path.join(a , a ) , map_location='''cpu''' )
# Rename keys in the transformers names
_A: str = list(temp.keys() )
for key in keys:
_A: str = temp.pop(a )
if tensors is None:
_A: int = temp
else:
for key in tensors.keys():
if any(key.endswith(a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
_A: Union[str, Any] = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
_A: int = torch.cat([tensors[key], temp[key]] , dim=a )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
_A: str = tensors[key] / pretraining_tp
torch.save(
a , os.path.join(
a , '''pytorch_model_{}-of-{}.bin'''.format(str(j + 1 ).zfill(5 ) , str(len(a ) ).zfill(5 ) ) , ) , )
for key in tensors.keys():
_A: Dict = tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype )
if key not in index_dict["weight_map"]:
_A: Union[str, Any] = '''pytorch_model_{}-of-{}.bin'''.format(
str(j + 1 ).zfill(5 ) , str(len(a ) ).zfill(5 ) )
_A: Union[str, Any] = BloomConfig()
_A: str = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
_A: Optional[int] = total_size
with open(a , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
with open(os.path.join(a , WEIGHTS_NAME + '''.index.json''' ) , '''w''' , encoding='''utf-8''' ) as f:
_A: Optional[Any] = json.dumps(a , indent=2 , sort_keys=a ) + '''\n'''
f.write(a )
else:
_A: int = BloomModel(a )
_A: Tuple = os.listdir(a )
_A: str = sorted(filter(lambda a : s.startswith('''layer''' ) and "model_00" in s , a ) )
_A: Optional[Any] = None
for i, file in enumerate(a ):
_A: str = None
for i in range(a ):
# load all TP files
_A: Tuple = file.replace('''model_00''' , f"""model_0{i}""" )
_A: List[Any] = torch.load(os.path.join(a , a ) , map_location='''cpu''' )
# Rename keys in the transformers names
_A: Tuple = list(temp.keys() )
for key in keys:
_A: int = temp.pop(a )
if tensors is None:
_A: Union[str, Any] = temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
_A: Dict = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
_A: Optional[int] = torch.cat([tensors[key], temp[key]] , dim=a )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
_A: List[Any] = tensors[key] / pretraining_tp
_A: str = model.load_state_dict(a , strict=a )
assert not other_keys.unexpected_keys, f"""The keys {other_keys.unexpected_keys} are unexpected"""
if missing_keys is None:
_A: Union[str, Any] = set(other_keys.missing_keys )
else:
_A: Any = missing_keys.intersection(set(other_keys.missing_keys ) )
assert not missing_keys, f"""The keys {missing_keys} are missing"""
# Save pytorch-model
os.makedirs(a , exist_ok=a )
_A: Optional[Any] = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
_A: Optional[int] = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
print(f"""Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}""" )
if config.torch_dtype is not None:
_A: List[str] = model.to(config.torch_dtype )
torch.save(model.state_dict() , a )
print(f"""Save configuration file to {pytorch_config_dump_path}""" )
with open(a , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
UpperCAmelCase__ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--bloom_checkpoint_path',
default=None,
type=str,
required=True,
help='Path to the Megatron-LM checkpoint path.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--bloom_config_file',
default='',
type=str,
help=(
'An optional config json file corresponding to the pre-trained model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--shard_model',
action='store_true',
help='An optional setting to shard the output model \nThis enables sharding the converted checkpoint',
)
parser.add_argument(
'--pretraining_tp',
default=4,
type=int,
help='Pretraining TP rank that has been used when training the model in Megatron-LM \n',
)
UpperCAmelCase__ : Any = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| 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 |
"""simple docstring"""
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : int ):
__lowercase = ""
__lowercase = ""
__lowercase = []
def _lowercase ( self : int, UpperCAmelCase__ : int, UpperCAmelCase__ : int ):
if m == -1:
return n + 1
elif n == -1:
return m + 1
elif self.dp[m][n] > -1:
return self.dp[m][n]
else:
if self.worda[m] == self.worda[n]:
__lowercase = self.__min_dist_top_down_dp(m - 1, n - 1 )
else:
__lowercase = self.__min_dist_top_down_dp(UpperCAmelCase__, n - 1 )
__lowercase = self.__min_dist_top_down_dp(m - 1, UpperCAmelCase__ )
__lowercase = self.__min_dist_top_down_dp(m - 1, n - 1 )
__lowercase = 1 + min(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ )
return self.dp[m][n]
def _lowercase ( self : Union[str, Any], UpperCAmelCase__ : str, UpperCAmelCase__ : str ):
__lowercase = worda
__lowercase = worda
__lowercase = [[-1 for _ in range(len(UpperCAmelCase__ ) )] for _ in range(len(UpperCAmelCase__ ) )]
return self.__min_dist_top_down_dp(len(UpperCAmelCase__ ) - 1, len(UpperCAmelCase__ ) - 1 )
def _lowercase ( self : int, UpperCAmelCase__ : str, UpperCAmelCase__ : str ):
__lowercase = worda
__lowercase = worda
__lowercase = len(UpperCAmelCase__ )
__lowercase = len(UpperCAmelCase__ )
__lowercase = [[0 for _ in range(n + 1 )] for _ in range(m + 1 )]
for i in range(m + 1 ):
for j in range(n + 1 ):
if i == 0: # first string is empty
__lowercase = j
elif j == 0: # second string is empty
__lowercase = i
elif worda[i - 1] == worda[j - 1]: # last characters are equal
__lowercase = self.dp[i - 1][j - 1]
else:
__lowercase = self.dp[i][j - 1]
__lowercase = self.dp[i - 1][j]
__lowercase = self.dp[i - 1][j - 1]
__lowercase = 1 + min(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ )
return self.dp[m][n]
if __name__ == "__main__":
_a = EditDistance()
print('****************** Testing Edit Distance DP Algorithm ******************')
print()
_a = input('Enter the first string: ').strip()
_a = input('Enter the second string: ').strip()
print()
print(F"The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}")
print(F"The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}")
print()
print('*************** End of Testing Edit Distance DP Algorithm ***************')
| 17 | """simple docstring"""
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> int:
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
lowercase__: Tuple = grid[0]
for row_n in range(1 , len(__UpperCAmelCase ) ):
lowercase__: Tuple = grid[row_n]
lowercase__: Dict = fill_row(__UpperCAmelCase , __UpperCAmelCase )
lowercase__: Union[str, Any] = grid[row_n]
return grid[-1][-1]
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase ) -> list:
current_row[0] += row_above[0]
for cell_n in range(1 , len(__UpperCAmelCase ) ):
current_row[cell_n] += min(current_row[cell_n - 1] , row_above[cell_n] )
return current_row
if __name__ == "__main__":
import doctest
doctest.testmod()
| 177 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_A = logging.getLogger(__name__)
@dataclass
class _lowerCamelCase :
_lowerCamelCase :str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
_lowerCamelCase :Optional[str] = field(
default=a_ , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
_lowerCamelCase :Optional[str] = field(
default="NER" , metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"} )
_lowerCamelCase :Optional[str] = field(
default=a_ , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
_lowerCamelCase :bool = field(default=a_ , metadata={"help": "Set this flag to use fast tokenization."} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_lowerCamelCase :Optional[str] = field(
default=a_ , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
@dataclass
class _lowerCamelCase :
_lowerCamelCase :str = field(
metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
_lowerCamelCase :Optional[str] = field(
default=a_ , metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."} , )
_lowerCamelCase :int = field(
default=128 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
_lowerCamelCase :bool = field(
default=a_ , metadata={"help": "Overwrite the cached training and evaluation sets"} )
def lowercase_ ( ) -> List[str]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowerCAmelCase__ : str = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : List[Any] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Optional[Any] = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
""" --overwrite_output_dir to overcome.""" )
lowerCAmelCase__ : Tuple = import_module("""tasks""" )
try:
lowerCAmelCase__ : Optional[int] = getattr(__UpperCAmelCase , model_args.task_type )
lowerCAmelCase__ : TokenClassificationTask = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , __UpperCAmelCase )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
lowerCAmelCase__ : Any = token_classification_task.get_labels(data_args.labels )
lowerCAmelCase__ : Dict[int, str] = dict(enumerate(__UpperCAmelCase ) )
lowerCAmelCase__ : Optional[Any] = len(__UpperCAmelCase )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowerCAmelCase__ : Optional[Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__UpperCAmelCase , idalabel=__UpperCAmelCase , labelaid={label: i for i, label in enumerate(__UpperCAmelCase )} , cache_dir=model_args.cache_dir , )
lowerCAmelCase__ : Optional[Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
lowerCAmelCase__ : Optional[int] = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__UpperCAmelCase , cache_dir=model_args.cache_dir , )
# Get datasets
lowerCAmelCase__ : Tuple = (
TokenClassificationDataset(
token_classification_task=__UpperCAmelCase , data_dir=data_args.data_dir , tokenizer=__UpperCAmelCase , labels=__UpperCAmelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
lowerCAmelCase__ : List[Any] = (
TokenClassificationDataset(
token_classification_task=__UpperCAmelCase , data_dir=data_args.data_dir , tokenizer=__UpperCAmelCase , labels=__UpperCAmelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(__UpperCAmelCase , __UpperCAmelCase ) -> Tuple[List[int], List[int]]:
lowerCAmelCase__ : Optional[int] = np.argmax(__UpperCAmelCase , axis=2 )
lowerCAmelCase__ , lowerCAmelCase__ : Tuple = preds.shape
lowerCAmelCase__ : Union[str, Any] = [[] for _ in range(__UpperCAmelCase )]
lowerCAmelCase__ : int = [[] for _ in range(__UpperCAmelCase )]
for i in range(__UpperCAmelCase ):
for j in range(__UpperCAmelCase ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(__UpperCAmelCase ) -> Dict:
lowerCAmelCase__ , lowerCAmelCase__ : str = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(__UpperCAmelCase , __UpperCAmelCase ),
"precision": precision_score(__UpperCAmelCase , __UpperCAmelCase ),
"recall": recall_score(__UpperCAmelCase , __UpperCAmelCase ),
"f1": fa_score(__UpperCAmelCase , __UpperCAmelCase ),
}
# Data collator
lowerCAmelCase__ : List[str] = DataCollatorWithPadding(__UpperCAmelCase , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowerCAmelCase__ : List[Any] = Trainer(
model=__UpperCAmelCase , args=__UpperCAmelCase , train_dataset=__UpperCAmelCase , eval_dataset=__UpperCAmelCase , compute_metrics=__UpperCAmelCase , data_collator=__UpperCAmelCase , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowerCAmelCase__ : List[Any] = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
lowerCAmelCase__ : List[str] = trainer.evaluate()
lowerCAmelCase__ : List[str] = os.path.join(training_args.output_dir , """eval_results.txt""" )
if trainer.is_world_process_zero():
with open(__UpperCAmelCase , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key, value in result.items():
logger.info(""" %s = %s""" , __UpperCAmelCase , __UpperCAmelCase )
writer.write("""%s = %s\n""" % (key, value) )
results.update(__UpperCAmelCase )
# Predict
if training_args.do_predict:
lowerCAmelCase__ : List[str] = TokenClassificationDataset(
token_classification_task=__UpperCAmelCase , data_dir=data_args.data_dir , tokenizer=__UpperCAmelCase , labels=__UpperCAmelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : List[str] = trainer.predict(__UpperCAmelCase )
lowerCAmelCase__ , lowerCAmelCase__ : List[Any] = align_predictions(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ : List[Any] = os.path.join(training_args.output_dir , """test_results.txt""" )
if trainer.is_world_process_zero():
with open(__UpperCAmelCase , """w""" ) as writer:
for key, value in metrics.items():
logger.info(""" %s = %s""" , __UpperCAmelCase , __UpperCAmelCase )
writer.write("""%s = %s\n""" % (key, value) )
# Save predictions
lowerCAmelCase__ : Tuple = os.path.join(training_args.output_dir , """test_predictions.txt""" )
if trainer.is_world_process_zero():
with open(__UpperCAmelCase , """w""" ) as writer:
with open(os.path.join(data_args.data_dir , """test.txt""" ) , """r""" ) as f:
token_classification_task.write_predictions_to_file(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
return results
def lowercase_ ( __UpperCAmelCase ) -> str:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 212 |
"""simple docstring"""
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowercase_ ( __UpperCAmelCase ) -> List[str]:
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class _lowerCamelCase ( a_ ):
@staticmethod
def _lowerCAmelCase ( UpperCamelCase : ArgumentParser ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase__ : int = parser.add_parser("""download""" )
download_parser.add_argument(
"""--cache-dir""" , type=UpperCamelCase , default=UpperCamelCase , help="""Path to location to store the models""" )
download_parser.add_argument(
"""--force""" , action="""store_true""" , help="""Force the model to be download even if already in cache-dir""" )
download_parser.add_argument(
"""--trust-remote-code""" , action="""store_true""" , help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine""" , )
download_parser.add_argument("""model""" , type=UpperCamelCase , help="""Name of the model to download""" )
download_parser.set_defaults(func=UpperCamelCase )
def __init__( self : Optional[int] , UpperCamelCase : str , UpperCamelCase : str , UpperCamelCase : bool , UpperCamelCase : bool ) -> Any:
"""simple docstring"""
lowerCAmelCase__ : int = model
lowerCAmelCase__ : Union[str, Any] = cache
lowerCAmelCase__ : Optional[int] = force
lowerCAmelCase__ : Dict = trust_remote_code
def _lowerCAmelCase ( self : int ) -> Any:
"""simple docstring"""
from ..models.auto import AutoModel, AutoTokenizer
AutoModel.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
AutoTokenizer.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
| 212 | 1 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __UpperCAmelCase ( ) -> str:
lowercase__ : Tuple = HfArgumentParser(__lowerCamelCase )
lowercase__ : Optional[Any] = parser.parse_args_into_dataclasses()[0]
lowercase__ : int = TensorFlowBenchmark(args=__lowerCamelCase )
try:
lowercase__ : Optional[int] = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowercase__ : Optional[Any] = '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
lowercase__ : List[str] = ''' '''.join(str(__lowerCamelCase ).split(''' ''' )[:-1] )
lowercase__ : Tuple = ''''''
lowercase__ : List[Any] = eval(str(__lowerCamelCase ).split(''' ''' )[-1] )
lowercase__ : Any = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(__lowerCamelCase )
if len(__lowerCamelCase ) > 0:
lowercase__ : Optional[int] = full_error_msg + begin_error_msg + str(__lowerCamelCase )
raise ValueError(__lowerCamelCase )
benchmark.run()
if __name__ == "__main__":
main()
| 16 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> int:
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise TypeError('''Input value must be a \'int\' type''' )
return bin(lowerCAmelCase__ ).count('''1''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 181 | 0 |
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : int , __magic_name__ : int , __magic_name__ : int ) -> int:
"""simple docstring"""
if index == number_of_items:
return 0
UpperCamelCase :int = 0
UpperCamelCase :Union[str, Any] = 0
UpperCamelCase :Optional[int] = knapsack(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , index + 1 )
if weights[index] <= max_weight:
UpperCamelCase :List[Any] = values[index] + knapsack(
__magic_name__ , __magic_name__ , __magic_name__ , max_weight - weights[index] , index + 1 )
return max(__magic_name__ , __magic_name__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DeformableDetrImageProcessor
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __init__( self : Union[str, Any] , __lowerCamelCase : int , __lowerCamelCase : Any=7 , __lowerCamelCase : Tuple=3 , __lowerCamelCase : Optional[Any]=30 , __lowerCamelCase : Union[str, Any]=400 , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : Tuple=None , __lowerCamelCase : int=True , __lowerCamelCase : Dict=[0.5, 0.5, 0.5] , __lowerCamelCase : int=[0.5, 0.5, 0.5] , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : str=1 / 255 , __lowerCamelCase : str=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
UpperCamelCase :List[Any] = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1_333}
UpperCamelCase :Tuple = parent
UpperCamelCase :int = batch_size
UpperCamelCase :str = num_channels
UpperCamelCase :Dict = min_resolution
UpperCamelCase :Any = max_resolution
UpperCamelCase :int = do_resize
UpperCamelCase :str = size
UpperCamelCase :Dict = do_normalize
UpperCamelCase :Tuple = image_mean
UpperCamelCase :Optional[int] = image_std
UpperCamelCase :Tuple = do_rescale
UpperCamelCase :Optional[Any] = rescale_factor
UpperCamelCase :List[Any] = do_pad
def _A ( self : List[Any] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def _A ( self : Dict , __lowerCamelCase : Any , __lowerCamelCase : Optional[int]=False ):
if not batched:
UpperCamelCase :Optional[Any] = image_inputs[0]
if isinstance(__lowerCamelCase , Image.Image ):
UpperCamelCase , UpperCamelCase :Union[str, Any] = image.size
else:
UpperCamelCase , UpperCamelCase :Optional[int] = image.shape[1], image.shape[2]
if w < h:
UpperCamelCase :int = int(self.size["""shortest_edge"""] * h / w )
UpperCamelCase :Tuple = self.size["""shortest_edge"""]
elif w > h:
UpperCamelCase :List[Any] = self.size["""shortest_edge"""]
UpperCamelCase :str = int(self.size["""shortest_edge"""] * w / h )
else:
UpperCamelCase :List[Any] = self.size["""shortest_edge"""]
UpperCamelCase :str = self.size["""shortest_edge"""]
else:
UpperCamelCase :List[Any] = []
for image in image_inputs:
UpperCamelCase , UpperCamelCase :int = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCamelCase :int = max(__lowerCamelCase , key=lambda __lowerCamelCase : item[0] )[0]
UpperCamelCase :Tuple = max(__lowerCamelCase , key=lambda __lowerCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE ( _a , unittest.TestCase ):
snake_case__ : Optional[int] = DeformableDetrImageProcessor if is_vision_available() else None
def _A ( self : Optional[Any] ):
UpperCamelCase :str = DeformableDetrImageProcessingTester(self )
@property
def _A ( self : Optional[Any] ):
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self : Dict ):
UpperCamelCase :int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__lowerCamelCase , """image_mean""" ) )
self.assertTrue(hasattr(__lowerCamelCase , """image_std""" ) )
self.assertTrue(hasattr(__lowerCamelCase , """do_normalize""" ) )
self.assertTrue(hasattr(__lowerCamelCase , """do_resize""" ) )
self.assertTrue(hasattr(__lowerCamelCase , """do_rescale""" ) )
self.assertTrue(hasattr(__lowerCamelCase , """do_pad""" ) )
self.assertTrue(hasattr(__lowerCamelCase , """size""" ) )
def _A ( self : str ):
UpperCamelCase :Optional[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 18, """longest_edge""": 1_333} )
self.assertEqual(image_processor.do_pad , __lowerCamelCase )
UpperCamelCase :int = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=__lowerCamelCase )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42, """longest_edge""": 84} )
self.assertEqual(image_processor.do_pad , __lowerCamelCase )
def _A ( self : List[Any] ):
pass
def _A ( self : Dict ):
# Initialize image_processing
UpperCamelCase :List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase :List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCamelCase , Image.Image )
# Test not batched input
UpperCamelCase :Dict = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
UpperCamelCase , UpperCamelCase :Optional[int] = self.image_processor_tester.get_expected_values(__lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase , UpperCamelCase :str = self.image_processor_tester.get_expected_values(__lowerCamelCase , batched=__lowerCamelCase )
UpperCamelCase :int = image_processing(__lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _A ( self : Tuple ):
# Initialize image_processing
UpperCamelCase :Any = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase :Union[str, Any] = 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
UpperCamelCase :Union[str, Any] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
UpperCamelCase , UpperCamelCase :Any = self.image_processor_tester.get_expected_values(__lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase :Dict = image_processing(__lowerCamelCase , return_tensors="""pt""" ).pixel_values
UpperCamelCase , UpperCamelCase :Optional[Any] = self.image_processor_tester.get_expected_values(__lowerCamelCase , batched=__lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _A ( self : Any ):
# Initialize image_processing
UpperCamelCase :Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase :List[str] = 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
UpperCamelCase :Tuple = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
UpperCamelCase , UpperCamelCase :List[str] = self.image_processor_tester.get_expected_values(__lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase :Union[str, Any] = image_processing(__lowerCamelCase , return_tensors="""pt""" ).pixel_values
UpperCamelCase , UpperCamelCase :List[str] = self.image_processor_tester.get_expected_values(__lowerCamelCase , batched=__lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def _A ( self : Optional[Any] ):
# prepare image and target
UpperCamelCase :int = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""" ) as f:
UpperCamelCase :str = json.loads(f.read() )
UpperCamelCase :List[Any] = {"""image_id""": 39_769, """annotations""": target}
# encode them
UpperCamelCase :Optional[int] = DeformableDetrImageProcessor()
UpperCamelCase :Dict = image_processing(images=__lowerCamelCase , annotations=__lowerCamelCase , return_tensors="""pt""" )
# verify pixel values
UpperCamelCase :Union[str, Any] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["""pixel_values"""].shape , __lowerCamelCase )
UpperCamelCase :Optional[Any] = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , __lowerCamelCase , atol=1E-4 ) )
# verify area
UpperCamelCase :str = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , __lowerCamelCase ) )
# verify boxes
UpperCamelCase :List[Any] = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , __lowerCamelCase )
UpperCamelCase :List[str] = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , __lowerCamelCase , atol=1E-3 ) )
# verify image_id
UpperCamelCase :Tuple = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , __lowerCamelCase ) )
# verify is_crowd
UpperCamelCase :List[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , __lowerCamelCase ) )
# verify class_labels
UpperCamelCase :Union[str, Any] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , __lowerCamelCase ) )
# verify orig_size
UpperCamelCase :Dict = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , __lowerCamelCase ) )
# verify size
UpperCamelCase :int = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , __lowerCamelCase ) )
@slow
def _A ( self : str ):
# prepare image, target and masks_path
UpperCamelCase :Any = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""" ) as f:
UpperCamelCase :Any = json.loads(f.read() )
UpperCamelCase :int = {"""file_name""": """000000039769.png""", """image_id""": 39_769, """segments_info""": target}
UpperCamelCase :Any = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )
# encode them
UpperCamelCase :Tuple = DeformableDetrImageProcessor(format="""coco_panoptic""" )
UpperCamelCase :Dict = image_processing(images=__lowerCamelCase , annotations=__lowerCamelCase , masks_path=__lowerCamelCase , return_tensors="""pt""" )
# verify pixel values
UpperCamelCase :Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["""pixel_values"""].shape , __lowerCamelCase )
UpperCamelCase :Optional[int] = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , __lowerCamelCase , atol=1E-4 ) )
# verify area
UpperCamelCase :List[str] = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , __lowerCamelCase ) )
# verify boxes
UpperCamelCase :List[str] = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , __lowerCamelCase )
UpperCamelCase :List[Any] = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , __lowerCamelCase , atol=1E-3 ) )
# verify image_id
UpperCamelCase :str = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , __lowerCamelCase ) )
# verify is_crowd
UpperCamelCase :Tuple = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , __lowerCamelCase ) )
# verify class_labels
UpperCamelCase :List[Any] = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , __lowerCamelCase ) )
# verify masks
UpperCamelCase :Union[str, Any] = 822_873
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , __lowerCamelCase )
# verify orig_size
UpperCamelCase :Tuple = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , __lowerCamelCase ) )
# verify size
UpperCamelCase :str = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , __lowerCamelCase ) )
| 62 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCamelCase__ = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
'LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST',
'LongT5EncoderModel',
'LongT5ForConditionalGeneration',
'LongT5Model',
'LongT5PreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
'FlaxLongT5ForConditionalGeneration',
'FlaxLongT5Model',
'FlaxLongT5PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longta import (
LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
LongTaEncoderModel,
LongTaForConditionalGeneration,
LongTaModel,
LongTaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_longta import (
FlaxLongTaForConditionalGeneration,
FlaxLongTaModel,
FlaxLongTaPreTrainedModel,
)
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 65 | def lowerCAmelCase_ ( __A, __A ) -> None:
'''simple docstring'''
UpperCAmelCase__ = len(__A )
print("The following activities are selected:" )
# The first activity is always selected
UpperCAmelCase__ = 0
print(__A, end="," )
# Consider rest of the activities
for j in range(__A ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(__A, end="," )
UpperCAmelCase__ = j
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase__ = [1, 3, 0, 5, 8, 5]
UpperCamelCase__ = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 65 | 1 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class a__ ( snake_case__ , snake_case__ , unittest.TestCase ):
_a : Optional[int] = IFPipeline
_a : str = TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
_a : str = TEXT_TO_IMAGE_BATCH_PARAMS
_a : Tuple = PipelineTesterMixin.required_optional_params - {'''latents'''}
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self._get_dummy_components()
def __SCREAMING_SNAKE_CASE( self , _A , _A=0 ):
"""simple docstring"""
if str(_snake_case ).startswith("mps" ):
__lowerCAmelCase = torch.manual_seed(_snake_case )
else:
__lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
__lowerCAmelCase = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda" , reason="float16 requires CUDA" )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self._test_save_load_local()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0" , variant="fp16" , torch_dtype=torch.floataa )
__lowerCAmelCase = IFSuperResolutionPipeline.from_pretrained(
"DeepFloyd/IF-II-L-v1.0" , variant="fp16" , torch_dtype=torch.floataa , text_encoder=_snake_case , tokenizer=_snake_case )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to("cuda" )
__lowerCAmelCase , __lowerCAmelCase = pipe_a.encode_prompt("anime turtle" , device="cuda" )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
__lowerCAmelCase = None
__lowerCAmelCase = None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(_snake_case , _snake_case , _snake_case , _snake_case )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
__lowerCAmelCase = IFImgaImgPipeline(**pipe_a.components )
__lowerCAmelCase = IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(_snake_case , _snake_case , _snake_case , _snake_case )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
__lowerCAmelCase = IFInpaintingPipeline(**pipe_a.components )
__lowerCAmelCase = IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(_snake_case , _snake_case , _snake_case , _snake_case )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A ):
"""simple docstring"""
_start_torch_memory_measurement()
__lowerCAmelCase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowerCAmelCase = pipe_a(
prompt_embeds=_snake_case , negative_prompt_embeds=_snake_case , num_inference_steps=2 , generator=_snake_case , output_type="np" , )
__lowerCAmelCase = output.images[0]
assert image.shape == (6_4, 6_4, 3)
__lowerCAmelCase = torch.cuda.max_memory_allocated()
assert mem_bytes < 1_3 * 1_0**9
__lowerCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy" )
assert_mean_pixel_difference(_snake_case , _snake_case )
# pipeline 2
_start_torch_memory_measurement()
__lowerCAmelCase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowerCAmelCase = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(0 ) ).to(_snake_case )
__lowerCAmelCase = pipe_a(
prompt_embeds=_snake_case , negative_prompt_embeds=_snake_case , image=_snake_case , generator=_snake_case , num_inference_steps=2 , output_type="np" , )
__lowerCAmelCase = output.images[0]
assert image.shape == (2_5_6, 2_5_6, 3)
__lowerCAmelCase = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 1_0**9
__lowerCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy" )
assert_mean_pixel_difference(_snake_case , _snake_case )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A ):
"""simple docstring"""
_start_torch_memory_measurement()
__lowerCAmelCase = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(0 ) ).to(_snake_case )
__lowerCAmelCase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowerCAmelCase = pipe_a(
prompt_embeds=_snake_case , negative_prompt_embeds=_snake_case , image=_snake_case , num_inference_steps=2 , generator=_snake_case , output_type="np" , )
__lowerCAmelCase = output.images[0]
assert image.shape == (6_4, 6_4, 3)
__lowerCAmelCase = torch.cuda.max_memory_allocated()
assert mem_bytes < 1_0 * 1_0**9
__lowerCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy" )
assert_mean_pixel_difference(_snake_case , _snake_case )
# pipeline 2
_start_torch_memory_measurement()
__lowerCAmelCase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowerCAmelCase = floats_tensor((1, 3, 2_5_6, 2_5_6) , rng=random.Random(0 ) ).to(_snake_case )
__lowerCAmelCase = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(0 ) ).to(_snake_case )
__lowerCAmelCase = pipe_a(
prompt_embeds=_snake_case , negative_prompt_embeds=_snake_case , image=_snake_case , original_image=_snake_case , generator=_snake_case , num_inference_steps=2 , output_type="np" , )
__lowerCAmelCase = output.images[0]
assert image.shape == (2_5_6, 2_5_6, 3)
__lowerCAmelCase = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 1_0**9
__lowerCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy" )
assert_mean_pixel_difference(_snake_case , _snake_case )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A ):
"""simple docstring"""
_start_torch_memory_measurement()
__lowerCAmelCase = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(0 ) ).to(_snake_case )
__lowerCAmelCase = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(1 ) ).to(_snake_case )
__lowerCAmelCase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowerCAmelCase = pipe_a(
prompt_embeds=_snake_case , negative_prompt_embeds=_snake_case , image=_snake_case , mask_image=_snake_case , num_inference_steps=2 , generator=_snake_case , output_type="np" , )
__lowerCAmelCase = output.images[0]
assert image.shape == (6_4, 6_4, 3)
__lowerCAmelCase = torch.cuda.max_memory_allocated()
assert mem_bytes < 1_0 * 1_0**9
__lowerCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy" )
assert_mean_pixel_difference(_snake_case , _snake_case )
# pipeline 2
_start_torch_memory_measurement()
__lowerCAmelCase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowerCAmelCase = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(0 ) ).to(_snake_case )
__lowerCAmelCase = floats_tensor((1, 3, 2_5_6, 2_5_6) , rng=random.Random(0 ) ).to(_snake_case )
__lowerCAmelCase = floats_tensor((1, 3, 2_5_6, 2_5_6) , rng=random.Random(1 ) ).to(_snake_case )
__lowerCAmelCase = pipe_a(
prompt_embeds=_snake_case , negative_prompt_embeds=_snake_case , image=_snake_case , mask_image=_snake_case , original_image=_snake_case , generator=_snake_case , num_inference_steps=2 , output_type="np" , )
__lowerCAmelCase = output.images[0]
assert image.shape == (2_5_6, 2_5_6, 3)
__lowerCAmelCase = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 1_0**9
__lowerCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy" )
assert_mean_pixel_difference(_snake_case , _snake_case )
def _a ( ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 357 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
elif electron_conc < 0:
raise ValueError("Electron concentration cannot be negative in a semiconductor" )
elif hole_conc < 0:
raise ValueError("Hole concentration cannot be negative in a semiconductor" )
elif intrinsic_conc < 0:
raise ValueError(
"Intrinsic concentration cannot be negative in a semiconductor" )
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc**2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc**2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 102 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.