code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from __future__ import annotations
def _snake_case ( __snake_case : list[list[int]] ):
"""simple docstring"""
_lowerCamelCase : Dict = len(__snake_case )
# We need to create solution object to save path.
_lowerCamelCase : int = [[0 for _ in range(__snake_case )] for _ in range(__snake_case )]
_lowerCamelCase : str = run_maze(__snake_case , 0 , 0 , __snake_case )
if solved:
print("""\n""".join(str(__snake_case ) for row in solutions ) )
else:
print("""No solution exists!""" )
return solved
def _snake_case ( __snake_case : list[list[int]] , __snake_case : int , __snake_case : int , __snake_case : list[list[int]] ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = len(__snake_case )
# Final check point.
if i == j == (size - 1):
_lowerCamelCase : List[str] = 1
return True
_lowerCamelCase : List[Any] = (not i < 0) and (not j < 0) # Check lower bounds
_lowerCamelCase : Union[str, Any] = (i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
_lowerCamelCase : List[str] = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
_lowerCamelCase : Union[str, Any] = 1
# check for directions
if (
run_maze(__snake_case , i + 1 , __snake_case , __snake_case )
or run_maze(__snake_case , __snake_case , j + 1 , __snake_case )
or run_maze(__snake_case , i - 1 , __snake_case , __snake_case )
or run_maze(__snake_case , __snake_case , j - 1 , __snake_case )
):
return True
_lowerCamelCase : str = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 88 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__lowerCamelCase : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : str = ["""GPTSw3Tokenizer"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_swa import GPTSwaTokenizer
else:
import sys
__lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 385 | 0 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _lowerCAmelCase ( lowercase : Callable , lowercase : float , lowercase : float , lowercase : float , lowercase : float ) ->np.array:
"""simple docstring"""
lowercase__ = int(np.ceil((x_end - xa) / step_size ) )
lowercase__ = np.zeros((n + 1,) )
lowercase__ = ya
lowercase__ = xa
for k in range(lowercase ):
lowercase__ = y[k] + step_size * ode_func(lowercase , y[k] )
lowercase__ = y[k] + (
(step_size / 2) * (ode_func(lowercase , y[k] ) + ode_func(x + step_size , lowercase ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 716 |
'''simple docstring'''
import numpy as np
import datasets
_lowerCAmelCase = "\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"
_lowerCAmelCase = "\\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"
_lowerCAmelCase = "\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 __A ( datasets.Metric ):
"""simple docstring"""
def snake_case_( self )-> Dict:
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 snake_case_( self , _lowerCamelCase , _lowerCamelCase )-> Any:
# convert to numpy arrays
lowercase__ = np.array(_lowerCamelCase )
lowercase__ = np.array(_lowerCamelCase )
# 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(_lowerCamelCase )
lowercase__ = np.cov(reference_distribution.T )
try:
lowercase__ = np.linalg.inv(_lowerCamelCase )
except np.linalg.LinAlgError:
lowercase__ = np.linalg.pinv(_lowerCamelCase )
lowercase__ = np.dot(_lowerCamelCase , _lowerCamelCase )
lowercase__ = np.dot(_lowerCamelCase , X_minus_mu.T ).diagonal()
return {"mahalanobis": mahal_dist}
| 318 | 0 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __lowerCAmelCase ( *_UpperCamelCase ) -> int:
'''simple docstring'''
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
lowerCamelCase__: List[Any] = list(__lowerCamelCase )
for i in range(len(__lowerCamelCase ) ):
lowerCamelCase__: List[Any] = None
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
return objects
def __lowerCAmelCase ( _UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase__: str = [
"""CUDA out of memory.""", # CUDA OOM
"""cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""", # CUDNN SNAFU
"""DefaultCPUAllocator: can\'t allocate memory""", # CPU OOM
]
if isinstance(__lowerCamelCase , __lowerCamelCase ) and len(exception.args ) == 1:
return any(err in exception.args[0] for err in _statements )
return False
def __lowerCAmelCase ( _UpperCamelCase = None , _UpperCamelCase = 128 ) -> List[Any]:
'''simple docstring'''
if function is None:
return functools.partial(__lowerCamelCase , starting_batch_size=__lowerCamelCase )
lowerCamelCase__: Optional[Any] = starting_batch_size
def decorator(*_UpperCamelCase , **_UpperCamelCase ):
nonlocal batch_size
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
lowerCamelCase__: str = list(inspect.signature(__lowerCamelCase ).parameters.keys() )
# Guard against user error
if len(__lowerCamelCase ) < (len(__lowerCamelCase ) + 1):
lowerCamelCase__: List[str] = """, """.join([f"""{arg}={value}""" for arg, value in zip(params[1:] , args[1:] )] )
raise TypeError(
f"""Batch size was passed into `{function.__name__}` as the first argument when called."""
f"""Remove this as the decorator already does so: `{function.__name__}({arg_str})`""" )
while True:
if batch_size == 0:
raise RuntimeError("""No executable batch size found, reached zero.""" )
try:
return function(__lowerCamelCase , *__lowerCamelCase , **__lowerCamelCase )
except Exception as e:
if should_reduce_batch_size(__lowerCamelCase ):
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
batch_size //= 2
else:
raise
return decorator
| 306 |
"""simple docstring"""
def A__ ( __lowerCamelCase, __lowerCamelCase ):
"""simple docstring"""
_validate_point(__lowerCamelCase )
_validate_point(__lowerCamelCase )
if len(__lowerCamelCase ) != len(__lowerCamelCase ):
raise ValueError('Both points must be in the same n-dimensional space' )
return float(sum(abs(a - b ) for a, b in zip(__lowerCamelCase, __lowerCamelCase ) ) )
def A__ ( __lowerCamelCase ):
"""simple docstring"""
if point:
if isinstance(__lowerCamelCase, __lowerCamelCase ):
for item in point:
if not isinstance(__lowerCamelCase, (int, float) ):
_lowerCAmelCase = (
'Expected a list of numbers as input, found '
F'''{type(__lowerCamelCase ).__name__}'''
)
raise TypeError(__lowerCamelCase )
else:
_lowerCAmelCase = F'''Expected a list of numbers as input, found {type(__lowerCamelCase ).__name__}'''
raise TypeError(__lowerCamelCase )
else:
raise ValueError('Missing an input' )
def A__ ( __lowerCamelCase, __lowerCamelCase ):
"""simple docstring"""
_validate_point(__lowerCamelCase )
_validate_point(__lowerCamelCase )
if len(__lowerCamelCase ) != len(__lowerCamelCase ):
raise ValueError('Both points must be in the same n-dimensional space' )
return float(sum(abs(x - y ) for x, y in zip(__lowerCamelCase, __lowerCamelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 589 | 0 |
"""simple docstring"""
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : Tuple = AutoConfig.from_pretrained(_UpperCamelCase )
__lowerCAmelCase : int = FlaxAutoModelForSeqaSeqLM.from_config(config=_UpperCamelCase )
__lowerCAmelCase : Optional[Any] = checkpoints.load_tax_checkpoint(_UpperCamelCase )
__lowerCAmelCase : Any = 'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']
if config.model_type == "t5":
__lowerCAmelCase : Optional[Any] = 'SelfAttention'
if config.model_type == "longt5" and config.encoder_attention_type == "local":
__lowerCAmelCase : Optional[int] = 'LocalSelfAttention'
elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
__lowerCAmelCase : Optional[int] = 'TransientGlobalSelfAttention'
else:
raise ValueError(
'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
' attribute with a value from [\'local\', \'transient-global].' )
# Encoder
for layer_index in range(config.num_layers ):
__lowerCAmelCase : Union[str, Any] = F"layers_{str(_UpperCamelCase )}"
# Self-Attention
__lowerCAmelCase : int = tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
__lowerCAmelCase : List[Any] = tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
__lowerCAmelCase : int = tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
__lowerCAmelCase : Tuple = tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
__lowerCAmelCase : Tuple = tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']
# Layer Normalization
__lowerCAmelCase : Optional[Any] = tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']
if split_mlp_wi:
__lowerCAmelCase : Dict = tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
__lowerCAmelCase : Tuple = tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
else:
__lowerCAmelCase : Any = tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']
__lowerCAmelCase : List[str] = tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']
# Layer Normalization
__lowerCAmelCase : Optional[int] = tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']
# Assigning
__lowerCAmelCase : Tuple = flax_model.params['encoder']['block'][str(_UpperCamelCase )]['layer']
__lowerCAmelCase : Union[str, Any] = tax_attention_key
__lowerCAmelCase : int = tax_attention_out
__lowerCAmelCase : List[str] = tax_attention_query
__lowerCAmelCase : List[Any] = tax_attention_value
__lowerCAmelCase : List[Any] = tax_attention_layer_norm
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
__lowerCAmelCase : Dict = tax_global_layer_norm
if split_mlp_wi:
__lowerCAmelCase : Optional[Any] = tax_mlp_wi_a
__lowerCAmelCase : Any = tax_mlp_wi_a
else:
__lowerCAmelCase : List[str] = tax_mlp_wi
__lowerCAmelCase : Tuple = tax_mlp_wo
__lowerCAmelCase : str = tax_mlp_layer_norm
__lowerCAmelCase : Tuple = flax_model_encoder_layer_block
# Only for layer 0:
__lowerCAmelCase : Dict = tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
__lowerCAmelCase : str = tax_encoder_rel_embedding
# Side/global relative position_bias + layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
__lowerCAmelCase : Dict = tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
__lowerCAmelCase : List[str] = tax_encoder_global_rel_embedding
# Assigning
__lowerCAmelCase : int = tax_model['target']['encoder']['encoder_norm']['scale']
__lowerCAmelCase : Tuple = tax_encoder_norm
# Decoder
for layer_index in range(config.num_layers ):
__lowerCAmelCase : str = F"layers_{str(_UpperCamelCase )}"
# Self-Attention
__lowerCAmelCase : Union[str, Any] = tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
__lowerCAmelCase : Any = tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
__lowerCAmelCase : int = tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
__lowerCAmelCase : int = tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']
# Layer Normalization
__lowerCAmelCase : int = tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
'scale'
]
# Encoder-Decoder-Attention
__lowerCAmelCase : Any = tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
__lowerCAmelCase : Dict = tax_enc_dec_attention_module['key']['kernel']
__lowerCAmelCase : Optional[Any] = tax_enc_dec_attention_module['out']['kernel']
__lowerCAmelCase : List[str] = tax_enc_dec_attention_module['query']['kernel']
__lowerCAmelCase : Any = tax_enc_dec_attention_module['value']['kernel']
# Layer Normalization
__lowerCAmelCase : str = tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']
# MLP
if split_mlp_wi:
__lowerCAmelCase : int = tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
__lowerCAmelCase : Dict = tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
else:
__lowerCAmelCase : Optional[int] = tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']
__lowerCAmelCase : str = tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']
# Layer Normalization
__lowerCAmelCase : Optional[int] = tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']
# Assigning
__lowerCAmelCase : Any = flax_model.params['decoder']['block'][str(_UpperCamelCase )]['layer']
__lowerCAmelCase : Any = tax_attention_key
__lowerCAmelCase : List[str] = tax_attention_out
__lowerCAmelCase : List[Any] = tax_attention_query
__lowerCAmelCase : List[Any] = tax_attention_value
__lowerCAmelCase : Any = tax_pre_attention_layer_norm
__lowerCAmelCase : Any = tax_enc_dec_attention_key
__lowerCAmelCase : Optional[Any] = tax_enc_dec_attention_out
__lowerCAmelCase : str = tax_enc_dec_attention_query
__lowerCAmelCase : Tuple = tax_enc_dec_attention_value
__lowerCAmelCase : Optional[Any] = tax_cross_layer_norm
if split_mlp_wi:
__lowerCAmelCase : Union[str, Any] = tax_mlp_wi_a
__lowerCAmelCase : str = tax_mlp_wi_a
else:
__lowerCAmelCase : Dict = tax_mlp_wi
__lowerCAmelCase : Union[str, Any] = tax_mlp_wo
__lowerCAmelCase : int = txa_mlp_layer_norm
__lowerCAmelCase : List[str] = flax_model_decoder_layer_block
# Decoder Normalization
__lowerCAmelCase : Optional[Any] = tax_model['target']['decoder']['decoder_norm']['scale']
__lowerCAmelCase : Optional[int] = txa_decoder_norm
# Only for layer 0:
__lowerCAmelCase : Tuple = tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
__lowerCAmelCase : List[str] = tax_decoder_rel_embedding
# Token Embeddings
__lowerCAmelCase : Tuple = tax_model['target']['token_embedder']['embedding']
__lowerCAmelCase : Dict = txa_token_embeddings
# LM Head (only in v1.1 and LongT5 checkpoints)
if "logits_dense" in tax_model["target"]["decoder"]:
__lowerCAmelCase : Tuple = tax_model['target']['decoder']['logits_dense']['kernel']
flax_model.save_pretrained(_UpperCamelCase )
print('T5X Model was sucessfully converted!' )
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
)
parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
parser.add_argument(
"""--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
)
lowerCamelCase__ = parser.parse_args()
convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path) | 549 |
"""simple docstring"""
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__ ( _lowerCamelCase):
A_ : Any = ['image_processor', 'tokenizer']
A_ : Optional[Any] = 'BlipImageProcessor'
A_ : int = 'AutoTokenizer'
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Union[str, Any] = False
super().__init__(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[Any] = self.image_processor
def __call__( self , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ):
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 : Optional[int] = self.tokenizer
__lowerCAmelCase : List[str] = self.tokenizer(
text=_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE , stride=_SCREAMING_SNAKE_CASE , pad_to_multiple_of=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , return_overflowing_tokens=_SCREAMING_SNAKE_CASE , return_special_tokens_mask=_SCREAMING_SNAKE_CASE , return_offsets_mapping=_SCREAMING_SNAKE_CASE , return_token_type_ids=_SCREAMING_SNAKE_CASE , return_length=_SCREAMING_SNAKE_CASE , verbose=_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
return text_encoding
# add pixel_values
__lowerCAmelCase : List[Any] = self.image_processor(_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE )
if text is not None:
__lowerCAmelCase : Optional[int] = self.tokenizer(
text=_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE , stride=_SCREAMING_SNAKE_CASE , pad_to_multiple_of=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , return_overflowing_tokens=_SCREAMING_SNAKE_CASE , return_special_tokens_mask=_SCREAMING_SNAKE_CASE , return_offsets_mapping=_SCREAMING_SNAKE_CASE , return_token_type_ids=_SCREAMING_SNAKE_CASE , return_length=_SCREAMING_SNAKE_CASE , verbose=_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
else:
__lowerCAmelCase : int = None
if text_encoding is not None:
encoding_image_processor.update(_SCREAMING_SNAKE_CASE )
return encoding_image_processor
def __lowerCamelCase ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def __lowerCamelCase ( self ):
__lowerCAmelCase : int = self.tokenizer.model_input_names
__lowerCAmelCase : Union[str, Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) ) | 549 | 1 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int = 1_00_00_00 ) -> int:
'''simple docstring'''
UpperCAmelCase_ = limit + 1
UpperCAmelCase_ = [0] * limit
for first_term in range(1 , snake_case_ ):
for n in range(snake_case_ , snake_case_ , snake_case_ ):
UpperCAmelCase_ = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
UpperCAmelCase_ = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f"{solution() = }")
| 78 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
__magic_name__ = '''tiny-wmt19-en-ru'''
# Build
# borrowed from a test
__magic_name__ = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''w</w>''',
'''r</w>''',
'''t</w>''',
'''lo''',
'''low''',
'''er</w>''',
'''low</w>''',
'''lowest</w>''',
'''newer</w>''',
'''wider</w>''',
'''<unk>''',
]
__magic_name__ = dict(zip(vocab, range(len(vocab))))
__magic_name__ = ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']
with tempfile.TemporaryDirectory() as tmpdirname:
__magic_name__ = Path(tmpdirname)
__magic_name__ = build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
__magic_name__ = build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
__magic_name__ = build_dir / VOCAB_FILES_NAMES['''merges_file''']
with open(src_vocab_file, '''w''') as fp:
fp.write(json.dumps(vocab_tokens))
with open(tgt_vocab_file, '''w''') as fp:
fp.write(json.dumps(vocab_tokens))
with open(merges_file, '''w''') as fp:
fp.write('''\n'''.join(merges))
__magic_name__ = FSMTTokenizer(
langs=['''en''', '''ru'''],
src_vocab_size=len(vocab),
tgt_vocab_size=len(vocab),
src_vocab_file=src_vocab_file,
tgt_vocab_file=tgt_vocab_file,
merges_file=merges_file,
)
__magic_name__ = FSMTConfig(
langs=['''ru''', '''en'''],
src_vocab_size=1_0_0_0,
tgt_vocab_size=1_0_0_0,
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
__magic_name__ = FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')
# Test
__magic_name__ = tokenizer(['''Making tiny model'''], return_tensors='''pt''')
__magic_name__ = tiny_model(**batch)
print('''test output:''', len(outputs.logits[0]))
# Save
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(f'''Generated {mname_tiny}''')
# Upload
# transformers-cli upload tiny-wmt19-en-ru
| 250 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[int] = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : Union[str, Any] = '''mgp-str'''
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any]=[3_2, 1_2_8] , SCREAMING_SNAKE_CASE__ : int=4 , SCREAMING_SNAKE_CASE__ : Tuple=3 , SCREAMING_SNAKE_CASE__ : List[str]=2_7 , SCREAMING_SNAKE_CASE__ : List[Any]=3_8 , SCREAMING_SNAKE_CASE__ : Any=5_0_2_5_7 , SCREAMING_SNAKE_CASE__ : List[Any]=3_0_5_2_2 , SCREAMING_SNAKE_CASE__ : Any=7_6_8 , SCREAMING_SNAKE_CASE__ : Optional[Any]=1_2 , SCREAMING_SNAKE_CASE__ : str=1_2 , SCREAMING_SNAKE_CASE__ : Tuple=4.0 , SCREAMING_SNAKE_CASE__ : List[str]=True , SCREAMING_SNAKE_CASE__ : int=False , SCREAMING_SNAKE_CASE__ : Union[str, Any]=1E-5 , SCREAMING_SNAKE_CASE__ : Any=0.0 , SCREAMING_SNAKE_CASE__ : Optional[Any]=0.0 , SCREAMING_SNAKE_CASE__ : str=0.0 , SCREAMING_SNAKE_CASE__ : Tuple=False , SCREAMING_SNAKE_CASE__ : List[Any]=0.02 , **SCREAMING_SNAKE_CASE__ : Union[str, Any] , ) -> List[str]:
super().__init__(**SCREAMING_SNAKE_CASE__ )
a_ : List[Any] = image_size
a_ : int = patch_size
a_ : List[str] = num_channels
a_ : Dict = max_token_length
a_ : List[Any] = num_character_labels
a_ : Union[str, Any] = num_bpe_labels
a_ : Optional[int] = num_wordpiece_labels
a_ : Optional[Any] = hidden_size
a_ : int = num_hidden_layers
a_ : List[Any] = num_attention_heads
a_ : Optional[Any] = mlp_ratio
a_ : List[str] = distilled
a_ : int = layer_norm_eps
a_ : int = drop_rate
a_ : Any = qkv_bias
a_ : List[str] = attn_drop_rate
a_ : Union[str, Any] = drop_path_rate
a_ : Optional[int] = output_aa_attentions
a_ : Dict = initializer_range
| 713 |
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,
)
UpperCAmelCase_ : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
UpperCAmelCase_ : str = '\n Examples:\n ```py\n >>> import torch\n >>> import numpy as np\n\n >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n >>> from transformers import pipeline\n >>> from diffusers.utils import load_image\n\n\n >>> def make_hint(image, depth_estimator):\n ... image = depth_estimator(image)["depth"]\n ... image = np.array(image)\n ... image = image[:, :, None]\n ... image = np.concatenate([image, image, image], axis=2)\n ... detected_map = torch.from_numpy(image).float() / 255.0\n ... hint = detected_map.permute(2, 0, 1)\n ... return hint\n\n\n >>> depth_estimator = pipeline("depth-estimation")\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n ... )\n >>> pipe_prior = pipe_prior.to("cuda")\n\n >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n ... "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to("cuda")\n\n\n >>> img = load_image(\n ... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n ... "/kandinsky/cat.png"\n ... ).resize((768, 768))\n\n >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n >>> prompt = "A robot, 4k photo"\n >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n >>> image_emb, zero_image_emb = pipe_prior(\n ... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n ... ).to_tuple()\n\n >>> images = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... hint=hint,\n ... num_inference_steps=50,\n ... generator=generator,\n ... height=768,\n ... width=768,\n ... ).images\n\n >>> images[0].save("robot_cat.png")\n ```\n'
def SCREAMING_SNAKE_CASE_ ( __A : Optional[Any] , __A : Tuple , __A : Tuple=8 ) -> Dict:
"""simple docstring"""
a_ : Optional[int] = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
a_ : int = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
def __init__( self : str , SCREAMING_SNAKE_CASE__ : UNetaDConditionModel , SCREAMING_SNAKE_CASE__ : DDPMScheduler , SCREAMING_SNAKE_CASE__ : VQModel , ) -> Any:
super().__init__()
self.register_modules(
unet=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ , movq=SCREAMING_SNAKE_CASE__ , )
a_ : List[str] = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def SCREAMING_SNAKE_CASE ( self : int , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> str:
if latents is None:
a_ : List[str] = randn_tensor(SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ , dtype=SCREAMING_SNAKE_CASE__ )
else:
if latents.shape != shape:
raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
a_ : Any = latents.to(SCREAMING_SNAKE_CASE__ )
a_ : Union[str, Any] = latents * scheduler.init_noise_sigma
return latents
def SCREAMING_SNAKE_CASE ( self : Tuple , SCREAMING_SNAKE_CASE__ : Optional[Any]=0 ) -> Dict:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
a_ : Union[str, Any] = torch.device(F"""cuda:{gpu_id}""" )
a_ : Optional[Any] = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : List[str] , SCREAMING_SNAKE_CASE__ : Optional[int]=0 ) -> Dict:
if is_accelerate_available() and is_accelerate_version('>=' , '0.17.0.dev0' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' )
a_ : Tuple = torch.device(F"""cuda:{gpu_id}""" )
if self.device.type != "cpu":
self.to('cpu' , silence_dtype_warnings=SCREAMING_SNAKE_CASE__ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
a_ : Dict = None
for cpu_offloaded_model in [self.unet, self.movq]:
a_ , a_ : Optional[Any] = cpu_offload_with_hook(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , prev_module_hook=SCREAMING_SNAKE_CASE__ )
# We'll offload the last model manually.
a_ : str = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> List[Any]:
if 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()
@replace_example_docstring(SCREAMING_SNAKE_CASE__ )
def __call__( self : Optional[int] , SCREAMING_SNAKE_CASE__ : Union[torch.FloatTensor, List[torch.FloatTensor]] , SCREAMING_SNAKE_CASE__ : Union[torch.FloatTensor, List[torch.FloatTensor]] , SCREAMING_SNAKE_CASE__ : torch.FloatTensor , SCREAMING_SNAKE_CASE__ : int = 5_1_2 , SCREAMING_SNAKE_CASE__ : int = 5_1_2 , SCREAMING_SNAKE_CASE__ : int = 1_0_0 , SCREAMING_SNAKE_CASE__ : float = 4.0 , SCREAMING_SNAKE_CASE__ : int = 1 , SCREAMING_SNAKE_CASE__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , SCREAMING_SNAKE_CASE__ : Optional[torch.FloatTensor] = None , SCREAMING_SNAKE_CASE__ : Optional[str] = "pil" , SCREAMING_SNAKE_CASE__ : bool = True , ) -> int:
a_ : Dict = self._execution_device
a_ : int = guidance_scale > 1.0
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
a_ : Union[str, Any] = torch.cat(SCREAMING_SNAKE_CASE__ , dim=0 )
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
a_ : Optional[Any] = torch.cat(SCREAMING_SNAKE_CASE__ , dim=0 )
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
a_ : Optional[int] = torch.cat(SCREAMING_SNAKE_CASE__ , dim=0 )
a_ : Dict = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
a_ : Optional[Any] = image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__ , dim=0 )
a_ : Optional[Any] = negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__ , dim=0 )
a_ : int = hint.repeat_interleave(SCREAMING_SNAKE_CASE__ , dim=0 )
a_ : int = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=SCREAMING_SNAKE_CASE__ )
a_ : List[Any] = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=SCREAMING_SNAKE_CASE__ )
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ )
a_ : Optional[Any] = self.scheduler.timesteps
a_ : Optional[int] = self.movq.config.latent_channels
a_ , a_ : Dict = downscale_height_and_width(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , self.movq_scale_factor )
# create initial latent
a_ : int = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , self.scheduler , )
for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE__ ) ):
# expand the latents if we are doing classifier free guidance
a_ : int = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
a_ : int = {'image_embeds': image_embeds, 'hint': hint}
a_ : str = self.unet(
sample=SCREAMING_SNAKE_CASE__ , timestep=SCREAMING_SNAKE_CASE__ , encoder_hidden_states=SCREAMING_SNAKE_CASE__ , added_cond_kwargs=SCREAMING_SNAKE_CASE__ , return_dict=SCREAMING_SNAKE_CASE__ , )[0]
if do_classifier_free_guidance:
a_ , a_ : Optional[int] = noise_pred.split(latents.shape[1] , dim=1 )
a_ , a_ : str = noise_pred.chunk(2 )
a_ , a_ : Union[str, Any] = variance_pred.chunk(2 )
a_ : Optional[int] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
a_ : str = 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"]
):
a_ , a_ : Optional[int] = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
a_ : str = self.scheduler.step(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , )[0]
# post-processing
a_ : str = self.movq.decode(SCREAMING_SNAKE_CASE__ , force_not_quantize=SCREAMING_SNAKE_CASE__ )['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"]:
a_ : str = image * 0.5 + 0.5
a_ : Union[str, Any] = image.clamp(0 , 1 )
a_ : Optional[int] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
a_ : List[str] = self.numpy_to_pil(SCREAMING_SNAKE_CASE__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__ )
| 443 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :int = 600_851_475_143 ):
try:
__UpperCAmelCase = int(snake_case_ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''' )
__UpperCAmelCase = 2
__UpperCAmelCase = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
__UpperCAmelCase = i
while n % i == 0:
__UpperCAmelCase = n // i
i += 1
return int(snake_case_ )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 49 | import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils import logging
logging.set_verbosity_info()
__magic_name__ = logging.get_logger(__name__)
def UpperCAmelCase__( __UpperCAmelCase : List[str] , __UpperCAmelCase : Dict ):
__snake_case : List[str] = []
for i in range(encoder_config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F"""encoder.deit.blocks.{i}.norm1.weight""", F"""encoder.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((F"""encoder.deit.blocks.{i}.norm1.bias""", F"""encoder.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(F"""encoder.deit.blocks.{i}.attn.proj.weight""", F"""encoder.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append(
(F"""encoder.deit.blocks.{i}.attn.proj.bias""", F"""encoder.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append(
(F"""encoder.deit.blocks.{i}.norm2.weight""", F"""encoder.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((F"""encoder.deit.blocks.{i}.norm2.bias""", F"""encoder.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append(
(F"""encoder.deit.blocks.{i}.mlp.fc1.weight""", F"""encoder.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append(
(F"""encoder.deit.blocks.{i}.mlp.fc1.bias""", F"""encoder.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append(
(F"""encoder.deit.blocks.{i}.mlp.fc2.weight""", F"""encoder.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((F"""encoder.deit.blocks.{i}.mlp.fc2.bias""", F"""encoder.encoder.layer.{i}.output.dense.bias""") )
# cls token, position embeddings and patch embeddings of encoder
rename_keys.extend(
[
('encoder.deit.cls_token', 'encoder.embeddings.cls_token'),
('encoder.deit.pos_embed', 'encoder.embeddings.position_embeddings'),
('encoder.deit.patch_embed.proj.weight', 'encoder.embeddings.patch_embeddings.projection.weight'),
('encoder.deit.patch_embed.proj.bias', 'encoder.embeddings.patch_embeddings.projection.bias'),
('encoder.deit.norm.weight', 'encoder.layernorm.weight'),
('encoder.deit.norm.bias', 'encoder.layernorm.bias'),
] )
return rename_keys
def UpperCAmelCase__( __UpperCAmelCase : List[Any] , __UpperCAmelCase : Tuple ):
for i in range(encoder_config.num_hidden_layers ):
# queries, keys and values (only weights, no biases)
__snake_case : int = state_dict.pop(F"""encoder.deit.blocks.{i}.attn.qkv.weight""" )
__snake_case : Tuple = in_proj_weight[
: encoder_config.hidden_size, :
]
__snake_case : Tuple = in_proj_weight[
encoder_config.hidden_size : encoder_config.hidden_size * 2, :
]
__snake_case : int = in_proj_weight[
-encoder_config.hidden_size :, :
]
def UpperCAmelCase__( __UpperCAmelCase : Tuple , __UpperCAmelCase : Dict , __UpperCAmelCase : Optional[Any] ):
__snake_case : Any = dct.pop(__UpperCAmelCase )
__snake_case : Optional[int] = val
def UpperCAmelCase__( __UpperCAmelCase : Tuple ):
if "handwritten" in checkpoint_url:
__snake_case : Any = 'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg' # industry
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" #
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
__snake_case : Union[str, Any] = 'https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'
__snake_case : Any = Image.open(requests.get(__UpperCAmelCase , stream=__UpperCAmelCase ).raw ).convert('RGB' )
return im
@torch.no_grad()
def UpperCAmelCase__( __UpperCAmelCase : List[str] , __UpperCAmelCase : str ):
__snake_case : List[Any] = ViTConfig(image_size=3_84 , qkv_bias=__UpperCAmelCase )
__snake_case : int = TrOCRConfig()
# size of the architecture
if "base" in checkpoint_url:
__snake_case : List[Any] = 7_68
elif "large" in checkpoint_url:
# use ViT-large encoder
__snake_case : str = 10_24
__snake_case : List[Any] = 40_96
__snake_case : Tuple = 24
__snake_case : Dict = 16
__snake_case : Union[str, Any] = 10_24
else:
raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL' )
# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
__snake_case : Optional[Any] = False
__snake_case : List[Any] = 'relu'
__snake_case : List[str] = 10_24
__snake_case : Union[str, Any] = True
__snake_case : Tuple = False
__snake_case : Optional[Any] = False
# load HuggingFace model
__snake_case : List[Any] = ViTModel(__UpperCAmelCase , add_pooling_layer=__UpperCAmelCase )
__snake_case : Dict = TrOCRForCausalLM(__UpperCAmelCase )
__snake_case : Optional[Any] = VisionEncoderDecoderModel(encoder=__UpperCAmelCase , decoder=__UpperCAmelCase )
model.eval()
# load state_dict of original model, rename some keys
__snake_case : Any = torch.hub.load_state_dict_from_url(__UpperCAmelCase , map_location='cpu' , check_hash=__UpperCAmelCase )['model']
__snake_case : int = create_rename_keys(__UpperCAmelCase , __UpperCAmelCase )
for src, dest in rename_keys:
rename_key(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
read_in_q_k_v(__UpperCAmelCase , __UpperCAmelCase )
# remove parameters we don't need
del state_dict["encoder.deit.head.weight"]
del state_dict["encoder.deit.head.bias"]
del state_dict["decoder.version"]
# add prefix to decoder keys
for key, val in state_dict.copy().items():
__snake_case : Dict = state_dict.pop(__UpperCAmelCase )
if key.startswith('decoder' ) and "output_projection" not in key:
__snake_case : Optional[int] = val
else:
__snake_case : Tuple = val
# load state dict
model.load_state_dict(__UpperCAmelCase )
# Check outputs on an image
__snake_case : Dict = ViTImageProcessor(size=encoder_config.image_size )
__snake_case : Union[str, Any] = RobertaTokenizer.from_pretrained('roberta-large' )
__snake_case : List[Any] = TrOCRProcessor(__UpperCAmelCase , __UpperCAmelCase )
__snake_case : Optional[Any] = processor(images=prepare_img(__UpperCAmelCase ) , return_tensors='pt' ).pixel_values
# verify logits
__snake_case : List[Any] = torch.tensor([[model.config.decoder.decoder_start_token_id]] )
__snake_case : List[Any] = model(pixel_values=__UpperCAmelCase , decoder_input_ids=__UpperCAmelCase )
__snake_case : List[Any] = outputs.logits
__snake_case : Dict = torch.Size([1, 1, 5_02_65] )
if "trocr-base-handwritten" in checkpoint_url:
__snake_case : List[str] = torch.tensor(
[-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311] )
elif "trocr-large-handwritten" in checkpoint_url:
__snake_case : Dict = torch.tensor(
[-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170] )
elif "trocr-base-printed" in checkpoint_url:
__snake_case : str = torch.tensor(
[-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210] )
elif "trocr-large-printed" in checkpoint_url:
__snake_case : int = torch.tensor(
[-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535] )
if "stage1" not in checkpoint_url:
assert logits.shape == expected_shape, "Shape of logits not as expected"
assert torch.allclose(logits[0, 0, :10] , __UpperCAmelCase , atol=1E-3 ), "First elements of logits not as expected"
Path(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )
print(F"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(__UpperCAmelCase )
print(F"""Saving processor to {pytorch_dump_folder_path}""" )
processor.save_pretrained(__UpperCAmelCase )
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_url''',
default='''https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt''',
type=str,
help='''URL to the original PyTorch checkpoint (.pth file).''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
__magic_name__ = parser.parse_args()
convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 576 | 0 |
'''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 A_ :
def lowerCAmelCase ( self : Tuple):
torch.manual_seed(0)
__lowerCamelCase : Optional[int] = TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')
torch.manual_seed(0)
__lowerCamelCase : Optional[Any] = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5')
torch.manual_seed(0)
__lowerCamelCase : List[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 : Dict = DDPMScheduler(
num_train_timesteps=1_0_0_0 ,beta_schedule='squaredcos_cap_v2' ,beta_start=0.0001 ,beta_end=0.02 ,thresholding=SCREAMING_SNAKE_CASE__ ,dynamic_thresholding_ratio=0.95 ,sample_max_value=1.0 ,prediction_type='epsilon' ,variance_type='learned_range' ,)
torch.manual_seed(0)
__lowerCamelCase : List[Any] = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def lowerCAmelCase ( self : Any):
torch.manual_seed(0)
__lowerCamelCase : int = TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')
torch.manual_seed(0)
__lowerCamelCase : Optional[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 : str = DDPMScheduler(
num_train_timesteps=1_0_0_0 ,beta_schedule='squaredcos_cap_v2' ,beta_start=0.0001 ,beta_end=0.02 ,thresholding=SCREAMING_SNAKE_CASE__ ,dynamic_thresholding_ratio=0.95 ,sample_max_value=1.0 ,prediction_type='epsilon' ,variance_type='learned_range' ,)
torch.manual_seed(0)
__lowerCamelCase : Union[str, Any] = DDPMScheduler(
num_train_timesteps=1_0_0_0 ,beta_schedule='squaredcos_cap_v2' ,beta_start=0.0001 ,beta_end=0.02 ,)
torch.manual_seed(0)
__lowerCamelCase : Any = 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 : str):
__lowerCamelCase : Union[str, Any] = self.get_dummy_components()
__lowerCamelCase : Tuple = self.pipeline_class(**SCREAMING_SNAKE_CASE__)
pipe.to(SCREAMING_SNAKE_CASE__)
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : str = inputs['prompt']
__lowerCamelCase : str = inputs['generator']
__lowerCamelCase : List[Any] = inputs['num_inference_steps']
__lowerCamelCase : Optional[Any] = inputs['output_type']
if "image" in inputs:
__lowerCamelCase : Dict = inputs['image']
else:
__lowerCamelCase : Optional[Any] = None
if "mask_image" in inputs:
__lowerCamelCase : Optional[int] = inputs['mask_image']
else:
__lowerCamelCase : Dict = None
if "original_image" in inputs:
__lowerCamelCase : Dict = inputs['original_image']
else:
__lowerCamelCase : Optional[Any] = None
__lowerCamelCase : Optional[Any] = pipe.encode_prompt(SCREAMING_SNAKE_CASE__)
# 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 : List[str] = image
if mask_image is not None:
__lowerCamelCase : List[Any] = 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(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Optional[Any] = pipe(**SCREAMING_SNAKE_CASE__)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[Any] = self.pipeline_class.from_pretrained(SCREAMING_SNAKE_CASE__)
pipe_loaded.to(SCREAMING_SNAKE_CASE__)
pipe_loaded.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__) is None ,F"`{optional_component}` did not stay set to None after loading." ,)
__lowerCamelCase : str = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Optional[int] = inputs['generator']
__lowerCamelCase : Any = inputs['num_inference_steps']
__lowerCamelCase : List[str] = inputs['output_type']
# inputs with prompt converted to embeddings
__lowerCamelCase : 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 : Optional[int] = image
if mask_image is not None:
__lowerCamelCase : int = mask_image
if original_image is not None:
__lowerCamelCase : int = original_image
__lowerCamelCase : List[Any] = pipe_loaded(**SCREAMING_SNAKE_CASE__)[0]
__lowerCamelCase : Dict = np.abs(to_np(SCREAMING_SNAKE_CASE__) - to_np(SCREAMING_SNAKE_CASE__)).max()
self.assertLess(SCREAMING_SNAKE_CASE__ ,1E-4)
def lowerCAmelCase ( self : List[Any]):
__lowerCamelCase : str = self.get_dummy_components()
__lowerCamelCase : Optional[int] = self.pipeline_class(**SCREAMING_SNAKE_CASE__)
pipe.to(SCREAMING_SNAKE_CASE__)
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[Any] = pipe(**SCREAMING_SNAKE_CASE__)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[Any] = self.pipeline_class.from_pretrained(SCREAMING_SNAKE_CASE__)
pipe_loaded.to(SCREAMING_SNAKE_CASE__)
pipe_loaded.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()) # For reproducibility tests
__lowerCamelCase : str = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Union[str, Any] = pipe_loaded(**SCREAMING_SNAKE_CASE__)[0]
__lowerCamelCase : int = np.abs(to_np(SCREAMING_SNAKE_CASE__) - to_np(SCREAMING_SNAKE_CASE__)).max()
self.assertLess(SCREAMING_SNAKE_CASE__ ,1E-4)
| 715 |
from math import factorial
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ = 1_0_0 ) -> int:
return sum(int(lowerCamelCase__ ) for x in str(factorial(lowerCamelCase__ ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 337 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __lowercase ( _UpperCAmelCase , unittest.TestCase):
"""simple docstring"""
_A : Tuple = FunnelTokenizer
_A : str = FunnelTokenizerFast
_A : Optional[Any] = True
_A : List[Any] = True
def __UpperCamelCase (self ):
super().setUp()
snake_case_ : List[str] = [
"""<unk>""",
"""<cls>""",
"""<sep>""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
snake_case_ : List[Any] = 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 __UpperCamelCase (self , **lowercase__ ):
return FunnelTokenizer.from_pretrained(self.tmpdirname , **lowercase__ )
def __UpperCamelCase (self , **lowercase__ ):
return FunnelTokenizerFast.from_pretrained(self.tmpdirname , **lowercase__ )
def __UpperCamelCase (self , lowercase__ ):
snake_case_ : str = """UNwant\u00E9d,running"""
snake_case_ : Optional[Any] = """unwanted, running"""
return input_text, output_text
def __UpperCamelCase (self ):
snake_case_ : Tuple = self.tokenizer_class(self.vocab_file )
snake_case_ : List[Any] = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(lowercase__ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__ ) , [7, 4, 5, 10, 8, 9] )
def __UpperCamelCase (self ):
snake_case_ : Union[str, Any] = self.get_tokenizers(do_lower_case=lowercase__ )
for tokenizer in tokenizers:
snake_case_ : List[Any] = tokenizer("""UNwant\u00E9d,running""" )
snake_case_ : Tuple = len(inputs["""input_ids"""] ) - 1
self.assertListEqual(inputs["""token_type_ids"""] , [2] + [0] * sentence_len )
snake_case_ : Dict = tokenizer("""UNwant\u00E9d,running""" , """UNwant\u00E9d,running""" )
self.assertListEqual(inputs["""token_type_ids"""] , [2] + [0] * sentence_len + [1] * sentence_len )
| 480 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a_ = logging.get_logger(__name__)
a_ = {
'''microsoft/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class __lowercase ( _UpperCAmelCase , _UpperCAmelCase):
"""simple docstring"""
_A : List[Any] = """swin"""
_A : Any = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__(self , lowercase__=2_24 , lowercase__=4 , lowercase__=3 , lowercase__=96 , lowercase__=[2, 2, 6, 2] , lowercase__=[3, 6, 12, 24] , lowercase__=7 , lowercase__=4.0 , lowercase__=True , lowercase__=0.0 , lowercase__=0.0 , lowercase__=0.1 , lowercase__="gelu" , lowercase__=False , lowercase__=0.02 , lowercase__=1e-5 , lowercase__=32 , lowercase__=None , lowercase__=None , **lowercase__ , ):
super().__init__(**lowercase__ )
snake_case_ : List[Any] = image_size
snake_case_ : Optional[Any] = patch_size
snake_case_ : Union[str, Any] = num_channels
snake_case_ : List[str] = embed_dim
snake_case_ : str = depths
snake_case_ : Tuple = len(lowercase__ )
snake_case_ : Optional[int] = num_heads
snake_case_ : Dict = window_size
snake_case_ : int = mlp_ratio
snake_case_ : List[Any] = qkv_bias
snake_case_ : List[Any] = hidden_dropout_prob
snake_case_ : Union[str, Any] = attention_probs_dropout_prob
snake_case_ : int = drop_path_rate
snake_case_ : Optional[Any] = hidden_act
snake_case_ : str = use_absolute_embeddings
snake_case_ : str = layer_norm_eps
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : List[Any] = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case_ : Optional[int] = int(embed_dim * 2 ** (len(lowercase__ ) - 1) )
snake_case_ : Optional[int] = ["""stem"""] + [f'stage{idx}' for idx in range(1 , len(lowercase__ ) + 1 )]
snake_case_ , snake_case_ : Optional[Any] = get_aligned_output_features_output_indices(
out_features=lowercase__ , out_indices=lowercase__ , stage_names=self.stage_names )
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
_A : str = version.parse("""1.11""")
@property
def __UpperCamelCase (self ):
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def __UpperCamelCase (self ):
return 1e-4
| 480 | 1 |
"""simple docstring"""
def lowerCAmelCase (__UpperCamelCase : int ):
"""simple docstring"""
__UpperCamelCase =n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 717 | """simple docstring"""
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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 TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class _lowercase :
"""simple docstring"""
lowercase__ = LEDConfig
lowercase__ = {}
lowercase__ = '''gelu'''
def __init__( self : Any , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[Any]=13 , UpperCamelCase__ : Optional[int]=7 , UpperCamelCase__ : List[Any]=True , UpperCamelCase__ : Optional[Any]=False , UpperCamelCase__ : List[str]=99 , UpperCamelCase__ : Dict=32 , UpperCamelCase__ : List[Any]=2 , UpperCamelCase__ : Tuple=4 , UpperCamelCase__ : int=37 , UpperCamelCase__ : str=0.1 , UpperCamelCase__ : Optional[int]=0.1 , UpperCamelCase__ : Optional[int]=20 , UpperCamelCase__ : str=2 , UpperCamelCase__ : Dict=1 , UpperCamelCase__ : Optional[Any]=0 , UpperCamelCase__ : Tuple=4 , ) -> str:
'''simple docstring'''
__UpperCamelCase =parent
__UpperCamelCase =batch_size
__UpperCamelCase =seq_length
__UpperCamelCase =is_training
__UpperCamelCase =use_labels
__UpperCamelCase =vocab_size
__UpperCamelCase =hidden_size
__UpperCamelCase =num_hidden_layers
__UpperCamelCase =num_attention_heads
__UpperCamelCase =intermediate_size
__UpperCamelCase =hidden_dropout_prob
__UpperCamelCase =attention_probs_dropout_prob
__UpperCamelCase =max_position_embeddings
__UpperCamelCase =eos_token_id
__UpperCamelCase =pad_token_id
__UpperCamelCase =bos_token_id
__UpperCamelCase =attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
__UpperCamelCase =self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
__UpperCamelCase =(
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def UpperCAmelCase_ ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
__UpperCamelCase =ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__UpperCamelCase =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__UpperCamelCase =tf.concat([input_ids, eos_tensor] , axis=1 )
__UpperCamelCase =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase =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 , attention_window=self.attention_window , **self.config_updates , )
__UpperCamelCase =prepare_led_inputs_dict(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
__UpperCamelCase =tf.concat(
[tf.zeros_like(UpperCamelCase__ )[:, :-1], tf.ones_like(UpperCamelCase__ )[:, -1:]] , axis=-1 , )
__UpperCamelCase =global_attention_mask
return config, inputs_dict
def UpperCAmelCase_ ( self : Dict , UpperCamelCase__ : Dict , UpperCamelCase__ : Dict ) -> Any:
'''simple docstring'''
__UpperCamelCase =TFLEDModel(config=UpperCamelCase__ ).get_decoder()
__UpperCamelCase =inputs_dict['''input_ids''']
__UpperCamelCase =input_ids[:1, :]
__UpperCamelCase =inputs_dict['''attention_mask'''][:1, :]
__UpperCamelCase =1
# first forward pass
__UpperCamelCase =model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , use_cache=UpperCamelCase__ )
__UpperCamelCase , __UpperCamelCase =outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__UpperCamelCase =ids_tensor((self.batch_size, 3) , config.vocab_size )
__UpperCamelCase =tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
__UpperCamelCase =tf.concat([input_ids, next_tokens] , axis=-1 )
__UpperCamelCase =tf.concat([attention_mask, next_attn_mask] , axis=-1 )
__UpperCamelCase =model(UpperCamelCase__ , attention_mask=UpperCamelCase__ )[0]
__UpperCamelCase =model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , past_key_values=UpperCamelCase__ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
__UpperCamelCase =int(ids_tensor((1,) , output_from_past.shape[-1] ) )
__UpperCamelCase =output_from_no_past[:, -3:, random_slice_idx]
__UpperCamelCase =output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(UpperCamelCase__ , UpperCamelCase__ , rtol=1E-3 )
def lowerCAmelCase (__UpperCamelCase : Any , __UpperCamelCase : Dict , __UpperCamelCase : Any , __UpperCamelCase : Any=None , __UpperCamelCase : List[str]=None , __UpperCamelCase : int=None , __UpperCamelCase : Tuple=None , ):
"""simple docstring"""
if attention_mask is None:
__UpperCamelCase =tf.cast(tf.math.not_equal(__UpperCamelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__UpperCamelCase =tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__UpperCamelCase =tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__UpperCamelCase =tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class _lowercase ( __a , __a , unittest.TestCase ):
"""simple docstring"""
lowercase__ = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
lowercase__ = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
lowercase__ = (
{
'''conversational''': TFLEDForConditionalGeneration,
'''feature-extraction''': TFLEDModel,
'''summarization''': TFLEDForConditionalGeneration,
'''text2text-generation''': TFLEDForConditionalGeneration,
'''translation''': TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowercase__ = True
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase_ ( self : int ) -> List[Any]:
'''simple docstring'''
__UpperCamelCase =TFLEDModelTester(self )
__UpperCamelCase =ConfigTester(self , config_class=UpperCamelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self : Dict ) -> Any:
'''simple docstring'''
__UpperCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase =tf.zeros_like(inputs_dict['''attention_mask'''] )
__UpperCamelCase =2
__UpperCamelCase =tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict['''global_attention_mask'''] , )
__UpperCamelCase =True
__UpperCamelCase =self.model_tester.seq_length
__UpperCamelCase =self.model_tester.encoder_seq_length
def check_decoder_attentions_output(UpperCamelCase__ : Tuple ):
__UpperCamelCase =outputs.decoder_attentions
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(UpperCamelCase__ : Dict ):
__UpperCamelCase =[t.numpy() for t in outputs.encoder_attentions]
__UpperCamelCase =[t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
__UpperCamelCase =True
__UpperCamelCase =False
__UpperCamelCase =False
__UpperCamelCase =model_class(UpperCamelCase__ )
__UpperCamelCase =model(self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
__UpperCamelCase =len(UpperCamelCase__ )
self.assertEqual(config.output_hidden_states , UpperCamelCase__ )
check_encoder_attentions_output(UpperCamelCase__ )
if self.is_encoder_decoder:
__UpperCamelCase =model_class(UpperCamelCase__ )
__UpperCamelCase =model(self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
self.assertEqual(config.output_hidden_states , UpperCamelCase__ )
check_decoder_attentions_output(UpperCamelCase__ )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
__UpperCamelCase =True
__UpperCamelCase =model_class(UpperCamelCase__ )
__UpperCamelCase =model(self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
self.assertEqual(config.output_hidden_states , UpperCamelCase__ )
check_encoder_attentions_output(UpperCamelCase__ )
# Check attention is always last and order is fine
__UpperCamelCase =True
__UpperCamelCase =True
__UpperCamelCase =model_class(UpperCamelCase__ )
__UpperCamelCase =model(self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(UpperCamelCase__ ) )
self.assertEqual(model.config.output_hidden_states , UpperCamelCase__ )
check_encoder_attentions_output(UpperCamelCase__ )
@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.''' )
def UpperCAmelCase_ ( self : Dict ) -> Dict:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
pass
def lowerCAmelCase (__UpperCamelCase : str ):
"""simple docstring"""
return tf.constant(__UpperCamelCase , dtype=tf.intaa )
__lowercase = 1e-4
@slow
@require_tf
class _lowercase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self : str ) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase =TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' ).led
# change to intended input here
__UpperCamelCase =_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
__UpperCamelCase =_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
__UpperCamelCase =prepare_led_inputs_dict(model.config , UpperCamelCase__ , UpperCamelCase__ )
__UpperCamelCase =model(**UpperCamelCase__ )[0]
__UpperCamelCase =(1, 1024, 768)
self.assertEqual(output.shape , UpperCamelCase__ )
# change to expected output here
__UpperCamelCase =tf.convert_to_tensor(
[[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]] , )
tf.debugging.assert_near(output[:, :3, :3] , UpperCamelCase__ , atol=1E-3 )
def UpperCAmelCase_ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase =TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' )
# change to intended input here
__UpperCamelCase =_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
__UpperCamelCase =_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
__UpperCamelCase =prepare_led_inputs_dict(model.config , UpperCamelCase__ , UpperCamelCase__ )
__UpperCamelCase =model(**UpperCamelCase__ )[0]
__UpperCamelCase =(1, 1024, model.config.vocab_size)
self.assertEqual(output.shape , UpperCamelCase__ )
# change to expected output here
__UpperCamelCase =tf.convert_to_tensor(
[[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]] , )
tf.debugging.assert_near(output[:, :3, :3] , UpperCamelCase__ , atol=1E-3 , rtol=1E-3 )
| 296 | 0 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__UpperCamelCase : int = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.14.0', 'To fix: pip install -r examples/pytorch/audio-classification/requirements.txt')
def _UpperCAmelCase ( UpperCAmelCase : Any , UpperCAmelCase : List[str] , UpperCAmelCase : Tuple = 16_000 ):
"""simple docstring"""
__lowerCamelCase : Tuple = int(round(sample_rate * max_length ) )
if len(lowercase__ ) <= sample_length:
return wav
__lowerCamelCase : List[str] = randint(0 , len(lowercase__ ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class _UpperCamelCase :
'''simple docstring'''
a_ : Optional[str] = field(default=__UpperCAmelCase,metadata={"help": "Name of a dataset from the datasets package"} )
a_ : Optional[str] = field(
default=__UpperCAmelCase,metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
a_ : Optional[str] = field(
default=__UpperCAmelCase,metadata={"help": "A file containing the training audio paths and labels."} )
a_ : Optional[str] = field(
default=__UpperCAmelCase,metadata={"help": "A file containing the validation audio paths and labels."} )
a_ : str = field(
default="train",metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
},)
a_ : str = field(
default="validation",metadata={
"help": (
"The name of the training data set split to use (via the datasets library). Defaults to 'validation'"
)
},)
a_ : str = field(
default="audio",metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},)
a_ : str = field(
default="label",metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'"} )
a_ : Optional[int] = field(
default=__UpperCAmelCase,metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},)
a_ : Optional[int] = field(
default=__UpperCAmelCase,metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},)
a_ : float = field(
default=20,metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."},)
@dataclass
class _UpperCamelCase :
'''simple docstring'''
a_ : str = field(
default="facebook/wav2vec2-base",metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"},)
a_ : Optional[str] = field(
default=__UpperCAmelCase,metadata={"help": "Pretrained config name or path if not the same as model_name"} )
a_ : Optional[str] = field(
default=__UpperCAmelCase,metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"} )
a_ : str = field(
default="main",metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},)
a_ : Optional[str] = field(
default=__UpperCAmelCase,metadata={"help": "Name or path of preprocessor config."} )
a_ : bool = field(
default=__UpperCAmelCase,metadata={"help": "Whether to freeze the feature encoder layers of the model."} )
a_ : bool = field(
default=__UpperCAmelCase,metadata={"help": "Whether to generate an attention mask in the feature extractor."} )
a_ : bool = field(
default=__UpperCAmelCase,metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
},)
a_ : Optional[bool] = field(
default=__UpperCAmelCase,metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
a_ : bool = field(
default=__UpperCAmelCase,metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},)
def _snake_case ( self : Any ):
'''simple docstring'''
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
"""The argument `--freeze_feature_extractor` is deprecated and """
"""will be removed in a future version. Use `--freeze_feature_encoder`"""
"""instead. Setting `freeze_feature_encoder==True`.""" , lowerCamelCase__ , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
"""The argument `--freeze_feature_extractor` is deprecated and """
"""should not be used in combination with `--freeze_feature_encoder`."""
"""Only make use of `--freeze_feature_encoder`.""" )
def _UpperCAmelCase ( ):
"""simple docstring"""
__lowerCamelCase : Optional[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase : Optional[int] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase : Optional[Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_audio_classification""" , lowercase__ , lowercase__ )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
__lowerCamelCase : Optional[Any] = training_args.get_process_log_level()
logger.setLevel(lowercase__ )
transformers.utils.logging.set_verbosity(lowercase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} """
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
__lowerCamelCase : List[Any] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowerCamelCase : Any = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
"""Use --overwrite_output_dir to train from scratch.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Initialize our dataset and prepare it for the audio classification task.
__lowerCamelCase : List[str] = DatasetDict()
__lowerCamelCase : Union[str, Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
__lowerCamelCase : List[str] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f"""--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. """
"""Make sure to set `--audio_column_name` to the correct audio column - one of """
f"""{", ".join(raw_datasets["train"].column_names )}.""" )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f"""--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. """
"""Make sure to set `--label_column_name` to the correct text column - one of """
f"""{", ".join(raw_datasets["train"].column_names )}.""" )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
__lowerCamelCase : Tuple = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
__lowerCamelCase : Optional[int] = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
__lowerCamelCase : str = feature_extractor.model_input_names[0]
def train_transforms(UpperCAmelCase : List[Any] ):
__lowerCamelCase : int = []
for audio in batch[data_args.audio_column_name]:
__lowerCamelCase : int = random_subsample(
audio["""array"""] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(lowercase__ )
__lowerCamelCase : int = feature_extractor(lowercase__ , sampling_rate=feature_extractor.sampling_rate )
__lowerCamelCase : int = {model_input_name: inputs.get(lowercase__ )}
__lowerCamelCase : Union[str, Any] = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(UpperCAmelCase : Tuple ):
__lowerCamelCase : List[str] = [audio["""array"""] for audio in batch[data_args.audio_column_name]]
__lowerCamelCase : int = feature_extractor(lowercase__ , sampling_rate=feature_extractor.sampling_rate )
__lowerCamelCase : int = {model_input_name: inputs.get(lowercase__ )}
__lowerCamelCase : List[Any] = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
__lowerCamelCase : Optional[Any] = raw_datasets["""train"""].features[data_args.label_column_name].names
__lowerCamelCase , __lowerCamelCase : str = {}, {}
for i, label in enumerate(lowercase__ ):
__lowerCamelCase : Dict = str(lowercase__ )
__lowerCamelCase : int = label
# Load the accuracy metric from the datasets package
__lowerCamelCase : str = evaluate.load("""accuracy""" )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(UpperCAmelCase : List[Any] ):
__lowerCamelCase : int = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=lowercase__ , references=eval_pred.label_ids )
__lowerCamelCase : Dict = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(lowercase__ ) , labelaid=lowercase__ , idalabel=lowercase__ , finetuning_task="""audio-classification""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
__lowerCamelCase : str = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=lowercase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
__lowerCamelCase : List[Any] = (
raw_datasets["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(lowercase__ , output_all_columns=lowercase__ )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
__lowerCamelCase : Tuple = (
raw_datasets["""eval"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(lowercase__ , output_all_columns=lowercase__ )
# Initialize our trainer
__lowerCamelCase : Optional[int] = Trainer(
model=lowercase__ , args=lowercase__ , train_dataset=raw_datasets["""train"""] if training_args.do_train else None , eval_dataset=raw_datasets["""eval"""] if training_args.do_eval else None , compute_metrics=lowercase__ , tokenizer=lowercase__ , )
# Training
if training_args.do_train:
__lowerCamelCase : int = None
if training_args.resume_from_checkpoint is not None:
__lowerCamelCase : Optional[int] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__lowerCamelCase : List[str] = last_checkpoint
__lowerCamelCase : List[str] = trainer.train(resume_from_checkpoint=lowercase__ )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
__lowerCamelCase : Any = trainer.evaluate()
trainer.log_metrics("""eval""" , lowercase__ )
trainer.save_metrics("""eval""" , lowercase__ )
# Write model card and (optionally) push to hub
__lowerCamelCase : str = {
"""finetuned_from""": model_args.model_name_or_path,
"""tasks""": """audio-classification""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""audio-classification"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase__ )
else:
trainer.create_model_card(**lowercase__ )
if __name__ == "__main__":
main()
| 519 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class A ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase : Tuple = DebertaTokenizer
lowerCamelCase : Any = True
lowerCamelCase : Dict = DebertaTokenizerFast
def A__ ( self ) -> List[str]:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase__ = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""[UNK]""",
]
lowercase__ = dict(zip(lowerCamelCase__ , range(len(lowerCamelCase__ ) ) ) )
lowercase__ = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
lowercase__ = {"""unk_token""": """[UNK]"""}
lowercase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
lowercase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(lowerCamelCase__ ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(lowerCamelCase__ ) )
def A__ ( self , **lowerCamelCase__ ) -> str:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowerCamelCase__ )
def A__ ( self , lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
lowercase__ = """lower newer"""
lowercase__ = """lower newer"""
return input_text, output_text
def A__ ( self ) -> Optional[int]:
'''simple docstring'''
lowercase__ = self.get_tokenizer()
lowercase__ = """lower newer"""
lowercase__ = ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
lowercase__ = tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
lowercase__ = tokens + [tokenizer.unk_token]
lowercase__ = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , lowerCamelCase__ )
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase__ = self.get_tokenizer()
lowercase__ = tokenizer("""Hello""" , """World""" )
lowercase__ = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
self.assertListEqual(tokd["""token_type_ids"""] , lowerCamelCase__ )
@slow
def A__ ( self ) -> Any:
'''simple docstring'''
lowercase__ = self.tokenizer_class.from_pretrained("""microsoft/deberta-base""" )
lowercase__ = tokenizer.encode("""sequence builders""" , add_special_tokens=lowerCamelCase__ )
lowercase__ = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowerCamelCase__ )
lowercase__ = tokenizer.encode(
"""sequence builders""" , add_special_tokens=lowerCamelCase__ , add_prefix_space=lowerCamelCase__ )
lowercase__ = tokenizer.encode(
"""sequence builders""" , """multi-sequence build""" , add_special_tokens=lowerCamelCase__ , add_prefix_space=lowerCamelCase__ )
lowercase__ = tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ )
lowercase__ = tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ , lowerCamelCase__ )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
@slow
def A__ ( self ) -> Tuple:
'''simple docstring'''
lowercase__ = [self.tokenizer_class]
if self.test_rust_tokenizer:
tokenizer_classes.append(self.rust_tokenizer_class )
for tokenizer_class in tokenizer_classes:
lowercase__ = tokenizer_class.from_pretrained("""microsoft/deberta-base""" )
lowercase__ = [
"""ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
"""ALBERT incorporates two parameter reduction techniques""",
"""The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
""" embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
""" vocabulary embedding.""",
]
lowercase__ = tokenizer(lowerCamelCase__ , padding=lowerCamelCase__ )
lowercase__ = [tokenizer.decode(lowerCamelCase__ , skip_special_tokens=lowerCamelCase__ ) for seq in encoding["""input_ids"""]]
# fmt: off
lowercase__ = {
"""input_ids""": [
[1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
],
"""token_type_ids""": [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
],
"""attention_mask""": [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
]
}
# fmt: on
lowercase__ = [
"""ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
"""ALBERT incorporates two parameter reduction techniques""",
"""The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
""" embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
""" vocabulary embedding.""",
]
self.assertDictEqual(encoding.data , lowerCamelCase__ )
for expected, decoded in zip(lowerCamelCase__ , lowerCamelCase__ ):
self.assertEqual(lowerCamelCase__ , lowerCamelCase__ )
| 325 | 0 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = """T5Config"""
def _lowercase ( a__ : str , a__ : Any , a__ : Optional[int] ) -> jnp.ndarray:
"""simple docstring"""
_UpperCamelCase = jnp.zeros_like(a__ )
_UpperCamelCase = shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
_UpperCamelCase = shifted_input_ids.at[:, 0].set(a__ )
_UpperCamelCase = jnp.where(shifted_input_ids == -1_00 , a__ , a__ )
return shifted_input_ids
class lowerCamelCase_ ( a__ ):
__lowercase : int = "mt5"
__lowercase : Dict = MTaConfig
class lowerCamelCase_ ( a__ ):
__lowercase : int = "mt5"
__lowercase : Any = MTaConfig
class lowerCamelCase_ ( a__ ):
__lowercase : Union[str, Any] = "mt5"
__lowercase : Tuple = MTaConfig
| 710 |
from ...configuration_utils import PretrainedConfig
class lowerCamelCase_ ( lowercase ):
__lowercase : Dict = "bert-generation"
def __init__( self , lowerCamelCase_=5_03_58 , lowerCamelCase_=10_24 , lowerCamelCase_=24 , lowerCamelCase_=16 , lowerCamelCase_=40_96 , lowerCamelCase_="gelu" , lowerCamelCase_=0.1 , lowerCamelCase_=0.1 , lowerCamelCase_=5_12 , lowerCamelCase_=0.02 , lowerCamelCase_=1E-12 , lowerCamelCase_=0 , lowerCamelCase_=2 , lowerCamelCase_=1 , lowerCamelCase_="absolute" , lowerCamelCase_=True , **lowerCamelCase_ , ) -> List[str]:
"""simple docstring"""
super().__init__(pad_token_id=lowerCamelCase_ , bos_token_id=lowerCamelCase_ , eos_token_id=lowerCamelCase_ , **lowerCamelCase_ )
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = hidden_act
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = initializer_range
_UpperCamelCase = layer_norm_eps
_UpperCamelCase = position_embedding_type
_UpperCamelCase = use_cache
| 589 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class snake_case (snake_case_ ):
@staticmethod
@abstractmethod
def _a ( UpperCAmelCase_ ) -> Union[str, Any]:
raise NotImplementedError()
@abstractmethod
def _a ( self ) -> Union[str, Any]:
raise NotImplementedError()
| 267 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def _UpperCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
if not postfix_notation:
return 0
__magic_name__ : Optional[Any] = {"+", "-", "*", "/"}
__magic_name__ : list[Any] = []
for token in postfix_notation:
if token in operations:
__magic_name__ , __magic_name__ : List[Any] = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(UpperCamelCase__ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod() | 436 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case_ (self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ (self ):
_UpperCAmelCase : Any = StableDiffusionKDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""" )
_UpperCAmelCase : int = sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
sd_pipe.set_scheduler("""sample_euler""" )
_UpperCAmelCase : Optional[int] = "A painting of a squirrel eating a burger"
_UpperCAmelCase : int = torch.manual_seed(0 )
_UpperCAmelCase : Dict = sd_pipe([prompt] , generator=__A , guidance_scale=9.0 , num_inference_steps=2_0 , output_type="""np""" )
_UpperCAmelCase : str = output.images
_UpperCAmelCase : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_UpperCAmelCase : Union[str, Any] = np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def snake_case_ (self ):
_UpperCAmelCase : Tuple = StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
_UpperCAmelCase : str = sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
sd_pipe.set_scheduler("""sample_euler""" )
_UpperCAmelCase : Dict = "A painting of a squirrel eating a burger"
_UpperCAmelCase : Tuple = torch.manual_seed(0 )
_UpperCAmelCase : int = sd_pipe([prompt] , generator=__A , guidance_scale=9.0 , num_inference_steps=2_0 , output_type="""np""" )
_UpperCAmelCase : Any = output.images
_UpperCAmelCase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_UpperCAmelCase : Any = np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def snake_case_ (self ):
_UpperCAmelCase : Optional[int] = StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
_UpperCAmelCase : List[Any] = sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
sd_pipe.set_scheduler("""sample_dpmpp_2m""" )
_UpperCAmelCase : int = "A painting of a squirrel eating a burger"
_UpperCAmelCase : str = torch.manual_seed(0 )
_UpperCAmelCase : Optional[Any] = sd_pipe(
[prompt] , generator=__A , guidance_scale=7.5 , num_inference_steps=1_5 , output_type="""np""" , use_karras_sigmas=__A , )
_UpperCAmelCase : Dict = output.images
_UpperCAmelCase : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_UpperCAmelCase : Any = np.array(
[0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 703 |
'''simple docstring'''
from __future__ import annotations
lowerCAmelCase_ : List[Any] = tuple[int, int, int]
lowerCAmelCase_ : Optional[Any] = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
lowerCAmelCase_ : Union[str, Any] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
# -------------------------- default selection --------------------------
# rotors --------------------------
lowerCAmelCase_ : Any = '''EGZWVONAHDCLFQMSIPJBYUKXTR'''
lowerCAmelCase_ : int = '''FOBHMDKEXQNRAULPGSJVTYICZW'''
lowerCAmelCase_ : Any = '''ZJXESIUQLHAVRMDOYGTNFWPBKC'''
# reflector --------------------------
lowerCAmelCase_ : str = {
'''A''': '''N''',
'''N''': '''A''',
'''B''': '''O''',
'''O''': '''B''',
'''C''': '''P''',
'''P''': '''C''',
'''D''': '''Q''',
'''Q''': '''D''',
'''E''': '''R''',
'''R''': '''E''',
'''F''': '''S''',
'''S''': '''F''',
'''G''': '''T''',
'''T''': '''G''',
'''H''': '''U''',
'''U''': '''H''',
'''I''': '''V''',
'''V''': '''I''',
'''J''': '''W''',
'''W''': '''J''',
'''K''': '''X''',
'''X''': '''K''',
'''L''': '''Y''',
'''Y''': '''L''',
'''M''': '''Z''',
'''Z''': '''M''',
}
# -------------------------- extra rotors --------------------------
lowerCAmelCase_ : int = '''RMDJXFUWGISLHVTCQNKYPBEZOA'''
lowerCAmelCase_ : List[str] = '''SGLCPQWZHKXAREONTFBVIYJUDM'''
lowerCAmelCase_ : List[Any] = '''HVSICLTYKQUBXDWAJZOMFGPREN'''
lowerCAmelCase_ : Union[str, Any] = '''RZWQHFMVDBKICJLNTUXAGYPSOE'''
lowerCAmelCase_ : Any = '''LFKIJODBEGAMQPXVUHYSTCZRWN'''
lowerCAmelCase_ : List[str] = '''KOAEGVDHXPQZMLFTYWJNBRCIUS'''
def __A ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
# Checks if there are 3 unique rotors
if (unique_rotsel := len(set(lowerCAmelCase_ ) )) < 3:
_UpperCAmelCase : List[str] = f"Please use 3 unique rotors (not {unique_rotsel})"
raise Exception(lowerCAmelCase_ )
# Checks if rotor positions are valid
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : str = rotpos
if not 0 < rotorposa <= len(lowerCAmelCase_ ):
_UpperCAmelCase : List[str] = f"First rotor position is not within range of 1..26 ({rotorposa}"
raise ValueError(lowerCAmelCase_ )
if not 0 < rotorposa <= len(lowerCAmelCase_ ):
_UpperCAmelCase : Dict = f"Second rotor position is not within range of 1..26 ({rotorposa})"
raise ValueError(lowerCAmelCase_ )
if not 0 < rotorposa <= len(lowerCAmelCase_ ):
_UpperCAmelCase : Dict = f"Third rotor position is not within range of 1..26 ({rotorposa})"
raise ValueError(lowerCAmelCase_ )
# Validates string and returns dict
_UpperCAmelCase : Union[str, Any] = _plugboard(lowerCAmelCase_ )
return rotpos, rotsel, pbdict
def __A ( lowerCAmelCase_ ):
# tests the input string if it
# a) is type string
# b) has even length (so pairs can be made)
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_UpperCAmelCase : Optional[Any] = f"Plugboard setting isn't type string ({type(lowerCAmelCase_ )})"
raise TypeError(lowerCAmelCase_ )
elif len(lowerCAmelCase_ ) % 2 != 0:
_UpperCAmelCase : Tuple = f"Odd number of symbols ({len(lowerCAmelCase_ )})"
raise Exception(lowerCAmelCase_ )
elif pbstring == "":
return {}
pbstring.replace(""" """ , """""" )
# Checks if all characters are unique
_UpperCAmelCase : List[Any] = set()
for i in pbstring:
if i not in abc:
_UpperCAmelCase : Dict = f"'{i}' not in list of symbols"
raise Exception(lowerCAmelCase_ )
elif i in tmppbl:
_UpperCAmelCase : Optional[Any] = f"Duplicate symbol ({i})"
raise Exception(lowerCAmelCase_ )
else:
tmppbl.add(lowerCAmelCase_ )
del tmppbl
# Created the dictionary
_UpperCAmelCase : List[Any] = {}
for j in range(0 , len(lowerCAmelCase_ ) - 1 , 2 ):
_UpperCAmelCase : Union[str, Any] = pbstring[j + 1]
_UpperCAmelCase : Dict = pbstring[j]
return pb
def __A ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = (rotora, rotora, rotora) , lowerCAmelCase_ = "" , ):
_UpperCAmelCase : Optional[Any] = text.upper()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : int = _validator(
lowerCAmelCase_ , lowerCAmelCase_ , plugb.upper() )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : str = rotor_position
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Dict = rotor_selection
rotorposa -= 1
rotorposa -= 1
rotorposa -= 1
_UpperCAmelCase : str = []
# encryption/decryption process --------------------------
for symbol in text:
if symbol in abc:
# 1st plugboard --------------------------
if symbol in plugboard:
_UpperCAmelCase : List[Any] = plugboard[symbol]
# rotor ra --------------------------
_UpperCAmelCase : Tuple = abc.index(lowerCAmelCase_ ) + rotorposa
_UpperCAmelCase : str = rotora[index % len(lowerCAmelCase_ )]
# rotor rb --------------------------
_UpperCAmelCase : int = abc.index(lowerCAmelCase_ ) + rotorposa
_UpperCAmelCase : int = rotora[index % len(lowerCAmelCase_ )]
# rotor rc --------------------------
_UpperCAmelCase : Union[str, Any] = abc.index(lowerCAmelCase_ ) + rotorposa
_UpperCAmelCase : Dict = rotora[index % len(lowerCAmelCase_ )]
# reflector --------------------------
# this is the reason you don't need another machine to decipher
_UpperCAmelCase : Optional[Any] = reflector[symbol]
# 2nd rotors
_UpperCAmelCase : Any = abc[rotora.index(lowerCAmelCase_ ) - rotorposa]
_UpperCAmelCase : Optional[int] = abc[rotora.index(lowerCAmelCase_ ) - rotorposa]
_UpperCAmelCase : Union[str, Any] = abc[rotora.index(lowerCAmelCase_ ) - rotorposa]
# 2nd plugboard
if symbol in plugboard:
_UpperCAmelCase : List[str] = plugboard[symbol]
# moves/resets rotor positions
rotorposa += 1
if rotorposa >= len(lowerCAmelCase_ ):
_UpperCAmelCase : int = 0
rotorposa += 1
if rotorposa >= len(lowerCAmelCase_ ):
_UpperCAmelCase : Union[str, Any] = 0
rotorposa += 1
if rotorposa >= len(lowerCAmelCase_ ):
_UpperCAmelCase : str = 0
# else:
# pass
# Error could be also raised
# raise ValueError(
# 'Invalid symbol('+repr(symbol)+')')
result.append(lowerCAmelCase_ )
return "".join(lowerCAmelCase_ )
if __name__ == "__main__":
lowerCAmelCase_ : str = '''This is my Python script that emulates the Enigma machine from WWII.'''
lowerCAmelCase_ : Union[str, Any] = (1, 1, 1)
lowerCAmelCase_ : Optional[Any] = '''pictures'''
lowerCAmelCase_ : List[Any] = (rotora, rotora, rotora)
lowerCAmelCase_ : Tuple = enigma(message, rotor_pos, rotor_sel, pb)
print('''Encrypted message:''', en)
print('''Decrypted message:''', enigma(en, rotor_pos, rotor_sel, pb))
| 156 | 0 |
'''simple docstring'''
def a_ ( UpperCamelCase_ ):
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_persistence() does not accept negative values" )
A_ = 0
A_ = str(UpperCamelCase_ )
while len(UpperCamelCase_ ) != 1:
A_ = [int(UpperCamelCase_ ) for i in num_string]
A_ = 1
for i in range(0 , len(UpperCamelCase_ ) ):
total *= numbers[i]
A_ = str(UpperCamelCase_ )
steps += 1
return steps
def a_ ( UpperCamelCase_ ):
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError("additive_persistence() only accepts integral values" )
if num < 0:
raise ValueError("additive_persistence() does not accept negative values" )
A_ = 0
A_ = str(UpperCamelCase_ )
while len(UpperCamelCase_ ) != 1:
A_ = [int(UpperCamelCase_ ) for i in num_string]
A_ = 0
for i in range(0 , len(UpperCamelCase_ ) ):
total += numbers[i]
A_ = str(UpperCamelCase_ )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 452 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
DYNAMO_BACKENDS,
_ask_field,
_ask_options,
_convert_dynamo_backend,
_convert_mixed_precision,
_convert_sagemaker_distributed_mode,
_convert_yes_no_to_bool,
)
if is_botoa_available():
import botoa # noqa: F401
def a_ ( UpperCamelCase_ ):
A_ = botoa.client("iam" )
A_ = {
"Version": "2012-10-17",
"Statement": [
{"Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"}, "Action": "sts:AssumeRole"}
],
}
try:
# create the role, associated with the chosen trust policy
iam_client.create_role(
RoleName=UpperCamelCase_ , AssumeRolePolicyDocument=json.dumps(UpperCamelCase_ , indent=2 ) )
A_ = {
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"sagemaker:*",
"ecr:GetDownloadUrlForLayer",
"ecr:BatchGetImage",
"ecr:BatchCheckLayerAvailability",
"ecr:GetAuthorizationToken",
"cloudwatch:PutMetricData",
"cloudwatch:GetMetricData",
"cloudwatch:GetMetricStatistics",
"cloudwatch:ListMetrics",
"logs:CreateLogGroup",
"logs:CreateLogStream",
"logs:DescribeLogStreams",
"logs:PutLogEvents",
"logs:GetLogEvents",
"s3:CreateBucket",
"s3:ListBucket",
"s3:GetBucketLocation",
"s3:GetObject",
"s3:PutObject",
],
"Resource": "*",
}
],
}
# attach policy to role
iam_client.put_role_policy(
RoleName=UpperCamelCase_ , PolicyName=f"{role_name}_policy_permission" , PolicyDocument=json.dumps(UpperCamelCase_ , indent=2 ) , )
except iam_client.exceptions.EntityAlreadyExistsException:
print(f"role {role_name} already exists. Using existing one" )
def a_ ( UpperCamelCase_ ):
A_ = botoa.client("iam" )
return iam_client.get_role(RoleName=UpperCamelCase_ )["Role"]["Arn"]
def a_ ( ):
A_ = _ask_options(
"How do you want to authorize?" , ["AWS Profile", "Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) "] , UpperCamelCase_ , )
A_ = None
if credentials_configuration == 0:
A_ = _ask_field("Enter your AWS Profile name: [default] " , default="default" )
A_ = aws_profile
else:
print(
"Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,"
"`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`" )
A_ = _ask_field("AWS Access Key ID: " )
A_ = aws_access_key_id
A_ = _ask_field("AWS Secret Access Key: " )
A_ = aws_secret_access_key
A_ = _ask_field("Enter your AWS Region: [us-east-1]" , default="us-east-1" )
A_ = aws_region
A_ = _ask_options(
"Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?" , ["Provide IAM Role name", "Create new IAM role using credentials"] , UpperCamelCase_ , )
if role_management == 0:
A_ = _ask_field("Enter your IAM role name: " )
else:
A_ = "accelerate_sagemaker_execution_role"
print(f"Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials" )
_create_iam_role_for_sagemaker(UpperCamelCase_ )
A_ = _ask_field(
"Do you want to use custom Docker image? [yes/NO]: " , _convert_yes_no_to_bool , default=UpperCamelCase_ , error_message="Please enter yes or no." , )
A_ = None
if is_custom_docker_image:
A_ = _ask_field("Enter your Docker image: " , lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower() )
A_ = _ask_field(
"Do you want to provide SageMaker input channels with data locations? [yes/NO]: " , _convert_yes_no_to_bool , default=UpperCamelCase_ , error_message="Please enter yes or no." , )
A_ = None
if is_sagemaker_inputs_enabled:
A_ = _ask_field(
"Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): " , lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower() , )
A_ = _ask_field(
"Do you want to enable SageMaker metrics? [yes/NO]: " , _convert_yes_no_to_bool , default=UpperCamelCase_ , error_message="Please enter yes or no." , )
A_ = None
if is_sagemaker_metrics_enabled:
A_ = _ask_field(
"Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): " , lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower() , )
A_ = _ask_options(
"What is the distributed mode?" , ["No distributed training", "Data parallelism"] , _convert_sagemaker_distributed_mode , )
A_ = {}
A_ = _ask_field(
"Do you wish to optimize your script with torch dynamo?[yes/NO]:" , _convert_yes_no_to_bool , default=UpperCamelCase_ , error_message="Please enter yes or no." , )
if use_dynamo:
A_ = "dynamo_"
A_ = _ask_options(
"Which dynamo backend would you like to use?" , [x.lower() for x in DYNAMO_BACKENDS] , _convert_dynamo_backend , default=2 , )
A_ = _ask_field(
"Do you want to customize the defaults sent to torch.compile? [yes/NO]: " , _convert_yes_no_to_bool , default=UpperCamelCase_ , error_message="Please enter yes or no." , )
if use_custom_options:
A_ = _ask_options(
"Which mode do you want to use?" , UpperCamelCase_ , lambda UpperCamelCase_ : TORCH_DYNAMO_MODES[int(UpperCamelCase_ )] , default="default" , )
A_ = _ask_field(
"Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: " , _convert_yes_no_to_bool , default=UpperCamelCase_ , error_message="Please enter yes or no." , )
A_ = _ask_field(
"Do you want to enable dynamic shape tracing? [yes/NO]: " , _convert_yes_no_to_bool , default=UpperCamelCase_ , error_message="Please enter yes or no." , )
A_ = "Which EC2 instance type you want to use for your training?"
if distributed_type != SageMakerDistributedType.NO:
A_ = _ask_options(
UpperCamelCase_ , UpperCamelCase_ , lambda UpperCamelCase_ : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(UpperCamelCase_ )] )
else:
eca_instance_query += "? [ml.p3.2xlarge]:"
A_ = _ask_field(UpperCamelCase_ , lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower() , default="ml.p3.2xlarge" )
A_ = 1
if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
A_ = _ask_field(
"How many machines do you want use? [1]: " , UpperCamelCase_ , default=1 , )
A_ = _ask_options(
"Do you wish to use FP16 or BF16 (mixed precision)?" , ["no", "fp16", "bf16", "fp8"] , _convert_mixed_precision , )
if use_dynamo and mixed_precision == "no":
print(
"Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts." )
return SageMakerConfig(
image_uri=UpperCamelCase_ , compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER , distributed_type=UpperCamelCase_ , use_cpu=UpperCamelCase_ , dynamo_config=UpperCamelCase_ , eca_instance_type=UpperCamelCase_ , profile=UpperCamelCase_ , region=UpperCamelCase_ , iam_role_name=UpperCamelCase_ , mixed_precision=UpperCamelCase_ , num_machines=UpperCamelCase_ , sagemaker_inputs_file=UpperCamelCase_ , sagemaker_metrics_file=UpperCamelCase_ , )
| 452 | 1 |
'''simple docstring'''
import requests
A : Any = """YOUR API KEY"""
def _a ( lowerCamelCase_ , lowerCamelCase_ = giphy_api_key ):
snake_case : Dict ='''+'''.join(query.split() )
snake_case : List[str] =F'''https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'''
snake_case : Union[str, Any] =requests.get(lowerCamelCase_ ).json()['''data''']
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print("""\n""".join(get_gifs("""space ship""")))
| 136 |
'''simple docstring'''
from __future__ import annotations
A : List[Any] = 8.9_88E9 # units = N * m^s * C^-2
def _a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
snake_case : Tuple =abs(chargea * chargea )
if (force, chargea, chargea, distance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if distance < 0:
raise ValueError('''Distance cannot be negative''' )
if force == 0:
snake_case : int =COULOMBS_CONSTANT * charge_product / (distance**2)
return {"force": force}
elif chargea == 0:
snake_case : Any =abs(lowerCamelCase_ ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge1": chargea}
elif chargea == 0:
snake_case : Optional[int] =abs(lowerCamelCase_ ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge2": chargea}
elif distance == 0:
snake_case : int =(COULOMBS_CONSTANT * charge_product / abs(lowerCamelCase_ )) ** 0.5
return {"distance": distance}
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 136 | 1 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCAmelCase_ ( __a ) -> float:
"""simple docstring"""
return np.dot(__a , __a )
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__(self : List[str] , *,
UpperCAmelCase_ : float = np.inf , UpperCAmelCase_ : str = "linear" , UpperCAmelCase_ : float = 0.0 , ) ->None:
'''simple docstring'''
lowerCamelCase__: Dict =regularization
lowerCamelCase__: Any =gamma
if kernel == "linear":
lowerCamelCase__: Dict =self.__linear
elif kernel == "rbf":
if self.gamma == 0:
raise ValueError("rbf kernel requires gamma")
if not isinstance(self.gamma , (float, int)):
raise ValueError("gamma must be float or int")
if not self.gamma > 0:
raise ValueError("gamma must be > 0")
lowerCamelCase__: Tuple =self.__rbf
# in the future, there could be a default value like in sklearn
# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
# previously it was 1/(n_features)
else:
lowerCamelCase__: Optional[Any] =F"""Unknown kernel: {kernel}"""
raise ValueError(UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : List[str] , UpperCAmelCase_ : ndarray , UpperCAmelCase_ : ndarray) ->float:
'''simple docstring'''
return np.dot(UpperCAmelCase_ , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : List[str] , UpperCAmelCase_ : ndarray , UpperCAmelCase_ : ndarray) ->float:
'''simple docstring'''
return np.exp(-(self.gamma * norm_squared(vectora - vectora)))
def SCREAMING_SNAKE_CASE_ (self : Tuple , UpperCAmelCase_ : list[ndarray] , UpperCAmelCase_ : ndarray) ->None:
'''simple docstring'''
lowerCamelCase__: Optional[Any] =observations
lowerCamelCase__: Optional[int] =classes
# using Wolfe's Dual to calculate w.
# Primal problem: minimize 1/2*norm_squared(w)
# constraint: yn(w . xn + b) >= 1
#
# With l a vector
# Dual problem: maximize sum_n(ln) -
# 1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
# constraint: self.C >= ln >= 0
# and sum_n(ln*yn) = 0
# Then we get w using w = sum_n(ln*yn*xn)
# At the end we can get b ~= mean(yn - w . xn)
#
# Since we use kernels, we only need l_star to calculate b
# and to classify observations
((lowerCamelCase__) , ): List[str] =np.shape(UpperCAmelCase_)
def to_minimize(UpperCAmelCase_ : ndarray) -> float:
lowerCamelCase__: int =0
((lowerCamelCase__) , ): Optional[Any] =np.shape(UpperCAmelCase_)
for i in range(UpperCAmelCase_):
for j in range(UpperCAmelCase_):
s += (
candidate[i]
* candidate[j]
* classes[i]
* classes[j]
* self.kernel(observations[i] , observations[j])
)
return 1 / 2 * s - sum(UpperCAmelCase_)
lowerCamelCase__: List[Any] =LinearConstraint(UpperCAmelCase_ , 0 , 0)
lowerCamelCase__: str =Bounds(0 , self.regularization)
lowerCamelCase__: Union[str, Any] =minimize(
UpperCAmelCase_ , np.ones(UpperCAmelCase_) , bounds=UpperCAmelCase_ , constraints=[ly_contraint]).x
lowerCamelCase__: str =l_star
# calculating mean offset of separation plane to points
lowerCamelCase__: Tuple =0
for i in range(UpperCAmelCase_):
for j in range(UpperCAmelCase_):
s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
observations[i] , observations[j])
lowerCamelCase__: int =s / n
def SCREAMING_SNAKE_CASE_ (self : Any , UpperCAmelCase_ : ndarray) ->int:
'''simple docstring'''
lowerCamelCase__: Optional[Any] =sum(
self.optimum[n]
* self.classes[n]
* self.kernel(self.observations[n] , UpperCAmelCase_)
for n in range(len(self.classes)))
return 1 if s + self.offset >= 0 else -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 59 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a__ : int = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Optional[int] = ["""YolosFeatureExtractor"""]
a__ : List[str] = ["""YolosImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Union[str, Any] = [
"""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
a__ : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 165 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''studio-ousia/luke-base''': '''https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json''',
'''studio-ousia/luke-large''': '''https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json''',
}
class UpperCamelCase_ (__A ):
__magic_name__ = '''luke'''
def __init__( self : List[str] , lowerCAmelCase_ : Tuple=50_267 , lowerCAmelCase_ : Dict=500_000 , lowerCAmelCase_ : int=768 , lowerCAmelCase_ : Optional[int]=256 , lowerCAmelCase_ : int=12 , lowerCAmelCase_ : Optional[Any]=12 , lowerCAmelCase_ : Optional[int]=3_072 , lowerCAmelCase_ : List[Any]="gelu" , lowerCAmelCase_ : Optional[int]=0.1 , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : List[str]=512 , lowerCAmelCase_ : str=2 , lowerCAmelCase_ : List[Any]=0.0_2 , lowerCAmelCase_ : List[str]=1e-12 , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Tuple=1 , lowerCAmelCase_ : str=0 , lowerCAmelCase_ : Any=2 , **lowerCAmelCase_ : Dict , ) -> Optional[Any]:
super().__init__(pad_token_id=lowerCAmelCase_ , bos_token_id=lowerCAmelCase_ , eos_token_id=lowerCAmelCase_ , **lowerCAmelCase_ )
UpperCAmelCase_ : Tuple = vocab_size
UpperCAmelCase_ : str = entity_vocab_size
UpperCAmelCase_ : str = hidden_size
UpperCAmelCase_ : Any = entity_emb_size
UpperCAmelCase_ : Tuple = num_hidden_layers
UpperCAmelCase_ : Dict = num_attention_heads
UpperCAmelCase_ : Optional[Any] = hidden_act
UpperCAmelCase_ : Tuple = intermediate_size
UpperCAmelCase_ : Optional[int] = hidden_dropout_prob
UpperCAmelCase_ : Tuple = attention_probs_dropout_prob
UpperCAmelCase_ : Union[str, Any] = max_position_embeddings
UpperCAmelCase_ : Dict = type_vocab_size
UpperCAmelCase_ : str = initializer_range
UpperCAmelCase_ : Tuple = layer_norm_eps
UpperCAmelCase_ : Optional[Any] = use_entity_aware_attention
UpperCAmelCase_ : Optional[Any] = classifier_dropout
| 718 |
"""simple docstring"""
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCamelCase_ :
__magic_name__ = None
def _SCREAMING_SNAKE_CASE ( self : Any ) -> List[Any]:
UpperCAmelCase_ : Tuple = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase_ : Union[str, Any] = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : str ) -> int:
UpperCAmelCase_ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ : List[Any] = os.path.join(lowerCAmelCase_ , "feat_extract.json" )
feat_extract_first.to_json_file(lowerCAmelCase_ )
UpperCAmelCase_ : Any = self.feature_extraction_class.from_json_file(lowerCAmelCase_ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def _SCREAMING_SNAKE_CASE ( self : int ) -> Dict:
UpperCAmelCase_ : Optional[Any] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ : Optional[Any] = feat_extract_first.save_pretrained(lowerCAmelCase_ )[0]
check_json_file_has_correct_format(lowerCAmelCase_ )
UpperCAmelCase_ : Any = self.feature_extraction_class.from_pretrained(lowerCAmelCase_ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Optional[Any]:
UpperCAmelCase_ : Optional[int] = self.feature_extraction_class()
self.assertIsNotNone(lowerCAmelCase_ )
| 463 | 0 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
_A : List[Any] = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
| 427 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : str , snake_case_ : str ) -> 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""" )
__lowerCAmelCase = len(snake_case_ )
__lowerCAmelCase = len(snake_case_ )
__lowerCAmelCase = [[0] * (texta_length + 1) for _ in range(texta_length + 1 )]
__lowerCAmelCase = 0
__lowerCAmelCase = 0
for i in range(1 , texta_length + 1 ):
for j in range(1 , texta_length + 1 ):
if texta[i - 1] == texta[j - 1]:
__lowerCAmelCase = 1 + dp[i - 1][j - 1]
if dp[i][j] > ans_length:
__lowerCAmelCase = i
__lowerCAmelCase = dp[i][j]
return texta[ans_index - ans_length : ans_index]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 427 | 1 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowerCamelCase_ , lowerCamelCase_ = 9, 14 # noqa: F841
lowerCamelCase_ = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
lowerCamelCase_ = defaultdict(lowercase__ )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
lowerCamelCase_ = mst(lowercase__ )
lowerCamelCase_ = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
lowerCamelCase_ = tuple(answer[:2] )
lowerCamelCase_ = tuple(edge[::-1] )
assert edge in result or reverse in result
| 719 |
from manim import *
class A( UpperCamelCase ):
'''simple docstring'''
def a__ ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ = Rectangle(height=0.5 , width=0.5 )
lowerCamelCase_ = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
lowerCamelCase_ = Rectangle(height=0.25 , width=0.25 )
lowerCamelCase_ = [mem.copy() for i in range(6 )]
lowerCamelCase_ = [mem.copy() for i in range(6 )]
lowerCamelCase_ = VGroup(*A_ ).arrange(A_ , buff=0 )
lowerCamelCase_ = VGroup(*A_ ).arrange(A_ , buff=0 )
lowerCamelCase_ = VGroup(A_ , A_ ).arrange(A_ , buff=0 )
lowerCamelCase_ = Text('CPU' , font_size=24 )
lowerCamelCase_ = Group(A_ , A_ ).arrange(A_ , buff=0.5 , aligned_edge=A_ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(A_ )
lowerCamelCase_ = [mem.copy() for i in range(4 )]
lowerCamelCase_ = VGroup(*A_ ).arrange(A_ , buff=0 )
lowerCamelCase_ = Text('GPU' , font_size=24 )
lowerCamelCase_ = Group(A_ , A_ ).arrange(A_ , buff=0.5 , aligned_edge=A_ )
gpu.move_to([-1, -1, 0] )
self.add(A_ )
lowerCamelCase_ = [mem.copy() for i in range(6 )]
lowerCamelCase_ = VGroup(*A_ ).arrange(A_ , buff=0 )
lowerCamelCase_ = Text('Model' , font_size=24 )
lowerCamelCase_ = Group(A_ , A_ ).arrange(A_ , buff=0.5 , aligned_edge=A_ )
model.move_to([3, -1.0, 0] )
self.add(A_ )
lowerCamelCase_ = []
lowerCamelCase_ = []
for i, rect in enumerate(A_ ):
lowerCamelCase_ = fill.copy().set_fill(A_ , opacity=0.8 )
target.move_to(A_ )
model_arr.append(A_ )
lowerCamelCase_ = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(A_ , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(A_ )
self.add(*A_ , *A_ )
lowerCamelCase_ = [meta_mem.copy() for i in range(6 )]
lowerCamelCase_ = [meta_mem.copy() for i in range(6 )]
lowerCamelCase_ = VGroup(*A_ ).arrange(A_ , buff=0 )
lowerCamelCase_ = VGroup(*A_ ).arrange(A_ , buff=0 )
lowerCamelCase_ = VGroup(A_ , A_ ).arrange(A_ , buff=0 )
lowerCamelCase_ = Text('Disk' , font_size=24 )
lowerCamelCase_ = Group(A_ , A_ ).arrange(A_ , buff=0.5 , aligned_edge=A_ )
disk.move_to([-4, -1.25, 0] )
self.add(A_ , A_ )
lowerCamelCase_ = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
lowerCamelCase_ = MarkupText(
f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(A_ , A_ )
lowerCamelCase_ = MarkupText(
f"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" , font_size=18 , )
blue_text.next_to(A_ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(A_ )
lowerCamelCase_ = MarkupText(
f"""Now watch as an input is passed through the model\nand how the memory is utilized and handled.""" , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(A_ ) )
lowerCamelCase_ = Square(0.3 )
input.set_fill(A_ , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , A_ , buff=0.5 )
self.play(Write(A_ ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=A_ , buff=0.02 )
self.play(MoveToTarget(A_ ) )
self.play(FadeOut(A_ ) )
lowerCamelCase_ = Arrow(start=A_ , end=A_ , color=A_ , buff=0.5 )
a.next_to(model_arr[0].get_left() , A_ , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
lowerCamelCase_ = MarkupText(
f"""As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.""" , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(A_ , run_time=3 ) )
lowerCamelCase_ = {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}
self.play(
Write(A_ ) , Circumscribe(model_arr[0] , color=A_ , **A_ ) , Circumscribe(model_cpu_arr[0] , color=A_ , **A_ ) , Circumscribe(gpu_rect[0] , color=A_ , **A_ ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
lowerCamelCase_ = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.02 , A_ , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02 )
lowerCamelCase_ = AnimationGroup(
FadeOut(A_ , run_time=0.5 ) , MoveToTarget(A_ , run_time=0.5 ) , FadeIn(A_ , run_time=0.5 ) , lag_ratio=0.2 )
self.play(A_ )
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
if i >= 1:
lowerCamelCase_ = 0.7
self.play(
Circumscribe(model_arr[i] , **A_ ) , Circumscribe(cpu_left_col_base[i] , **A_ ) , Circumscribe(cpu_left_col_base[i + 1] , color=A_ , **A_ ) , Circumscribe(gpu_rect[0] , color=A_ , **A_ ) , Circumscribe(model_arr[i + 1] , color=A_ , **A_ ) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i] ) , MoveToTarget(model_cpu_arr[i + 1] ) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7 ) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7 ) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2 )
self.play(
Circumscribe(model_arr[-1] , color=A_ , **A_ ) , Circumscribe(cpu_left_col_base[-1] , color=A_ , **A_ ) , Circumscribe(gpu_rect[0] , color=A_ , **A_ ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
lowerCamelCase_ = a_c
lowerCamelCase_ = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5 )
self.play(
FadeOut(A_ ) , FadeOut(A_ , run_time=0.5 ) , )
lowerCamelCase_ = MarkupText(f"""Inference on a model too large for GPU memory\nis successfully completed.""" , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(A_ , run_time=3 ) , MoveToTarget(A_ ) )
self.wait()
| 651 | 0 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__lowerCAmelCase : Union[str, Any] =logging.get_logger(__name__)
class _lowercase :
'''simple docstring'''
def __init__( self :Union[str, Any] , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Dict ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE : Optional[int] = question_encoder
__SCREAMING_SNAKE_CASE : int = generator
__SCREAMING_SNAKE_CASE : str = self.question_encoder
def __magic_name__( self :List[str] , lowerCAmelCase__ :int ) -> Any:
if os.path.isfile(lowerCAmelCase__ ):
raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(lowerCAmelCase__ , exist_ok=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Tuple = os.path.join(lowerCAmelCase__ , '''question_encoder_tokenizer''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.join(lowerCAmelCase__ , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(lowerCAmelCase__ )
self.generator.save_pretrained(lowerCAmelCase__ )
@classmethod
def __magic_name__( cls :str , lowerCAmelCase__ :str , **lowerCAmelCase__ :Union[str, Any] ) -> int:
# dynamically import AutoTokenizer
from ..auto.tokenization_auto import AutoTokenizer
__SCREAMING_SNAKE_CASE : Any = kwargs.pop('''config''' , lowerCAmelCase__ )
if config is None:
__SCREAMING_SNAKE_CASE : Dict = RagConfig.from_pretrained(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoTokenizer.from_pretrained(
lowerCAmelCase__ , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
__SCREAMING_SNAKE_CASE : Any = AutoTokenizer.from_pretrained(
lowerCAmelCase__ , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=lowerCAmelCase__ , generator=lowerCAmelCase__ )
def __call__( self :Dict , *lowerCAmelCase__ :Optional[Any] , **lowerCAmelCase__ :Tuple ) -> str:
return self.current_tokenizer(*lowerCAmelCase__ , **lowerCAmelCase__ )
def __magic_name__( self :List[str] , *lowerCAmelCase__ :Any , **lowerCAmelCase__ :List[Any] ) -> List[str]:
return self.generator.batch_decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
def __magic_name__( self :Optional[int] , *lowerCAmelCase__ :int , **lowerCAmelCase__ :Tuple ) -> Tuple:
return self.generator.decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
def __magic_name__( self :Dict ) -> Tuple:
__SCREAMING_SNAKE_CASE : Tuple = self.question_encoder
def __magic_name__( self :Union[str, Any] ) -> int:
__SCREAMING_SNAKE_CASE : List[Any] = self.generator
def __magic_name__( self :Tuple , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Optional[List[str]] = None , lowerCAmelCase__ :Optional[int] = None , lowerCAmelCase__ :Optional[int] = None , lowerCAmelCase__ :str = "longest" , lowerCAmelCase__ :str = None , lowerCAmelCase__ :bool = True , **lowerCAmelCase__ :Optional[int] , ) -> BatchEncoding:
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , lowerCAmelCase__ , )
if max_length is None:
__SCREAMING_SNAKE_CASE : Optional[int] = self.current_tokenizer.model_max_length
__SCREAMING_SNAKE_CASE : Dict = self(
lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , **lowerCAmelCase__ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
__SCREAMING_SNAKE_CASE : List[Any] = self.current_tokenizer.model_max_length
__SCREAMING_SNAKE_CASE : Union[str, Any] = self(
text_target=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , padding=lowerCAmelCase__ , max_length=lowerCAmelCase__ , truncation=lowerCAmelCase__ , **lowerCAmelCase__ , )
__SCREAMING_SNAKE_CASE : Optional[Any] = labels['''input_ids''']
return model_inputs
| 696 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class _lowercase ( A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[List[np.ndarray], torch.FloatTensor]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 696 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import ceil, floor, sqrt
def lowercase_ ( _lowercase : int = 2_00_00_00 ):
'''simple docstring'''
UpperCAmelCase : list[int] = [0]
UpperCAmelCase : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbers.append(triangle_numbers[-1] + idx )
# we want this to be as close as possible to target
UpperCAmelCase : int = 0
# the area corresponding to the grid that gives the product closest to target
UpperCAmelCase : int = 0
# an estimate of b, using the quadratic formula
UpperCAmelCase : float
# the largest integer less than b_estimate
UpperCAmelCase : int
# the largest integer less than b_estimate
UpperCAmelCase : int
# the triangle number corresponding to b_floor
UpperCAmelCase : int
# the triangle number corresponding to b_ceil
UpperCAmelCase : int
for idx_a, triangle_a in enumerate(triangle_numbers[1:] , 1 ):
UpperCAmelCase : int = (-1 + sqrt(1 + 8 * target / triangle_a )) / 2
UpperCAmelCase : Optional[Any] = floor(_lowercase )
UpperCAmelCase : Union[str, Any] = ceil(_lowercase )
UpperCAmelCase : Tuple = triangle_numbers[b_floor]
UpperCAmelCase : Dict = triangle_numbers[b_ceil]
if abs(target - triangle_b_first_guess * triangle_a ) < abs(
target - best_product ):
UpperCAmelCase : str = triangle_b_first_guess * triangle_a
UpperCAmelCase : List[Any] = idx_a * b_floor
if abs(target - triangle_b_second_guess * triangle_a ) < abs(
target - best_product ):
UpperCAmelCase : Tuple = triangle_b_second_guess * triangle_a
UpperCAmelCase : Union[str, Any] = idx_a * b_ceil
return area
if __name__ == "__main__":
print(f'''{solution() = }''')
| 292 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
snake_case_ : Dict = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""MvpTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : List[Any] = ["""MvpTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Any = [
"""MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MvpForCausalLM""",
"""MvpForConditionalGeneration""",
"""MvpForQuestionAnswering""",
"""MvpForSequenceClassification""",
"""MvpModel""",
"""MvpPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
snake_case_ : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 292 | 1 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig
_lowerCamelCase = logging.get_logger(__name__)
# General docstring
_lowerCamelCase = """PoolFormerConfig"""
# Base docstring
_lowerCamelCase = """sail/poolformer_s12"""
_lowerCamelCase = [1, 512, 7, 7]
# Image classification docstring
_lowerCamelCase = """sail/poolformer_s12"""
_lowerCamelCase = """tabby, tabby cat"""
_lowerCamelCase = [
"""sail/poolformer_s12""",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def a__ ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : float = 0.0 , _SCREAMING_SNAKE_CASE : bool = False ) -> Tuple:
"""simple docstring"""
if drop_prob == 0.0 or not training:
return input
UpperCAmelCase_ : Any = 1 - drop_prob
UpperCAmelCase_ : Dict = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
UpperCAmelCase_ : Dict = keep_prob + torch.rand(_SCREAMING_SNAKE_CASE , dtype=input.dtype , device=input.device )
random_tensor.floor_() # binarize
UpperCAmelCase_ : Optional[Any] = input.div(_SCREAMING_SNAKE_CASE ) * random_tensor
return output
class _snake_case (nn.Module):
def __init__( self ,_snake_case = None ):
super().__init__()
UpperCAmelCase_ : List[Any] = drop_prob
def UpperCamelCase__ ( self ,_snake_case ):
return drop_path(_snake_case ,self.drop_prob ,self.training )
def UpperCamelCase__ ( self ):
return "p={}".format(self.drop_prob )
class _snake_case (nn.Module):
def __init__( self ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case=None ):
super().__init__()
UpperCAmelCase_ : int = patch_size if isinstance(_snake_case ,collections.abc.Iterable ) else (patch_size, patch_size)
UpperCAmelCase_ : Optional[int] = stride if isinstance(_snake_case ,collections.abc.Iterable ) else (stride, stride)
UpperCAmelCase_ : Union[str, Any] = padding if isinstance(_snake_case ,collections.abc.Iterable ) else (padding, padding)
UpperCAmelCase_ : Optional[int] = nn.Convad(_snake_case ,_snake_case ,kernel_size=_snake_case ,stride=_snake_case ,padding=_snake_case )
UpperCAmelCase_ : Optional[Any] = norm_layer(_snake_case ) if norm_layer else nn.Identity()
def UpperCamelCase__ ( self ,_snake_case ):
UpperCAmelCase_ : Union[str, Any] = self.projection(_snake_case )
UpperCAmelCase_ : Any = self.norm(_snake_case )
return embeddings
class _snake_case (nn.GroupNorm):
def __init__( self ,_snake_case ,**_snake_case ):
super().__init__(1 ,_snake_case ,**_snake_case )
class _snake_case (nn.Module):
def __init__( self ,_snake_case ):
super().__init__()
UpperCAmelCase_ : List[Any] = nn.AvgPoolad(_snake_case ,stride=1 ,padding=pool_size // 2 ,count_include_pad=_snake_case )
def UpperCamelCase__ ( self ,_snake_case ):
return self.pool(_snake_case ) - hidden_states
class _snake_case (nn.Module):
def __init__( self ,_snake_case ,_snake_case ,_snake_case ,_snake_case ):
super().__init__()
UpperCAmelCase_ : Optional[Any] = nn.Convad(_snake_case ,_snake_case ,1 )
UpperCAmelCase_ : int = nn.Convad(_snake_case ,_snake_case ,1 )
UpperCAmelCase_ : Tuple = PoolFormerDropPath(_snake_case )
if isinstance(config.hidden_act ,_snake_case ):
UpperCAmelCase_ : Dict = ACTaFN[config.hidden_act]
else:
UpperCAmelCase_ : List[Any] = config.hidden_act
def UpperCamelCase__ ( self ,_snake_case ):
UpperCAmelCase_ : Optional[Any] = self.conva(_snake_case )
UpperCAmelCase_ : Tuple = self.act_fn(_snake_case )
UpperCAmelCase_ : Tuple = self.drop(_snake_case )
UpperCAmelCase_ : Union[str, Any] = self.conva(_snake_case )
UpperCAmelCase_ : str = self.drop(_snake_case )
return hidden_states
class _snake_case (nn.Module):
def __init__( self ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ):
super().__init__()
UpperCAmelCase_ : int = PoolFormerPooling(_snake_case )
UpperCAmelCase_ : Any = PoolFormerOutput(_snake_case ,_snake_case ,_snake_case ,_snake_case )
UpperCAmelCase_ : List[str] = PoolFormerGroupNorm(_snake_case )
UpperCAmelCase_ : Tuple = PoolFormerGroupNorm(_snake_case )
# Useful for training neural nets
UpperCAmelCase_ : Optional[Any] = PoolFormerDropPath(_snake_case ) if drop_path > 0.0 else nn.Identity()
UpperCAmelCase_ : Union[str, Any] = config.use_layer_scale
if config.use_layer_scale:
UpperCAmelCase_ : str = nn.Parameter(
config.layer_scale_init_value * torch.ones((_snake_case) ) ,requires_grad=_snake_case )
UpperCAmelCase_ : List[Any] = nn.Parameter(
config.layer_scale_init_value * torch.ones((_snake_case) ) ,requires_grad=_snake_case )
def UpperCamelCase__ ( self ,_snake_case ):
if self.use_layer_scale:
UpperCAmelCase_ : Dict = self.pooling(self.before_norm(_snake_case ) )
UpperCAmelCase_ : Tuple = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
# First residual connection
UpperCAmelCase_ : Optional[int] = hidden_states + self.drop_path(_snake_case )
UpperCAmelCase_ : Optional[Any] = ()
UpperCAmelCase_ : List[str] = self.output(self.after_norm(_snake_case ) )
UpperCAmelCase_ : Any = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
# Second residual connection
UpperCAmelCase_ : List[str] = hidden_states + self.drop_path(_snake_case )
UpperCAmelCase_ : Any = (output,) + outputs
return outputs
else:
UpperCAmelCase_ : Tuple = self.drop_path(self.pooling(self.before_norm(_snake_case ) ) )
# First residual connection
UpperCAmelCase_ : List[Any] = pooling_output + hidden_states
UpperCAmelCase_ : Optional[int] = ()
# Second residual connection inside the PoolFormerOutput block
UpperCAmelCase_ : Optional[int] = self.drop_path(self.output(self.after_norm(_snake_case ) ) )
UpperCAmelCase_ : List[Any] = hidden_states + layer_output
UpperCAmelCase_ : Optional[Any] = (output,) + outputs
return outputs
class _snake_case (nn.Module):
def __init__( self ,_snake_case ):
super().__init__()
UpperCAmelCase_ : Any = config
# stochastic depth decay rule
UpperCAmelCase_ : Optional[Any] = [x.item() for x in torch.linspace(0 ,config.drop_path_rate ,sum(config.depths ) )]
# patch embeddings
UpperCAmelCase_ : str = []
for i in range(config.num_encoder_blocks ):
embeddings.append(
PoolFormerEmbeddings(
patch_size=config.patch_sizes[i] ,stride=config.strides[i] ,padding=config.padding[i] ,num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] ,hidden_size=config.hidden_sizes[i] ,) )
UpperCAmelCase_ : List[Any] = nn.ModuleList(_snake_case )
# Transformer blocks
UpperCAmelCase_ : int = []
UpperCAmelCase_ : List[Any] = 0
for i in range(config.num_encoder_blocks ):
# each block consists of layers
UpperCAmelCase_ : str = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i] ):
layers.append(
PoolFormerLayer(
_snake_case ,num_channels=config.hidden_sizes[i] ,pool_size=config.pool_size ,hidden_size=config.hidden_sizes[i] ,intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) ,drop_path=dpr[cur + j] ,) )
blocks.append(nn.ModuleList(_snake_case ) )
UpperCAmelCase_ : List[Any] = nn.ModuleList(_snake_case )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case=False ,_snake_case=True ):
UpperCAmelCase_ : Tuple = () if output_hidden_states else None
UpperCAmelCase_ : str = pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings ,self.block ) ):
UpperCAmelCase_ , UpperCAmelCase_ : Optional[int] = layers
# Get patch embeddings from hidden_states
UpperCAmelCase_ : Optional[int] = embedding_layer(_snake_case )
# Send the embeddings through the blocks
for _, blk in enumerate(_snake_case ):
UpperCAmelCase_ : int = blk(_snake_case )
UpperCAmelCase_ : Optional[Any] = layer_outputs[0]
if output_hidden_states:
UpperCAmelCase_ : List[Any] = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=_snake_case ,hidden_states=_snake_case )
class _snake_case (__SCREAMING_SNAKE_CASE):
__A : List[str] =PoolFormerConfig
__A : Dict ="poolformer"
__A : Any ="pixel_values"
__A : Optional[Any] =True
def UpperCamelCase__ ( self ,_snake_case ):
if isinstance(_snake_case ,(nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 ,std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(_snake_case ,nn.LayerNorm ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case=False ):
if isinstance(_snake_case ,_snake_case ):
UpperCAmelCase_ : Any = value
_lowerCamelCase = R"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
_lowerCamelCase = R"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`PoolFormerImageProcessor.__call__`] for details.
"""
@add_start_docstrings(
"The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top." , __SCREAMING_SNAKE_CASE , )
class _snake_case (__SCREAMING_SNAKE_CASE):
def __init__( self ,_snake_case ):
super().__init__(_snake_case )
UpperCAmelCase_ : str = config
UpperCAmelCase_ : str = PoolFormerEncoder(_snake_case )
# Initialize weights and apply final processing
self.post_init()
def UpperCamelCase__ ( self ):
return self.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(_snake_case )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC ,output_type=_snake_case ,config_class=_CONFIG_FOR_DOC ,modality="vision" ,expected_output=_EXPECTED_OUTPUT_SHAPE ,)
def UpperCamelCase__ ( self ,_snake_case = None ,_snake_case = None ,_snake_case = None ,):
UpperCAmelCase_ : int = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase_ : List[Any] = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values" )
UpperCAmelCase_ : str = self.encoder(
_snake_case ,output_hidden_states=_snake_case ,return_dict=_snake_case ,)
UpperCAmelCase_ : Tuple = encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseModelOutputWithNoAttention(
last_hidden_state=_snake_case ,hidden_states=encoder_outputs.hidden_states ,)
class _snake_case (nn.Module):
def __init__( self ,_snake_case ):
super().__init__()
UpperCAmelCase_ : List[Any] = nn.Linear(config.hidden_size ,config.hidden_size )
def UpperCamelCase__ ( self ,_snake_case ):
UpperCAmelCase_ : Dict = self.dense(_snake_case )
return output
@add_start_docstrings(
"\n PoolFormer Model transformer with an image classification head on top\n " , __SCREAMING_SNAKE_CASE , )
class _snake_case (__SCREAMING_SNAKE_CASE):
def __init__( self ,_snake_case ):
super().__init__(_snake_case )
UpperCAmelCase_ : List[Any] = config.num_labels
UpperCAmelCase_ : Any = PoolFormerModel(_snake_case )
# Final norm
UpperCAmelCase_ : Optional[int] = PoolFormerGroupNorm(config.hidden_sizes[-1] )
# Classifier head
UpperCAmelCase_ : Optional[Any] = (
nn.Linear(config.hidden_sizes[-1] ,config.num_labels ) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_snake_case )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=_snake_case ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,)
def UpperCamelCase__ ( self ,_snake_case = None ,_snake_case = None ,_snake_case = None ,_snake_case = None ,):
UpperCAmelCase_ : Optional[int] = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase_ : Union[str, Any] = self.poolformer(
_snake_case ,output_hidden_states=_snake_case ,return_dict=_snake_case ,)
UpperCAmelCase_ : Any = outputs[0]
UpperCAmelCase_ : str = self.classifier(self.norm(_snake_case ).mean([-2, -1] ) )
UpperCAmelCase_ : Tuple = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
UpperCAmelCase_ : List[str] = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
UpperCAmelCase_ : Optional[int] = "single_label_classification"
else:
UpperCAmelCase_ : Tuple = "multi_label_classification"
if self.config.problem_type == "regression":
UpperCAmelCase_ : str = MSELoss()
if self.num_labels == 1:
UpperCAmelCase_ : int = loss_fct(logits.squeeze() ,labels.squeeze() )
else:
UpperCAmelCase_ : List[str] = loss_fct(_snake_case ,_snake_case )
elif self.config.problem_type == "single_label_classification":
UpperCAmelCase_ : Any = CrossEntropyLoss()
UpperCAmelCase_ : List[str] = loss_fct(logits.view(-1 ,self.num_labels ) ,labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
UpperCAmelCase_ : Optional[int] = BCEWithLogitsLoss()
UpperCAmelCase_ : Optional[int] = loss_fct(_snake_case ,_snake_case )
if not return_dict:
UpperCAmelCase_ : Any = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=_snake_case ,logits=_snake_case ,hidden_states=outputs.hidden_states )
| 71 |
from math import factorial
def _a ( UpperCamelCase_ : int = 20 ) -> int:
"""simple docstring"""
lowerCAmelCase__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
lowerCAmelCase__ = n // 2
return int(factorial(UpperCamelCase_ ) / (factorial(UpperCamelCase_ ) * factorial(n - k )) )
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution(20))
else:
try:
a_ = int(sys.argv[1])
print(solution(n))
except ValueError:
print('''Invalid entry - please enter a number.''')
| 339 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Any ) -> Optional[int]:
_UpperCAmelCase : Optional[Any] = os.path.join(args.tf_model_dir , "parameters.json" )
_UpperCAmelCase : List[str] = 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" ):
_UpperCAmelCase : Union[str, Any] = args.output + ".pt"
_UpperCAmelCase : Dict = OrderedDict()
with tf.device("/CPU:0" ):
_UpperCAmelCase : int = tf.train.load_checkpoint(args.tf_model_dir )
_UpperCAmelCase : int = reader.get_variable_to_shape_map()
for key_name in shapes.keys():
_UpperCAmelCase : Dict = 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" ):
_UpperCAmelCase : int = int(key_name[9] )
elif key_name.startswith("pasts/out" ):
_UpperCAmelCase : Any = 8
_UpperCAmelCase : str = "model.sqout.%d.weight" % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
_UpperCAmelCase : Optional[Any] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : str = torch.tensor(lowerCAmelCase )
elif key_name.startswith("model/moe" ):
_UpperCAmelCase : List[str] = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/switch_gating/kernel" ):
_UpperCAmelCase : Union[str, Any] = "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
_UpperCAmelCase : List[str] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : Tuple = torch.tensor(lowerCAmelCase )
elif key_name.endswith("/softmlp/kernel" ):
_UpperCAmelCase : List[str] = "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
_UpperCAmelCase : List[str] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : Union[str, Any] = torch.tensor(lowerCAmelCase )
elif key_name.endswith("/wo/kernel" ) or key_name.endswith("/wi/kernel" ):
_UpperCAmelCase : str = key_name[-9:-7]
for i in range(16 ):
_UpperCAmelCase : Optional[int] = "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
_UpperCAmelCase : Optional[int] = (
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
_UpperCAmelCase : Dict = torch.tensor(lowerCAmelCase )
elif key_name.startswith("model/mlp" ):
_UpperCAmelCase : Tuple = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/p1/kernel" ):
_UpperCAmelCase : Any = "model.blocks.%d.feed_forward.mlp.wi.weight" % player
_UpperCAmelCase : int = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : List[str] = torch.tensor(lowerCAmelCase )
elif key_name.endswith("/p1/bias" ):
_UpperCAmelCase : Optional[Any] = "model.blocks.%d.feed_forward.mlp.wi.bias" % player
_UpperCAmelCase : Tuple = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : Any = torch.tensor(lowerCAmelCase )
elif key_name.endswith("/p2/kernel" ):
_UpperCAmelCase : str = "model.blocks.%d.feed_forward.mlp.wo.weight" % player
_UpperCAmelCase : Union[str, Any] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : List[Any] = torch.tensor(lowerCAmelCase )
elif key_name.endswith("/p2/bias" ):
_UpperCAmelCase : int = "model.blocks.%d.feed_forward.mlp.wo.bias" % player
_UpperCAmelCase : Optional[int] = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : str = torch.tensor(lowerCAmelCase )
elif key_name.startswith("model/ln" ):
_UpperCAmelCase : Union[str, Any] = int(key_name[8:].split("/" )[0] )
if key_name.endswith("/b" ):
_UpperCAmelCase : Union[str, Any] = "model.blocks.%d.feed_forward.norm.bias" % player
_UpperCAmelCase : int = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : Union[str, Any] = torch.tensor(lowerCAmelCase )
elif key_name.endswith("/g" ):
_UpperCAmelCase : Optional[Any] = "model.blocks.%d.feed_forward.norm.weight" % player
_UpperCAmelCase : Union[str, Any] = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : List[Any] = torch.tensor(lowerCAmelCase )
elif key_name.startswith("model/att" ):
_UpperCAmelCase : Optional[Any] = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/qkv/kernel" ):
_UpperCAmelCase : List[Any] = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
_UpperCAmelCase : Dict = state[:, 0, :, :]
_UpperCAmelCase : List[Any] = state[:, 1, :, :]
_UpperCAmelCase : Tuple = state[:, 2, :, :]
_UpperCAmelCase : List[str] = (
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
_UpperCAmelCase : int = (
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
_UpperCAmelCase : Tuple = (
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
_UpperCAmelCase : int = "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
_UpperCAmelCase : Any = torch.tensor(lowerCAmelCase )
_UpperCAmelCase : List[Any] = "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
_UpperCAmelCase : Any = torch.tensor(lowerCAmelCase )
_UpperCAmelCase : Union[str, Any] = "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
_UpperCAmelCase : str = torch.tensor(lowerCAmelCase )
elif key_name.endswith("/o/kernel" ):
_UpperCAmelCase : Dict = "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
_UpperCAmelCase : Tuple = (
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : List[str] = torch.tensor(lowerCAmelCase )
elif key_name.startswith("model/an" ):
_UpperCAmelCase : Optional[int] = int(key_name[8:].split("/" )[0] )
if key_name.endswith("/b" ):
_UpperCAmelCase : Any = "model.blocks.%d.self_attn.norm.bias" % player
_UpperCAmelCase : Any = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : Dict = torch.tensor(lowerCAmelCase )
elif key_name.endswith("/g" ):
_UpperCAmelCase : Dict = "model.blocks.%d.self_attn.norm.weight" % player
_UpperCAmelCase : Any = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : Tuple = torch.tensor(lowerCAmelCase )
elif (
key_name.startswith("model/wte" )
or key_name.startswith("model/wpe" )
or key_name.startswith("model/ete" )
):
_UpperCAmelCase : Optional[Any] = {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
key_name[-3:]
]
_UpperCAmelCase : Dict = "model.%s.weight" % nlayer
_UpperCAmelCase : Optional[Any] = vnp.copy() # same in embedded
_UpperCAmelCase : List[Any] = torch.tensor(lowerCAmelCase )
if key_name.startswith("model/wte" ):
_UpperCAmelCase : List[str] = "lm_head.weight"
_UpperCAmelCase : Optional[int] = vnp.copy() # same in embedded
_UpperCAmelCase : List[str] = torch.tensor(lowerCAmelCase )
elif key_name.startswith("model/wob" ):
_UpperCAmelCase : Dict = "final_logits_bias"
_UpperCAmelCase : Optional[Any] = vnp.copy() # same in embedded
_UpperCAmelCase : str = state.reshape((1, -1) )
_UpperCAmelCase : Dict = torch.tensor(lowerCAmelCase )
elif key_name == "model/dense/kernel":
_UpperCAmelCase : List[Any] = "model.last_project.weight"
_UpperCAmelCase : int = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : str = torch.tensor(lowerCAmelCase )
elif key_name == "model/dense_1/bias":
_UpperCAmelCase : List[Any] = "model.last_project.bias"
_UpperCAmelCase : Any = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : Any = torch.tensor(lowerCAmelCase )
torch.save(lowerCAmelCase , args.output )
if __name__ == "__main__":
SCREAMING_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')
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_tf_gptsan_to_pt(args)
| 467 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Dict , lowerCAmelCase: Union[str, Any] ) -> Union[str, Any]:
_UpperCAmelCase : Dict = b.T
_UpperCAmelCase : Dict = np.sum(np.square(lowerCAmelCase ) , axis=1 )
_UpperCAmelCase : Optional[Any] = np.sum(np.square(lowerCAmelCase ) , axis=0 )
_UpperCAmelCase : str = np.matmul(lowerCAmelCase , lowerCAmelCase )
_UpperCAmelCase : Any = aa[:, None] - 2 * ab + ba[None, :]
return d
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Union[str, Any] , lowerCAmelCase: Dict ) -> int:
_UpperCAmelCase : Any = x.reshape(-1 , 3 )
_UpperCAmelCase : List[str] = squared_euclidean_distance(lowerCAmelCase , lowerCAmelCase )
return np.argmin(lowerCAmelCase , axis=1 )
class a ( UpperCAmelCase ):
_lowercase = ["pixel_values"]
def __init__( self , A_ = None , A_ = True , A_ = None , A_ = PILImageResampling.BILINEAR , A_ = True , A_ = True , **A_ , ):
'''simple docstring'''
super().__init__(**A_ )
_UpperCAmelCase : Optional[Any] = size if size is not None else {"height": 256, "width": 256}
_UpperCAmelCase : Optional[int] = get_size_dict(A_ )
_UpperCAmelCase : Union[str, Any] = np.array(A_ ) if clusters is not None else None
_UpperCAmelCase : int = do_resize
_UpperCAmelCase : Union[str, Any] = size
_UpperCAmelCase : Optional[Any] = resample
_UpperCAmelCase : str = do_normalize
_UpperCAmelCase : List[str] = do_color_quantize
def _UpperCAmelCase ( self , A_ , A_ , A_ = PILImageResampling.BILINEAR , A_ = None , **A_ , ):
'''simple docstring'''
_UpperCAmelCase : int = get_size_dict(A_ )
if "height" not in size or "width" not in size:
raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}' )
return resize(
A_ , size=(size["height"], size["width"]) , resample=A_ , data_format=A_ , **A_ )
def _UpperCAmelCase ( self , A_ , A_ = None , ):
'''simple docstring'''
_UpperCAmelCase : Dict = rescale(image=A_ , scale=1 / 1_27.5 , data_format=A_ )
_UpperCAmelCase : List[Any] = image - 1
return image
def _UpperCAmelCase ( self , A_ , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = ChannelDimension.FIRST , **A_ , ):
'''simple docstring'''
_UpperCAmelCase : Optional[int] = do_resize if do_resize is not None else self.do_resize
_UpperCAmelCase : Any = size if size is not None else self.size
_UpperCAmelCase : Dict = get_size_dict(A_ )
_UpperCAmelCase : List[Any] = resample if resample is not None else self.resample
_UpperCAmelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
_UpperCAmelCase : Optional[Any] = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
_UpperCAmelCase : Any = clusters if clusters is not None else self.clusters
_UpperCAmelCase : Optional[int] = np.array(A_ )
_UpperCAmelCase : List[str] = make_list_of_images(A_ )
if not valid_images(A_ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_color_quantize and clusters is None:
raise ValueError("Clusters must be specified if do_color_quantize is True." )
# All transformations expect numpy arrays.
_UpperCAmelCase : List[str] = [to_numpy_array(A_ ) for image in images]
if do_resize:
_UpperCAmelCase : int = [self.resize(image=A_ , size=A_ , resample=A_ ) for image in images]
if do_normalize:
_UpperCAmelCase : List[str] = [self.normalize(image=A_ ) for image in images]
if do_color_quantize:
_UpperCAmelCase : Tuple = [to_channel_dimension_format(A_ , ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
_UpperCAmelCase : List[str] = np.array(A_ )
_UpperCAmelCase : List[Any] = color_quantize(A_ , A_ ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
_UpperCAmelCase : Any = images.shape[0]
_UpperCAmelCase : List[Any] = images.reshape(A_ , -1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
_UpperCAmelCase : Union[str, Any] = list(A_ )
else:
_UpperCAmelCase : Optional[Any] = [to_channel_dimension_format(A_ , A_ ) for image in images]
_UpperCAmelCase : List[Any] = {"input_ids": images}
return BatchFeature(data=A_ , tensor_type=A_ )
| 467 | 1 |
import re
def a__ ( __UpperCamelCase ):
return [char.split() for char in re.split(r"[^ a-z A-Z 0-9 \s]" , str_ )]
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = split_input(str_ )
return "".join(
["".join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
try:
SCREAMING_SNAKE_CASE_ = split_input(__UpperCamelCase )
if upper:
SCREAMING_SNAKE_CASE_ = "".join(
[
separator.join([char.upper() for char in sub_str] )
for sub_str in string_split
] )
else:
SCREAMING_SNAKE_CASE_ = "".join(
[
separator.join([char.lower() for char in sub_str] )
for sub_str in string_split
] )
return res_str
except IndexError:
return "not valid string"
def a__ ( __UpperCamelCase ):
return to_simple_case(__UpperCamelCase )
def a__ ( __UpperCamelCase ):
try:
SCREAMING_SNAKE_CASE_ = to_simple_case(__UpperCamelCase )
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def a__ ( __UpperCamelCase , __UpperCamelCase ):
return to_complex_case(__UpperCamelCase , __UpperCamelCase , "_" )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
return to_complex_case(__UpperCamelCase , __UpperCamelCase , "-" )
if __name__ == "__main__":
__import__("doctest").testmod()
| 140 | import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
A : Optional[int] = get_tests_dir("fixtures/test_sentencepiece.model")
if is_sentencepiece_available():
import sentencepiece as sp
A : Dict = 5
A : Optional[int] = 10
@require_sentencepiece
@require_tokenizers
class lowerCamelCase (SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = SpeechaTextTokenizer
lowerCamelCase__ = False
lowerCamelCase__ = True
def __A ( self : Optional[Any] ) -> str:
super().setUp()
SCREAMING_SNAKE_CASE_ = sp.SentencePieceProcessor()
spm_model.Load(__magic_name__ )
SCREAMING_SNAKE_CASE_ = ["<s>", "<pad>", "</s>", "<unk>"]
vocab += [spm_model.IdToPiece(id_ ) for id_ in range(len(__magic_name__ ) )]
SCREAMING_SNAKE_CASE_ = dict(zip(__magic_name__ , range(len(__magic_name__ ) ) ) )
SCREAMING_SNAKE_CASE_ = Path(self.tmpdirname )
save_json(__magic_name__ , save_dir / VOCAB_FILES_NAMES["vocab_file"] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(__magic_name__ , save_dir / VOCAB_FILES_NAMES["spm_file"] )
SCREAMING_SNAKE_CASE_ = SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def __A ( self : Any ) -> Tuple:
SCREAMING_SNAKE_CASE_ = "<pad>"
SCREAMING_SNAKE_CASE_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__magic_name__ ) , __magic_name__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__magic_name__ ) , __magic_name__ )
def __A ( self : Any ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "j" )
self.assertEqual(len(__magic_name__ ) , 1_001 )
def __A ( self : List[Any] ) -> Dict:
self.assertEqual(self.get_tokenizer().vocab_size , 1_001 )
def __A ( self : Union[str, Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE_ = tokenizer.tokenize("This is a test" )
self.assertListEqual(__magic_name__ , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__magic_name__ ) , [289, 50, 14, 174, 386] , )
SCREAMING_SNAKE_CASE_ = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
__magic_name__ , [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", "."] , )
SCREAMING_SNAKE_CASE_ = tokenizer.convert_tokens_to_ids(__magic_name__ )
self.assertListEqual(__magic_name__ , [12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8] )
SCREAMING_SNAKE_CASE_ = tokenizer.convert_ids_to_tokens(__magic_name__ )
self.assertListEqual(
__magic_name__ , [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>", "."] , )
@slow
def __A ( self : List[str] ) -> List[Any]:
# fmt: off
SCREAMING_SNAKE_CASE_ = {"input_ids": [[3_791, 797, 31, 11, 64, 797, 31, 2_429, 433, 12, 1_176, 12, 20, 786, 915, 142, 2_413, 240, 37, 3_238, 797, 31, 11, 35, 93, 915, 142, 2_413, 240, 37, 5_540, 567, 1_276, 93, 37, 610, 40, 62, 455, 657, 1_042, 123, 780, 177, 37, 309, 241, 1_298, 514, 20, 292, 2_737, 114, 2_469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3_388, 511, 459, 4, 3_555, 40, 321, 302, 705, 4, 3_388, 511, 583, 326, 5, 5, 5, 62, 3_310, 560, 177, 2_680, 217, 1_508, 32, 31, 853, 418, 64, 583, 511, 1_605, 62, 35, 93, 560, 177, 2_680, 217, 1_508, 1_521, 64, 583, 511, 519, 62, 20, 1_515, 764, 20, 149, 261, 5_625, 7_972, 20, 5_540, 567, 1_276, 93, 3_925, 1_675, 11, 15, 802, 7_972, 576, 217, 1_508, 11, 35, 93, 1_253, 2_441, 15, 289, 652, 31, 416, 321, 3_842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2_681, 1_153, 3_434, 20, 5_540, 37, 567, 126, 1_253, 2_441, 3_376, 449, 210, 431, 1_563, 177, 767, 5_540, 11, 1_203, 472, 11, 2_953, 685, 285, 364, 706, 1_153, 20, 6_799, 20, 2_869, 20, 4_464, 126, 40, 2_429, 20, 1_040, 866, 2_664, 418, 20, 318, 20, 1_726, 186, 20, 265, 522, 35, 93, 2_191, 4_634, 20, 1_040, 12, 6_799, 15, 228, 2_356, 142, 31, 11, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_575, 2_666, 684, 1_582, 1_176, 12, 627, 149, 619, 20, 4_902, 563, 11, 20, 149, 261, 3_420, 2_356, 174, 142, 4_714, 131, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=__magic_name__ , model_name="facebook/s2t-small-mustc-en-de-st" , revision="a14f04cf0776c02f62a8cb800cf7909e15ea23ad" , )
@require_sentencepiece
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = '''valhalla/s2t_mustc_multilinguial_medium'''
lowerCamelCase__ = '''C\'est trop cool'''
lowerCamelCase__ = '''Esto es genial'''
@classmethod
def __A ( cls : List[Any] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name )
return cls
def __A ( self : str ) -> int:
self.assertEqual(self.tokenizer.lang_code_to_id["pt"] , 4 )
self.assertEqual(self.tokenizer.lang_code_to_id["ru"] , 6 )
self.assertEqual(self.tokenizer.lang_code_to_id["it"] , 9 )
self.assertEqual(self.tokenizer.lang_code_to_id["de"] , 11 )
def __A ( self : List[Any] ) -> Union[str, Any]:
self.assertEqual(self.tokenizer.vocab_size , 10_000 )
def __A ( self : Any ) -> int:
self.assertIn(__magic_name__ , self.tokenizer.all_special_ids )
SCREAMING_SNAKE_CASE_ = [ES_CODE, 4, 1_601, 47, 7_647, 2]
SCREAMING_SNAKE_CASE_ = self.tokenizer.decode(__magic_name__ , skip_special_tokens=__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__magic_name__ )
self.assertEqual(__magic_name__ , __magic_name__ )
self.assertNotIn(self.tokenizer.eos_token , __magic_name__ )
def __A ( self : Dict ) -> Tuple:
SCREAMING_SNAKE_CASE_ = "fr"
SCREAMING_SNAKE_CASE_ = self.tokenizer(self.french_text ).input_ids
self.assertEqual(encoded[0] , __magic_name__ )
self.assertEqual(encoded[-1] , self.tokenizer.eos_token_id )
def __A ( self : Any ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = "fr"
self.assertListEqual(self.tokenizer.prefix_tokens , [FR_CODE] )
SCREAMING_SNAKE_CASE_ = "es"
self.assertListEqual(self.tokenizer.prefix_tokens , [ES_CODE] )
| 140 | 1 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCAmelCase__ = "."
# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
lowerCAmelCase__ = [
"Assert",
"AssignVariableOp",
"EmptyTensorList",
"MergeV2Checkpoints",
"ReadVariableOp",
"ResourceGather",
"RestoreV2",
"SaveV2",
"ShardedFilename",
"StatefulPartitionedCall",
"StaticRegexFullMatch",
"VarHandleOp",
]
def __lowerCamelCase ( __a : int , __a : Dict , __a : Union[str, Any] ) -> Tuple:
_lowercase =SavedModel()
_lowercase =[]
with open(os.path.join(__a , "utils" , "tf_ops" , "onnx.json" ) ) as f:
_lowercase =json.load(__a )["opsets"]
for i in range(1 , opset + 1 ):
onnx_ops.extend(onnx_opsets[str(__a )] )
with open(__a , "rb" ) as f:
saved_model.ParseFromString(f.read() )
_lowercase =set()
# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
for meta_graph in saved_model.meta_graphs:
# Add operations in the graph definition
model_op_names.update(node.op for node in meta_graph.graph_def.node )
# Go through the functions in the graph definition
for func in meta_graph.graph_def.library.function:
# Add operations in each function
model_op_names.update(node.op for node in func.node_def )
# Convert to list, sorted if you want
_lowercase =sorted(__a )
_lowercase =[]
for op in model_op_names:
if op not in onnx_ops and op not in INTERNAL_OPS:
incompatible_ops.append(__a )
if strict and len(__a ) > 0:
raise Exception(f'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops )
elif len(__a ) > 0:
print(f'''Found the following incompatible ops for the opset {opset}:''' )
print(*__a , sep="\n" )
else:
print(f'''The saved model {saved_model_path} can properly be converted with ONNX.''' )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
parser.add_argument(
"--opset", default=1_2, type=int, help="The ONNX opset against which the model has to be tested."
)
parser.add_argument(
"--framework", choices=["onnx"], default="onnx", help="Frameworks against which to test the saved model."
)
parser.add_argument(
"--strict", action="store_true", help="Whether make the checking strict (raise errors) or not (raise warnings)"
)
lowerCAmelCase__ = parser.parse_args()
if args.framework == "onnx":
onnx_compliancy(args.saved_model_path, args.strict, args.opset)
| 713 | 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 _a ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def __lowerCAmelCase ( self ):
_lowercase , _lowercase =FlaxStableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2" , revision="bf16" , dtype=jnp.bfloataa , )
_lowercase ="A painting of a squirrel eating a burger"
_lowercase =jax.device_count()
_lowercase =num_samples * [prompt]
_lowercase =sd_pipe.prepare_inputs(lowerCAmelCase_ )
_lowercase =replicate(lowerCAmelCase_ )
_lowercase =shard(lowerCAmelCase_ )
_lowercase =jax.random.PRNGKey(0 )
_lowercase =jax.random.split(lowerCAmelCase_ , jax.device_count() )
_lowercase =sd_pipe(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , num_inference_steps=25 , jit=lowerCAmelCase_ )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
_lowercase =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
_lowercase =images[0, 253:256, 253:256, -1]
_lowercase =jnp.asarray(jax.device_get(image_slice.flatten() ) )
_lowercase =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 __lowerCAmelCase ( self ):
_lowercase ="stabilityai/stable-diffusion-2"
_lowercase , _lowercase =FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCAmelCase_ , subfolder="scheduler" )
_lowercase , _lowercase =FlaxStableDiffusionPipeline.from_pretrained(
lowerCAmelCase_ , scheduler=lowerCAmelCase_ , revision="bf16" , dtype=jnp.bfloataa , )
_lowercase =scheduler_params
_lowercase ="A painting of a squirrel eating a burger"
_lowercase =jax.device_count()
_lowercase =num_samples * [prompt]
_lowercase =sd_pipe.prepare_inputs(lowerCAmelCase_ )
_lowercase =replicate(lowerCAmelCase_ )
_lowercase =shard(lowerCAmelCase_ )
_lowercase =jax.random.PRNGKey(0 )
_lowercase =jax.random.split(lowerCAmelCase_ , jax.device_count() )
_lowercase =sd_pipe(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , num_inference_steps=25 , jit=lowerCAmelCase_ )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
_lowercase =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
_lowercase =images[0, 253:256, 253:256, -1]
_lowercase =jnp.asarray(jax.device_get(image_slice.flatten() ) )
_lowercase =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
| 594 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __SCREAMING_SNAKE_CASE (metaclass=lowercase_ ):
"""simple docstring"""
_a : Optional[Any] = ['''torch''', '''scipy''']
def __init__( self , *UpperCamelCase__ , **UpperCamelCase__ ):
"""simple docstring"""
requires_backends(self , ['torch', 'scipy'] )
@classmethod
def _a ( cls , *UpperCamelCase__ , **UpperCamelCase__ ):
"""simple docstring"""
requires_backends(cls , ['torch', 'scipy'] )
@classmethod
def _a ( cls , *UpperCamelCase__ , **UpperCamelCase__ ):
"""simple docstring"""
requires_backends(cls , ['torch', 'scipy'] )
| 536 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
# TODO Update this
__snake_case : Union[str, Any] = {
'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.json',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
__snake_case = 'esm'
def __init__( self : Tuple , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : int=7_68 , lowerCAmelCase_ : Optional[Any]=12 , lowerCAmelCase_ : int=12 , lowerCAmelCase_ : List[str]=30_72 , lowerCAmelCase_ : int=0.1 , lowerCAmelCase_ : Any=0.1 , lowerCAmelCase_ : Dict=10_26 , lowerCAmelCase_ : int=0.02 , lowerCAmelCase_ : int=1e-12 , lowerCAmelCase_ : List[Any]="absolute" , lowerCAmelCase_ : Dict=True , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : str=False , lowerCAmelCase_ : List[str]=False , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Union[str, Any]=None , **lowerCAmelCase_ : int , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(pad_token_id=lowerCAmelCase_ , mask_token_id=lowerCAmelCase_ , **lowerCAmelCase_ )
A__ : Any =vocab_size
A__ : Optional[Any] =hidden_size
A__ : Tuple =num_hidden_layers
A__ : List[str] =num_attention_heads
A__ : Tuple =intermediate_size
A__ : int =hidden_dropout_prob
A__ : str =attention_probs_dropout_prob
A__ : Tuple =max_position_embeddings
A__ : List[Any] =initializer_range
A__ : Optional[Any] =layer_norm_eps
A__ : Union[str, Any] =position_embedding_type
A__ : str =use_cache
A__ : Optional[int] =emb_layer_norm_before
A__ : Union[str, Any] =token_dropout
A__ : Tuple =is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("""No esmfold_config supplied for folding model, using default values.""" )
A__ : Optional[int] =EsmFoldConfig()
elif isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
A__ : Dict =EsmFoldConfig(**lowerCAmelCase_ )
A__ : Union[str, Any] =esmfold_config
if vocab_list is None:
logger.warning("""No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!""" )
A__ : List[str] =get_default_vocab_list()
else:
A__ : List[str] =vocab_list
else:
A__ : Union[str, Any] =None
A__ : List[Any] =None
if self.esmfold_config is not None and getattr(self.esmfold_config , """use_esm_attn_map""" , lowerCAmelCase_ ):
raise ValueError("""The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!""" )
def lowercase__ ( self : Dict ) -> Any:
'''simple docstring'''
A__ : Dict =super().to_dict()
if isinstance(self.esmfold_config , lowerCAmelCase_ ):
A__ : Union[str, Any] =self.esmfold_config.to_dict()
return output
@dataclass
class lowerCamelCase :
'''simple docstring'''
__snake_case = None
__snake_case = True
__snake_case = False
__snake_case = False
__snake_case = False
__snake_case = 0
__snake_case = True
__snake_case = False
__snake_case = 128
__snake_case = None
def lowercase__ ( self : Tuple ) -> List[str]:
'''simple docstring'''
if self.trunk is None:
A__ : int =TrunkConfig()
elif isinstance(self.trunk , lowerCAmelCase_ ):
A__ : str =TrunkConfig(**self.trunk )
def lowercase__ ( self : Optional[Any] ) -> Tuple:
'''simple docstring'''
A__ : List[Any] =asdict(self )
A__ : Tuple =self.trunk.to_dict()
return output
@dataclass
class lowerCamelCase :
'''simple docstring'''
__snake_case = 48
__snake_case = 1024
__snake_case = 128
__snake_case = 32
__snake_case = 32
__snake_case = 32
__snake_case = 0
__snake_case = 0
__snake_case = False
__snake_case = 4
__snake_case = 128
__snake_case = None
def lowercase__ ( self : Dict ) -> Any:
'''simple docstring'''
if self.structure_module is None:
A__ : Dict =StructureModuleConfig()
elif isinstance(self.structure_module , lowerCAmelCase_ ):
A__ : Union[str, Any] =StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(f"`max_recycles` should be positive, got {self.max_recycles}." )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"""`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"""
f" {self.sequence_state_dim} and {self.sequence_state_dim}." )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"""`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"""
f" {self.pairwise_state_dim} and {self.pairwise_state_dim}." )
A__ : List[str] =self.sequence_state_dim // self.sequence_head_width
A__ : Optional[int] =self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"""`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"""
f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}." )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"""`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"""
f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}." )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}." )
if self.dropout >= 0.4:
raise ValueError(f"`dropout` should not be greater than 0.4, got {self.dropout}." )
def lowercase__ ( self : str ) -> List[Any]:
'''simple docstring'''
A__ : int =asdict(self )
A__ : Optional[Any] =self.structure_module.to_dict()
return output
@dataclass
class lowerCamelCase :
'''simple docstring'''
__snake_case = 384
__snake_case = 128
__snake_case = 16
__snake_case = 128
__snake_case = 12
__snake_case = 4
__snake_case = 8
__snake_case = 0.1
__snake_case = 8
__snake_case = 1
__snake_case = 2
__snake_case = 7
__snake_case = 10
__snake_case = 1E-8
__snake_case = 1E5
def lowercase__ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
return asdict(self )
def __lowerCamelCase ( ) -> Union[str, Any]:
"""simple docstring"""
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 215 | 0 |
'''simple docstring'''
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__A : Dict = 'src/transformers'
# This is to make sure the transformers module imported is the one in the repo.
__A : Tuple = importlib.util.spec_from_file_location(
'transformers',
os.path.join(PATH_TO_TRANSFORMERS, '__init__.py'),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
__A : Optional[int] = spec.loader.load_module()
__A : str = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
__A : List[Any] = re.compile('\[(.+?)\]\((https://huggingface\.co/.+?)\)')
__A : int = {
'CLIPConfigMixin',
'DecisionTransformerConfigMixin',
'EncoderDecoderConfigMixin',
'RagConfigMixin',
'SpeechEncoderDecoderConfigMixin',
'VisionEncoderDecoderConfigMixin',
'VisionTextDualEncoderConfigMixin',
}
def UpperCAmelCase ( ):
'''simple docstring'''
snake_case_ : str = []
for config_class in list(CONFIG_MAPPING.values() ):
snake_case_ : List[Any] = False
# source code of `config_class`
snake_case_ : List[str] = inspect.getsource(a_ )
snake_case_ : Optional[Any] = _re_checkpoint.findall(a_ )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
snake_case_ : List[Any] = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
snake_case_ : List[str] = F'''https://huggingface.co/{ckpt_name}'''
if ckpt_link == ckpt_link_from_name:
snake_case_ : Dict = True
break
snake_case_ : int = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(a_ )
if len(a_ ) > 0:
snake_case_ : Any = '''\n'''.join(sorted(a_ ) )
raise ValueError(F'''The following configurations don\'t contain any valid checkpoint:\n{message}''' )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints() | 713 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :str ):
'''simple docstring'''
return " ".join(
"""""".join(word[::-1] ) if len(lowerCamelCase_ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('Hey wollef sroirraw')) | 267 | 0 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
)
from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase__ :
"""simple docstring"""
def __init__( self : Union[str, Any] , A__ : str , A__ : int=1_3 , A__ : int=7 , A__ : Dict=True , A__ : Optional[int]=True , A__ : Tuple=True , A__ : Dict=True , A__ : List[Any]=9_9 , A__ : str=1_6 , A__ : Union[str, Any]=3_6 , A__ : Tuple=6 , A__ : List[str]=6 , A__ : Optional[Any]=6 , A__ : str=3_7 , A__ : Tuple="gelu" , A__ : Dict=0.1 , A__ : int=0.1 , A__ : Optional[int]=5_1_2 , A__ : Union[str, Any]=1_6 , A__ : Dict=2 , A__ : int=0.02 , A__ : Optional[Any]=3 , A__ : Optional[Any]=4 , A__ : List[Any]=None , ) -> List[str]:
'''simple docstring'''
a__ : Tuple = parent
a__ : int = batch_size
a__ : Dict = seq_length
a__ : Dict = is_training
a__ : Optional[Any] = use_input_mask
a__ : Tuple = use_token_type_ids
a__ : Optional[Any] = use_labels
a__ : Optional[int] = vocab_size
a__ : int = embedding_size
a__ : str = hidden_size
a__ : Optional[int] = num_hidden_layers
a__ : Dict = num_hidden_groups
a__ : Union[str, Any] = num_attention_heads
a__ : str = intermediate_size
a__ : Tuple = hidden_act
a__ : Optional[int] = hidden_dropout_prob
a__ : str = attention_probs_dropout_prob
a__ : List[str] = max_position_embeddings
a__ : Optional[int] = type_vocab_size
a__ : List[str] = type_sequence_label_size
a__ : Any = initializer_range
a__ : str = num_labels
a__ : Optional[Any] = num_choices
a__ : Optional[int] = scope
def __lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
a__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a__ : Any = None
if self.use_input_mask:
a__ : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
a__ : List[Any] = None
if self.use_token_type_ids:
a__ : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a__ : Tuple = None
a__ : List[Any] = None
a__ : List[Any] = None
if self.use_labels:
a__ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a__ : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a__ : str = ids_tensor([self.batch_size] , self.num_choices )
a__ : Optional[int] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self : int ) -> Optional[Any]:
'''simple docstring'''
return AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , num_hidden_groups=self.num_hidden_groups , )
def __lowerCAmelCase ( self : int , A__ : Optional[Any] , A__ : Tuple , A__ : Any , A__ : List[str] , A__ : List[Any] , A__ : Dict , A__ : Dict ) -> List[str]:
'''simple docstring'''
a__ : Any = AlbertModel(config=A__ )
model.to(A__ )
model.eval()
a__ : Tuple = model(A__ , attention_mask=A__ , token_type_ids=A__ )
a__ : str = model(A__ , token_type_ids=A__ )
a__ : List[Any] = 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 __lowerCAmelCase ( self : List[str] , A__ : Dict , A__ : int , A__ : int , A__ : Optional[Any] , A__ : Any , A__ : Tuple , A__ : str ) -> Optional[Any]:
'''simple docstring'''
a__ : Union[str, Any] = AlbertForPreTraining(config=A__ )
model.to(A__ )
model.eval()
a__ : str = model(
A__ , attention_mask=A__ , token_type_ids=A__ , labels=A__ , sentence_order_label=A__ , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.sop_logits.shape , (self.batch_size, config.num_labels) )
def __lowerCAmelCase ( self : Union[str, Any] , A__ : Dict , A__ : int , A__ : List[str] , A__ : Dict , A__ : int , A__ : List[str] , A__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
a__ : List[str] = AlbertForMaskedLM(config=A__ )
model.to(A__ )
model.eval()
a__ : Dict = model(A__ , attention_mask=A__ , token_type_ids=A__ , labels=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self : Optional[int] , A__ : Optional[Any] , A__ : List[Any] , A__ : Optional[Any] , A__ : List[str] , A__ : Optional[int] , A__ : Optional[int] , A__ : Optional[Any] ) -> int:
'''simple docstring'''
a__ : Union[str, Any] = AlbertForQuestionAnswering(config=A__ )
model.to(A__ )
model.eval()
a__ : Dict = model(
A__ , attention_mask=A__ , token_type_ids=A__ , start_positions=A__ , end_positions=A__ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowerCAmelCase ( self : Dict , A__ : Optional[int] , A__ : Optional[int] , A__ : Dict , A__ : str , A__ : int , A__ : Union[str, Any] , A__ : Any ) -> Union[str, Any]:
'''simple docstring'''
a__ : List[Any] = self.num_labels
a__ : List[Any] = AlbertForSequenceClassification(A__ )
model.to(A__ )
model.eval()
a__ : List[str] = model(A__ , attention_mask=A__ , token_type_ids=A__ , labels=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self : int , A__ : List[Any] , A__ : Dict , A__ : str , A__ : Any , A__ : Union[str, Any] , A__ : List[str] , A__ : Optional[Any] ) -> str:
'''simple docstring'''
a__ : List[str] = self.num_labels
a__ : Optional[int] = AlbertForTokenClassification(config=A__ )
model.to(A__ )
model.eval()
a__ : Dict = model(A__ , attention_mask=A__ , token_type_ids=A__ , labels=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCAmelCase ( self : Dict , A__ : List[Any] , A__ : List[str] , A__ : int , A__ : Union[str, Any] , A__ : str , A__ : Optional[Any] , A__ : List[str] ) -> List[Any]:
'''simple docstring'''
a__ : List[Any] = self.num_choices
a__ : Optional[Any] = AlbertForMultipleChoice(config=A__ )
model.to(A__ )
model.eval()
a__ : List[str] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a__ : List[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a__ : Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a__ : Optional[Any] = model(
A__ , attention_mask=A__ , token_type_ids=A__ , labels=A__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCAmelCase ( self : Tuple ) -> List[Any]:
'''simple docstring'''
a__ : int = self.prepare_config_and_inputs()
(
(
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) ,
) : Optional[Any] = config_and_inputs
a__ : Any = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class lowerCAmelCase__ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
__UpperCamelCase = (
{
"feature-extraction": AlbertModel,
"fill-mask": AlbertForMaskedLM,
"question-answering": AlbertForQuestionAnswering,
"text-classification": AlbertForSequenceClassification,
"token-classification": AlbertForTokenClassification,
"zero-shot": AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCamelCase = True
def __lowerCAmelCase ( self : str , A__ : Union[str, Any] , A__ : List[str] , A__ : str=False ) -> Optional[int]:
'''simple docstring'''
a__ : List[str] = super()._prepare_for_class(A__ , A__ , return_labels=A__ )
if return_labels:
if model_class in get_values(A__ ):
a__ : int = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=A__ )
a__ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A__ )
return inputs_dict
def __lowerCAmelCase ( self : Tuple ) -> str:
'''simple docstring'''
a__ : Tuple = AlbertModelTester(self )
a__ : str = ConfigTester(self , config_class=A__ , hidden_size=3_7 )
def __lowerCAmelCase ( self : Union[str, Any] ) -> int:
'''simple docstring'''
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self : List[Any] ) -> Any:
'''simple docstring'''
a__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def __lowerCAmelCase ( self : Any ) -> Any:
'''simple docstring'''
a__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*A__ )
def __lowerCAmelCase ( self : Optional[Any] ) -> Any:
'''simple docstring'''
a__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A__ )
def __lowerCAmelCase ( self : str ) -> int:
'''simple docstring'''
a__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A__ )
def __lowerCAmelCase ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
a__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A__ )
def __lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
a__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A__ )
def __lowerCAmelCase ( self : Tuple ) -> int:
'''simple docstring'''
a__ : int = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
a__ : Tuple = type
self.model_tester.create_and_check_model(*A__ )
@slow
def __lowerCAmelCase ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : int = AlbertModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
@require_torch
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def __lowerCAmelCase ( self : Union[str, Any] ) -> List[str]:
'''simple docstring'''
a__ : Union[str, Any] = AlbertModel.from_pretrained('''albert-base-v2''' )
a__ : Optional[Any] = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
a__ : Optional[Any] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
a__ : Any = model(A__ , attention_mask=A__ )[0]
a__ : str = torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , A__ )
a__ : Any = torch.tensor(
[[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , A__ , atol=1E-4 ) )
| 688 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def __a ( lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : str ):
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
a__ : Dict = TapasConfig.from_json_file(lowerCAmelCase__ )
# set absolute/relative position embeddings parameter
a__ : List[Any] = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
a__ : Optional[Any] = TapasForQuestionAnswering(config=lowerCAmelCase__ )
elif task == "WTQ":
# run_task_main.py hparams
a__ : List[str] = 4
a__ : Optional[int] = True
# hparam_utils.py hparams
a__ : List[Any] = 0.664694
a__ : List[Any] = 0.207951
a__ : Union[str, Any] = 0.121194
a__ : Optional[Any] = True
a__ : Optional[int] = True
a__ : List[str] = False
a__ : Union[str, Any] = 0.0352513
a__ : Any = TapasForQuestionAnswering(config=lowerCAmelCase__ )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
a__ : Tuple = 4
a__ : Dict = False
# hparam_utils.py hparams
a__ : str = 36.4519
a__ : str = 0.903421
a__ : Optional[Any] = 222.088
a__ : Dict = True
a__ : Dict = True
a__ : Dict = True
a__ : str = 0.763141
a__ : List[Any] = TapasForQuestionAnswering(config=lowerCAmelCase__ )
elif task == "TABFACT":
a__ : List[str] = TapasForSequenceClassification(config=lowerCAmelCase__ )
elif task == "MLM":
a__ : Tuple = TapasForMaskedLM(config=lowerCAmelCase__ )
elif task == "INTERMEDIATE_PRETRAINING":
a__ : List[str] = TapasModel(config=lowerCAmelCase__ )
else:
raise ValueError(F'Task {task} not supported.' )
print(F'Building PyTorch model from configuration: {config}' )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# Save pytorch-model (weights and configuration)
print(F'Save PyTorch model to {pytorch_dump_path}' )
model.save_pretrained(lowerCAmelCase__ )
# Save tokenizer files
print(F'Save tokenizer files to {pytorch_dump_path}' )
a__ : Optional[Any] = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + '''vocab.txt''' , model_max_length=512 )
tokenizer.save_pretrained(lowerCAmelCase__ )
print('''Used relative position embeddings:''' , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
)
parser.add_argument(
'--reset_position_index_per_cell',
default=False,
action='store_true',
help='Whether to use relative position embeddings or not. Defaults to True.',
)
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--tapas_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained TAPAS 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.'
)
__SCREAMING_SNAKE_CASE = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 688 | 1 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase : List[Any] = get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ (a ,unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase = GPTSwaTokenizer
_UpperCamelCase = False
_UpperCamelCase = True
_UpperCamelCase = False
def UpperCamelCase_ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase__ = GPTSwaTokenizer(_lowerCAmelCase ,eos_token="""<unk>""" ,bos_token="""<unk>""" ,pad_token="""<unk>""" )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
lowerCamelCase__ = """This is a test"""
lowerCamelCase__ = """This is a test"""
return input_text, output_text
def UpperCamelCase_ ( self ):
lowerCamelCase__ = """<s>"""
lowerCamelCase__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCAmelCase ) ,_lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCAmelCase ) ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"""<unk>""" )
self.assertEqual(vocab_keys[1] ,"""<s>""" )
self.assertEqual(vocab_keys[-1] ,"""j""" )
self.assertEqual(len(_lowerCAmelCase ) ,20_00 )
def UpperCamelCase_ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size ,20_00 )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = GPTSwaTokenizer(_lowerCAmelCase )
lowerCamelCase__ = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(_lowerCAmelCase ,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase ) ,[4_65, 2_87, 2_65, 6_31, 8_42] )
lowerCamelCase__ = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
# fmt: off
self.assertListEqual(
_lowerCAmelCase ,["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""] ,)
# fmt: on
lowerCamelCase__ = tokenizer.convert_tokens_to_ids(_lowerCAmelCase )
self.assertListEqual(
_lowerCAmelCase ,[2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60] ,)
lowerCamelCase__ = tokenizer.convert_ids_to_tokens(_lowerCAmelCase )
# fmt: off
self.assertListEqual(
_lowerCAmelCase ,["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""] )
# fmt: on
def UpperCamelCase_ ( self ):
lowerCamelCase__ = GPTSwaTokenizer(_lowerCAmelCase )
lowerCamelCase__ = ["""This is a test""", """I was born in 92000, and this is falsé."""]
lowerCamelCase__ = [
[4_65, 2_87, 2_65, 6_31, 8_42],
[2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
]
# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
for text, expected_ids in zip(_lowerCAmelCase ,_lowerCAmelCase ):
self.assertListEqual(tokenizer.encode_fast(_lowerCAmelCase ) ,_lowerCAmelCase )
# Test that decode_fast returns the input text
for text, token_ids in zip(_lowerCAmelCase ,_lowerCAmelCase ):
self.assertEqual(tokenizer.decode_fast(_lowerCAmelCase ) ,_lowerCAmelCase )
@slow
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [
"""<|python|>def fibonacci(n)\n if n < 0:\n print('Incorrect input')""",
"""Hey there, how are you doing this fine day?""",
"""This is a text with a trailing spaces followed by a dot .""",
"""Häj sväjs lillebrör! =)""",
"""Det är inget fel på Mr. Cool""",
]
# fmt: off
lowerCamelCase__ = {"""input_ids""": [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 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]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_lowerCAmelCase ,model_name="""AI-Sweden/gpt-sw3-126m""" ,sequences=_lowerCAmelCase ,)
| 9 |
'''simple docstring'''
import argparse
import struct
import unittest
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ):
lowerCamelCase__ = data
# Initialize hash values
lowerCamelCase__ = [
0x6a_09_e6_67,
0xbb_67_ae_85,
0x3c_6e_f3_72,
0xa5_4f_f5_3a,
0x51_0e_52_7f,
0x9b_05_68_8c,
0x1f_83_d9_ab,
0x5b_e0_cd_19,
]
# Initialize round constants
lowerCamelCase__ = [
0x42_8a_2f_98,
0x71_37_44_91,
0xb5_c0_fb_cf,
0xe9_b5_db_a5,
0x39_56_c2_5b,
0x59_f1_11_f1,
0x92_3f_82_a4,
0xab_1c_5e_d5,
0xd8_07_aa_98,
0x12_83_5b_01,
0x24_31_85_be,
0x55_0c_7d_c3,
0x72_be_5d_74,
0x80_de_b1_fe,
0x9b_dc_06_a7,
0xc1_9b_f1_74,
0xe4_9b_69_c1,
0xef_be_47_86,
0x0f_c1_9d_c6,
0x24_0c_a1_cc,
0x2d_e9_2c_6f,
0x4a_74_84_aa,
0x5c_b0_a9_dc,
0x76_f9_88_da,
0x98_3e_51_52,
0xa8_31_c6_6d,
0xb0_03_27_c8,
0xbf_59_7f_c7,
0xc6_e0_0b_f3,
0xd5_a7_91_47,
0x06_ca_63_51,
0x14_29_29_67,
0x27_b7_0a_85,
0x2e_1b_21_38,
0x4d_2c_6d_fc,
0x53_38_0d_13,
0x65_0a_73_54,
0x76_6a_0a_bb,
0x81_c2_c9_2e,
0x92_72_2c_85,
0xa2_bf_e8_a1,
0xa8_1a_66_4b,
0xc2_4b_8b_70,
0xc7_6c_51_a3,
0xd1_92_e8_19,
0xd6_99_06_24,
0xf4_0e_35_85,
0x10_6a_a0_70,
0x19_a4_c1_16,
0x1e_37_6c_08,
0x27_48_77_4c,
0x34_b0_bc_b5,
0x39_1c_0c_b3,
0x4e_d8_aa_4a,
0x5b_9c_ca_4f,
0x68_2e_6f_f3,
0x74_8f_82_ee,
0x78_a5_63_6f,
0x84_c8_78_14,
0x8c_c7_02_08,
0x90_be_ff_fa,
0xa4_50_6c_eb,
0xbe_f9_a3_f7,
0xc6_71_78_f2,
]
lowerCamelCase__ = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def UpperCamelCase_ ( _lowerCAmelCase ):
lowerCamelCase__ = B"""\x80""" + (B"""\x00""" * (63 - (len(_lowerCAmelCase ) + 8) % 64))
lowerCamelCase__ = struct.pack(""">Q""" ,(len(_lowerCAmelCase ) * 8) )
return data + padding + big_endian_integer
def UpperCamelCase_ ( self ):
# Convert into blocks of 64 bytes
lowerCamelCase__ = [
self.preprocessed_data[x : x + 64]
for x in range(0 ,len(self.preprocessed_data ) ,64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
lowerCamelCase__ = list(struct.unpack(""">16L""" ,_lowerCAmelCase ) )
# add 48 0-ed integers
words += [0] * 48
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = self.hashes
for index in range(0 ,64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
lowerCamelCase__ = (
self.ror(words[index - 15] ,7 )
^ self.ror(words[index - 15] ,18 )
^ (words[index - 15] >> 3)
)
lowerCamelCase__ = (
self.ror(words[index - 2] ,17 )
^ self.ror(words[index - 2] ,19 )
^ (words[index - 2] >> 10)
)
lowerCamelCase__ = (
words[index - 16] + sa + words[index - 7] + sa
) % 0x1_00_00_00_00
# Compression
lowerCamelCase__ = self.ror(_lowerCAmelCase ,6 ) ^ self.ror(_lowerCAmelCase ,11 ) ^ self.ror(_lowerCAmelCase ,25 )
lowerCamelCase__ = (e & f) ^ ((~e & 0xff_ff_ff_ff) & g)
lowerCamelCase__ = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0x1_00_00_00_00
lowerCamelCase__ = self.ror(_lowerCAmelCase ,2 ) ^ self.ror(_lowerCAmelCase ,13 ) ^ self.ror(_lowerCAmelCase ,22 )
lowerCamelCase__ = (a & b) ^ (a & c) ^ (b & c)
lowerCamelCase__ = (sa + maj) % 0x1_00_00_00_00
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = (
g,
f,
e,
((d + tempa) % 0x1_00_00_00_00),
c,
b,
a,
((tempa + tempa) % 0x1_00_00_00_00),
)
lowerCamelCase__ = [a, b, c, d, e, f, g, h]
# Modify final values
lowerCamelCase__ = [
((element + mutated_hash_values[index]) % 0x1_00_00_00_00)
for index, element in enumerate(self.hashes )
]
lowerCamelCase__ = """""".join([hex(_lowerCAmelCase )[2:].zfill(8 ) for value in self.hashes] )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
return 0xff_ff_ff_ff & (value << (32 - rotations)) | (value >> rotations)
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
import hashlib
lowerCamelCase__ = bytes("""Test String""" ,"""utf-8""" )
self.assertEqual(SHAaaa(_lowerCAmelCase ).hash ,hashlib.shaaaa(_lowerCAmelCase ).hexdigest() )
def A__ ( ):
import doctest
doctest.testmod()
lowerCamelCase__ = argparse.ArgumentParser()
parser.add_argument(
"""-s""" , """--string""" , dest="""input_string""" , default="""Hello World!! Welcome to Cryptography""" , help="""Hash the string""" , )
parser.add_argument(
"""-f""" , """--file""" , dest="""input_file""" , help="""Hash contents of a file""" )
lowerCamelCase__ = parser.parse_args()
lowerCamelCase__ = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , """rb""" ) as f:
lowerCamelCase__ = f.read()
else:
lowerCamelCase__ = bytes(__lowerCAmelCase , """utf-8""" )
print(SHAaaa(__lowerCAmelCase ).hash )
if __name__ == "__main__":
main()
| 9 | 1 |
'''simple docstring'''
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class A :
def __init__( self : Dict , lowerCAmelCase_ : str , lowerCAmelCase_ : Tuple=99 , lowerCAmelCase_ : int=13 , lowerCAmelCase_ : Tuple=7 , lowerCAmelCase_ : Tuple=9 , lowerCAmelCase_ : int=True , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : Optional[int]=False , lowerCAmelCase_ : List[str]=32 , lowerCAmelCase_ : Optional[int]=5 , lowerCAmelCase_ : Any=4 , lowerCAmelCase_ : Tuple=37 , lowerCAmelCase_ : Any=8 , lowerCAmelCase_ : int=0.1 , lowerCAmelCase_ : Any=0.0_0_2 , lowerCAmelCase_ : int=1 , lowerCAmelCase_ : Union[str, Any]=0 , lowerCAmelCase_ : List[Any]=0 , lowerCAmelCase_ : Tuple=None , lowerCAmelCase_ : List[Any]=None , ) -> List[Any]:
"""simple docstring"""
_a = parent
_a = batch_size
_a = encoder_seq_length
_a = decoder_seq_length
# For common tests
_a = self.decoder_seq_length
_a = is_training
_a = use_attention_mask
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = d_ff
_a = relative_attention_num_buckets
_a = dropout_rate
_a = initializer_factor
_a = eos_token_id
_a = pad_token_id
_a = decoder_start_token_id
_a = None
_a = decoder_layers
def __lowerCAmelCase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
return TaConfig.from_pretrained('''google/umt5-base''' )
def __lowerCAmelCase ( self : Dict , lowerCAmelCase_ : str , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : str=None , lowerCAmelCase_ : str=None , lowerCAmelCase_ : Union[str, Any]=None , ) -> List[str]:
"""simple docstring"""
if attention_mask is None:
_a = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
_a = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
_a = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=lowerCAmelCase_ )
if decoder_head_mask is None:
_a = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=lowerCAmelCase_ )
if cross_attn_head_mask is None:
_a = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=lowerCAmelCase_ )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def __lowerCAmelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
_a = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
_a = input_ids.clamp(self.pad_token_id + 1 )
_a = decoder_input_ids.clamp(self.pad_token_id + 1 )
_a = self.get_config()
_a = config.num_attention_heads
_a = self.prepare_inputs_dict(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
return config, input_dict
def __lowerCAmelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.prepare_config_and_inputs()
return config, inputs_dict
def __lowerCAmelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
return TaConfig(
vocab_size=1_66 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def __lowerCAmelCase ( self : int ) -> str:
"""simple docstring"""
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def __lowerCAmelCase ( self : str , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Any , lowerCAmelCase_ : List[Any] , ) -> Optional[Any]:
"""simple docstring"""
_a = UMTaModel(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_a = model(
input_ids=lowerCAmelCase_ , decoder_input_ids=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , decoder_attention_mask=lowerCAmelCase_ , )
_a = model(input_ids=lowerCAmelCase_ , decoder_input_ids=lowerCAmelCase_ )
_a = result.last_hidden_state
_a = result.past_key_values
_a = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(lowerCAmelCase_ ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def __lowerCAmelCase ( self : Dict , lowerCAmelCase_ : Any , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Tuple , ) -> Union[str, Any]:
"""simple docstring"""
_a = UMTaModel(config=lowerCAmelCase_ ).get_decoder().to(lowerCAmelCase_ ).eval()
# first forward pass
_a = model(lowerCAmelCase_ , use_cache=lowerCAmelCase_ )
_a = model(lowerCAmelCase_ )
_a = model(lowerCAmelCase_ , use_cache=lowerCAmelCase_ )
self.parent.assertTrue(len(lowerCAmelCase_ ) == len(lowerCAmelCase_ ) )
self.parent.assertTrue(len(lowerCAmelCase_ ) == len(lowerCAmelCase_ ) + 1 )
_a , _a = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_a = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
_a = torch.cat([input_ids, next_tokens] , dim=-1 )
_a = model(lowerCAmelCase_ )['''last_hidden_state''']
_a = model(lowerCAmelCase_ , past_key_values=lowerCAmelCase_ )['''last_hidden_state''']
# select random slice
_a = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_a = output_from_no_past[:, -1, random_slice_idx].detach()
_a = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCAmelCase_ , lowerCAmelCase_ , atol=1e-3 ) )
def __lowerCAmelCase ( self : List[str] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : int , ) -> Optional[int]:
"""simple docstring"""
_a = UMTaModel(config=lowerCAmelCase_ ).to(lowerCAmelCase_ ).half().eval()
_a = model(**lowerCAmelCase_ )['''last_hidden_state''']
self.parent.assertFalse(torch.isnan(lowerCAmelCase_ ).any().item() )
@require_torch
class A ( _a ,_a ,_a ,unittest.TestCase ):
lowercase_ = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
lowercase_ = (UMTaForConditionalGeneration,) if is_torch_available() else ()
lowercase_ = (
{
'conversational': UMTaForConditionalGeneration,
'feature-extraction': UMTaModel,
'summarization': UMTaForConditionalGeneration,
'text2text-generation': UMTaForConditionalGeneration,
'translation': UMTaForConditionalGeneration,
'question-answering': UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
lowercase_ = True
lowercase_ = False
lowercase_ = False
lowercase_ = True
lowercase_ = True
# The small UMT5 model needs higher percentages for CPU/MP tests
lowercase_ = [0.8, 0.9]
def __lowerCAmelCase ( self : str ) -> int:
"""simple docstring"""
_a = UMTaModelTester(self )
@unittest.skip('''Test has a segmentation fault on torch 1.8.0''' )
def __lowerCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
_a = UMTaModel(config_and_inputs[0] ).to(lowerCAmelCase_ )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
lowerCAmelCase_ , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , F'{tmpdirname}/t5_test.onnx' , export_params=lowerCAmelCase_ , opset_version=9 , input_names=['''input_ids''', '''decoder_input_ids'''] , )
@unittest.skipIf(torch_device == '''cpu''' , '''Cant do half precision''' )
def __lowerCAmelCase ( self : List[Any] ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*lowerCAmelCase_ )
def __lowerCAmelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
_a = ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
_a = self.model_tester.prepare_config_and_inputs()
_a = config_and_inputs[0]
_a = UMTaForConditionalGeneration(lowerCAmelCase_ ).eval()
model.to(lowerCAmelCase_ )
_a = {
'''head_mask''': torch.zeros(config.num_layers , config.num_heads , device=lowerCAmelCase_ ),
'''decoder_head_mask''': torch.zeros(config.num_decoder_layers , config.num_heads , device=lowerCAmelCase_ ),
'''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers , config.num_heads , device=lowerCAmelCase_ ),
}
for attn_name, (name, mask) in zip(lowerCAmelCase_ , head_masking.items() ):
_a = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
_a = torch.ones(
config.num_decoder_layers , config.num_heads , device=lowerCAmelCase_ )
_a = model.generate(
config_and_inputs[1]['''input_ids'''] , num_beams=1 , max_length=3 , output_attentions=lowerCAmelCase_ , return_dict_in_generate=lowerCAmelCase_ , **lowerCAmelCase_ , )
# We check the state of decoder_attentions and cross_attentions just from the last step
_a = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.''' )
def __lowerCAmelCase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class A ( unittest.TestCase ):
@slow
@unittest.skip(
'''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged''' )
def __lowerCAmelCase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
_a = UMTaForConditionalGeneration.from_pretrained('''google/umt5-small''' , return_dict=lowerCAmelCase_ ).to(lowerCAmelCase_ )
_a = AutoTokenizer.from_pretrained('''google/umt5-small''' , use_fast=lowerCAmelCase_ , legacy=lowerCAmelCase_ )
_a = [
'''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
'''No se como puedo <extra_id_0>.''',
'''This is the reason why we <extra_id_0> them.''',
'''The <extra_id_0> walks in <extra_id_1>, seats''',
'''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
]
_a = tokenizer(lowerCAmelCase_ , return_tensors='''pt''' , padding=lowerCAmelCase_ ).input_ids
# fmt: off
_a = torch.tensor(
[
[ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
] )
# fmt: on
torch.testing.assert_allclose(lowerCAmelCase_ , lowerCAmelCase_ )
_a = model.generate(input_ids.to(lowerCAmelCase_ ) )
_a = [
'''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
'''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
]
_a = tokenizer.batch_decode(lowerCAmelCase_ )
self.assertEqual(lowerCAmelCase_ , lowerCAmelCase_ )
| 22 |
from __future__ import annotations
import math
import random
from typing import Any
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self: Dict ):
lowercase__ : list[Any] = []
lowercase__ : int = 0
lowercase__ : int = 0
def snake_case__( self: Optional[int] ):
return self.head == self.tail
def snake_case__( self: Any, lowerCamelCase_: Any ):
self.data.append(lowerCamelCase_ )
lowercase__ : int = self.tail + 1
def snake_case__( self: Tuple ):
lowercase__ : Optional[Any] = self.data[self.head]
lowercase__ : Optional[int] = self.head + 1
return ret
def snake_case__( self: List[Any] ):
return self.tail - self.head
def snake_case__( self: List[Any] ):
print(self.data )
print('**************' )
print(self.data[self.head : self.tail] )
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self: Union[str, Any], lowerCamelCase_: Any ):
lowercase__ : List[Any] = data
lowercase__ : MyNode | None = None
lowercase__ : MyNode | None = None
lowercase__ : int = 1
def snake_case__( self: Optional[Any] ):
return self.data
def snake_case__( self: List[str] ):
return self.left
def snake_case__( self: Optional[int] ):
return self.right
def snake_case__( self: Optional[Any] ):
return self.height
def snake_case__( self: Optional[int], lowerCamelCase_: Any ):
lowercase__ : Tuple = data
def snake_case__( self: Optional[int], lowerCamelCase_: MyNode | None ):
lowercase__ : Dict = node
def snake_case__( self: Union[str, Any], lowerCamelCase_: MyNode | None ):
lowercase__ : Optional[Any] = node
def snake_case__( self: List[Any], lowerCamelCase_: int ):
lowercase__ : Union[str, Any] = height
def SCREAMING_SNAKE_CASE__ ( _lowercase : MyNode | None ) -> int:
'''simple docstring'''
if node is None:
return 0
return node.get_height()
def SCREAMING_SNAKE_CASE__ ( _lowercase : int , _lowercase : int ) -> int:
'''simple docstring'''
if a > b:
return a
return b
def SCREAMING_SNAKE_CASE__ ( _lowercase : MyNode ) -> MyNode:
'''simple docstring'''
print('left rotation node:' , node.get_data() )
lowercase__ : Optional[int] = node.get_left()
assert ret is not None
node.set_left(ret.get_right() )
ret.set_right(_lowercase )
lowercase__ : List[str] = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(_lowercase )
lowercase__ : Optional[Any] = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(_lowercase )
return ret
def SCREAMING_SNAKE_CASE__ ( _lowercase : MyNode ) -> MyNode:
'''simple docstring'''
print('right rotation node:' , node.get_data() )
lowercase__ : Union[str, Any] = node.get_right()
assert ret is not None
node.set_right(ret.get_left() )
ret.set_left(_lowercase )
lowercase__ : str = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(_lowercase )
lowercase__ : List[str] = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(_lowercase )
return ret
def SCREAMING_SNAKE_CASE__ ( _lowercase : MyNode ) -> MyNode:
'''simple docstring'''
lowercase__ : str = node.get_left()
assert left_child is not None
node.set_left(left_rotation(_lowercase ) )
return right_rotation(_lowercase )
def SCREAMING_SNAKE_CASE__ ( _lowercase : MyNode ) -> MyNode:
'''simple docstring'''
lowercase__ : Optional[Any] = node.get_right()
assert right_child is not None
node.set_right(right_rotation(_lowercase ) )
return left_rotation(_lowercase )
def SCREAMING_SNAKE_CASE__ ( _lowercase : MyNode | None , _lowercase : Any ) -> MyNode | None:
'''simple docstring'''
if node is None:
return MyNode(_lowercase )
if data < node.get_data():
node.set_left(insert_node(node.get_left() , _lowercase ) )
if (
get_height(node.get_left() ) - get_height(node.get_right() ) == 2
): # an unbalance detected
lowercase__ : Tuple = node.get_left()
assert left_child is not None
if (
data < left_child.get_data()
): # new node is the left child of the left child
lowercase__ : Optional[Any] = right_rotation(_lowercase )
else:
lowercase__ : List[str] = lr_rotation(_lowercase )
else:
node.set_right(insert_node(node.get_right() , _lowercase ) )
if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
lowercase__ : Dict = node.get_right()
assert right_child is not None
if data < right_child.get_data():
lowercase__ : Dict = rl_rotation(_lowercase )
else:
lowercase__ : Tuple = left_rotation(_lowercase )
lowercase__ : List[Any] = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(_lowercase )
return node
def SCREAMING_SNAKE_CASE__ ( _lowercase : MyNode ) -> Any:
'''simple docstring'''
while True:
lowercase__ : List[Any] = root.get_right()
if right_child is None:
break
lowercase__ : List[str] = right_child
return root.get_data()
def SCREAMING_SNAKE_CASE__ ( _lowercase : MyNode ) -> Any:
'''simple docstring'''
while True:
lowercase__ : Any = root.get_left()
if left_child is None:
break
lowercase__ : List[str] = left_child
return root.get_data()
def SCREAMING_SNAKE_CASE__ ( _lowercase : MyNode , _lowercase : Any ) -> MyNode | None:
'''simple docstring'''
lowercase__ : List[Any] = root.get_left()
lowercase__ : List[Any] = root.get_right()
if root.get_data() == data:
if left_child is not None and right_child is not None:
lowercase__ : Dict = get_left_most(_lowercase )
root.set_data(_lowercase )
root.set_right(del_node(_lowercase , _lowercase ) )
elif left_child is not None:
lowercase__ : List[Any] = left_child
elif right_child is not None:
lowercase__ : Dict = right_child
else:
return None
elif root.get_data() > data:
if left_child is None:
print('No such data' )
return root
else:
root.set_left(del_node(_lowercase , _lowercase ) )
else: # root.get_data() < data
if right_child is None:
return root
else:
root.set_right(del_node(_lowercase , _lowercase ) )
if get_height(_lowercase ) - get_height(_lowercase ) == 2:
assert right_child is not None
if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
lowercase__ : List[Any] = left_rotation(_lowercase )
else:
lowercase__ : Optional[Any] = rl_rotation(_lowercase )
elif get_height(_lowercase ) - get_height(_lowercase ) == -2:
assert left_child is not None
if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
lowercase__ : Optional[int] = right_rotation(_lowercase )
else:
lowercase__ : Dict = lr_rotation(_lowercase )
lowercase__ : Any = my_max(get_height(root.get_right() ) , get_height(root.get_left() ) ) + 1
root.set_height(_lowercase )
return root
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self: str ):
lowercase__ : MyNode | None = None
def snake_case__( self: Optional[Any] ):
return get_height(self.root )
def snake_case__( self: List[Any], lowerCamelCase_: Any ):
print('insert:' + str(lowerCamelCase_ ) )
lowercase__ : int = insert_node(self.root, lowerCamelCase_ )
def snake_case__( self: Tuple, lowerCamelCase_: Any ):
print('delete:' + str(lowerCamelCase_ ) )
if self.root is None:
print('Tree is empty!' )
return
lowercase__ : int = del_node(self.root, lowerCamelCase_ )
def __str__( self: str, ): # a level traversale, gives a more intuitive look on the tree
lowercase__ : Optional[int] = ''
lowercase__ : Any = MyQueue()
q.push(self.root )
lowercase__ : Dict = self.get_height()
if layer == 0:
return output
lowercase__ : Optional[int] = 0
while not q.is_empty():
lowercase__ : Dict = q.pop()
lowercase__ : Optional[Any] = ' ' * int(math.pow(2, layer - 1 ) )
output += space
if node is None:
output += "*"
q.push(lowerCamelCase_ )
q.push(lowerCamelCase_ )
else:
output += str(node.get_data() )
q.push(node.get_left() )
q.push(node.get_right() )
output += space
lowercase__ : Union[str, Any] = cnt + 1
for i in range(100 ):
if cnt == math.pow(2, lowerCamelCase_ ) - 1:
lowercase__ : Optional[int] = layer - 1
if layer == 0:
output += "\n*************************************"
return output
output += "\n"
break
output += "\n*************************************"
return output
def SCREAMING_SNAKE_CASE__ ( ) -> None:
'''simple docstring'''
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
__UpperCamelCase: int = AVLtree()
__UpperCamelCase: int = list(range(1_0))
random.shuffle(lst)
for i in lst:
t.insert(i)
print(str(t))
random.shuffle(lst)
for i in lst:
t.del_node(i)
print(str(t))
| 266 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Union[str, Any] ={
"""configuration_x_clip""": [
"""XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XCLIPConfig""",
"""XCLIPTextConfig""",
"""XCLIPVisionConfig""",
],
"""processing_x_clip""": ["""XCLIPProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Any =[
"""XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XCLIPModel""",
"""XCLIPPreTrainedModel""",
"""XCLIPTextModel""",
"""XCLIPVisionModel""",
]
if TYPE_CHECKING:
from .configuration_x_clip import (
XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
XCLIPConfig,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .processing_x_clip import XCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_x_clip import (
XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
else:
import sys
A_ : Dict =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 222 |
"""simple docstring"""
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class __a ( lowerCAmelCase__ ):
def __init__( self , a__ , a__=13 , a__=7 , a__=True , a__=True , a__=True , a__=True , a__=99 , a__=32 , a__=5 , a__=4 , a__=37 , a__="gelu" , a__=0.1 , a__=0.1 , a__=5_12 , a__=16 , a__=2 , a__=0.02 , a__=False , a__=True , a__="None" , a__=3 , a__=4 , a__=None , ):
_lowerCamelCase = parent
_lowerCamelCase = batch_size
_lowerCamelCase = seq_length
_lowerCamelCase = is_training
_lowerCamelCase = use_input_mask
_lowerCamelCase = use_token_type_ids
_lowerCamelCase = use_labels
_lowerCamelCase = vocab_size
_lowerCamelCase = hidden_size
_lowerCamelCase = num_hidden_layers
_lowerCamelCase = num_attention_heads
_lowerCamelCase = intermediate_size
_lowerCamelCase = hidden_act
_lowerCamelCase = hidden_dropout_prob
_lowerCamelCase = attention_probs_dropout_prob
_lowerCamelCase = max_position_embeddings
_lowerCamelCase = type_vocab_size
_lowerCamelCase = type_sequence_label_size
_lowerCamelCase = initializer_range
_lowerCamelCase = num_labels
_lowerCamelCase = num_choices
_lowerCamelCase = relative_attention
_lowerCamelCase = position_biased_input
_lowerCamelCase = pos_att_type
_lowerCamelCase = scope
def snake_case_ ( self ):
_lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCamelCase = None
if self.use_input_mask:
_lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
_lowerCamelCase = None
if self.use_token_type_ids:
_lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_lowerCamelCase = None
_lowerCamelCase = None
_lowerCamelCase = None
if self.use_labels:
_lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowerCamelCase = ids_tensor([self.batch_size] , self.num_choices )
_lowerCamelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case_ ( self ):
return DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def snake_case_ ( self , a__ ):
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def snake_case_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
_lowerCamelCase = DebertaVaModel(config=a__ )
model.to(a__ )
model.eval()
_lowerCamelCase = model(a__ , attention_mask=a__ , token_type_ids=a__ )[0]
_lowerCamelCase = model(a__ , token_type_ids=a__ )[0]
_lowerCamelCase = model(a__ )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def snake_case_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
_lowerCamelCase = DebertaVaForMaskedLM(config=a__ )
model.to(a__ )
model.eval()
_lowerCamelCase = model(a__ , attention_mask=a__ , token_type_ids=a__ , labels=a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
_lowerCamelCase = self.num_labels
_lowerCamelCase = DebertaVaForSequenceClassification(a__ )
model.to(a__ )
model.eval()
_lowerCamelCase = model(a__ , attention_mask=a__ , token_type_ids=a__ , labels=a__ )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(a__ )
def snake_case_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
_lowerCamelCase = self.num_labels
_lowerCamelCase = DebertaVaForTokenClassification(config=a__ )
model.to(a__ )
model.eval()
_lowerCamelCase = model(a__ , attention_mask=a__ , token_type_ids=a__ , labels=a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def snake_case_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
_lowerCamelCase = DebertaVaForQuestionAnswering(config=a__ )
model.to(a__ )
model.eval()
_lowerCamelCase = model(
a__ , attention_mask=a__ , token_type_ids=a__ , start_positions=a__ , end_positions=a__ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def snake_case_ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ):
_lowerCamelCase = DebertaVaForMultipleChoice(config=a__ )
model.to(a__ )
model.eval()
_lowerCamelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_lowerCamelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_lowerCamelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_lowerCamelCase = model(
a__ , attention_mask=a__ , token_type_ids=a__ , labels=a__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def snake_case_ ( self ):
_lowerCamelCase = self.prepare_config_and_inputs()
(
(
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) ,
) = config_and_inputs
_lowerCamelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class __a ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
SCREAMING_SNAKE_CASE__ : int = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE__ : Tuple = (
{
"feature-extraction": DebertaVaModel,
"fill-mask": DebertaVaForMaskedLM,
"question-answering": DebertaVaForQuestionAnswering,
"text-classification": DebertaVaForSequenceClassification,
"token-classification": DebertaVaForTokenClassification,
"zero-shot": DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE__ : Optional[int] = True
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
SCREAMING_SNAKE_CASE__ : Union[str, Any] = False
SCREAMING_SNAKE_CASE__ : str = False
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
def snake_case_ ( self ):
_lowerCamelCase = DebertaVaModelTester(self )
_lowerCamelCase = ConfigTester(self , config_class=a__ , hidden_size=37 )
def snake_case_ ( self ):
self.config_tester.run_common_tests()
def snake_case_ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*a__ )
def snake_case_ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*a__ )
def snake_case_ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*a__ )
def snake_case_ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*a__ )
def snake_case_ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*a__ )
def snake_case_ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*a__ )
@slow
def snake_case_ ( self ):
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase = DebertaVaModel.from_pretrained(a__ )
self.assertIsNotNone(a__ )
@require_torch
@require_sentencepiece
@require_tokenizers
class __a ( unittest.TestCase ):
@unittest.skip(reason='Model not available yet' )
def snake_case_ ( self ):
pass
@slow
def snake_case_ ( self ):
_lowerCamelCase = DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge' )
_lowerCamelCase = torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
_lowerCamelCase = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
_lowerCamelCase = model(a__ , attention_mask=a__ )[0]
# compare the actual values for a slice.
_lowerCamelCase = torch.tensor(
[[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a__ , atol=1e-4 ) , F'{output[:, 1:4, 1:4]}' )
| 222 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
snake_case : int = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case : str = ["""NllbTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case : Dict = ["""NllbTokenizerFast"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
snake_case : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 545 |
"""simple docstring"""
from maths.prime_check import is_prime
def A ( __snake_case: int ) -> int:
"""simple docstring"""
if not isinstance(__snake_case , __snake_case ):
__magic_name__ = F"""Input value of [number={number}] must be an integer"""
raise TypeError(__snake_case )
if is_prime(__snake_case ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod() | 545 | 1 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def UpperCAmelCase ( _lowerCamelCase : List[str] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Dict , _lowerCamelCase : Any=1_024 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = [], []
SCREAMING_SNAKE_CASE__ : Union[str, Any] = list(zip(_lowerCamelCase , _lowerCamelCase ) )
SCREAMING_SNAKE_CASE__ : List[Any] = sorted_examples[0]
def is_too_big(_lowerCamelCase : int ):
return tok(_lowerCamelCase , return_tensors="pt" ).input_ids.shape[1] > max_tokens
for src, tgt in tqdm(sorted_examples[1:] ):
SCREAMING_SNAKE_CASE__ : int = new_src + " " + src
SCREAMING_SNAKE_CASE__ : str = new_tgt + " " + tgt
if is_too_big(_lowerCamelCase ) or is_too_big(_lowerCamelCase ): # cant fit, finalize example
finished_src.append(_lowerCamelCase )
finished_tgt.append(_lowerCamelCase )
SCREAMING_SNAKE_CASE__ : Optional[int] = src, tgt
else: # can fit, keep adding
SCREAMING_SNAKE_CASE__ : Optional[Any] = cand_src, cand_tgt
# cleanup
if new_src:
assert new_tgt
finished_src.append(_lowerCamelCase )
finished_tgt.append(_lowerCamelCase )
return finished_src, finished_tgt
def UpperCAmelCase ( _lowerCamelCase : List[Any] , _lowerCamelCase : Path , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = Path(_lowerCamelCase )
save_path.mkdir(exist_ok=_lowerCamelCase )
for split in ["train"]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
SCREAMING_SNAKE_CASE__ : str = [x.rstrip() for x in Path(_lowerCamelCase ).open().readlines()]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [x.rstrip() for x in Path(_lowerCamelCase ).open().readlines()]
SCREAMING_SNAKE_CASE__ : int = pack_examples(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
print(f"""packed {split} split from {len(_lowerCamelCase )} examples -> {len(_lowerCamelCase )}.""" )
Path(save_path / f"""{split}.source""" ).open("w" ).write("\n".join(_lowerCamelCase ) )
Path(save_path / f"""{split}.target""" ).open("w" ).write("\n".join(_lowerCamelCase ) )
for split in ["val", "test"]:
SCREAMING_SNAKE_CASE__ : List[str] = data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
shutil.copyfile(_lowerCamelCase , save_path / f"""{split}.source""" )
shutil.copyfile(_lowerCamelCase , save_path / f"""{split}.target""" )
def UpperCAmelCase ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = argparse.ArgumentParser()
parser.add_argument("--tok_name" , type=_lowerCamelCase , help="like facebook/bart-large-cnn,t5-base, etc." )
parser.add_argument("--max_seq_len" , type=_lowerCamelCase , default=128 )
parser.add_argument("--data_dir" , type=_lowerCamelCase )
parser.add_argument("--save_path" , type=_lowerCamelCase )
SCREAMING_SNAKE_CASE__ : List[str] = parser.parse_args()
SCREAMING_SNAKE_CASE__ : int = AutoTokenizer.from_pretrained(args.tok_name )
return pack_data_dir(_lowerCamelCase , Path(args.data_dir ) , args.max_seq_len , args.save_path )
if __name__ == "__main__":
packer_cli() | 709 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
__lowercase :List[Any] = "python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append("importlib_metadata")
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")
def UpperCAmelCase ( _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[Any]=None ):
'''simple docstring'''
require_version(deps[pkg] , _lowerCamelCase ) | 26 | 0 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCAmelCase__ ( __lowercase , unittest.TestCase ):
a__ : Tuple = DDIMPipeline
a__ : List[str] = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
a__ : Optional[int] = PipelineTesterMixin.required_optional_params - {
"""num_images_per_prompt""",
"""latents""",
"""callback""",
"""callback_steps""",
}
a__ : Optional[int] = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
a__ : str = False
def __A ( self : Optional[int] ) -> Any:
torch.manual_seed(0 )
__lowerCamelCase = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
__lowerCamelCase = DDIMScheduler()
__lowerCamelCase = {'''unet''': unet, '''scheduler''': scheduler}
return components
def __A ( self : List[Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : int=0 ) -> Tuple:
if str(SCREAMING_SNAKE_CASE__ ).startswith('''mps''' ):
__lowerCamelCase = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
else:
__lowerCamelCase = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = {
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def __A ( self : Tuple ) -> List[Any]:
__lowerCamelCase = '''cpu'''
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = self.pipeline_class(**SCREAMING_SNAKE_CASE__ )
pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = pipe(**SCREAMING_SNAKE_CASE__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
__lowerCamelCase = np.array(
[1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04] )
__lowerCamelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(SCREAMING_SNAKE_CASE__ , 1e-3 )
def __A ( self : Optional[int] ) -> Any:
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def __A ( self : Union[str, Any] ) -> Optional[Any]:
super().test_save_load_local(expected_max_difference=3e-3 )
def __A ( self : Optional[int] ) -> List[Any]:
super().test_save_load_optional_components(expected_max_difference=3e-3 )
def __A ( self : List[Any] ) -> Dict:
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
def __A ( self : List[Any] ) -> Tuple:
__lowerCamelCase = '''google/ddpm-cifar10-32'''
__lowerCamelCase = UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = DDIMScheduler()
__lowerCamelCase = DDIMPipeline(unet=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ )
ddim.to(SCREAMING_SNAKE_CASE__ )
ddim.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = ddim(generator=SCREAMING_SNAKE_CASE__ , eta=0.0 , output_type='''numpy''' ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowerCamelCase = np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __A ( self : List[str] ) -> Optional[int]:
__lowerCamelCase = '''google/ddpm-ema-bedroom-256'''
__lowerCamelCase = UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = DDIMPipeline(unet=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ )
ddpm.to(SCREAMING_SNAKE_CASE__ )
ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = ddpm(generator=SCREAMING_SNAKE_CASE__ , output_type='''numpy''' ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
__lowerCamelCase = np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 298 |
import unittest
import numpy as np
import requests
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_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
SCREAMING_SNAKE_CASE__ : Dict = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class lowerCAmelCase__ ( unittest.TestCase ):
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[Any]=7 , SCREAMING_SNAKE_CASE__ : str=3 , SCREAMING_SNAKE_CASE__ : List[str]=18 , SCREAMING_SNAKE_CASE__ : Optional[int]=30 , SCREAMING_SNAKE_CASE__ : Optional[Any]=4_00 , SCREAMING_SNAKE_CASE__ : Any=None , SCREAMING_SNAKE_CASE__ : Union[str, Any]=True , SCREAMING_SNAKE_CASE__ : Union[str, Any]=True , SCREAMING_SNAKE_CASE__ : List[str]=None , ) -> Dict:
__lowerCamelCase = size if size is not None else {'''height''': 20, '''width''': 20}
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = num_channels
__lowerCamelCase = image_size
__lowerCamelCase = min_resolution
__lowerCamelCase = max_resolution
__lowerCamelCase = size
__lowerCamelCase = do_normalize
__lowerCamelCase = do_convert_rgb
__lowerCamelCase = [5_12, 10_24, 20_48, 40_96]
__lowerCamelCase = patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
def __A ( self : Union[str, Any] ) -> Optional[int]:
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def __A ( self : int ) -> Dict:
__lowerCamelCase = '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
__lowerCamelCase = Image.open(requests.get(SCREAMING_SNAKE_CASE__ , stream=SCREAMING_SNAKE_CASE__ ).raw ).convert('''RGB''' )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`.""" , )
@require_torch
@require_vision
class lowerCAmelCase__ ( __lowercase , unittest.TestCase ):
a__ : Optional[Any] = PixaStructImageProcessor if is_vision_available() else None
def __A ( self : Any ) -> Tuple:
__lowerCamelCase = PixaStructImageProcessingTester(self )
@property
def __A ( self : Any ) -> Dict:
return self.image_processor_tester.prepare_image_processor_dict()
def __A ( self : List[str] ) -> Tuple:
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''do_normalize''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''do_convert_rgb''' ) )
def __A ( self : Optional[Any] ) -> List[str]:
__lowerCamelCase = self.image_processor_tester.prepare_dummy_image()
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
__lowerCamelCase = 20_48
__lowerCamelCase = image_processor(SCREAMING_SNAKE_CASE__ , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE__ )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0606 ) , atol=1e-3 , rtol=1e-3 ) )
def __A ( self : Optional[int] ) -> Union[str, Any]:
# Initialize image_processor
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowerCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , Image.Image )
# Test not batched input
__lowerCamelCase = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
__lowerCamelCase = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__lowerCamelCase = image_processor(
SCREAMING_SNAKE_CASE__ , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __A ( self : Any ) -> Dict:
# Initialize image_processor
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowerCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , Image.Image )
# Test not batched input
__lowerCamelCase = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
__lowerCamelCase = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE__ ).flattened_patches
__lowerCamelCase = '''Hello'''
__lowerCamelCase = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE__ , header_text=SCREAMING_SNAKE_CASE__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__lowerCamelCase = image_processor(
SCREAMING_SNAKE_CASE__ , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE__ , header_text=SCREAMING_SNAKE_CASE__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __A ( self : int ) -> Union[str, Any]:
# Initialize image_processor
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__lowerCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE__ , numpify=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , np.ndarray )
__lowerCamelCase = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
__lowerCamelCase = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__lowerCamelCase = image_processor(
SCREAMING_SNAKE_CASE__ , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __A ( self : Any ) -> int:
# Initialize image_processor
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__lowerCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE__ , torchify=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , torch.Tensor )
# Test not batched input
__lowerCamelCase = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
__lowerCamelCase = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__lowerCamelCase = image_processor(
SCREAMING_SNAKE_CASE__ , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`.""" , )
@require_torch
@require_vision
class lowerCAmelCase__ ( __lowercase , unittest.TestCase ):
a__ : Optional[int] = PixaStructImageProcessor if is_vision_available() else None
def __A ( self : List[str] ) -> Optional[Any]:
__lowerCamelCase = PixaStructImageProcessingTester(self , num_channels=4 )
__lowerCamelCase = 3
@property
def __A ( self : List[Any] ) -> Union[str, Any]:
return self.image_processor_tester.prepare_image_processor_dict()
def __A ( self : Optional[int] ) -> Any:
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''do_normalize''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''do_convert_rgb''' ) )
def __A ( self : Optional[int] ) -> Any:
# Initialize image_processor
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowerCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , Image.Image )
# Test not batched input
__lowerCamelCase = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
__lowerCamelCase = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__lowerCamelCase = image_processor(
SCREAMING_SNAKE_CASE__ , return_tensors='''pt''' , max_patches=SCREAMING_SNAKE_CASE__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 298 | 1 |
def _lowerCAmelCase ( __magic_name__ :list[int] , __magic_name__ :list[int] ):
# Check if the input is valid
if not len(__magic_name__ ) == len(__magic_name__ ) == 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
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ = equationa
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ = equationa
# Calculate the determinants of the matrices
UpperCAmelCase_ = aa * ba - aa * ba
UpperCAmelCase_ = ca * ba - ca * ba
UpperCAmelCase_ = 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:
UpperCAmelCase_ = determinant_x / determinant
UpperCAmelCase_ = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 407 |
def _lowerCAmelCase ( __magic_name__ :int ):
UpperCAmelCase_ = int(__magic_name__ )
if decimal in (0, 1): # Exit cases for the recursion
return str(__magic_name__ )
UpperCAmelCase_, UpperCAmelCase_ = divmod(__magic_name__ , 2 )
return binary_recursive(__magic_name__ ) + str(__magic_name__ )
def _lowerCAmelCase ( __magic_name__ :str ):
UpperCAmelCase_ = str(__magic_name__ ).strip()
if not number:
raise ValueError('''No input value was provided''' )
UpperCAmelCase_ = '''-''' if number.startswith('''-''' ) else ''''''
UpperCAmelCase_ = number.lstrip('''-''' )
if not number.isnumeric():
raise ValueError('''Input value is not an integer''' )
return F'''{negative}0b{binary_recursive(int(__magic_name__ ) )}'''
if __name__ == "__main__":
from doctest import testmod
testmod()
| 407 | 1 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class lowerCAmelCase_ ( lowerCamelCase_ ):
@slow
@require_torch
def snake_case ( self ):
SCREAMING_SNAKE_CASE_ : List[str] = EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny' ,'prajjwal1/bert-tiny' )
SCREAMING_SNAKE_CASE_ : Tuple = BertTokenizer.from_pretrained('bert-base-uncased' )
SCREAMING_SNAKE_CASE_ : Dict = bertabert.config.encoder.vocab_size
SCREAMING_SNAKE_CASE_ : Optional[int] = tokenizer.sep_token_id
SCREAMING_SNAKE_CASE_ : List[Any] = tokenizer.cls_token_id
SCREAMING_SNAKE_CASE_ : Any = 128
SCREAMING_SNAKE_CASE_ : List[str] = datasets.load_dataset('cnn_dailymail' ,'3.0.0' ,split='train[:1%]' )
SCREAMING_SNAKE_CASE_ : Dict = datasets.load_dataset('cnn_dailymail' ,'3.0.0' ,split='validation[:1%]' )
SCREAMING_SNAKE_CASE_ : List[Any] = train_dataset.select(range(32 ) )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = val_dataset.select(range(16 ) )
SCREAMING_SNAKE_CASE_ : str = 4
def _map_to_encoder_decoder_inputs(snake_case__ ):
# Tokenizer will automatically set [BOS] <text> [EOS]
SCREAMING_SNAKE_CASE_ : Optional[int] = tokenizer(batch['article'] ,padding='max_length' ,truncation=snake_case__ ,max_length=512 )
SCREAMING_SNAKE_CASE_ : int = tokenizer(batch['highlights'] ,padding='max_length' ,truncation=snake_case__ ,max_length=128 )
SCREAMING_SNAKE_CASE_ : Any = inputs.input_ids
SCREAMING_SNAKE_CASE_ : Optional[int] = inputs.attention_mask
SCREAMING_SNAKE_CASE_ : List[str] = outputs.input_ids
SCREAMING_SNAKE_CASE_ : int = outputs.input_ids.copy()
SCREAMING_SNAKE_CASE_ : Optional[Any] = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
]
SCREAMING_SNAKE_CASE_ : int = outputs.attention_mask
assert all(len(snake_case__ ) == 512 for x in inputs.input_ids )
assert all(len(snake_case__ ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(snake_case__ ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = pred.label_ids
SCREAMING_SNAKE_CASE_ : Any = pred.predictions
# all unnecessary tokens are removed
SCREAMING_SNAKE_CASE_ : Any = tokenizer.batch_decode(snake_case__ ,skip_special_tokens=snake_case__ )
SCREAMING_SNAKE_CASE_ : Optional[Any] = tokenizer.batch_decode(snake_case__ ,skip_special_tokens=snake_case__ )
SCREAMING_SNAKE_CASE_ : List[Any] = sum([int(pred_str[i] == label_str[i] ) for i in range(len(snake_case__ ) )] ) / len(snake_case__ )
return {"accuracy": accuracy}
# map train dataset
SCREAMING_SNAKE_CASE_ : Optional[int] = train_dataset.map(
_map_to_encoder_decoder_inputs ,batched=snake_case__ ,batch_size=snake_case__ ,remove_columns=['article', 'highlights'] ,)
train_dataset.set_format(
type='torch' ,columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'] ,)
# same for validation dataset
SCREAMING_SNAKE_CASE_ : Dict = val_dataset.map(
_map_to_encoder_decoder_inputs ,batched=snake_case__ ,batch_size=snake_case__ ,remove_columns=['article', 'highlights'] ,)
val_dataset.set_format(
type='torch' ,columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'] ,)
SCREAMING_SNAKE_CASE_ : List[Any] = self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE_ : List[str] = SeqaSeqTrainingArguments(
output_dir=snake_case__ ,per_device_train_batch_size=snake_case__ ,per_device_eval_batch_size=snake_case__ ,predict_with_generate=snake_case__ ,evaluation_strategy='steps' ,do_train=snake_case__ ,do_eval=snake_case__ ,warmup_steps=0 ,eval_steps=2 ,logging_steps=2 ,)
# instantiate trainer
SCREAMING_SNAKE_CASE_ : str = SeqaSeqTrainer(
model=snake_case__ ,args=snake_case__ ,compute_metrics=_compute_metrics ,train_dataset=snake_case__ ,eval_dataset=snake_case__ ,tokenizer=snake_case__ ,)
# start training
trainer.train()
| 105 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a = {
"""configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = [
"""GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GPTNeoForCausalLM""",
"""GPTNeoForQuestionAnswering""",
"""GPTNeoForSequenceClassification""",
"""GPTNeoForTokenClassification""",
"""GPTNeoModel""",
"""GPTNeoPreTrainedModel""",
"""load_tf_weights_in_gpt_neo""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = [
"""FlaxGPTNeoForCausalLM""",
"""FlaxGPTNeoModel""",
"""FlaxGPTNeoPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
a = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 687 | 0 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
__UpperCAmelCase = "0.12" # assumed parallelism: 8
@require_flax
@is_staging_test
class a_( unittest.TestCase ):
"""simple docstring"""
@classmethod
def __UpperCamelCase ( cls : List[str]) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = TOKEN
HfFolder.save_token(lowerCAmelCase__)
@classmethod
def __UpperCamelCase ( cls : Dict) -> int:
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id='test-model-flax')
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-model-flax-org')
except HTTPError:
pass
def __UpperCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7)
SCREAMING_SNAKE_CASE = FlaxBertModel(lowerCAmelCase__)
model.push_to_hub('test-model-flax' , use_auth_token=self._token)
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(f'''{USER}/test-model-flax''')
SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(model.params))
SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(new_model.params))
for key in base_params.keys():
SCREAMING_SNAKE_CASE = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(lowerCAmelCase__ , 1e-3 , msg=f'''{key} not identical''')
# Reset repo
delete_repo(token=self._token , repo_id='test-model-flax')
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowerCAmelCase__ , repo_id='test-model-flax' , push_to_hub=lowerCAmelCase__ , use_auth_token=self._token)
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(f'''{USER}/test-model-flax''')
SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(model.params))
SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(new_model.params))
for key in base_params.keys():
SCREAMING_SNAKE_CASE = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(lowerCAmelCase__ , 1e-3 , msg=f'''{key} not identical''')
def __UpperCamelCase ( self : List[Any]) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE = BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7)
SCREAMING_SNAKE_CASE = FlaxBertModel(lowerCAmelCase__)
model.push_to_hub('valid_org/test-model-flax-org' , use_auth_token=self._token)
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained('valid_org/test-model-flax-org')
SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(model.params))
SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(new_model.params))
for key in base_params.keys():
SCREAMING_SNAKE_CASE = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(lowerCAmelCase__ , 1e-3 , msg=f'''{key} not identical''')
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-model-flax-org')
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
lowerCAmelCase__ , repo_id='valid_org/test-model-flax-org' , push_to_hub=lowerCAmelCase__ , use_auth_token=self._token)
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained('valid_org/test-model-flax-org')
SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(model.params))
SCREAMING_SNAKE_CASE = flatten_dict(unfreeze(new_model.params))
for key in base_params.keys():
SCREAMING_SNAKE_CASE = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(lowerCAmelCase__ , 1e-3 , msg=f'''{key} not identical''')
def A_ ( lowercase_ , lowercase_ ) ->List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = flatten_dict(modela.params )
SCREAMING_SNAKE_CASE = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1e-4:
SCREAMING_SNAKE_CASE = False
return models_are_equal
@require_flax
class a_( unittest.TestCase ):
"""simple docstring"""
def __UpperCamelCase ( self : int) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = BertConfig.from_pretrained('hf-internal-testing/tiny-bert-flax-only')
SCREAMING_SNAKE_CASE = FlaxBertModel(lowerCAmelCase__)
SCREAMING_SNAKE_CASE = 'bert'
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(lowerCAmelCase__ , lowerCAmelCase__))
with self.assertRaises(lowerCAmelCase__):
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(lowerCAmelCase__)
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(lowerCAmelCase__ , subfolder=lowerCAmelCase__)
self.assertTrue(check_models_equal(lowerCAmelCase__ , lowerCAmelCase__))
def __UpperCamelCase ( self : Optional[int]) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = BertConfig.from_pretrained('hf-internal-testing/tiny-bert-flax-only')
SCREAMING_SNAKE_CASE = FlaxBertModel(lowerCAmelCase__)
SCREAMING_SNAKE_CASE = 'bert'
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(lowerCAmelCase__ , lowerCAmelCase__) , max_shard_size='10KB')
with self.assertRaises(lowerCAmelCase__):
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(lowerCAmelCase__)
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(lowerCAmelCase__ , subfolder=lowerCAmelCase__)
self.assertTrue(check_models_equal(lowerCAmelCase__ , lowerCAmelCase__))
def __UpperCamelCase ( self : Union[str, Any]) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'bert'
SCREAMING_SNAKE_CASE = 'hf-internal-testing/tiny-random-bert-subfolder'
with self.assertRaises(lowerCAmelCase__):
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(lowerCAmelCase__)
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(lowerCAmelCase__ , subfolder=lowerCAmelCase__)
self.assertIsNotNone(lowerCAmelCase__)
def __UpperCamelCase ( self : Any) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'bert'
SCREAMING_SNAKE_CASE = 'hf-internal-testing/tiny-random-bert-sharded-subfolder'
with self.assertRaises(lowerCAmelCase__):
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(lowerCAmelCase__)
SCREAMING_SNAKE_CASE = FlaxBertModel.from_pretrained(lowerCAmelCase__ , subfolder=lowerCAmelCase__)
self.assertIsNotNone(lowerCAmelCase__)
| 259 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
class a_( lowercase__ ):
"""simple docstring"""
__snake_case : int ='''van'''
def __init__( self : Tuple , lowerCAmelCase__ : Optional[int]=2_2_4 , lowerCAmelCase__ : Optional[Any]=3 , lowerCAmelCase__ : Tuple=[7, 3, 3, 3] , lowerCAmelCase__ : Any=[4, 2, 2, 2] , lowerCAmelCase__ : Optional[int]=[6_4, 1_2_8, 3_2_0, 5_1_2] , lowerCAmelCase__ : Optional[Any]=[3, 3, 1_2, 3] , lowerCAmelCase__ : str=[8, 8, 4, 4] , lowerCAmelCase__ : List[str]="gelu" , lowerCAmelCase__ : Union[str, Any]=0.02 , lowerCAmelCase__ : Any=1e-6 , lowerCAmelCase__ : Optional[Any]=1e-2 , lowerCAmelCase__ : Union[str, Any]=0.0 , lowerCAmelCase__ : Any=0.0 , **lowerCAmelCase__ : Tuple , ) -> Any:
"""simple docstring"""
super().__init__(**lowerCAmelCase__)
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = patch_sizes
SCREAMING_SNAKE_CASE = strides
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = mlp_ratios
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = layer_scale_init_value
SCREAMING_SNAKE_CASE = drop_path_rate
SCREAMING_SNAKE_CASE = dropout_rate
| 259 | 1 |
import numpy as np
import qiskit
def lowercase__ ( A_: int = 8 , A_: int | None = None ) -> str:
"""simple docstring"""
__UpperCAmelCase =np.random.default_rng(seed=A_ )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
__UpperCAmelCase =6 * key_len
# Measurement basis for Alice's qubits.
__UpperCAmelCase =rng.integers(2 , size=A_ )
# The set of states Alice will prepare.
__UpperCAmelCase =rng.integers(2 , size=A_ )
# Measurement basis for Bob's qubits.
__UpperCAmelCase =rng.integers(2 , size=A_ )
# Quantum Circuit to simulate BB84
__UpperCAmelCase =qiskit.QuantumCircuit(A_ , name="""BB84""" )
# Alice prepares her qubits according to rules above.
for index, _ in enumerate(A_ ):
if alice_state[index] == 1:
bbaa_circ.x(A_ )
if alice_basis[index] == 1:
bbaa_circ.h(A_ )
bbaa_circ.barrier()
# Bob measures the received qubits according to rules above.
for index, _ in enumerate(A_ ):
if bob_basis[index] == 1:
bbaa_circ.h(A_ )
bbaa_circ.barrier()
bbaa_circ.measure_all()
# Simulate the quantum circuit.
__UpperCAmelCase =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.
__UpperCAmelCase =qiskit.execute(A_ , A_ , shots=1 , seed_simulator=A_ )
# Returns the result of measurement.
__UpperCAmelCase =job.result().get_counts(A_ ).most_frequent()
# Extracting the generated key from the simulation results.
# Only keep measurement results where Alice and Bob chose the same basis.
__UpperCAmelCase ="""""".join(
[
result_bit
for alice_basis_bit, bob_basis_bit, result_bit in zip(
A_ , A_ , A_ )
if alice_basis_bit == bob_basis_bit
] )
# Get final key. Pad with 0 if too short, otherwise truncate.
__UpperCAmelCase =gen_key[:key_len] if len(A_ ) >= key_len else gen_key.ljust(A_ , """0""" )
return key
if __name__ == "__main__":
print(F"""The generated key is : {bbaa(8, seed=0)}""")
from doctest import testmod
testmod()
| 68 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : List[str] = "roformer"
def __init__( self : Any ,_snake_case : str=50_000 ,_snake_case : int=None ,_snake_case : int=768 ,_snake_case : Tuple=12 ,_snake_case : Dict=12 ,_snake_case : Dict=3_072 ,_snake_case : Tuple="gelu" ,_snake_case : List[Any]=0.1 ,_snake_case : List[Any]=0.1 ,_snake_case : Optional[Any]=1_536 ,_snake_case : Dict=2 ,_snake_case : Union[str, Any]=0.02 ,_snake_case : Optional[Any]=1e-12 ,_snake_case : Optional[Any]=0 ,_snake_case : Tuple=False ,_snake_case : Optional[int]=True ,**_snake_case : Optional[int] ,) -> Tuple:
"""simple docstring"""
super().__init__(pad_token_id=_snake_case ,**_snake_case )
lowercase__ : Optional[int] = vocab_size
lowercase__ : int = hidden_size if embedding_size is None else embedding_size
lowercase__ : Union[str, Any] = hidden_size
lowercase__ : Any = num_hidden_layers
lowercase__ : Union[str, Any] = num_attention_heads
lowercase__ : str = hidden_act
lowercase__ : Union[str, Any] = intermediate_size
lowercase__ : Dict = hidden_dropout_prob
lowercase__ : Optional[Any] = attention_probs_dropout_prob
lowercase__ : List[Any] = max_position_embeddings
lowercase__ : List[str] = type_vocab_size
lowercase__ : Optional[int] = initializer_range
lowercase__ : List[Any] = layer_norm_eps
lowercase__ : Optional[Any] = rotary_value
lowercase__ : Optional[int] = use_cache
class __A ( A_ ):
'''simple docstring'''
@property
def UpperCAmelCase ( self : Any ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
lowercase__ : Union[str, Any] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowercase__ : List[Any] = {0: '''batch''', 1: '''sequence'''}
lowercase__ : Optional[Any] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 560 | 0 |
'''simple docstring'''
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Any = tmp_path / 'cache'
UpperCAmelCase : int = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCAmelCase : Any = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ , keep_in_memory=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize(
'features' , [
None,
{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
{'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
{'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
{'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
] , )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Any = tmp_path / 'cache'
UpperCAmelCase : List[str] = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
UpperCAmelCase : int = features.copy() if features else default_expected_features
UpperCAmelCase : Tuple = (
Features({feature: Value(SCREAMING_SNAKE_CASE_ ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCAmelCase : Union[str, Any] = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , features=SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize('split' , [None, NamedSplit('train' ), 'train', 'test'] )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : List[Any] = tmp_path / 'cache'
UpperCAmelCase : List[Any] = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
UpperCAmelCase : List[str] = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ , split=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('path_type' , [str, list] )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
if issubclass(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCAmelCase : Union[str, Any] = parquet_path
elif issubclass(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCAmelCase : Dict = [parquet_path]
UpperCAmelCase : Any = tmp_path / 'cache'
UpperCAmelCase : int = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
UpperCAmelCase : int = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_=("train",) ):
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for split in splits:
UpperCAmelCase : Any = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : str = tmp_path / 'cache'
UpperCAmelCase : Dict = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCAmelCase : Any = ParquetDatasetReader(
{'train': parquet_path} , cache_dir=SCREAMING_SNAKE_CASE_ , keep_in_memory=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_datasetdict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize(
'features' , [
None,
{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
{'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
{'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
{'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
] , )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Optional[Any] = tmp_path / 'cache'
UpperCAmelCase : List[Any] = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
UpperCAmelCase : List[Any] = features.copy() if features else default_expected_features
UpperCAmelCase : Tuple = (
Features({feature: Value(SCREAMING_SNAKE_CASE_ ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCAmelCase : Optional[int] = ParquetDatasetReader({'train': parquet_path} , features=SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_datasetdict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize('split' , [None, NamedSplit('train' ), 'train', 'test'] )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
if split:
UpperCAmelCase : Optional[int] = {split: parquet_path}
else:
UpperCAmelCase : Optional[Any] = 'train'
UpperCAmelCase : Optional[Any] = {'train': parquet_path, 'test': parquet_path}
UpperCAmelCase : Union[str, Any] = tmp_path / 'cache'
UpperCAmelCase : List[str] = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
UpperCAmelCase : Union[str, Any] = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_datasetdict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Tuple = ParquetDatasetWriter(SCREAMING_SNAKE_CASE_ , tmp_path / 'foo.parquet' )
assert writer.write() > 0
UpperCAmelCase : Tuple = pq.ParquetFile(tmp_path / 'foo.parquet' )
UpperCAmelCase : Optional[Any] = pf.read()
assert dataset.data.table == output_table
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Tuple = str(shared_datadir / 'test_image_rgb.jpg' )
UpperCAmelCase : Any = {'image': [image_path]}
UpperCAmelCase : Optional[int] = Features({'image': Image()} )
UpperCAmelCase : Tuple = Dataset.from_dict(SCREAMING_SNAKE_CASE_ , features=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase : Optional[int] = ParquetDatasetWriter(SCREAMING_SNAKE_CASE_ , tmp_path / 'foo.parquet' )
assert writer.write() > 0
UpperCAmelCase : Optional[int] = Dataset.from_parquet(str(tmp_path / 'foo.parquet' ) )
assert dataset.features == reloaded_dataset.features
UpperCAmelCase : Tuple = ParquetDatasetReader(str(tmp_path / 'foo.parquet' ) , streaming=SCREAMING_SNAKE_CASE_ ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
'feature, expected' , [
(Features({'foo': Value('int32' )} ), None),
(Features({'image': Image(), 'foo': Value('int32' )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({'nested': Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
assert get_writer_batch_size(SCREAMING_SNAKE_CASE_ ) == expected
| 700 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
return [sentence[i : i + ngram_size] for i in range(len(UpperCAmelCase_ ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 695 | 0 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__lowerCAmelCase : Optional[Any] = datasets.load_iris()
__lowerCAmelCase : int = np.array(data["data"])
__lowerCAmelCase : Any = np.array(data["target"])
__lowerCAmelCase : List[str] = data["target_names"]
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Optional[int] = train_test_split(X, y)
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
return np.linalg.norm(np.array(__lowerCAmelCase ) - np.array(__lowerCAmelCase ) )
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=5 ) -> Union[str, Any]:
__lowercase : str = zip(__lowerCAmelCase , __lowerCAmelCase )
# List of distances of all points from the point to be classified
__lowercase : Dict = []
for data_point in data:
__lowercase : int = euclidean_distance(data_point[0] , __lowerCAmelCase )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
__lowercase : Tuple = [i[1] for i in sorted(__lowerCAmelCase )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
__lowercase : Union[str, Any] = Counter(__lowerCAmelCase ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
| 509 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging
__lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
from transformers import Trainer
class __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
def __init__( self : Tuple , _snake_case : int = 101 ):
__lowercase : Tuple = length
def __len__( self : str ):
return self.length
def __getitem__( self : List[Any] , _snake_case : List[str] ):
return i
class __lowerCAmelCase :
"""simple docstring"""
def __call__( self : Union[str, Any] , _snake_case : List[str] ):
return {"input_ids": torch.tensor(_snake_case ), "labels": torch.tensor(_snake_case )}
class __lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Any ):
super().__init__()
# Add some (unused) params otherwise DDP will complain.
__lowercase : str = nn.Linear(120 , 80 )
def snake_case_ ( self : Tuple , _snake_case : List[str] , _snake_case : Optional[int]=None ):
if labels is not None:
return torch.tensor(0.0 , device=input_ids.device ), input_ids
else:
return input_ids
class __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
@require_torch_neuroncore
def snake_case_ ( self : Tuple ):
__lowercase : List[str] = F'--nproc_per_node=2\n --master_port={get_torch_dist_unique_port()}\n {self.test_file_dir}/test_trainer_distributed.py\n '.split()
__lowercase : Any = self.get_auto_remove_tmp_dir()
__lowercase : List[str] = F'--output_dir {output_dir}'.split()
__lowercase : Optional[Any] = ['''torchrun'''] + distributed_args + args
execute_subprocess_async(_snake_case , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
class __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
@require_torch_multi_gpu
def snake_case_ ( self : str ):
__lowercase : Tuple = F'--nproc_per_node={torch.cuda.device_count()}\n --master_port={get_torch_dist_unique_port()}\n {self.test_file_dir}/test_trainer_distributed.py\n '.split()
__lowercase : Dict = self.get_auto_remove_tmp_dir()
__lowercase : str = F'--output_dir {output_dir}'.split()
__lowercase : Dict = ['''torchrun'''] + distributed_args + args
execute_subprocess_async(_snake_case , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
#
# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py
__lowerCAmelCase : int = HfArgumentParser((TrainingArguments,))
__lowerCAmelCase : int = parser.parse_args_into_dataclasses()[0]
logger.warning(
F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '
F'distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'
)
# Essentially, what we want to verify in the distributed case is that we get all samples back,
# in the right order. (this is crucial for prediction for instance)
for dataset_length in [101, 40, 7]:
__lowerCAmelCase : str = DummyDataset(dataset_length)
def UpperCAmelCase_ ( __lowerCAmelCase ) -> Dict:
__lowercase : Dict = list(range(len(__lowerCAmelCase ) ) )
__lowercase : Optional[Any] = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
if not success and training_args.local_rank == 0:
logger.warning(
'''Predictions and/or labels do not match expected results:\n - predictions: '''
F'{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}' )
return {"success": success}
__lowerCAmelCase : Tuple = Trainer(
model=DummyModel(),
args=training_args,
data_collator=DummyDataCollator(),
eval_dataset=dataset,
compute_metrics=compute_metrics,
)
__lowerCAmelCase : Optional[int] = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
__lowerCAmelCase : Optional[Any] = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
__lowerCAmelCase : Optional[int] = 2
__lowerCAmelCase : str = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
__lowerCAmelCase : Any = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
__lowerCAmelCase : List[str] = None
| 509 | 1 |
"""simple docstring"""
from collections import namedtuple
import requests
from lxml import html # type: ignore
lowerCAmelCase__ = namedtuple('''covid_data''', '''cases deaths recovered''')
def a__ ( _SCREAMING_SNAKE_CASE = "https://www.worldometers.info/coronavirus/" ):
"""simple docstring"""
UpperCamelCase = "//div[@class = \"maincounter-number\"]/span/text()"
return covid_data(*html.fromstring(requests.get(__snake_case ).content ).xpath(__snake_case ) )
lowerCAmelCase__ = '''Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}'''
print(fmt.format(*covid_stats()))
| 705 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = test_file.split(os.path.sep )
if components[0:2] != ["tests", "models"]:
raise ValueError(
"`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got "
F"{test_file} instead." )
UpperCamelCase = components[-1]
if not test_fn.endswith("py" ):
raise ValueError(F"`test_file` should be a python file. Got {test_fn} instead." )
if not test_fn.startswith("test_modeling_" ):
raise ValueError(
F"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead." )
UpperCamelCase = components[:-1] + [test_fn.replace(".py" , "" )]
UpperCamelCase = ".".join(_SCREAMING_SNAKE_CASE )
return test_module_path
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = get_module_path(_SCREAMING_SNAKE_CASE )
UpperCamelCase = importlib.import_module(_SCREAMING_SNAKE_CASE )
return test_module
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = []
UpperCamelCase = get_test_module(_SCREAMING_SNAKE_CASE )
for attr in dir(_SCREAMING_SNAKE_CASE ):
if attr.endswith("ModelTester" ):
tester_classes.append(getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = []
UpperCamelCase = get_test_module(_SCREAMING_SNAKE_CASE )
for attr in dir(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
# `all_model_classes` is not empty (which also excludes other special classes).
UpperCamelCase = getattr(_SCREAMING_SNAKE_CASE , "all_model_classes" , [] )
if len(_SCREAMING_SNAKE_CASE ) > 0:
test_classes.append(_SCREAMING_SNAKE_CASE )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = get_test_classes(_SCREAMING_SNAKE_CASE )
UpperCamelCase = set()
for test_class in test_classes:
model_classes.update(test_class.all_model_classes )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = test_class()
if hasattr(_SCREAMING_SNAKE_CASE , "setUp" ):
test.setUp()
UpperCamelCase = None
if hasattr(_SCREAMING_SNAKE_CASE , "model_tester" ):
# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
if test.model_tester is not None:
UpperCamelCase = test.model_tester.__class__
return model_tester
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = get_test_classes(_SCREAMING_SNAKE_CASE )
UpperCamelCase = []
for test_class in test_classes:
if model_class in test_class.all_model_classes:
target_test_classes.append(_SCREAMING_SNAKE_CASE )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = get_test_classes_for_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = []
for test_class in test_classes:
UpperCamelCase = get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE )
if tester_class is not None:
tester_classes.append(_SCREAMING_SNAKE_CASE )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = get_test_classes(_SCREAMING_SNAKE_CASE )
UpperCamelCase = {test_class: get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE ) for test_class in test_classes}
return test_tester_mapping
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = get_model_classes(_SCREAMING_SNAKE_CASE )
UpperCamelCase = {
model_class: get_test_classes_for_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for model_class in model_classes
}
return model_test_mapping
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = get_model_classes(_SCREAMING_SNAKE_CASE )
UpperCamelCase = {
model_class: get_tester_classes_for_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for model_class in model_classes
}
return model_to_tester_mapping
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return o
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return o.__name__
elif isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) ):
return [to_json(_SCREAMING_SNAKE_CASE ) for x in o]
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return {to_json(_SCREAMING_SNAKE_CASE ): to_json(_SCREAMING_SNAKE_CASE ) for k, v in o.items()}
else:
return o
| 544 | 0 |
'''simple docstring'''
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
lowercase__ = HfArgumentParser(InitializationArguments)
lowercase__ = parser.parse_args()
# Load codeparrot tokenizer trained for Python code tokenization
lowercase__ = AutoTokenizer.from_pretrained(args.tokenizer_name)
# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase__ = {
"vocab_size": len(tokenizer),
"scale_attn_by_inverse_layer_idx": True,
"reorder_and_upcast_attn": True,
}
# Load model config (GPT-2 large in this case)
lowercase__ = AutoConfig.from_pretrained(args.config_name, **config_kwargs)
# Initialize new model with config
lowercase__ = AutoModelForCausalLM.from_config(config)
# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
| 638 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..utils import cached_file
# docstyle-ignore
lowercase__ = "\nHuman: <<task>>\n\nAssistant: "
lowercase__ = "huggingface-tools/default-prompts"
lowercase__ = {"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__="run" ) -> Dict:
'''simple docstring'''
if prompt_or_repo_id is None:
snake_case : Union[str, Any] = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search('''\\s''' , SCREAMING_SNAKE_CASE__ ) is not None:
return prompt_or_repo_id
snake_case : List[Any] = cached_file(
SCREAMING_SNAKE_CASE__ , PROMPT_FILES[mode] , repo_type='''dataset''' , user_agent={'''agent''': agent_name} )
with open(SCREAMING_SNAKE_CASE__ , '''r''' , encoding='''utf-8''' ) as f:
return f.read()
| 638 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( a : list[int | float] , a : int , a : int ):
if len(a ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(a )
or left < -len(a )
or right >= len(a )
or right < -len(a )
):
raise IndexError('list index out of range' )
if left == right:
return nums[left]
a__ = (left + right) >> 1 # the middle
a__ = find_max(a , a , a ) # find max in range[left, mid]
a__ = find_max(a , mid + 1 , a ) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 705 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipImageProcessor,
InstructBlipConfig,
InstructBlipForConditionalGeneration,
InstructBlipProcessor,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
LlamaConfig,
LlamaTokenizerFast,
TaConfig,
TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def lowerCAmelCase_ ( ):
a__ = 'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
a__ = Image.open(requests.get(a , stream=a ).raw ).convert('RGB' )
return image
def lowerCAmelCase_ ( a : int ):
a__ = []
# fmt: off
# vision encoder
rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding') )
rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding') )
rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight') )
rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias') )
rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight') )
rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias') )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.weight''', f'''vision_model.encoder.layers.{i}.layer_norm1.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.bias''', f'''vision_model.encoder.layers.{i}.layer_norm1.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.weight''', f'''vision_model.encoder.layers.{i}.layer_norm2.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.bias''', f'''vision_model.encoder.layers.{i}.layer_norm2.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.attn.qkv.weight''', f'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.weight''', f'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',) )
rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.bias''', f'''vision_model.encoder.layers.{i}.self_attn.projection.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc1.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc1.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc2.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc2.bias''') )
# QFormer
rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight') )
rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias') )
# fmt: on
return rename_keys
def lowerCAmelCase_ ( a : int , a : Dict , a : Optional[Any] ):
a__ = dct.pop(a )
a__ = val
def lowerCAmelCase_ ( a : List[Any] , a : Any ):
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
a__ = state_dict.pop(f'''visual_encoder.blocks.{i}.attn.q_bias''' )
a__ = state_dict.pop(f'''visual_encoder.blocks.{i}.attn.v_bias''' )
# next, set bias in the state dict
a__ = torch.cat((q_bias, torch.zeros_like(a , requires_grad=a ), v_bias) )
a__ = qkv_bias
def lowerCAmelCase_ ( a : List[Any] ):
a__ = 364 if 'coco' in model_name else 224
a__ = InstructBlipVisionConfig(image_size=a ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "t5-xl" in model_name:
a__ = TaConfig.from_pretrained('google/flan-t5-xl' , dense_act_fn='gelu' , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
a__ = TaConfig.from_pretrained('google/flan-t5-xxl' , dense_act_fn='gelu' , bos_token_id=1 ).to_dict()
elif "vicuna-7b" in model_name:
a__ = LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf' , vocab_size=32001 ).to_dict()
elif "vicuna-13b" in model_name:
a__ = LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf' , vocab_size=32001 ).to_dict()
else:
raise ValueError('Model name not supported' )
# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
a__ = InstructBlipQFormerConfig(vocab_size=30523 ).to_dict()
a__ = InstructBlipConfig(vision_config=a , text_config=a , qformer_config=a )
return config, image_size
@torch.no_grad()
def lowerCAmelCase_ ( a : Optional[int] , a : Optional[Any]=None , a : Any=False ):
a__ = AutoTokenizer.from_pretrained('bert-base-uncased' , truncation_side='left' )
qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'} )
if "t5" in model_name:
a__ = TaTokenizerFast.from_pretrained('google/flan-t5-xl' , truncation_side='left' )
elif "vicuna" in model_name:
# the following was used in the original implementation:
# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
# tokenizer.add_special_tokens({"bos_token": "</s>"})
# tokenizer.add_special_tokens({"eos_token": "</s>"})
# tokenizer.add_special_tokens({"unk_token": "</s>"})
a__ = LlamaTokenizerFast.from_pretrained(
'huggyllama/llama-7b' , truncation_side='left' , bos_token='</s>' , unk_token='</s>' )
tokenizer.add_special_tokens({'pad_token': '[PAD]'} )
a__ , a__ = get_blipa_config(a )
a__ = InstructBlipForConditionalGeneration(a ).eval()
a__ = {
'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
}
a__ , a__ = model_name_to_original[model_name]
# load original model
print('Loading original model...' )
a__ = 'cuda:1' if torch.cuda.is_available() else 'cpu'
a__ = 'cuda:2' if torch.cuda.is_available() else 'cpu'
a__ , a__ , a__ = load_model_and_preprocess(
name=a , model_type=a , is_eval=a , device=a )
original_model.eval()
print('Done!' )
# update state dict keys
a__ = original_model.state_dict()
a__ = create_rename_keys(a )
for src, dest in rename_keys:
rename_key(a , a , a )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
a__ = state_dict.pop(a )
if key.startswith('Qformer.bert' ):
a__ = key.replace('Qformer.bert' , 'qformer' )
if "attention.self" in key:
a__ = key.replace('self' , 'attention' )
if "llm_proj" in key:
a__ = key.replace('llm_proj' , 'language_projection' )
if "t5_proj" in key:
a__ = key.replace('t5_proj' , 'language_projection' )
if key.startswith('llm_model' ):
a__ = key.replace('llm_model' , 'language_model' )
if key.startswith('t5' ):
a__ = key.replace('t5' , 'language' )
a__ = val
# read in qv biases
read_in_q_v_bias(a , a )
# note: weights get loaded in torch.float32 by default
hf_model.load_state_dict(a , strict=a )
a__ = load_demo_image()
a__ = 'What is unusual about this image?'
# create processor
a__ = BlipImageProcessor(
size={'height': image_size, 'width': image_size} , image_mean=a , image_std=a )
a__ = InstructBlipProcessor(
image_processor=a , tokenizer=a , qformer_tokenizer=a , )
a__ = processor(images=a , text=a , return_tensors='pt' ).to(a )
# make sure processor creates exact same pixel values
a__ = vis_processors['eval'](a ).unsqueeze(0 ).to(a )
a__ = inputs.pixel_values
assert torch.allclose(original_pixel_values.to(pixel_values.device ) , a )
original_model.to(a )
hf_model.to(a )
with torch.no_grad():
if "vicuna" in model_name:
a__ = original_model({'image': original_pixel_values, 'text_input': [prompt]} ).logits
a__ = hf_model(**a ).logits
else:
a__ = original_model(
{'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']} ).logits
a__ = tokenizer('\n' , return_tensors='pt' ).input_ids.to(a )
a__ = label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id , -100 )
a__ = hf_model(**a , labels=a ).logits
print('First values of original logits:' , original_logits[0, :3, :3] )
print('First values of HF logits:' , logits[0, :3, :3] )
# assert values
assert original_logits.shape == logits.shape
a__ = 1e-4 if 'vicuna' in model_name else 1e-5
assert torch.allclose(original_logits.to(logits.device ) , a , atol=a )
print('Looks ok!' )
print('Generating with original model...' )
a__ = original_model.generate({'image': original_pixel_values, 'prompt': prompt} , num_beams=5 )
# important: we need to cast the weights of the HF model to the appropriate type
print('Generating with HF model...' )
a__ = hf_model.generate(
**a , do_sample=a , num_beams=5 , max_length=256 , min_length=1 , top_p=0.9 , repetition_penalty=1.5 , length_penalty=1.0 , temperature=1 , )
if "vicuna" in model_name:
# convert output id 0 to 2 (eos_token_id)
# TODO add this in the generate method?
a__ = 2
print('Original generation:' , a )
a__ = processor.batch_decode(a , skip_special_tokens=a )
a__ = [text.strip() for text in output_text]
print('HF generation:' , a )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(a )
hf_model.save_pretrained(a )
if push_to_hub:
processor.push_to_hub(f'''Salesforce/{model_name}''' )
hf_model.push_to_hub(f'''Salesforce/{model_name}''' )
if __name__ == "__main__":
__A : List[str] = argparse.ArgumentParser()
__A : Tuple = [
'instructblip-vicuna-7b',
'instructblip-vicuna-13b',
'instructblip-flan-t5-xl',
'instructblip-flan-t5-xxl',
]
parser.add_argument(
'--model_name',
default='instructblip-flan-t5-xl',
choices=choices,
type=str,
help='Path to hf config.json of model to convert',
)
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model and processor to the hub after converting',
)
__A : Union[str, Any] = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 126 | 0 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class lowerCAmelCase_ ( unittest.TestCase ):
def __init__( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=13, SCREAMING_SNAKE_CASE_=7, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=99, SCREAMING_SNAKE_CASE_=32, SCREAMING_SNAKE_CASE_=5, SCREAMING_SNAKE_CASE_=4, SCREAMING_SNAKE_CASE_=37, SCREAMING_SNAKE_CASE_="gelu", SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=512, SCREAMING_SNAKE_CASE_=16, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=0.02, SCREAMING_SNAKE_CASE_=4, ) -> Optional[int]:
UpperCamelCase : Optional[Any] = parent
UpperCamelCase : List[Any] = batch_size
UpperCamelCase : int = seq_length
UpperCamelCase : Union[str, Any] = is_training
UpperCamelCase : Tuple = use_attention_mask
UpperCamelCase : Dict = use_token_type_ids
UpperCamelCase : Tuple = use_labels
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : str = hidden_size
UpperCamelCase : Optional[Any] = num_hidden_layers
UpperCamelCase : List[Any] = num_attention_heads
UpperCamelCase : Any = intermediate_size
UpperCamelCase : Union[str, Any] = hidden_act
UpperCamelCase : List[Any] = hidden_dropout_prob
UpperCamelCase : List[Any] = attention_probs_dropout_prob
UpperCamelCase : str = max_position_embeddings
UpperCamelCase : str = type_vocab_size
UpperCamelCase : List[str] = type_sequence_label_size
UpperCamelCase : Dict = initializer_range
UpperCamelCase : str = num_choices
def snake_case_ ( self ) -> List[str]:
UpperCamelCase : int = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
UpperCamelCase : Dict = None
if self.use_attention_mask:
UpperCamelCase : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Dict = None
if self.use_token_type_ids:
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
UpperCamelCase : Optional[Any] = RobertaPreLayerNormConfig(
vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=SCREAMING_SNAKE_CASE_, initializer_range=self.initializer_range, )
return config, input_ids, token_type_ids, attention_mask
def snake_case_ ( self ) -> List[str]:
UpperCamelCase : Any = self.prepare_config_and_inputs()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : List[Any] = config_and_inputs
UpperCamelCase : List[str] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def snake_case_ ( self ) -> int:
UpperCamelCase : List[str] = self.prepare_config_and_inputs()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : List[Any] = config_and_inputs
UpperCamelCase : Any = True
UpperCamelCase : Dict = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCamelCase : List[str] = ids_tensor([self.batch_size, self.seq_length], vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class lowerCAmelCase_ ( a__ , unittest.TestCase ):
UpperCAmelCase__ : List[str] = True
UpperCAmelCase__ : Union[str, Any] = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def snake_case_ ( self ) -> List[Any]:
UpperCamelCase : List[Any] = FlaxRobertaPreLayerNormModelTester(self )
@slow
def snake_case_ ( self ) -> Dict:
for model_class_name in self.all_model_classes:
UpperCamelCase : Tuple = model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40', from_pt=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = model(np.ones((1, 1) ) )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_flax
class lowerCAmelCase_ ( unittest.TestCase ):
@slow
def snake_case_ ( self ) -> Optional[int]:
UpperCamelCase : Optional[int] = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40', from_pt=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]], dtype=jnp.intaa )
UpperCamelCase : Optional[int] = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[Any] = [1, 11, 5_0265]
self.assertEqual(list(output.shape ), SCREAMING_SNAKE_CASE_ )
# compare the actual values for a slice.
UpperCamelCase : int = np.array(
[[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]], dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3], SCREAMING_SNAKE_CASE_, atol=1e-4 ) )
@slow
def snake_case_ ( self ) -> Optional[int]:
UpperCamelCase : int = FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40', from_pt=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]], dtype=jnp.intaa )
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ )[0]
# compare the actual values for a slice.
UpperCamelCase : List[str] = np.array(
[[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]], dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3], SCREAMING_SNAKE_CASE_, atol=1e-4 ) )
| 40 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__UpperCAmelCase = ["""transformers""", """torch""", """note_seq"""]
def __init__( self : Dict , *snake_case_ : Any , **snake_case_ : List[Any] ):
'''simple docstring'''
requires_backends(self , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def __magic_name__ ( cls : Optional[int] , *snake_case_ : Union[str, Any] , **snake_case_ : List[Any] ):
'''simple docstring'''
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def __magic_name__ ( cls : List[Any] , *snake_case_ : Any , **snake_case_ : int ):
'''simple docstring'''
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
| 347 | 0 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :int , a :List[str] , a :str=1_3 , a :Tuple=7 , a :List[str]=True , a :List[Any]=True , a :Dict=True , a :Any=True , a :int=9_9 , a :Dict=3_2 , a :Tuple=2 , a :Tuple=4 , a :Optional[int]=3_7 , a :Optional[int]="gelu" , a :List[str]=0.1 , a :Union[str, Any]=0.1 , a :List[str]=5_1_2 , a :Tuple=1_6 , a :Tuple=2 , a :Dict=0.02 , a :Optional[int]=3 , a :List[Any]=4 , a :Union[str, Any]=None , ) -> Dict:
__UpperCamelCase : Any = parent
__UpperCamelCase : Tuple = 1_3
__UpperCamelCase : List[Any] = 7
__UpperCamelCase : Any = True
__UpperCamelCase : Optional[int] = True
__UpperCamelCase : Optional[Any] = True
__UpperCamelCase : Optional[Any] = True
__UpperCamelCase : int = 9_9
__UpperCamelCase : Dict = 3_8_4
__UpperCamelCase : List[str] = 2
__UpperCamelCase : Any = 4
__UpperCamelCase : int = 3_7
__UpperCamelCase : int = '''gelu'''
__UpperCamelCase : List[Any] = 0.1
__UpperCamelCase : Any = 0.1
__UpperCamelCase : Dict = 5_1_2
__UpperCamelCase : List[Any] = 1_6
__UpperCamelCase : Optional[int] = 2
__UpperCamelCase : Optional[Any] = 0.02
__UpperCamelCase : Dict = 3
__UpperCamelCase : List[str] = 4
__UpperCamelCase : int = 1_2_8
__UpperCamelCase : List[str] = 2
__UpperCamelCase : int = 9
__UpperCamelCase : Tuple = 1
__UpperCamelCase : str = None
def _lowerCamelCase ( self :List[str] ) -> int:
__UpperCamelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase : Union[str, Any] = None
if self.use_input_mask:
__UpperCamelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
__UpperCamelCase : List[str] = None
if self.use_token_type_ids:
__UpperCamelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__UpperCamelCase : Optional[Any] = None
__UpperCamelCase : Any = None
__UpperCamelCase : Union[str, Any] = None
if self.use_labels:
__UpperCamelCase : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCamelCase : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
__UpperCamelCase : Dict = ConvBertConfig(
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 , return_dict=UpperCAmelCase__ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowerCamelCase ( self :Union[str, Any] , a :Dict , a :int , a :List[Any] , a :str , a :Union[str, Any] , a :int , a :Any ) -> List[Any]:
__UpperCamelCase : Tuple = TFConvBertModel(config=UpperCAmelCase__ )
__UpperCamelCase : List[Any] = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__UpperCamelCase : Dict = [input_ids, input_mask]
__UpperCamelCase : Tuple = model(UpperCAmelCase__ )
__UpperCamelCase : Dict = model(UpperCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCamelCase ( self :str , a :int , a :Any , a :Optional[Any] , a :int , a :List[Any] , a :Dict , a :Optional[Any] ) -> Optional[Any]:
__UpperCamelCase : Dict = TFConvBertForMaskedLM(config=UpperCAmelCase__ )
__UpperCamelCase : str = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
__UpperCamelCase : int = model(UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCamelCase ( self :Dict , a :int , a :Tuple , a :List[Any] , a :Optional[int] , a :List[str] , a :int , a :Any ) -> Dict:
__UpperCamelCase : str = self.num_labels
__UpperCamelCase : Optional[Any] = TFConvBertForSequenceClassification(config=UpperCAmelCase__ )
__UpperCamelCase : Optional[int] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
__UpperCamelCase : Optional[int] = model(UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowerCamelCase ( self :Optional[int] , a :str , a :int , a :Optional[Any] , a :Dict , a :Union[str, Any] , a :Union[str, Any] , a :str ) -> Optional[int]:
__UpperCamelCase : Optional[Any] = self.num_choices
__UpperCamelCase : List[Any] = TFConvBertForMultipleChoice(config=UpperCAmelCase__ )
__UpperCamelCase : Optional[int] = tf.tile(tf.expand_dims(UpperCAmelCase__ , 1 ) , (1, self.num_choices, 1) )
__UpperCamelCase : List[Any] = tf.tile(tf.expand_dims(UpperCAmelCase__ , 1 ) , (1, self.num_choices, 1) )
__UpperCamelCase : Optional[Any] = tf.tile(tf.expand_dims(UpperCAmelCase__ , 1 ) , (1, self.num_choices, 1) )
__UpperCamelCase : str = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
__UpperCamelCase : List[str] = model(UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowerCamelCase ( self :int , a :Tuple , a :Optional[Any] , a :List[str] , a :Tuple , a :Union[str, Any] , a :Tuple , a :Dict ) -> List[Any]:
__UpperCamelCase : Optional[int] = self.num_labels
__UpperCamelCase : Optional[Any] = TFConvBertForTokenClassification(config=UpperCAmelCase__ )
__UpperCamelCase : Optional[Any] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
__UpperCamelCase : Optional[Any] = model(UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowerCamelCase ( self :List[str] , a :int , a :Optional[Any] , a :Dict , a :Union[str, Any] , a :List[Any] , a :Tuple , a :int ) -> Optional[int]:
__UpperCamelCase : int = TFConvBertForQuestionAnswering(config=UpperCAmelCase__ )
__UpperCamelCase : Any = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
__UpperCamelCase : Any = model(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 :Optional[int] ) -> int:
__UpperCamelCase : Any = self.prepare_config_and_inputs()
(
__UpperCamelCase
) : str = config_and_inputs
__UpperCamelCase : Dict = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class lowerCamelCase__ ( lowercase__ , lowercase__ , unittest.TestCase):
'''simple docstring'''
_A = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
_A = (
{
'feature-extraction': TFConvBertModel,
'fill-mask': TFConvBertForMaskedLM,
'question-answering': TFConvBertForQuestionAnswering,
'text-classification': TFConvBertForSequenceClassification,
'token-classification': TFConvBertForTokenClassification,
'zero-shot': TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
_A = False
_A = False
_A = False
def _lowerCamelCase ( self :str ) -> Optional[Any]:
__UpperCamelCase : Tuple = TFConvBertModelTester(self )
__UpperCamelCase : Any = ConfigTester(self , config_class=UpperCAmelCase__ , hidden_size=3_7 )
def _lowerCamelCase ( self :Union[str, Any] ) -> Union[str, Any]:
self.config_tester.run_common_tests()
def _lowerCamelCase ( self :Optional[Any] ) -> Union[str, Any]:
__UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def _lowerCamelCase ( self :List[str] ) -> List[Any]:
__UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__ )
def _lowerCamelCase ( self :List[str] ) -> Tuple:
__UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase__ )
def _lowerCamelCase ( self :Optional[Any] ) -> Dict:
__UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__ )
def _lowerCamelCase ( self :Any ) -> int:
__UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__ )
def _lowerCamelCase ( self :Dict ) -> List[Any]:
__UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__ )
@slow
def _lowerCamelCase ( self :Tuple ) -> List[Any]:
__UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase : List[str] = True
__UpperCamelCase : str = True
if hasattr(UpperCAmelCase__ , "use_cache" ):
__UpperCamelCase : List[Any] = True
__UpperCamelCase : Tuple = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length )
__UpperCamelCase : Dict = getattr(self.model_tester , "key_length" , UpperCAmelCase__ )
for model_class in self.all_model_classes:
__UpperCamelCase : Tuple = self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ )
__UpperCamelCase : List[str] = model_class(UpperCAmelCase__ )
__UpperCamelCase : int = len(model(UpperCAmelCase__ ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(UpperCAmelCase__ , saved_model=UpperCAmelCase__ )
__UpperCamelCase : Optional[int] = os.path.join(UpperCAmelCase__ , "saved_model" , "1" )
__UpperCamelCase : Any = tf.keras.models.load_model(UpperCAmelCase__ )
__UpperCamelCase : int = model(UpperCAmelCase__ )
if self.is_encoder_decoder:
__UpperCamelCase : List[Any] = outputs['''encoder_hidden_states''']
__UpperCamelCase : Optional[Any] = outputs['''encoder_attentions''']
else:
__UpperCamelCase : Dict = outputs['''hidden_states''']
__UpperCamelCase : Union[str, Any] = outputs['''attentions''']
self.assertEqual(len(UpperCAmelCase__ ) , UpperCAmelCase__ )
__UpperCamelCase : Optional[int] = getattr(
self.model_tester , "expected_num_hidden_layers" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(UpperCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def _lowerCamelCase ( self :Any ) -> Optional[int]:
__UpperCamelCase : Union[str, Any] = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
self.assertIsNotNone(UpperCAmelCase__ )
def _lowerCamelCase ( self :Optional[int] ) -> Union[str, Any]:
__UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase : Dict = True
__UpperCamelCase : Optional[int] = getattr(self.model_tester , "decoder_seq_length" , self.model_tester.seq_length )
__UpperCamelCase : Optional[Any] = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length )
__UpperCamelCase : Dict = getattr(self.model_tester , "key_length" , UpperCAmelCase__ )
__UpperCamelCase : List[Any] = getattr(self.model_tester , "key_length" , UpperCAmelCase__ )
def check_decoder_attentions_output(a :Union[str, Any] ):
__UpperCamelCase : str = len(UpperCAmelCase__ )
self.assertEqual(out_len % 2 , 0 )
__UpperCamelCase : Tuple = outputs.decoder_attentions
self.assertEqual(len(UpperCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(a :int ):
__UpperCamelCase : Any = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(UpperCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
__UpperCamelCase : int = True
__UpperCamelCase : str = False
__UpperCamelCase : int = model_class(UpperCAmelCase__ )
__UpperCamelCase : Dict = model(self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ ) )
__UpperCamelCase : Tuple = len(UpperCAmelCase__ )
self.assertEqual(config.output_hidden_states , UpperCAmelCase__ )
check_encoder_attentions_output(UpperCAmelCase__ )
if self.is_encoder_decoder:
__UpperCamelCase : Tuple = model_class(UpperCAmelCase__ )
__UpperCamelCase : Union[str, Any] = model(self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ ) )
self.assertEqual(config.output_hidden_states , UpperCAmelCase__ )
check_decoder_attentions_output(UpperCAmelCase__ )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
__UpperCamelCase : Optional[int] = True
__UpperCamelCase : Optional[Any] = model_class(UpperCAmelCase__ )
__UpperCamelCase : List[Any] = model(self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ ) )
self.assertEqual(config.output_hidden_states , UpperCAmelCase__ )
check_encoder_attentions_output(UpperCAmelCase__ )
# Check attention is always last and order is fine
__UpperCamelCase : Any = True
__UpperCamelCase : Dict = True
__UpperCamelCase : Optional[Any] = model_class(UpperCAmelCase__ )
__UpperCamelCase : Any = model(self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(UpperCAmelCase__ ) )
self.assertEqual(model.config.output_hidden_states , UpperCAmelCase__ )
check_encoder_attentions_output(UpperCAmelCase__ )
@require_tf
class lowerCamelCase__ ( unittest.TestCase):
'''simple docstring'''
@slow
def _lowerCamelCase ( self :Optional[Any] ) -> Optional[Any]:
__UpperCamelCase : List[str] = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
__UpperCamelCase : Optional[Any] = tf.constant([[0, 1, 2, 3, 4, 5]] )
__UpperCamelCase : List[str] = model(UpperCAmelCase__ )[0]
__UpperCamelCase : Any = [1, 6, 7_6_8]
self.assertEqual(output.shape , UpperCAmelCase__ )
__UpperCamelCase : int = tf.constant(
[
[
[-0.03475493, -0.4686034, -0.30638832],
[0.22637248, -0.26988646, -0.7423424],
[0.10324868, -0.45013508, -0.58280784],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , UpperCAmelCase__ , atol=1E-4 )
| 700 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> None:
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase : Optional[Any] = analyze_text(_lowerCamelCase)
__UpperCamelCase : List[str] = list(" " + ascii_lowercase)
# what is our total sum of probabilities.
__UpperCamelCase : Any = sum(single_char_strings.values())
# one length string
__UpperCamelCase : Optional[Any] = 0
# for each alpha we go in our dict and if it is in it we calculate entropy
for ch in my_alphas:
if ch in single_char_strings:
__UpperCamelCase : List[Any] = single_char_strings[ch]
__UpperCamelCase : List[str] = my_str / all_sum
my_fir_sum += prob * math.loga(_lowerCamelCase) # entropy formula.
# print entropy
print(F'{round(-1 * my_fir_sum):.1f}')
# two len string
__UpperCamelCase : Optional[Any] = sum(two_char_strings.values())
__UpperCamelCase : Tuple = 0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
__UpperCamelCase : List[str] = cha + cha
if sequence in two_char_strings:
__UpperCamelCase : Optional[Any] = two_char_strings[sequence]
__UpperCamelCase : Any = int(_lowerCamelCase) / all_sum
my_sec_sum += prob * math.loga(_lowerCamelCase)
# print second entropy
print(F'{round(-1 * my_sec_sum):.1f}')
# print the difference between them
print(F'{round((-1 * my_sec_sum) - (-1 * my_fir_sum)):.1f}')
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> tuple[dict, dict]:
'''simple docstring'''
__UpperCamelCase : Tuple = Counter() # type: ignore
__UpperCamelCase : Any = Counter() # type: ignore
single_char_strings[text[-1]] += 1
# first case when we have space at start.
two_char_strings[" " + text[0]] += 1
for i in range(0 , len(_lowerCamelCase) - 1):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def _SCREAMING_SNAKE_CASE ( ) -> List[str]:
'''simple docstring'''
import doctest
doctest.testmod()
# text = (
# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
# "jointure saw horrible. He private he on be imagine suppose. Fertile "
# "beloved evident through no service elderly is. Blind there if every no so "
# "at. Own neglected you preferred way sincerity delivered his attempted. To "
# "of message cottage windows do besides against uncivil. Delightful "
# "unreserved impossible few estimating men favourable see entreaties. She "
# "propriety immediate was improving. He or entrance humoured likewise "
# "moderate. Much nor game son say feel. Fat make met can must form into "
# "gate. Me we offending prevailed discovery. "
# )
# calculate_prob(text)
if __name__ == "__main__":
main() | 94 | 0 |
"""simple docstring"""
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
__lowercase : List[Any] = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase )
class _A ( _UpperCAmelCase ):
"""simple docstring"""
def __init__( self : List[Any] , *A_ : List[str] , **A_ : Dict ) -> List[Any]:
super().__init__(*A_ , **A_ )
requires_backends(self , '''decord''' )
self.check_model_type(A_ )
def lowercase ( self : int , A_ : Tuple=None , A_ : Optional[Any]=None , A_ : Optional[int]=None ) -> Optional[Any]:
__snake_case = {}
if frame_sampling_rate is not None:
__snake_case = frame_sampling_rate
if num_frames is not None:
__snake_case = num_frames
__snake_case = {}
if top_k is not None:
__snake_case = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : str , A_ : Union[str, List[str]] , **A_ : List[str] ) -> List[Any]:
return super().__call__(A_ , **A_ )
def lowercase ( self : str , A_ : List[Any] , A_ : List[Any]=None , A_ : Union[str, Any]=1 ) -> Tuple:
if num_frames is None:
__snake_case = self.model.config.num_frames
if video.startswith('''http://''' ) or video.startswith('''https://''' ):
__snake_case = BytesIO(requests.get(A_ ).content )
__snake_case = VideoReader(A_ )
videoreader.seek(0 )
__snake_case = 0
__snake_case = num_frames * frame_sampling_rate - 1
__snake_case = np.linspace(A_ , A_ , num=A_ , dtype=np.intaa )
__snake_case = videoreader.get_batch(A_ ).asnumpy()
__snake_case = list(A_ )
__snake_case = self.image_processor(A_ , return_tensors=self.framework )
return model_inputs
def lowercase ( self : Union[str, Any] , A_ : List[Any] ) -> Union[str, Any]:
__snake_case = self.model(**A_ )
return model_outputs
def lowercase ( self : Tuple , A_ : int , A_ : Dict=5 ) -> List[str]:
if top_k > self.model.config.num_labels:
__snake_case = self.model.config.num_labels
if self.framework == "pt":
__snake_case = model_outputs.logits.softmax(-1 )[0]
__snake_case , __snake_case = probs.topk(A_ )
else:
raise ValueError(f"Unsupported framework: {self.framework}" )
__snake_case = scores.tolist()
__snake_case = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(A_ , A_ )] | 564 | """simple docstring"""
from math import sqrt
def SCREAMING_SNAKE_CASE ( snake_case = 1_00_00_00):
__snake_case = 0
__snake_case = 0
__snake_case = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2, 2 * max_cuboid_size + 1):
if sqrt(sum_shortest_sides**2 + max_cuboid_size**2).is_integer():
num_cuboids += (
min(snake_case, sum_shortest_sides // 2)
- max(1, sum_shortest_sides - max_cuboid_size)
+ 1
)
return max_cuboid_size
if __name__ == "__main__":
print(F"""{solution() = }""") | 564 | 1 |
'''simple docstring'''
from PIL import Image
def __magic_name__( lowerCamelCase, lowerCamelCase):
def brightness(lowerCamelCase) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -2_55.0 <= level <= 2_55.0:
raise ValueError('''level must be between -255.0 (black) and 255.0 (white)''')
return img.point(lowerCamelCase)
if __name__ == "__main__":
# Load image
with Image.open("""image_data/lena.jpg""") as img:
# Change brightness to 100
_UpperCAmelCase : str = change_brightness(img, 1_0_0)
brigt_img.save("""image_data/lena_brightness.png""", format="""png""")
| 474 |
'''simple docstring'''
from torch import nn
def __magic_name__( lowerCamelCase):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"""Unsupported activation function: {act_fn}""")
| 474 | 1 |
import json
import sys
def UpperCamelCase ( snake_case__ : Optional[Any] , snake_case__ : Dict ) -> Dict:
with open(snake_case__ , encoding='utf-8' ) as f:
UpperCamelCase : Optional[Any] = json.load(snake_case__ )
UpperCamelCase : int = ['<details>', '<summary>Show updated benchmarks!</summary>', ' ']
for benchmark_name in sorted(snake_case__ ):
UpperCamelCase : List[str] = results[benchmark_name]
UpperCamelCase : Tuple = benchmark_name.split('/' )[-1]
output_md.append(F"""### Benchmark: {benchmark_file_name}""" )
UpperCamelCase : Optional[Any] = '| metric |'
UpperCamelCase : List[Any] = '|--------|'
UpperCamelCase : str = '| new / old (diff) |'
for metric_name in sorted(snake_case__ ):
UpperCamelCase : Dict = benchmark_res[metric_name]
UpperCamelCase : str = metric_vals['new']
UpperCamelCase : Optional[int] = metric_vals.get('old' , snake_case__ )
UpperCamelCase : Optional[Any] = metric_vals.get('diff' , snake_case__ )
UpperCamelCase : Union[str, Any] = F""" {new_val:f}""" if isinstance(snake_case__ , (int, float) ) else 'None'
if old_val is not None:
val_str += F""" / {old_val:f}""" if isinstance(snake_case__ , (int, float) ) else "None"
if dif_val is not None:
val_str += F""" ({dif_val:f})""" if isinstance(snake_case__ , (int, float) ) else "None"
title += " " + metric_name + " |"
lines += "---|"
value += val_str + " |"
output_md += [title, lines, value, " "]
output_md.append('</details>' )
with open(snake_case__ , 'w' , encoding='utf-8' ) as f:
f.writelines('\n'.join(snake_case__ ) )
if __name__ == "__main__":
__UpperCAmelCase = sys.argv[1]
__UpperCAmelCase = sys.argv[2]
format_json_to_md(input_json_file, output_md_file)
| 40 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
__snake_case = 1
__snake_case = 1
while repunit:
__snake_case = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def _UpperCamelCase (_lowerCamelCase : int = 1_00_00_00 )-> int:
'''simple docstring'''
__snake_case = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(_lowerCamelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(F"""{solution() = }""")
| 24 | 0 |
"""simple docstring"""
import numpy as np
def __UpperCAmelCase ( _snake_case : np.ndarray, _snake_case : float ):
return np.where(vector > 0, _snake_case, (alpha * (np.exp(_snake_case ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 227 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : Tuple = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig",
"PoolFormerOnnxConfig",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : str = ["PoolFormerFeatureExtractor"]
__UpperCamelCase : List[str] = ["PoolFormerImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Tuple = [
"POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"PoolFormerForImageClassification",
"PoolFormerModel",
"PoolFormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_poolformer import (
POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
PoolFormerConfig,
PoolFormerOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_poolformer import PoolFormerFeatureExtractor
from .image_processing_poolformer import PoolFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_poolformer import (
POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
PoolFormerForImageClassification,
PoolFormerModel,
PoolFormerPreTrainedModel,
)
else:
import sys
__UpperCamelCase : Any = _LazyModule(__name__, globals()["__file__"], _import_structure) | 227 | 1 |
import os
from pathlib import Path
def __magic_name__ ( ):
'''simple docstring'''
from torch.utils.cpp_extension import load
UpperCamelCase__ = Path(__a ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
UpperCamelCase__ = [
root / filename
for filename in [
"""vision.cpp""",
os.path.join("""cpu""" , """ms_deform_attn_cpu.cpp""" ),
os.path.join("""cuda""" , """ms_deform_attn_cuda.cu""" ),
]
]
load(
"""MultiScaleDeformableAttention""" , __a , with_cuda=__a , extra_include_paths=[str(__a )] , extra_cflags=["""-DWITH_CUDA=1"""] , extra_cuda_cflags=[
"""-DCUDA_HAS_FP16=1""",
"""-D__CUDA_NO_HALF_OPERATORS__""",
"""-D__CUDA_NO_HALF_CONVERSIONS__""",
"""-D__CUDA_NO_HALF2_OPERATORS__""",
] , )
import MultiScaleDeformableAttention as MSDA
return MSDA
| 513 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase=False ) -> List[Any]:
try:
snake_case : Tuple = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
snake_case : Tuple = default
else:
# KEY is set, convert it to True or False.
try:
snake_case : Tuple = strtobool(lowercase )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(f"""If set, {key} must be yes or no.""" )
return _value
lowerCamelCase : Tuple = parse_flag_from_env('RUN_SLOW', default=False)
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Optional[int]:
return unittest.skip("""Test was skipped""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Union[str, Any]:
return unittest.skipUnless(_run_slow_tests ,"""test is slow""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Any:
return unittest.skipUnless(not torch.cuda.is_available() ,"""test requires only a CPU""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Union[str, Any]:
return unittest.skipUnless(torch.cuda.is_available() ,"""test requires a GPU""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Optional[int]:
return unittest.skipUnless(is_xpu_available() ,"""test requires a XPU""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Optional[Any]:
return unittest.skipUnless(is_mps_available() ,"""test requires a `mps` backend support in `torch`""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Union[str, Any]:
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() ,"""test requires the Hugging Face suite""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Optional[Any]:
return unittest.skipUnless(is_bnb_available() ,"""test requires the bitsandbytes library""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Optional[int]:
return unittest.skipUnless(is_tpu_available() ,"""test requires TPU""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Any:
return unittest.skipUnless(torch.cuda.device_count() == 1 ,"""test requires a GPU""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> List[str]:
return unittest.skipUnless(torch.xpu.device_count() == 1 ,"""test requires a XPU""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Dict:
return unittest.skipUnless(torch.cuda.device_count() > 1 ,"""test requires multiple GPUs""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> List[Any]:
return unittest.skipUnless(torch.xpu.device_count() > 1 ,"""test requires multiple XPUs""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Optional[Any]:
return unittest.skipUnless(is_safetensors_available() ,"""test requires safetensors""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Any:
return unittest.skipUnless(is_deepspeed_available() ,"""test requires DeepSpeed""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Union[str, Any]:
return unittest.skipUnless(is_torch_version(""">=""" ,"""1.12.0""" ) ,"""test requires torch version >= 1.12.0""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase=None ,lowercase=None ) -> Optional[int]:
if test_case is None:
return partial(lowercase ,version=lowercase )
return unittest.skipUnless(is_torch_version(""">=""" ,lowercase ) ,f"""test requires torch version >= {version}""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Tuple:
return unittest.skipUnless(is_tensorboard_available() ,"""test requires Tensorboard""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Union[str, Any]:
return unittest.skipUnless(is_wandb_available() ,"""test requires wandb""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Union[str, Any]:
return unittest.skipUnless(is_comet_ml_available() ,"""test requires comet_ml""" )(lowercase )
lowerCamelCase : Union[str, Any] = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Optional[int]:
return unittest.skipUnless(
_atleast_one_tracker_available ,"""test requires at least one tracker to be available and for `comet_ml` to not be installed""" ,)(lowercase )
class __lowercase (unittest.TestCase ):
"""simple docstring"""
_snake_case = True
@classmethod
def UpperCAmelCase ( cls ) -> int:
snake_case : int = tempfile.mkdtemp()
@classmethod
def UpperCAmelCase ( cls ) -> str:
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def UpperCAmelCase ( self ) -> Tuple:
if self.clear_on_setup:
for path in Path(self.tmpdir ).glob("""**/*""" ):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(A )
class __lowercase (unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> Optional[Any]:
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class __lowercase (unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self , A ) -> Union[str, Any]:
snake_case : List[str] = mocks if isinstance(A , (tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> str:
snake_case : Optional[int] = AcceleratorState()
snake_case : int = tensor[None].clone().to(state.device )
snake_case : Dict = gather(lowercase ).cpu()
snake_case : str = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] ,lowercase ):
return False
return True
class __lowercase :
"""simple docstring"""
def __init__( self , A , A , A ) -> Optional[int]:
snake_case : Tuple = returncode
snake_case : str = stdout
snake_case : int = stderr
async def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> str:
while True:
snake_case : Any = await stream.readline()
if line:
callback(lowercase )
else:
break
async def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase=None ,lowercase=None ,lowercase=None ,lowercase=False ,lowercase=False ) -> _RunOutput:
if echo:
print("""\nRunning: """ ,""" """.join(lowercase ) )
snake_case : Optional[int] = await asyncio.create_subprocess_exec(
cmd[0] ,*cmd[1:] ,stdin=lowercase ,stdout=asyncio.subprocess.PIPE ,stderr=asyncio.subprocess.PIPE ,env=lowercase ,)
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
snake_case : Dict = []
snake_case : Union[str, Any] = []
def tee(lowercase ,lowercase ,lowercase ,lowercase="" ):
snake_case : str = line.decode("""utf-8""" ).rstrip()
sink.append(lowercase )
if not quiet:
print(lowercase ,lowercase ,file=lowercase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout ,lambda lowercase : tee(lowercase ,lowercase ,sys.stdout ,label="""stdout:""" ) ) ),
asyncio.create_task(_read_stream(p.stderr ,lambda lowercase : tee(lowercase ,lowercase ,sys.stderr ,label="""stderr:""" ) ) ),
] ,timeout=lowercase ,)
return _RunOutput(await p.wait() ,lowercase ,lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase=None ,lowercase=None ,lowercase=180 ,lowercase=False ,lowercase=True ) -> _RunOutput:
snake_case : str = asyncio.get_event_loop()
snake_case : Union[str, Any] = loop.run_until_complete(
_stream_subprocess(lowercase ,env=lowercase ,stdin=lowercase ,timeout=lowercase ,quiet=lowercase ,echo=lowercase ) )
snake_case : List[str] = """ """.join(lowercase )
if result.returncode > 0:
snake_case : List[Any] = """\n""".join(result.stderr )
raise RuntimeError(
f"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
f"""The combined stderr from workers follows:\n{stderr}""" )
return result
class __lowercase (UpperCamelCase__ ):
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase=False ) -> List[str]:
try:
snake_case : List[str] = subprocess.check_output(lowercase ,stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(lowercase ,"""decode""" ):
snake_case : List[str] = output.decode("""utf-8""" )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
f"""Command `{" ".join(lowercase )}` failed with the following error:\n\n{e.output.decode()}""" ) from e
| 587 | 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_ = logging.get_logger(__name__)
a_ = {
'facebook/data2vec-vision-base-ft': (
'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
),
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='data2vec-vision'
def __init__( self : Tuple , a : Any=768 , a : Dict=12 , a : Tuple=12 , a : Any=3072 , a : Any="gelu" , a : Dict=0.0 , a : Optional[int]=0.0 , a : Union[str, Any]=0.02 , a : List[Any]=1e-12 , a : str=224 , a : List[Any]=16 , a : str=3 , a : Union[str, Any]=False , a : List[str]=False , a : int=False , a : Any=False , a : int=0.1 , a : Any=0.1 , a : Optional[Any]=True , a : Union[str, Any]=[3, 5, 7, 11] , a : Any=[1, 2, 3, 6] , a : str=True , a : int=0.4 , a : Optional[int]=256 , a : Tuple=1 , a : Dict=False , a : str=255 , **a : Tuple , ) -> Dict:
"""simple docstring"""
super().__init__(**a )
SCREAMING_SNAKE_CASE : int = hidden_size
SCREAMING_SNAKE_CASE : Any = num_hidden_layers
SCREAMING_SNAKE_CASE : str = num_attention_heads
SCREAMING_SNAKE_CASE : int = intermediate_size
SCREAMING_SNAKE_CASE : List[str] = hidden_act
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : List[str] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : List[Any] = initializer_range
SCREAMING_SNAKE_CASE : List[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE : List[str] = image_size
SCREAMING_SNAKE_CASE : Optional[int] = patch_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE : Optional[Any] = use_mask_token
SCREAMING_SNAKE_CASE : List[Any] = use_absolute_position_embeddings
SCREAMING_SNAKE_CASE : Dict = use_relative_position_bias
SCREAMING_SNAKE_CASE : Optional[Any] = use_shared_relative_position_bias
SCREAMING_SNAKE_CASE : Any = layer_scale_init_value
SCREAMING_SNAKE_CASE : Union[str, Any] = drop_path_rate
SCREAMING_SNAKE_CASE : Tuple = use_mean_pooling
# decode head attributes (semantic segmentation)
SCREAMING_SNAKE_CASE : Any = out_indices
SCREAMING_SNAKE_CASE : int = pool_scales
# auxiliary head attributes (semantic segmentation)
SCREAMING_SNAKE_CASE : List[Any] = use_auxiliary_head
SCREAMING_SNAKE_CASE : Optional[Any] = auxiliary_loss_weight
SCREAMING_SNAKE_CASE : str = auxiliary_channels
SCREAMING_SNAKE_CASE : Optional[int] = auxiliary_num_convs
SCREAMING_SNAKE_CASE : Optional[Any] = auxiliary_concat_input
SCREAMING_SNAKE_CASE : Union[str, Any] = semantic_loss_ignore_index
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =version.parse('1.11' )
@property
def __UpperCamelCase ( self : List[str] ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def __UpperCamelCase ( self : Any ) -> float:
"""simple docstring"""
return 1e-4 | 709 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='gptj'
lowerCamelCase__ ={
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self : Dict , a : Optional[Any]=5_0400 , a : List[str]=2048 , a : List[Any]=4096 , a : int=28 , a : Union[str, Any]=16 , a : List[Any]=64 , a : int=None , a : Optional[int]="gelu_new" , a : Optional[Any]=0.0 , a : Any=0.0 , a : Union[str, Any]=0.0 , a : Union[str, Any]=1e-5 , a : Any=0.02 , a : Optional[int]=True , a : Tuple=5_0256 , a : Union[str, Any]=5_0256 , a : List[Any]=False , **a : str , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = vocab_size
SCREAMING_SNAKE_CASE : int = n_positions
SCREAMING_SNAKE_CASE : Tuple = n_embd
SCREAMING_SNAKE_CASE : Tuple = n_layer
SCREAMING_SNAKE_CASE : List[Any] = n_head
SCREAMING_SNAKE_CASE : Tuple = n_inner
SCREAMING_SNAKE_CASE : Any = rotary_dim
SCREAMING_SNAKE_CASE : str = activation_function
SCREAMING_SNAKE_CASE : int = resid_pdrop
SCREAMING_SNAKE_CASE : Optional[int] = embd_pdrop
SCREAMING_SNAKE_CASE : Tuple = attn_pdrop
SCREAMING_SNAKE_CASE : List[str] = layer_norm_epsilon
SCREAMING_SNAKE_CASE : int = initializer_range
SCREAMING_SNAKE_CASE : Tuple = use_cache
SCREAMING_SNAKE_CASE : Union[str, Any] = bos_token_id
SCREAMING_SNAKE_CASE : List[Any] = eos_token_id
super().__init__(
bos_token_id=a , eos_token_id=a , tie_word_embeddings=a , **a )
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : Optional[int] , a : PretrainedConfig , a : str = "default" , a : List[PatchingSpec] = None , a : bool = False , ) -> Any:
"""simple docstring"""
super().__init__(a , task=a , patching_specs=a , use_past=a )
if not getattr(self._config , "pad_token_id" , a ):
# TODO: how to do that better?
SCREAMING_SNAKE_CASE : Dict = 0
@property
def __UpperCamelCase ( self : Any ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
self.fill_with_past_key_values_(a , direction="inputs" )
SCREAMING_SNAKE_CASE : int = {0: "batch", 1: "past_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE : Any = {0: "batch", 1: "sequence"}
return common_inputs
@property
def __UpperCamelCase ( self : Any ) -> int:
"""simple docstring"""
return self._config.n_layer
@property
def __UpperCamelCase ( self : str ) -> int:
"""simple docstring"""
return self._config.n_head
def __UpperCamelCase ( self : str , a : PreTrainedTokenizer , a : int = -1 , a : int = -1 , a : bool = False , a : Optional[TensorType] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = super(a , self ).generate_dummy_inputs(
a , batch_size=a , seq_length=a , is_pair=a , framework=a )
# We need to order the input in the way they appears in the forward()
SCREAMING_SNAKE_CASE : Tuple = OrderedDict({"input_ids": common_inputs["input_ids"]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[Any] = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE : Any = seqlen + 2
SCREAMING_SNAKE_CASE : Dict = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
SCREAMING_SNAKE_CASE : str = [
(torch.zeros(a ), torch.zeros(a )) for _ in range(self.num_layers )
]
SCREAMING_SNAKE_CASE : Optional[int] = common_inputs["attention_mask"]
if self.use_past:
SCREAMING_SNAKE_CASE : List[str] = ordered_inputs["attention_mask"].dtype
SCREAMING_SNAKE_CASE : Any = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(a , a , dtype=a )] , dim=1 )
return ordered_inputs
@property
def __UpperCamelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
return 13 | 193 | 0 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Union[dict, list, tuple, torch.Tensor] ) -> List[Tuple[int, ...]]:
UpperCAmelCase_ = []
if isinstance(__UpperCamelCase , __UpperCamelCase ):
for v in tree.values():
shapes.extend(_fetch_dims(__UpperCamelCase ) )
elif isinstance(__UpperCamelCase , (list, tuple) ):
for t in tree:
shapes.extend(_fetch_dims(__UpperCamelCase ) )
elif isinstance(__UpperCamelCase , torch.Tensor ):
shapes.append(tree.shape )
else:
raise ValueError('''Not supported''' )
return shapes
@torch.jit.ignore
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int , __UpperCamelCase : Tuple[int, ...] ) -> Tuple[int, ...]:
UpperCAmelCase_ = []
for d in reversed(__UpperCamelCase ):
idx.append(flat_idx % d )
UpperCAmelCase_ = flat_idx // d
return tuple(reversed(__UpperCamelCase ) )
@torch.jit.ignore
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Sequence[int] , __UpperCamelCase : Sequence[int] , __UpperCamelCase : Sequence[int] , __UpperCamelCase : Optional[Sequence[bool]] = None , __UpperCamelCase : Optional[Sequence[bool]] = None , ) -> List[Tuple[slice, ...]]:
def reduce_edge_list(__UpperCamelCase : List[bool] ) -> None:
UpperCAmelCase_ = True
for i in range(len(__UpperCamelCase ) ):
UpperCAmelCase_ = -1 * (i + 1)
l[reversed_idx] &= tally
UpperCAmelCase_ = l[reversed_idx]
if start_edges is None:
UpperCAmelCase_ = [s == 0 for s in start]
reduce_edge_list(__UpperCamelCase )
if end_edges is None:
UpperCAmelCase_ = [e == (d - 1) for e, d in zip(__UpperCamelCase , __UpperCamelCase )]
reduce_edge_list(__UpperCamelCase )
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(__UpperCamelCase ) == 0:
return [()]
elif len(__UpperCamelCase ) == 1:
return [(slice(start[0] , end[0] + 1 ),)]
UpperCAmelCase_ = []
UpperCAmelCase_ = []
# Dimensions common to start and end can be selected directly
for s, e in zip(__UpperCamelCase , __UpperCamelCase ):
if s == e:
path_list.append(slice(__UpperCamelCase , s + 1 ) )
else:
break
UpperCAmelCase_ = tuple(__UpperCamelCase )
UpperCAmelCase_ = len(__UpperCamelCase )
# start == end, and we're done
if divergence_idx == len(__UpperCamelCase ):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
UpperCAmelCase_ = start[divergence_idx]
return tuple(
path + (slice(__UpperCamelCase , sdi + 1 ),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :] , [d - 1 for d in dims[divergence_idx + 1 :]] , dims[divergence_idx + 1 :] , start_edges=start_edges[divergence_idx + 1 :] , end_edges=[True for _ in end_edges[divergence_idx + 1 :]] , ) )
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
UpperCAmelCase_ = end[divergence_idx]
return tuple(
path + (slice(__UpperCamelCase , edi + 1 ),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]] , end[divergence_idx + 1 :] , dims[divergence_idx + 1 :] , start_edges=[True for _ in start_edges[divergence_idx + 1 :]] , end_edges=end_edges[divergence_idx + 1 :] , ) )
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] + 1 ),) )
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] ),) )
slices.extend(lower() )
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper() )
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] + 1 ),) )
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper() )
UpperCAmelCase_ = end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] ),) )
slices.extend(lower() )
return slices
@torch.jit.ignore
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : torch.Tensor , __UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : int ) -> torch.Tensor:
UpperCAmelCase_ = t.shape[:no_batch_dims]
UpperCAmelCase_ = list(_flat_idx_to_idx(__UpperCamelCase , __UpperCamelCase ) )
# _get_minimal_slice_set is inclusive
UpperCAmelCase_ = list(_flat_idx_to_idx(flat_end - 1 , __UpperCamelCase ) )
# Get an ordered list of slices to perform
UpperCAmelCase_ = _get_minimal_slice_set(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )
UpperCAmelCase_ = [t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Callable , __UpperCamelCase : Dict[str, Any] , __UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : bool = False , __UpperCamelCase : Any = None , __UpperCamelCase : bool = False , ) -> Any:
if not (len(__UpperCamelCase ) > 0):
raise ValueError('''Must provide at least one input''' )
UpperCAmelCase_ = [shape[:no_batch_dims] for shape in _fetch_dims(__UpperCamelCase )]
UpperCAmelCase_ = tuple([max(__UpperCamelCase ) for s in zip(*__UpperCamelCase )] )
def _prep_inputs(__UpperCamelCase : torch.Tensor ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
UpperCAmelCase_ = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
UpperCAmelCase_ = t.reshape(-1 , *t.shape[no_batch_dims:] )
else:
UpperCAmelCase_ = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
UpperCAmelCase_ = tensor_tree_map(_prep_inputs , __UpperCamelCase )
UpperCAmelCase_ = None
if _out is not None:
UpperCAmelCase_ = tensor_tree_map(lambda __UpperCamelCase : t.view([-1] + list(t.shape[no_batch_dims:] ) ) , _out )
UpperCAmelCase_ = 1
for d in orig_batch_dims:
flat_batch_dim *= d
UpperCAmelCase_ = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(__UpperCamelCase : torch.Tensor ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
UpperCAmelCase_ = 0
UpperCAmelCase_ = prepped_outputs
for _ in range(__UpperCamelCase ):
# Chunk the input
if not low_mem:
UpperCAmelCase_ = _select_chunk
else:
UpperCAmelCase_ = partial(
_chunk_slice , flat_start=__UpperCamelCase , flat_end=min(__UpperCamelCase , i + chunk_size ) , no_batch_dims=len(__UpperCamelCase ) , )
UpperCAmelCase_ = tensor_tree_map(__UpperCamelCase , __UpperCamelCase )
# Run the layer on the chunk
UpperCAmelCase_ = layer(**__UpperCamelCase )
# Allocate space for the output
if out is None:
UpperCAmelCase_ = tensor_tree_map(lambda __UpperCamelCase : t.new_zeros((flat_batch_dim,) + t.shape[1:] ) , __UpperCamelCase )
# Put the chunk in its pre-allocated space
if isinstance(__UpperCamelCase , __UpperCamelCase ):
def assign(__UpperCamelCase : dict , __UpperCamelCase : dict ) -> None:
for k, v in da.items():
if isinstance(__UpperCamelCase , __UpperCamelCase ):
assign(__UpperCamelCase , da[k] )
else:
if _add_into_out:
v[i : i + chunk_size] += da[k]
else:
UpperCAmelCase_ = da[k]
assign(__UpperCamelCase , __UpperCamelCase )
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
for xa, xa in zip(__UpperCamelCase , __UpperCamelCase ):
if _add_into_out:
xa[i : i + chunk_size] += xa
else:
UpperCAmelCase_ = xa
elif isinstance(__UpperCamelCase , torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
UpperCAmelCase_ = output_chunk
else:
raise ValueError('''Not supported''' )
i += chunk_size
UpperCAmelCase_ = tensor_tree_map(lambda __UpperCamelCase : t.view(orig_batch_dims + t.shape[1:] ) , __UpperCamelCase )
return out
class a :
'''simple docstring'''
def __init__( self : Any , __snake_case : Any = 5_12 , ):
UpperCAmelCase_ = max_chunk_size
UpperCAmelCase_ = None
UpperCAmelCase_ = None
def lowerCamelCase_ ( self : Any , __snake_case : List[Any] , __snake_case : Dict , __snake_case : Optional[int] ):
logging.info('''Tuning chunk size...''' )
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
UpperCAmelCase_ = [2**l for l in range(int(math.log(self.max_chunk_size , 2 ) ) + 1 )]
UpperCAmelCase_ = [c for c in candidates if c > min_chunk_size]
UpperCAmelCase_ = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(__snake_case : List[str] ) -> bool:
try:
with torch.no_grad():
fn(*_lowerCamelCase , chunk_size=_lowerCamelCase )
return True
except RuntimeError:
return False
UpperCAmelCase_ = 0
UpperCAmelCase_ = len(_lowerCamelCase ) - 1
while i > min_viable_chunk_size_index:
UpperCAmelCase_ = test_chunk_size(candidates[i] )
if not viable:
UpperCAmelCase_ = (min_viable_chunk_size_index + i) // 2
else:
UpperCAmelCase_ = i
UpperCAmelCase_ = (i + len(_lowerCamelCase ) - 1) // 2
return candidates[min_viable_chunk_size_index]
def lowerCamelCase_ ( self : List[Any] , __snake_case : List[Any] , __snake_case : int ):
UpperCAmelCase_ = True
for aa, aa in zip(_lowerCamelCase , _lowerCamelCase ):
assert type(_lowerCamelCase ) == type(_lowerCamelCase )
if isinstance(_lowerCamelCase , (list, tuple) ):
consistent &= self._compare_arg_caches(_lowerCamelCase , _lowerCamelCase )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
UpperCAmelCase_ = [v for _, v in sorted(aa.items() , key=lambda __snake_case : x[0] )]
UpperCAmelCase_ = [v for _, v in sorted(aa.items() , key=lambda __snake_case : x[0] )]
consistent &= self._compare_arg_caches(_lowerCamelCase , _lowerCamelCase )
else:
consistent &= aa == aa
return consistent
def lowerCamelCase_ ( self : Optional[int] , __snake_case : Any , __snake_case : List[str] , __snake_case : str , ):
UpperCAmelCase_ = True
UpperCAmelCase_ = tree_map(lambda __snake_case : a.shape if isinstance(_lowerCamelCase , torch.Tensor ) else a , _lowerCamelCase , _lowerCamelCase )
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data ) == len(_lowerCamelCase )
UpperCAmelCase_ = self._compare_arg_caches(self.cached_arg_data , _lowerCamelCase )
else:
# Otherwise, we can reuse the precomputed value
UpperCAmelCase_ = False
if not consistent:
UpperCAmelCase_ = self._determine_favorable_chunk_size(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , )
UpperCAmelCase_ = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 144 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
_lowerCAmelCase = "\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n"
_lowerCAmelCase = "\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"
_lowerCAmelCase = "\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - 'id': id of the question-answer pair as given in the references (see below)\n - 'prediction_text': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\n references: List of question-answers dictionaries with the following key-values:\n - 'id': id of the question-answer pair (see above),\n - 'answers': a Dict in the CUAD dataset format\n {\n 'text': list of possible texts for the answer, as a list of strings\n 'answer_start': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n 'exact_match': Exact match (the normalized answer exactly match the gold answer)\n 'f1': The F-score of predicted tokens versus the gold answer\n 'aupr': Area Under the Precision-Recall curve\n 'prec_at_80_recall': Precision at 80% recall\n 'prec_at_90_recall': Precision at 90% recall\nExamples:\n >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n >>> cuad_metric = datasets.load_metric(\"cuad\")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
"""simple docstring"""
def snake_case_( self )-> int:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': {
'''id''': datasets.Value('''string''' ),
'''prediction_text''': datasets.features.Sequence(datasets.Value('''string''' ) ),
},
'''references''': {
'''id''': datasets.Value('''string''' ),
'''answers''': datasets.features.Sequence(
{
'''text''': datasets.Value('''string''' ),
'''answer_start''': datasets.Value('''int32''' ),
} ),
},
} ) , codebase_urls=['''https://www.atticusprojectai.org/cuad'''] , reference_urls=['''https://www.atticusprojectai.org/cuad'''] , )
def snake_case_( self , _lowerCamelCase , _lowerCamelCase )-> Optional[int]:
lowercase__ = {prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
lowercase__ = [
{
'''paragraphs''': [
{
'''qas''': [
{
'''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
'''id''': ref['''id'''],
}
for ref in references
]
}
]
}
]
lowercase__ = evaluate(dataset=_lowerCamelCase , predictions=_lowerCamelCase )
return score
| 161 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : int ):
"""simple docstring"""
while b:
_lowerCamelCase , _lowerCamelCase : List[str] = b, a % b
return a
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : int ):
"""simple docstring"""
return a if b == 0 else euclidean_gcd_recursive(_lowerCAmelCase , a % b )
def A_ ( ):
"""simple docstring"""
print(F'euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}' )
print(F'euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}' )
print(F'euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}' )
print(F'euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}' )
print(F'euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}' )
print(F'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}' )
print(F'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}' )
print(F'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}' )
print(F'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}' )
print(F'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}' )
if __name__ == "__main__":
main() | 11 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[int] = {'vocab_file': 'spiece.model'}
UpperCAmelCase_ : Any = {
'vocab_file': {
'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
}
}
UpperCAmelCase_ : str = {
'AI-Sweden/gpt-sw3-126m': 2048,
'AI-Sweden/gpt-sw3-350m': 2048,
'AI-Sweden/gpt-sw3-1.6b': 2048,
'AI-Sweden/gpt-sw3-6.7b': 2048,
'AI-Sweden/gpt-sw3-20b': 2048,
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = VOCAB_FILES_NAMES
lowerCAmelCase_ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ = ['input_ids', 'attention_mask']
def __init__( self : Dict,__A : List[str],__A : Any=False,__A : Tuple=False,__A : Dict=False,__A : str=None,__A : List[str]=None,__A : Any=None,__A : str=None,__A : Optional[Dict[str, Any]] = None,**__A : str,):
_lowerCamelCase : List[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
_lowerCamelCase : int = kwargs.get("name_or_path" )
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored" )
_lowerCamelCase : Union[str, Any] = "None"
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
_lowerCamelCase : Tuple = "<|endoftext|>" if eos_token is None else eos_token
_lowerCamelCase : List[str] = "<unk>" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
_lowerCamelCase : Union[str, Any] = unk_token if pad_token is None else pad_token
_lowerCamelCase : str = eos_token if bos_token is None else bos_token
else:
_lowerCamelCase : List[str] = "<pad>" if pad_token is None else pad_token
_lowerCamelCase : str = "<s>" if bos_token is None else bos_token
super().__init__(
do_lower_case=__A,remove_space=__A,keep_accents=__A,bos_token=__A,eos_token=__A,unk_token=__A,pad_token=__A,sp_model_kwargs=self.sp_model_kwargs,**__A,)
_lowerCamelCase : Union[str, Any] = do_lower_case
_lowerCamelCase : List[Any] = remove_space
_lowerCamelCase : str = keep_accents
_lowerCamelCase : List[Any] = vocab_file
_lowerCamelCase : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__A )
# Used for whitespace normalization in input texts
# fmt : off
_lowerCamelCase : Union[str, Any] = {" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "", ""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
_lowerCamelCase : int = re.compile(
f'[{"".join(map(__A,list(range(0,9 ) ) + list(range(1_1,3_2 ) ) + list(range(1_2_7,1_6_0 ) ) + [1_6_0, 1_7_3, 8_2_0_3] ) )}]' )
def __getstate__( self : Dict ):
_lowerCamelCase : int = self.__dict__.copy()
_lowerCamelCase : Optional[Any] = None
return state
def __setstate__( self : Tuple,__A : int ):
_lowerCamelCase : Optional[int] = d
# for backward compatibility
if not hasattr(self,"sp_model_kwargs" ):
_lowerCamelCase : List[str] = {}
_lowerCamelCase : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def lowerCamelCase_ ( self : int ):
return len(self.sp_model )
def lowerCamelCase_ ( self : Dict,__A : str ):
_lowerCamelCase : Union[str, Any] = self.non_printing_characters_re.sub("",__A )
# Normalize whitespaces
_lowerCamelCase : Optional[Any] = "".join([char if char not in self.whitespaces else " " for char in text] )
# NFC Unicode normalization
_lowerCamelCase : Optional[Any] = unicodedata.normalize("NFC",__A )
return text
def lowerCamelCase_ ( self : Union[str, Any],__A : str,**__A : Optional[int] ):
_lowerCamelCase : str = self.preprocess_text(__A )
return self.sp_model.encode(__A,out_type=__A )
def lowerCamelCase_ ( self : int,__A : str ):
return self.sp_model.PieceToId(__A )
def lowerCamelCase_ ( self : Optional[int],__A : int ):
return self.sp_model.IdToPiece(__A )
@staticmethod
def lowerCamelCase_ ( __A : str ):
return out_string
def lowerCamelCase_ ( self : str,__A : List[str] ):
_lowerCamelCase : str = []
_lowerCamelCase : List[Any] = ""
_lowerCamelCase : Tuple = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(__A ) + token
_lowerCamelCase : Optional[int] = True
_lowerCamelCase : Optional[Any] = []
else:
current_sub_tokens.append(__A )
_lowerCamelCase : str = False
out_string += self.sp_model.decode(__A )
return out_string
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : Optional[int] = {self.convert_ids_to_tokens(__A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCamelCase_ ( self : Optional[Any],__A : str,__A : Optional[str] = None ):
if not os.path.isdir(__A ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_lowerCamelCase : List[Any] = 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:
_lowerCamelCase : str = self.sp_model.serialized_model_proto()
fi.write(__A )
return (out_vocab_file,)
def lowerCamelCase_ ( self : Optional[int],__A : Union[str, List[str]],__A : Union[str, bool] = False ):
if isinstance(__A,__A ):
_lowerCamelCase : List[Any] = self.preprocess_text(__A )
_lowerCamelCase : Optional[Any] = self.sp_model.encode(__A )
else:
_lowerCamelCase : List[str] = [self.preprocess_text(__A ) for t in text]
_lowerCamelCase : int = self.sp_model.encode(__A )
if return_tensors is True or return_tensors == "pt":
_lowerCamelCase : str = torch.tensor(__A )
return token_ids
def lowerCamelCase_ ( self : List[Any],__A : Union[int, List[int]] ):
return self.sp_model.decode(__A )
def lowerCamelCase_ ( self : Optional[int],__A : "Conversation" ):
_lowerCamelCase : Any = [f'User: {text}' if is_user else f'Bot: {text}' for is_user, text in conversation.iter_texts()]
_lowerCamelCase : Tuple = (
f'{self.eos_token}{self.bos_token}' + f'{self.bos_token}'.join(__A ) + f'{self.bos_token}Bot:'
)
return self.encode(text=__A ) | 11 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class a_ ( unittest.TestCase ):
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = tempfile.mkdtemp()
UpperCamelCase = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
UpperCamelCase = 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] ) )
UpperCamelCase = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
"""image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
}
UpperCamelCase = os.path.join(self.tmpdirname , _SCREAMING_SNAKE_CASE )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_SCREAMING_SNAKE_CASE )
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_SCREAMING_SNAKE_CASE )
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Any:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
UpperCamelCase = [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = self.get_rust_tokenizer()
UpperCamelCase = self.get_image_processor()
UpperCamelCase = AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
processor_slow.save_pretrained(self.tmpdirname )
UpperCamelCase = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_SCREAMING_SNAKE_CASE )
UpperCamelCase = AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
processor_fast.save_pretrained(self.tmpdirname )
UpperCamelCase = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _SCREAMING_SNAKE_CASE )
self.assertIsInstance(processor_fast.tokenizer , _SCREAMING_SNAKE_CASE )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _SCREAMING_SNAKE_CASE )
self.assertIsInstance(processor_fast.image_processor , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
UpperCamelCase = self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE , padding_value=1.0 )
UpperCamelCase = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_SCREAMING_SNAKE_CASE , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _SCREAMING_SNAKE_CASE )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = image_processor(_SCREAMING_SNAKE_CASE , return_tensors="""np""" )
UpperCamelCase = processor(images=_SCREAMING_SNAKE_CASE , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
UpperCamelCase = """lower newer"""
UpperCamelCase = processor(text=_SCREAMING_SNAKE_CASE )
UpperCamelCase = tokenizer(_SCREAMING_SNAKE_CASE , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
UpperCamelCase = """lower newer"""
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = processor(text=_SCREAMING_SNAKE_CASE , images=_SCREAMING_SNAKE_CASE )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_SCREAMING_SNAKE_CASE ):
processor()
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
UpperCamelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCamelCase = processor.batch_decode(_SCREAMING_SNAKE_CASE )
UpperCamelCase = tokenizer.batch_decode(_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
UpperCamelCase = """lower newer"""
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = processor(text=_SCREAMING_SNAKE_CASE , images=_SCREAMING_SNAKE_CASE )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 301 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class a_ ( lowerCamelCase ):
lowercase = ["""image_processor""", """tokenizer"""]
lowercase = """ViltImageProcessor"""
lowercase = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
UpperCamelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , _SCREAMING_SNAKE_CASE , )
UpperCamelCase = kwargs.pop("""feature_extractor""" )
UpperCamelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = self.image_processor
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> BatchEncoding:
"""simple docstring"""
UpperCamelCase = self.tokenizer(
text=_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE , stride=_SCREAMING_SNAKE_CASE , pad_to_multiple_of=_SCREAMING_SNAKE_CASE , return_token_type_ids=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , return_overflowing_tokens=_SCREAMING_SNAKE_CASE , return_special_tokens_mask=_SCREAMING_SNAKE_CASE , return_offsets_mapping=_SCREAMING_SNAKE_CASE , return_length=_SCREAMING_SNAKE_CASE , verbose=_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
# add pixel_values + pixel_mask
UpperCamelCase = self.image_processor(_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE )
encoding.update(_SCREAMING_SNAKE_CASE )
return encoding
def A__ ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def A__ ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@property
def A__ ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase = self.tokenizer.model_input_names
UpperCamelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def A__ ( self ) -> List[Any]:
"""simple docstring"""
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , _SCREAMING_SNAKE_CASE , )
return self.image_processor_class
@property
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , _SCREAMING_SNAKE_CASE , )
return self.image_processor
| 301 | 1 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Optional[int] = logging.get_logger(__name__)
A_ : Optional[Any] = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}
class __snake_case ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCamelCase__ = '''xlnet'''
lowerCamelCase__ = ['''mems''']
lowerCamelCase__ = {
'''n_token''': '''vocab_size''', # Backward compatibility
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self , __SCREAMING_SNAKE_CASE=3_2_0_0_0 , __SCREAMING_SNAKE_CASE=1_0_2_4 , __SCREAMING_SNAKE_CASE=2_4 , __SCREAMING_SNAKE_CASE=1_6 , __SCREAMING_SNAKE_CASE=4_0_9_6 , __SCREAMING_SNAKE_CASE="gelu" , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE="bi" , __SCREAMING_SNAKE_CASE=0.02 , __SCREAMING_SNAKE_CASE=1e-1_2 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=5_1_2 , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=-1 , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE="last" , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE="tanh" , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=5 , __SCREAMING_SNAKE_CASE=5 , __SCREAMING_SNAKE_CASE=5 , __SCREAMING_SNAKE_CASE=1 , __SCREAMING_SNAKE_CASE=2 , **__SCREAMING_SNAKE_CASE , ):
snake_case__ : Optional[int] = vocab_size
snake_case__ : Any = d_model
snake_case__ : Union[str, Any] = n_layer
snake_case__ : Union[str, Any] = n_head
if d_model % n_head != 0:
raise ValueError(f"'d_model % n_head' ({d_model % n_head}) should be equal to 0" )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})" )
snake_case__ : str = d_model // n_head
snake_case__ : int = ff_activation
snake_case__ : Dict = d_inner
snake_case__ : int = untie_r
snake_case__ : Optional[int] = attn_type
snake_case__ : Optional[Any] = initializer_range
snake_case__ : Dict = layer_norm_eps
snake_case__ : Union[str, Any] = dropout
snake_case__ : Union[str, Any] = mem_len
snake_case__ : Optional[int] = reuse_len
snake_case__ : List[str] = bi_data
snake_case__ : Any = clamp_len
snake_case__ : Dict = same_length
snake_case__ : Optional[Any] = summary_type
snake_case__ : Union[str, Any] = summary_use_proj
snake_case__ : Optional[Any] = summary_activation
snake_case__ : Optional[Any] = summary_last_dropout
snake_case__ : List[str] = start_n_top
snake_case__ : int = end_n_top
snake_case__ : Any = bos_token_id
snake_case__ : Optional[Any] = pad_token_id
snake_case__ : Union[str, Any] = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
"""The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"""
""" instead.""" , __snake_case , )
snake_case__ : Optional[int] = kwargs['''use_cache''']
snake_case__ : Optional[int] = use_mems_eval
snake_case__ : Optional[int] = use_mems_train
super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case )
@property
def __UpperCamelCase ( self ):
logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit." )
return -1
@max_position_embeddings.setter
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE ):
# Message copied from Transformer-XL documentation
raise NotImplementedError(
f"The model {self.model_type} is one of the few models that has no sequence length limit." )
| 705 |
'''simple docstring'''
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 419 | 0 |
'''simple docstring'''
from math import sqrt
def __UpperCAmelCase ( a_: str ):
_UpperCAmelCase : Tuple = 0
for i in range(1, int(sqrt(snake_case__ ) + 1 ) ):
if n % i == 0 and i != sqrt(snake_case__ ):
total += i + n // i
elif i == sqrt(snake_case__ ):
total += i
return total - n
def __UpperCAmelCase ( a_: Optional[Any] = 10_000 ):
_UpperCAmelCase : str = sum(
i
for i in range(1, snake_case__ )
if sum_of_divisors(sum_of_divisors(snake_case__ ) ) == i and sum_of_divisors(snake_case__ ) != i )
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip()))) | 494 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"huggingface/informer-tourism-monthly": (
"https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
),
# See all Informer models at https://huggingface.co/models?filter=informer
}
class SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_A : Optional[Any] = """informer"""
_A : Dict = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
"""num_hidden_layers""": """encoder_layers""",
}
def __init__(self , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = "student_t" , lowerCAmelCase_ = "nll" , lowerCAmelCase_ = 1 , lowerCAmelCase_ = None , lowerCAmelCase_ = "mean" , lowerCAmelCase_ = 0 , lowerCAmelCase_ = 0 , lowerCAmelCase_ = 0 , lowerCAmelCase_ = 0 , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = 64 , lowerCAmelCase_ = 32 , lowerCAmelCase_ = 32 , lowerCAmelCase_ = 2 , lowerCAmelCase_ = 2 , lowerCAmelCase_ = 2 , lowerCAmelCase_ = 2 , lowerCAmelCase_ = True , lowerCAmelCase_ = "gelu" , lowerCAmelCase_ = 0.05 , lowerCAmelCase_ = 0.1 , lowerCAmelCase_ = 0.1 , lowerCAmelCase_ = 0.1 , lowerCAmelCase_ = 0.1 , lowerCAmelCase_ = 100 , lowerCAmelCase_ = 0.02 , lowerCAmelCase_=True , lowerCAmelCase_ = "prob" , lowerCAmelCase_ = 5 , lowerCAmelCase_ = True , **lowerCAmelCase_ , ):
# time series specific configuration
A_ : Optional[Any] = prediction_length
A_ : Dict = context_length or prediction_length
A_ : Dict = distribution_output
A_ : Tuple = loss
A_ : Dict = input_size
A_ : Union[str, Any] = num_time_features
A_ : List[str] = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
A_ : Optional[int] = scaling
A_ : Optional[Any] = num_dynamic_real_features
A_ : Tuple = num_static_real_features
A_ : Tuple = num_static_categorical_features
# set cardinality
if cardinality and num_static_categorical_features > 0:
if len(lowerCAmelCase_ ) != num_static_categorical_features:
raise ValueError(
"""The cardinality should be a list of the same length as `num_static_categorical_features`""" )
A_ : List[str] = cardinality
else:
A_ : List[Any] = [0]
# set embedding_dimension
if embedding_dimension and num_static_categorical_features > 0:
if len(lowerCAmelCase_ ) != num_static_categorical_features:
raise ValueError(
"""The embedding dimension should be a list of the same length as `num_static_categorical_features`""" )
A_ : int = embedding_dimension
else:
A_ : Optional[int] = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
A_ : Optional[int] = num_parallel_samples
# Transformer architecture configuration
A_ : Union[str, Any] = input_size * len(self.lags_sequence ) + self._number_of_features
A_ : Dict = d_model
A_ : Dict = encoder_attention_heads
A_ : Dict = decoder_attention_heads
A_ : Any = encoder_ffn_dim
A_ : Tuple = decoder_ffn_dim
A_ : Tuple = encoder_layers
A_ : Optional[int] = decoder_layers
A_ : List[str] = dropout
A_ : List[str] = attention_dropout
A_ : Any = activation_dropout
A_ : Any = encoder_layerdrop
A_ : List[Any] = decoder_layerdrop
A_ : str = activation_function
A_ : Optional[Any] = init_std
A_ : Optional[int] = use_cache
# Informer
A_ : Dict = attention_type
A_ : List[Any] = sampling_factor
A_ : List[Any] = distil
super().__init__(is_encoder_decoder=lowerCAmelCase_ , **lowerCAmelCase_ )
@property
def lowerCamelCase(self ):
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 180 | 0 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))
print("""Googling.....""")
_SCREAMING_SNAKE_CASE = F'''https://www.google.com/search?q={query}&num=100'''
_SCREAMING_SNAKE_CASE = requests.get(
url,
headers={"""User-Agent""": str(UserAgent().random)},
)
try:
_SCREAMING_SNAKE_CASE = (
BeautifulSoup(res.text, """html.parser""")
.find("""div""", attrs={"""class""": """yuRUbf"""})
.find("""a""")
.get("""href""")
)
except AttributeError:
_SCREAMING_SNAKE_CASE = parse_qs(
BeautifulSoup(res.text, """html.parser""")
.find("""div""", attrs={"""class""": """kCrYT"""})
.find("""a""")
.get("""href""")
)["""url"""][0]
webbrowser.open(link)
| 534 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
@property
def UpperCAmelCase_ ( self : Any ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ : List[Any] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
def UpperCAmelCase_ ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
snake_case_ : List[Any] = self.dummy_uncond_unet
snake_case_ : Optional[Any] = ScoreSdeVeScheduler()
snake_case_ : Tuple = ScoreSdeVePipeline(unet=_A , scheduler=_A )
sde_ve.to(_A )
sde_ve.set_progress_bar_config(disable=_A )
snake_case_ : Optional[Any] = torch.manual_seed(0 )
snake_case_ : Tuple = sde_ve(num_inference_steps=2 , output_type='numpy' , generator=_A ).images
snake_case_ : int = torch.manual_seed(0 )
snake_case_ : Optional[int] = sde_ve(num_inference_steps=2 , output_type='numpy' , generator=_A , return_dict=_A )[
0
]
snake_case_ : List[str] = image[0, -3:, -3:, -1]
snake_case_ : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case_ : List[Any] = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
def UpperCAmelCase_ ( self : Dict ) -> Dict:
"""simple docstring"""
snake_case_ : Dict = 'google/ncsnpp-church-256'
snake_case_ : List[Any] = UNetaDModel.from_pretrained(_A )
snake_case_ : str = ScoreSdeVeScheduler.from_pretrained(_A )
snake_case_ : Optional[Any] = ScoreSdeVePipeline(unet=_A , scheduler=_A )
sde_ve.to(_A )
sde_ve.set_progress_bar_config(disable=_A )
snake_case_ : Any = torch.manual_seed(0 )
snake_case_ : Optional[int] = sde_ve(num_inference_steps=10 , output_type='numpy' , generator=_A ).images
snake_case_ : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
snake_case_ : Dict = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 534 | 1 |
_UpperCAmelCase : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCAmelCase : Dict = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCAmelCase : Dict = {
0: """Sunday""",
1: """Monday""",
2: """Tuesday""",
3: """Wednesday""",
4: """Thursday""",
5: """Friday""",
6: """Saturday""",
}
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> str:
assert len(str(_UpperCAmelCase ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
lowerCamelCase__ : Any = year // 100
lowerCamelCase__ : int = (5 * (century % 4) + 2) % 7
lowerCamelCase__ : Optional[int] = year % 100
lowerCamelCase__ : List[str] = centurian % 12
lowerCamelCase__ : Any = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
lowerCamelCase__ : Any = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
else DOOMSDAY_LEAP[month - 1]
)
lowerCamelCase__ : Tuple = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 295 |
import pytest
_UpperCAmelCase : Optional[int] = """__dummy_dataset1__"""
_UpperCAmelCase : Dict = """
import json
import os
import datasets
REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}
class __DummyDataset1__(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features(
{
\"tokens\": datasets.Sequence(datasets.Value(\"string\")),
\"ner_tags\": datasets.Sequence(
datasets.features.ClassLabel(
names=[
\"O\",
\"B-PER\",
\"I-PER\",
\"B-ORG\",
\"I-ORG\",
\"B-LOC\",
\"I-LOC\",
]
)
),
\"langs\": datasets.Sequence(datasets.Value(\"string\")),
\"spans\": datasets.Sequence(datasets.Value(\"string\")),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),
datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),
]
def _generate_examples(self, filepath):
with open(filepath, \"r\", encoding=\"utf-8\") as f:
for i, line in enumerate(f):
yield i, json.loads(line)
"""
@pytest.fixture
def SCREAMING_SNAKE_CASE ( ) -> Dict:
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def SCREAMING_SNAKE_CASE ( ) -> List[Any]:
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> List[Any]:
lowerCamelCase__ : int = dataset_loading_script_name
lowerCamelCase__ : Any = tmp_path / 'datasets' / script_name
script_dir.mkdir(parents=_UpperCAmelCase )
lowerCamelCase__ : str = script_dir / F"""{script_name}.py"""
with open(_UpperCAmelCase , 'w' ) as f:
f.write(_UpperCAmelCase )
return str(_UpperCAmelCase )
| 295 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__magic_name__ : Optional[int] = 50_000
__magic_name__ : Tuple = 5_000
__magic_name__ : List[Any] = os.path.split(__file__)
__magic_name__ : int = os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def A__ ( A_ , A_ ) -> List[str]:
for i in range(A_ ):
_lowercase = dataset[i]
@get_duration
def A__ ( A_ , A_ , A_ ) -> List[Any]:
for i in range(0 , len(A_ ) , A_ ):
_lowercase = dataset[i : i + batch_size]
@get_duration
def A__ ( A_ , A_ , A_ ) -> List[str]:
with dataset.formatted_as(type=A_ ):
for i in range(A_ ):
_lowercase = dataset[i]
@get_duration
def A__ ( A_ , A_ , A_ , A_ ) -> Tuple:
with dataset.formatted_as(type=A_ ):
for i in range(0 , A_ , A_ ):
_lowercase = dataset[i : i + batch_size]
def A__ ( ) -> int:
_lowercase = {"num examples": SPEED_TEST_N_EXAMPLES}
_lowercase = [
(read, {"length": SMALL_TEST}),
(read, {"length": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_000}),
(read_formatted, {"type": "numpy", "length": SMALL_TEST}),
(read_formatted, {"type": "pandas", "length": SMALL_TEST}),
(read_formatted, {"type": "torch", "length": SMALL_TEST}),
(read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
(read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
(read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_000}),
]
_lowercase = [
(read, {"length": SMALL_TEST}),
(read, {"length": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_000}),
(read_formatted, {"type": "numpy", "length": SMALL_TEST}),
(read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
(read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_000}),
]
with tempfile.TemporaryDirectory() as tmp_dir:
print("generating dataset" )
_lowercase = datasets.Features(
{"list": datasets.Sequence(datasets.Value("float32" ) ), "numbers": datasets.Value("float32" )} )
_lowercase = generate_example_dataset(
os.path.join(A_ , "dataset.arrow" ) , A_ , num_examples=A_ , seq_shapes={"list": (100,)} , )
print("first set of iterations" )
for func, kwargs in functions:
print(func.__name__ , str(A_ ) )
_lowercase = func(A_ , **A_ )
print("shuffling dataset" )
_lowercase = dataset.shuffle()
print("Second set of iterations (after shuffling" )
for func, kwargs in functions_shuffled:
print("shuffled " , func.__name__ , str(A_ ) )
_lowercase = func(
A_ , **A_ )
with open(A_ , "wb" ) as f:
f.write(json.dumps(A_ ).encode("utf-8" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_iterating()
| 706 |
'''simple docstring'''
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Any , __A : Union[str, Any] ):
"""simple docstring"""
_lowercase = parent
def snake_case ( self : Optional[Any] ):
"""simple docstring"""
return {}
def A__ ( ) -> str:
_lowercase = "<HTML>\n\n <HEAD>\n <TITLE>sample document</TITLE>\n </HEAD>\n\n <BODY BGCOLOR=\"FFFFFF\">\n <HR>\n <a href=\"http://google.com\">Goog</a>\n <H1>This is one header</H1>\n <H2>This is a another Header</H2>\n <P>Travel from\n <P>\n <B>SFO to JFK</B>\n <BR>\n <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n <HR>\n <div style=\"color:#0000FF\">\n <h3>Traveler <b> name </b> is\n <p> John Doe </p>\n </div>"
_lowercase = "\n <!DOCTYPE html>\n <html>\n <body>\n\n <h1>My First Heading</h1>\n <p>My first paragraph.</p>\n\n </body>\n </html>\n "
return [html_string_a, html_string_a]
@require_bsa
class UpperCamelCase__ ( lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ = MarkupLMFeatureExtractor if is_bsa_available() else None
def snake_case ( self : Union[str, Any] ):
"""simple docstring"""
_lowercase = MarkupLMFeatureExtractionTester(self )
@property
def snake_case ( self : List[str] ):
"""simple docstring"""
return self.feature_extract_tester.prepare_feat_extract_dict()
def snake_case ( self : Dict ):
"""simple docstring"""
# Initialize feature_extractor
_lowercase = self.feature_extraction_class()
# Test not batched input
_lowercase = get_html_strings()[0]
_lowercase = feature_extractor(__A )
# fmt: off
_lowercase = [["sample document", "Goog", "This is one header", "This is a another Header", "Travel from", "SFO to JFK", "on May 2, 2015 at 2:00 pm. For details go to confirm.com", "Traveler", "name", "is", "John Doe"]]
_lowercase = [["/html/head/title", "/html/body/a", "/html/body/h1", "/html/body/h2", "/html/body/p", "/html/body/p/p/b[1]", "/html/body/p/p/b[2]/i", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/b", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/p"]]
# fmt: on
self.assertEqual(encoding.nodes , __A )
self.assertEqual(encoding.xpaths , __A )
# Test batched
_lowercase = get_html_strings()
_lowercase = feature_extractor(__A )
# fmt: off
_lowercase = expected_nodes + [["My First Heading", "My first paragraph."]]
_lowercase = expected_xpaths + [["/html/body/h1", "/html/body/p"]]
self.assertEqual(len(encoding.nodes ) , 2 )
self.assertEqual(len(encoding.xpaths ) , 2 )
self.assertEqual(encoding.nodes , __A )
self.assertEqual(encoding.xpaths , __A )
| 602 | 0 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
a_ = get_logger(__name__)
class __lowerCAmelCase :
lowerCAmelCase__ = """dummy_data"""
lowerCAmelCase__ = """datasets"""
lowerCAmelCase__ = False
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = False , __UpperCAmelCase = True , __UpperCAmelCase = None , ):
'''simple docstring'''
__lowerCamelCase = 0
__lowerCamelCase = dataset_name
__lowerCamelCase = cache_dir
__lowerCamelCase = use_local_dummy_data
__lowerCamelCase = config
# download_callbacks take a single url as input
__lowerCamelCase = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
__lowerCamelCase = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
__lowerCamelCase = str(__UpperCAmelCase )
# to be downloaded
__lowerCamelCase = None
__lowerCamelCase = None
@property
def lowerCamelCase ( self ):
'''simple docstring'''
if self._dummy_file is None:
__lowerCamelCase = self.download_dummy_data()
return self._dummy_file
@property
def lowerCamelCase ( self ):
'''simple docstring'''
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join('''dummy''' , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join('''dummy''' , self.version_name )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return os.path.join(self.dummy_data_folder , '''dummy_data.zip''' )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
__lowerCamelCase = cached_path(
__UpperCAmelCase , cache_dir=self.cache_dir , extract_compressed_file=__UpperCAmelCase , force_extract=__UpperCAmelCase )
return os.path.join(__UpperCAmelCase , self.dummy_file_name )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
if self._bucket_url is None:
__lowerCamelCase = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , '''/''' ) )
return self._bucket_url
@property
def lowerCamelCase ( self ):
'''simple docstring'''
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , '''/''' ).split('''/''' )[:-1] )
def lowerCamelCase ( self , __UpperCAmelCase , *__UpperCAmelCase ):
'''simple docstring'''
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
__lowerCamelCase = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
__lowerCamelCase = self.dummy_file_name
# special case when data_url is a dict
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
return self.create_dummy_data_dict(__UpperCAmelCase , __UpperCAmelCase )
elif isinstance(__UpperCAmelCase , (list, tuple) ):
return self.create_dummy_data_list(__UpperCAmelCase , __UpperCAmelCase )
else:
return self.create_dummy_data_single(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , *__UpperCAmelCase ):
'''simple docstring'''
return self.download_and_extract(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
return self.download_and_extract(__UpperCAmelCase )
def lowerCamelCase ( self , __UpperCAmelCase , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
return path
def lowerCamelCase ( self ):
'''simple docstring'''
return {}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
for single_url in single_urls:
download_callback(__UpperCAmelCase )
else:
__lowerCamelCase = single_urls
download_callback(__UpperCAmelCase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
__lowerCamelCase = [os.path.join(__UpperCAmelCase , urllib.parse.quote_plus(Path(__UpperCAmelCase ).name ) ) for x in single_urls]
else:
__lowerCamelCase = single_urls
__lowerCamelCase = os.path.join(__UpperCAmelCase , urllib.parse.quote_plus(Path(__UpperCAmelCase ).name ) )
__lowerCamelCase = value
# make sure that values are unique
if all(isinstance(__UpperCAmelCase , __UpperCAmelCase ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
__lowerCamelCase = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
__lowerCamelCase = all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}''' , __UpperCAmelCase ) ) for url in data_url )
__lowerCamelCase = all(
url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed''' ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
__lowerCamelCase = [data_url[0]] * len(__UpperCAmelCase )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(__UpperCAmelCase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
__lowerCamelCase = os.path.join(__UpperCAmelCase , urllib.parse.quote_plus(single_url.split('''/''' )[-1] ) )
dummy_data_list.append(__UpperCAmelCase )
return dummy_data_list
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
for download_callback in self.download_callbacks:
download_callback(__UpperCAmelCase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
__lowerCamelCase = os.path.join(__UpperCAmelCase , urllib.parse.quote_plus(data_url.split('''/''' )[-1] ) )
if os.path.exists(__UpperCAmelCase ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
def _iter_archive_members(__UpperCAmelCase ):
# this preserves the order of the members inside the ZIP archive
__lowerCamelCase = Path(self.dummy_file ).parent
__lowerCamelCase = path.relative_to(__UpperCAmelCase )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
__lowerCamelCase = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(__UpperCAmelCase )
__lowerCamelCase = Path(__UpperCAmelCase )
__lowerCamelCase = _iter_archive_members(__UpperCAmelCase ) if self.use_local_dummy_data else path.rglob('''*''' )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''') ):
yield file_path.relative_to(__UpperCAmelCase ).as_posix(), file_path.open('''rb''' )
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
__lowerCamelCase = [paths]
for path in paths:
if os.path.isfile(__UpperCAmelCase ):
if os.path.basename(__UpperCAmelCase ).startswith(('''.''', '''__''') ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(__UpperCAmelCase ):
if os.path.basename(__UpperCAmelCase ).startswith(('''.''', '''__''') ):
continue
dirnames.sort()
for filename in sorted(__UpperCAmelCase ):
if filename.startswith(('''.''', '''__''') ):
continue
yield os.path.join(__UpperCAmelCase , __UpperCAmelCase )
| 175 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
a_ = {
"""iou_prediction_head.layers.0""": """iou_prediction_head.proj_in""",
"""iou_prediction_head.layers.1""": """iou_prediction_head.layers.0""",
"""iou_prediction_head.layers.2""": """iou_prediction_head.proj_out""",
"""mask_decoder.output_upscaling.0""": """mask_decoder.upscale_conv1""",
"""mask_decoder.output_upscaling.1""": """mask_decoder.upscale_layer_norm""",
"""mask_decoder.output_upscaling.3""": """mask_decoder.upscale_conv2""",
"""mask_downscaling.0""": """mask_embed.conv1""",
"""mask_downscaling.1""": """mask_embed.layer_norm1""",
"""mask_downscaling.3""": """mask_embed.conv2""",
"""mask_downscaling.4""": """mask_embed.layer_norm2""",
"""mask_downscaling.6""": """mask_embed.conv3""",
"""point_embeddings""": """point_embed""",
"""pe_layer.positional_encoding_gaussian_matrix""": """shared_embedding.positional_embedding""",
"""image_encoder""": """vision_encoder""",
"""neck.0""": """neck.conv1""",
"""neck.1""": """neck.layer_norm1""",
"""neck.2""": """neck.conv2""",
"""neck.3""": """neck.layer_norm2""",
"""patch_embed.proj""": """patch_embed.projection""",
""".norm""": """.layer_norm""",
"""blocks""": """layers""",
}
def a__ ( _UpperCamelCase : Optional[Any] ):
__lowerCamelCase = {}
state_dict.pop('''pixel_mean''' ,_UpperCamelCase )
state_dict.pop('''pixel_std''' ,_UpperCamelCase )
__lowerCamelCase = R'''.*.output_hypernetworks_mlps.(\d+).layers.(\d+).*'''
for key, value in state_dict.items():
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
__lowerCamelCase = key.replace(_UpperCamelCase ,_UpperCamelCase )
if re.match(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = int(re.match(_UpperCamelCase ,_UpperCamelCase ).group(2 ) )
if layer_nb == 0:
__lowerCamelCase = key.replace('''layers.0''' ,'''proj_in''' )
elif layer_nb == 1:
__lowerCamelCase = key.replace('''layers.1''' ,'''layers.0''' )
elif layer_nb == 2:
__lowerCamelCase = key.replace('''layers.2''' ,'''proj_out''' )
__lowerCamelCase = value
__lowerCamelCase = model_state_dict[
'''prompt_encoder.shared_embedding.positional_embedding'''
]
return model_state_dict
def a__ ( _UpperCamelCase : Tuple ,_UpperCamelCase : Dict ,_UpperCamelCase : Any ,_UpperCamelCase : Optional[int]="ybelkada/segment-anything" ):
__lowerCamelCase = hf_hub_download(_UpperCamelCase ,F"""checkpoints/{model_name}.pth""" )
if "sam_vit_b" in model_name:
__lowerCamelCase = SamConfig()
elif "sam_vit_l" in model_name:
__lowerCamelCase = SamVisionConfig(
hidden_size=10_24 ,num_hidden_layers=24 ,num_attention_heads=16 ,global_attn_indexes=[5, 11, 17, 23] ,)
__lowerCamelCase = SamConfig(
vision_config=_UpperCamelCase ,)
elif "sam_vit_h" in model_name:
__lowerCamelCase = SamVisionConfig(
hidden_size=12_80 ,num_hidden_layers=32 ,num_attention_heads=16 ,global_attn_indexes=[7, 15, 23, 31] ,)
__lowerCamelCase = SamConfig(
vision_config=_UpperCamelCase ,)
__lowerCamelCase = torch.load(_UpperCamelCase ,map_location='''cpu''' )
__lowerCamelCase = replace_keys(_UpperCamelCase )
__lowerCamelCase = SamImageProcessor()
__lowerCamelCase = SamProcessor(image_processor=_UpperCamelCase )
__lowerCamelCase = SamModel(_UpperCamelCase )
hf_model.load_state_dict(_UpperCamelCase )
__lowerCamelCase = hf_model.to('''cuda''' )
__lowerCamelCase = '''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
__lowerCamelCase = Image.open(requests.get(_UpperCamelCase ,stream=_UpperCamelCase ).raw ).convert('''RGB''' )
__lowerCamelCase = [[[4_00, 6_50]]]
__lowerCamelCase = [[1]]
__lowerCamelCase = processor(images=np.array(_UpperCamelCase ) ,return_tensors='''pt''' ).to('''cuda''' )
with torch.no_grad():
__lowerCamelCase = hf_model(**_UpperCamelCase )
__lowerCamelCase = output.iou_scores.squeeze()
if model_name == "sam_vit_h_4b8939":
assert scores[-1].item() == 0.579_890_251_159_668
__lowerCamelCase = processor(
images=np.array(_UpperCamelCase ) ,input_points=_UpperCamelCase ,input_labels=_UpperCamelCase ,return_tensors='''pt''' ).to('''cuda''' )
with torch.no_grad():
__lowerCamelCase = hf_model(**_UpperCamelCase )
__lowerCamelCase = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_712_603_092_193_604
__lowerCamelCase = ((75, 2_75, 17_25, 8_50),)
__lowerCamelCase = processor(images=np.array(_UpperCamelCase ) ,input_boxes=_UpperCamelCase ,return_tensors='''pt''' ).to('''cuda''' )
with torch.no_grad():
__lowerCamelCase = hf_model(**_UpperCamelCase )
__lowerCamelCase = output.iou_scores.squeeze()
assert scores[-1].item() == 0.8_686_015_605_926_514
# Test with 2 points and 1 image.
__lowerCamelCase = [[[4_00, 6_50], [8_00, 6_50]]]
__lowerCamelCase = [[1, 1]]
__lowerCamelCase = processor(
images=np.array(_UpperCamelCase ) ,input_points=_UpperCamelCase ,input_labels=_UpperCamelCase ,return_tensors='''pt''' ).to('''cuda''' )
with torch.no_grad():
__lowerCamelCase = hf_model(**_UpperCamelCase )
__lowerCamelCase = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_936_047_792_434_692
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
a_ = ["""sam_vit_b_01ec64""", """sam_vit_h_4b8939""", """sam_vit_l_0b3195"""]
parser.add_argument(
"""--model_name""",
default="""sam_vit_h_4b8939""",
choices=choices,
type=str,
help="""Path to hf config.json of model to convert""",
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether to push the model and processor to the hub after converting""",
)
parser.add_argument(
"""--model_hub_id""",
default="""ybelkada/segment-anything""",
choices=choices,
type=str,
help="""Path to hf config.json of model to convert""",
)
a_ = parser.parse_args()
convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)
| 175 | 1 |
'''simple docstring'''
def UpperCamelCase ( a = 10**12 ) -> int:
'''simple docstring'''
__magic_name__ = 1
__magic_name__ = 0
__magic_name__ = 1
__magic_name__ = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 * prev_numerator
prev_denominator += 2 * denominator
denominator += 2 * prev_denominator
return (denominator + 1) // 2
if __name__ == "__main__":
print(F'''{solution() = }''')
| 245 |
'''simple docstring'''
def UpperCamelCase ( a , a , a , a , a , a ) -> Tuple:
'''simple docstring'''
if index == r:
for j in range(a ):
print(data[j] , end=''' ''' )
print(''' ''' )
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
__magic_name__ = arr[i]
combination_util(a , a , a , index + 1 , a , i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(a , a , a , a , a , i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def UpperCamelCase ( a , a , a ) -> List[str]:
'''simple docstring'''
# A temporary array to store all combination one by one
__magic_name__ = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(a , a , a , 0 , a , 0 )
if __name__ == "__main__":
# Driver code to check the function above
_lowerCAmelCase = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 245 | 1 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__UpperCamelCase : Dict = {
'''gwf-440k''': {
'''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
'''sample_rate''': 48000,
'''sample_size''': 65536,
},
'''jmann-small-190k''': {
'''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
'''sample_rate''': 48000,
'''sample_size''': 65536,
},
'''jmann-large-580k''': {
'''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
'''sample_rate''': 48000,
'''sample_size''': 131072,
},
'''maestro-uncond-150k''': {
'''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
'''sample_rate''': 16000,
'''sample_size''': 65536,
},
'''unlocked-uncond-250k''': {
'''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
'''sample_rate''': 16000,
'''sample_size''': 65536,
},
'''honk-140k''': {
'''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
'''sample_rate''': 16000,
'''sample_size''': 65536,
},
}
def _UpperCAmelCase ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str] ):
"""simple docstring"""
return torch.atana(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) / math.pi * 2
def _UpperCAmelCase ( UpperCAmelCase : Optional[int] ):
"""simple docstring"""
__lowerCamelCase : Any = torch.sin(t * math.pi / 2 ) ** 2
__lowerCamelCase : str = (1 - sigma**2) ** 0.5
return alpha_sigma_to_t(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
class _UpperCamelCase ( _a ):
'''simple docstring'''
pass
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] , _lowerCamelCase : str ):
'''simple docstring'''
super().__init__()
__lowerCamelCase : Tuple = DiffusionAttnUnetaD(__UpperCamelCase , n_attn_layers=4 )
__lowerCamelCase : int = deepcopy(self.diffusion )
__lowerCamelCase : Union[str, Any] = torch.quasirandom.SobolEngine(1 , scramble=__UpperCamelCase )
def _UpperCAmelCase ( UpperCAmelCase : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase : List[Any] = MODELS_MAP[model_name]["""url"""]
os.system(f"""wget {url} ./""" )
return f"""./{model_name}.ckpt"""
__UpperCamelCase : Tuple = {
'''1''': '''resnets.0''',
'''2''': '''attentions.0''',
'''3''': '''resnets.1''',
'''4''': '''attentions.1''',
'''5''': '''resnets.2''',
'''6''': '''attentions.2''',
}
__UpperCamelCase : Any = {
'''8''': '''resnets.0''',
'''9''': '''attentions.0''',
'''10''': '''resnets.1''',
'''11''': '''attentions.1''',
'''12''': '''resnets.2''',
'''13''': '''attentions.2''',
}
__UpperCamelCase : str = {
'''1''': '''resnets.0''',
'''2''': '''attentions.0''',
'''3''': '''resnets.1''',
'''4''': '''attentions.1''',
'''5''': '''resnets.2''',
'''6''': '''attentions.2''',
'''8''': '''resnets.3''',
'''9''': '''attentions.3''',
'''10''': '''resnets.4''',
'''11''': '''attentions.4''',
'''12''': '''resnets.5''',
'''13''': '''attentions.5''',
}
__UpperCamelCase : Optional[int] = {
'''0''': '''resnets.0''',
'''1''': '''resnets.1''',
'''2''': '''resnets.2''',
'''4''': '''resnets.0''',
'''5''': '''resnets.1''',
'''6''': '''resnets.2''',
}
__UpperCamelCase : List[str] = {
'''skip''': '''conv_skip''',
'''main.0''': '''conv_1''',
'''main.1''': '''group_norm_1''',
'''main.3''': '''conv_2''',
'''main.4''': '''group_norm_2''',
}
__UpperCamelCase : Any = {
'''norm''': '''group_norm''',
'''qkv_proj''': ['''query''', '''key''', '''value'''],
'''out_proj''': ['''proj_attn'''],
}
def _UpperCAmelCase ( UpperCAmelCase : Union[str, Any] ):
"""simple docstring"""
if name.startswith("""skip""" ):
return name.replace("""skip""" , RES_CONV_MAP["""skip"""] )
# name has to be of format main.{digit}
if not name.startswith("""main.""" ):
raise ValueError(f"""ResConvBlock error with {name}""" )
return name.replace(name[:6] , RES_CONV_MAP[name[:6]] )
def _UpperCAmelCase ( UpperCAmelCase : Optional[int] ):
"""simple docstring"""
for key, value in ATTN_MAP.items():
if name.startswith(SCREAMING_SNAKE_CASE_ ) and not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return name.replace(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
elif name.startswith(SCREAMING_SNAKE_CASE_ ):
return [name.replace(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for v in value]
raise ValueError(f"""Attn error with {name}""" )
def _UpperCAmelCase ( UpperCAmelCase : int , UpperCAmelCase : str=13 ):
"""simple docstring"""
__lowerCamelCase : Tuple = input_string
if string.split(""".""" )[0] == "timestep_embed":
return string.replace("""timestep_embed""" , """time_proj""" )
__lowerCamelCase : Any = 0
if string.startswith("""net.3.""" ):
depth += 1
__lowerCamelCase : str = string[6:]
elif string.startswith("""net.""" ):
__lowerCamelCase : Tuple = string[4:]
while string.startswith("""main.7.""" ):
depth += 1
__lowerCamelCase : Any = string[7:]
if string.startswith("""main.""" ):
__lowerCamelCase : Tuple = string[5:]
# mid block
if string[:2].isdigit():
__lowerCamelCase : List[str] = string[:2]
__lowerCamelCase : Optional[int] = string[2:]
else:
__lowerCamelCase : Any = string[0]
__lowerCamelCase : Optional[int] = string[1:]
if depth == max_depth:
__lowerCamelCase : Union[str, Any] = MID_NUM_TO_LAYER[layer_num]
__lowerCamelCase : Any = """mid_block"""
elif depth > 0 and int(SCREAMING_SNAKE_CASE_ ) < 7:
__lowerCamelCase : Any = DOWN_NUM_TO_LAYER[layer_num]
__lowerCamelCase : Union[str, Any] = f"""down_blocks.{depth}"""
elif depth > 0 and int(SCREAMING_SNAKE_CASE_ ) > 7:
__lowerCamelCase : int = UP_NUM_TO_LAYER[layer_num]
__lowerCamelCase : Any = f"""up_blocks.{max_depth - depth - 1}"""
elif depth == 0:
__lowerCamelCase : Dict = DEPTH_0_TO_LAYER[layer_num]
__lowerCamelCase : Union[str, Any] = f"""up_blocks.{max_depth - 1}""" if int(SCREAMING_SNAKE_CASE_ ) > 3 else """down_blocks.0"""
if not string_left.startswith(""".""" ):
raise ValueError(f"""Naming error with {input_string} and string_left: {string_left}.""" )
__lowerCamelCase : List[str] = string_left[1:]
if "resnets" in new_layer:
__lowerCamelCase : Any = convert_resconv_naming(SCREAMING_SNAKE_CASE_ )
elif "attentions" in new_layer:
__lowerCamelCase : Optional[int] = convert_attn_naming(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : List[str] = new_string_left
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
__lowerCamelCase : Dict = prefix + """.""" + new_layer + """.""" + string_left
else:
__lowerCamelCase : Union[str, Any] = [prefix + """.""" + new_layer + """.""" + s for s in string_left]
return new_string
def _UpperCAmelCase ( UpperCAmelCase : str ):
"""simple docstring"""
__lowerCamelCase : List[str] = {}
for k, v in state_dict.items():
if k.endswith("""kernel""" ):
# up- and downsample layers, don't have trainable weights
continue
__lowerCamelCase : Union[str, Any] = rename(SCREAMING_SNAKE_CASE_ )
# check if we need to transform from Conv => Linear for attention
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
__lowerCamelCase : Optional[Any] = transform_conv_attns(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
else:
__lowerCamelCase : Dict = v
return new_state_dict
def _UpperCAmelCase ( UpperCAmelCase : List[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Union[str, Any] ):
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE_ ) == 1:
if len(v.shape ) == 3:
# weight
__lowerCamelCase : Optional[Any] = v[:, :, 0]
else:
# bias
__lowerCamelCase : str = v
else:
# qkv matrices
__lowerCamelCase : List[str] = v.shape[0]
__lowerCamelCase : Any = trippled_shape // 3
for i in range(3 ):
if len(v.shape ) == 3:
__lowerCamelCase : Tuple = v[i * single_shape : (i + 1) * single_shape, :, 0]
else:
__lowerCamelCase : Union[str, Any] = v[i * single_shape : (i + 1) * single_shape]
return new_state_dict
def _UpperCAmelCase ( UpperCAmelCase : List[Any] ):
"""simple docstring"""
__lowerCamelCase : Union[str, Any] = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
__lowerCamelCase : int = args.model_path.split("""/""" )[-1].split(""".""" )[0]
if not os.path.isfile(args.model_path ):
assert (
model_name == args.model_path
), f"""Make sure to provide one of the official model names {MODELS_MAP.keys()}"""
__lowerCamelCase : str = download(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : List[str] = MODELS_MAP[model_name]["""sample_rate"""]
__lowerCamelCase : Union[str, Any] = MODELS_MAP[model_name]["""sample_size"""]
__lowerCamelCase : Optional[Any] = Object()
__lowerCamelCase : Union[str, Any] = sample_size
__lowerCamelCase : str = sample_rate
__lowerCamelCase : Tuple = 0
__lowerCamelCase : Optional[Any] = UNetaDModel(sample_size=SCREAMING_SNAKE_CASE_ , sample_rate=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Optional[int] = diffusers_model.state_dict()
__lowerCamelCase : Optional[int] = DiffusionUncond(SCREAMING_SNAKE_CASE_ )
orig_model.load_state_dict(torch.load(args.model_path , map_location=SCREAMING_SNAKE_CASE_ )["""state_dict"""] )
__lowerCamelCase : Optional[int] = orig_model.diffusion_ema.eval()
__lowerCamelCase : Optional[int] = orig_model.state_dict()
__lowerCamelCase : List[str] = rename_orig_weights(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Optional[Any] = set(renamed_state_dict.keys() ) - set(diffusers_state_dict.keys() )
__lowerCamelCase : Tuple = set(diffusers_state_dict.keys() ) - set(renamed_state_dict.keys() )
assert len(SCREAMING_SNAKE_CASE_ ) == 0, f"""Problem with {renamed_minus_diffusers}"""
assert all(k.endswith("""kernel""" ) for k in list(SCREAMING_SNAKE_CASE_ ) ), f"""Problem with {diffusers_minus_renamed}"""
for key, value in renamed_state_dict.items():
assert (
diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
), f"""Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"""
if key == "time_proj.weight":
__lowerCamelCase : str = value.squeeze()
__lowerCamelCase : Optional[Any] = value
diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Union[str, Any] = 100
__lowerCamelCase : Any = 33
__lowerCamelCase : int = IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Union[str, Any] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Optional[int] = torch.randn([1, 2, config.sample_size] , generator=SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : str = torch.linspace(1 , 0 , steps + 1 , device=SCREAMING_SNAKE_CASE_ )[:-1]
__lowerCamelCase : Optional[Any] = get_crash_schedule(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Tuple = DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : List[str] = torch.manual_seed(33 )
__lowerCamelCase : Any = pipe(num_inference_steps=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ ).audios
__lowerCamelCase : List[str] = sampling.iplms_sample(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , {} )
__lowerCamelCase : int = generated.clamp(-1 , 1 )
__lowerCamelCase : int = (generated - audio).abs().sum()
__lowerCamelCase : int = (generated - audio).abs().max()
if args.save:
pipe.save_pretrained(args.checkpoint_path )
print("""Diff sum""" , SCREAMING_SNAKE_CASE_ )
print("""Diff max""" , SCREAMING_SNAKE_CASE_ )
assert diff_max < 1e-3, f"""Diff max: {diff_max} is too much :-/"""
print(f"""Conversion for {model_name} successful!""" )
if __name__ == "__main__":
__UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
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=None, type=str, required=True, help='Path to the output model.')
__UpperCamelCase : Any = parser.parse_args()
main(args)
| 519 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : Dict = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextConfig''',
'''XCLIPVisionConfig''',
],
'''processing_x_clip''': ['''XCLIPProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = [
'''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XCLIPModel''',
'''XCLIPPreTrainedModel''',
'''XCLIPTextModel''',
'''XCLIPVisionModel''',
]
if TYPE_CHECKING:
from .configuration_x_clip import (
XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
XCLIPConfig,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .processing_x_clip import XCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_x_clip import (
XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
else:
import sys
A : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 287 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class _lowercase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : str = ProphetNetTokenizer
_SCREAMING_SNAKE_CASE : List[Any] = False
def a ( self : int ) -> Tuple:
super().setUp()
__lowerCAmelCase = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
__lowerCAmelCase = 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 a ( self : int , SCREAMING_SNAKE_CASE__ : Any ) -> Any:
__lowerCAmelCase = """UNwant\u00E9d,running"""
__lowerCAmelCase = """unwanted, running"""
return input_text, output_text
def a ( self : Union[str, Any] ) -> int:
__lowerCAmelCase = self.tokenizer_class(self.vocab_file )
__lowerCAmelCase = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ ) , [9, 6, 7, 12, 10, 11] )
def a ( self : Optional[int] ) -> Tuple:
__lowerCAmelCase = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] )
def a ( self : int ) -> Optional[Any]:
__lowerCAmelCase = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def a ( self : Optional[Any] ) -> Union[str, Any]:
__lowerCAmelCase = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] )
def a ( self : Dict ) -> List[Any]:
__lowerCAmelCase = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def a ( self : Union[str, Any] ) -> str:
__lowerCAmelCase = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def a ( self : Optional[Any] ) -> int:
__lowerCAmelCase = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def a ( self : Union[str, Any] ) -> Optional[int]:
__lowerCAmelCase = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def a ( self : Tuple ) -> List[str]:
__lowerCAmelCase = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def a ( self : List[str] ) -> int:
__lowerCAmelCase = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ , never_split=["""[UNK]"""] )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )
def a ( self : Dict ) -> int:
__lowerCAmelCase = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]
__lowerCAmelCase = {}
for i, token in enumerate(SCREAMING_SNAKE_CASE__ ):
__lowerCAmelCase = i
__lowerCAmelCase = WordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE__ , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] )
@require_torch
def a ( self : List[str] ) -> List[str]:
__lowerCAmelCase = self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased""" )
__lowerCAmelCase = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
__lowerCAmelCase = [10_37, 21_46, 2_04_23, 20_05, 76_80, 78_49, 39_89, 10_12, 1_02]
__lowerCAmelCase = tokenizer(SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , return_tensors="""pt""" )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = list(batch.input_ids.numpy()[0] )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def a ( self : Optional[int] ) -> Dict:
self.assertTrue(_is_whitespace(""" """ ) )
self.assertTrue(_is_whitespace("""\t""" ) )
self.assertTrue(_is_whitespace("""\r""" ) )
self.assertTrue(_is_whitespace("""\n""" ) )
self.assertTrue(_is_whitespace("""\u00A0""" ) )
self.assertFalse(_is_whitespace("""A""" ) )
self.assertFalse(_is_whitespace("""-""" ) )
def a ( self : str ) -> Any:
self.assertTrue(_is_control("""\u0005""" ) )
self.assertFalse(_is_control("""A""" ) )
self.assertFalse(_is_control(""" """ ) )
self.assertFalse(_is_control("""\t""" ) )
self.assertFalse(_is_control("""\r""" ) )
def a ( self : List[Any] ) -> Union[str, Any]:
self.assertTrue(_is_punctuation("""-""" ) )
self.assertTrue(_is_punctuation("""$""" ) )
self.assertTrue(_is_punctuation("""`""" ) )
self.assertTrue(_is_punctuation(""".""" ) )
self.assertFalse(_is_punctuation("""A""" ) )
self.assertFalse(_is_punctuation(""" """ ) )
@slow
def a ( self : Union[str, Any] ) -> Any:
__lowerCAmelCase = self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased""" )
__lowerCAmelCase = tokenizer.encode("""sequence builders""" , add_special_tokens=SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = tokenizer.encode("""multi-sequence build""" , add_special_tokens=SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
assert encoded_sentence == text + [1_02]
assert encoded_pair == text + [1_02] + text_a + [1_02]
| 330 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Union[str, Any] = logging.get_logger(__name__)
_A : Union[str, Any] = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
),
'''facebook/dpr-question_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
),
'''facebook/dpr-reader-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
),
'''facebook/dpr-ctx_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
),
'''facebook/dpr-question_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
),
'''facebook/dpr-reader-multiset-base''': (
'''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
),
}
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : str = """dpr"""
def __init__( self : Dict , SCREAMING_SNAKE_CASE__ : Tuple=3_05_22 , SCREAMING_SNAKE_CASE__ : List[Any]=7_68 , SCREAMING_SNAKE_CASE__ : int=12 , SCREAMING_SNAKE_CASE__ : List[str]=12 , SCREAMING_SNAKE_CASE__ : Tuple=30_72 , SCREAMING_SNAKE_CASE__ : int="gelu" , SCREAMING_SNAKE_CASE__ : Optional[int]=0.1 , SCREAMING_SNAKE_CASE__ : str=0.1 , SCREAMING_SNAKE_CASE__ : List[str]=5_12 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=2 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=0.0_2 , SCREAMING_SNAKE_CASE__ : Optional[Any]=1e-1_2 , SCREAMING_SNAKE_CASE__ : Optional[Any]=0 , SCREAMING_SNAKE_CASE__ : int="absolute" , SCREAMING_SNAKE_CASE__ : int = 0 , **SCREAMING_SNAKE_CASE__ : str , ) -> Tuple:
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = vocab_size
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = hidden_act
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = max_position_embeddings
__lowerCAmelCase = type_vocab_size
__lowerCAmelCase = initializer_range
__lowerCAmelCase = layer_norm_eps
__lowerCAmelCase = projection_dim
__lowerCAmelCase = position_embedding_type
| 330 | 1 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
SCREAMING_SNAKE_CASE : Dict = logging.getLogger(__name__)
@dataclass
class _lowerCamelCase:
lowercase_ : Optional[str] = field(
default="""tab_fact""", metadata={"""help""": """The name of the dataset to use (via the datasets library)."""} )
lowercase_ : Optional[str] = field(
default="""tab_fact""", metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}, )
lowercase_ : int = field(
default=10_24, metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
}, )
lowercase_ : bool = field(
default=_a, metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
lowercase_ : bool = field(
default=_a, metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
}, )
lowercase_ : Optional[int] = field(
default=_a, metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
}, )
lowercase_ : Optional[int] = field(
default=_a, metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
}, )
lowercase_ : Optional[int] = field(
default=_a, metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
}, )
lowercase_ : Optional[str] = field(
default=_a, metadata={"""help""": """A csv or a json file containing the training data."""} )
lowercase_ : Optional[str] = field(
default=_a, metadata={"""help""": """A csv or a json file containing the validation data."""} )
lowercase_ : Optional[str] = field(default=_a, metadata={"""help""": """A csv or a json file containing the test data."""} )
def UpperCamelCase ( self) -> Dict:
"""simple docstring"""
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.')
else:
_lowercase : int = self.train_file.split('.')[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
_lowercase : Tuple = self.validation_file.split('.')[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class _lowerCamelCase:
lowercase_ : str = field(
default=_a, metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
lowercase_ : Optional[str] = field(
default=_a, metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
lowercase_ : Optional[str] = field(
default=_a, metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
lowercase_ : Optional[str] = field(
default=_a, metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}, )
lowercase_ : bool = field(
default=_a, metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}, )
lowercase_ : str = field(
default="""main""", metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}, )
lowercase_ : bool = field(
default=_a, metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
}, )
def UpperCamelCase_( ) -> Optional[int]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_lowercase : Optional[int] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_lowercase , _lowercase , _lowercase : Tuple = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowercase , _lowercase , _lowercase : Union[str, Any] = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
_lowercase : Union[str, Any] = training_args.get_process_log_level()
logger.setLevel(lowerCamelCase_ )
datasets.utils.logging.set_verbosity(lowerCamelCase_ )
transformers.utils.logging.set_verbosity(lowerCamelCase_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
_lowercase : Optional[int] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_lowercase : Dict = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
_lowercase : Dict = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
_lowercase : Optional[Any] = {'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
_lowercase : Tuple = data_args.train_file.split('.' )[-1]
_lowercase : int = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
_lowercase : Any = data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(F'''load a local file for {key}: {data_files[key]}''' )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
_lowercase : str = load_dataset('csv' , data_files=lowerCamelCase_ , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
_lowercase : Optional[int] = load_dataset('json' , data_files=lowerCamelCase_ , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
_lowercase : Optional[Any] = raw_datasets['train'].features['label'].names
_lowercase : Any = len(lowerCamelCase_ )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowercase : List[Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowerCamelCase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
_lowercase : str = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=lowerCamelCase_ , )
_lowercase : Tuple = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=lowerCamelCase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
_lowercase : int = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
_lowercase : str = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
_lowercase : List[Any] = {'Refused': 0, 'Entailed': 1}
_lowercase : Union[str, Any] = {0: 'Refused', 1: 'Entailed'}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'''
F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.''' )
_lowercase : List[str] = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(lowerCamelCase_ ):
# Tokenize the texts
def _convert_table_text_to_pandas(lowerCamelCase_ ):
_lowercase : int = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
_lowercase : Any = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
_lowercase : List[Any] = examples['statement']
_lowercase : Optional[Any] = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
_lowercase : Union[str, Any] = tokenizer(lowerCamelCase_ , lowerCamelCase_ , padding=lowerCamelCase_ , max_length=lowerCamelCase_ , truncation=lowerCamelCase_ )
_lowercase : Any = examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
_lowercase : str = raw_datasets.map(
lowerCamelCase_ , batched=lowerCamelCase_ , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
_lowercase : Any = raw_datasets['train']
if data_args.max_train_samples is not None:
_lowercase : str = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
_lowercase : str = raw_datasets['validation']
if data_args.max_eval_samples is not None:
_lowercase : List[Any] = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
_lowercase : Optional[int] = raw_datasets['test']
if data_args.max_predict_samples is not None:
_lowercase : List[str] = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(lowerCamelCase_ ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(lowerCamelCase_ ):
_lowercase : Dict = p.predictions[0] if isinstance(p.predictions , lowerCamelCase_ ) else p.predictions
_lowercase : Tuple = np.argmax(lowerCamelCase_ , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
_lowercase : Any = default_data_collator
elif training_args.fpaa:
_lowercase : str = DataCollatorWithPadding(lowerCamelCase_ , pad_to_multiple_of=8 )
else:
_lowercase : Optional[Any] = None
# Initialize our Trainer
_lowercase : List[str] = Trainer(
model=lowerCamelCase_ , args=lowerCamelCase_ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=lowerCamelCase_ , tokenizer=lowerCamelCase_ , data_collator=lowerCamelCase_ , )
# Training
if training_args.do_train:
_lowercase : Optional[int] = None
if training_args.resume_from_checkpoint is not None:
_lowercase : List[Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_lowercase : Optional[Any] = last_checkpoint
_lowercase : Optional[Any] = trainer.train(resume_from_checkpoint=lowerCamelCase_ )
_lowercase : List[Any] = train_result.metrics
_lowercase : Dict = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCamelCase_ )
)
_lowercase : int = min(lowerCamelCase_ , len(lowerCamelCase_ ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , lowerCamelCase_ )
trainer.save_metrics('train' , lowerCamelCase_ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
_lowercase : Tuple = trainer.evaluate(eval_dataset=lowerCamelCase_ )
_lowercase : Any = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase_ )
_lowercase : Optional[int] = min(lowerCamelCase_ , len(lowerCamelCase_ ) )
trainer.log_metrics('eval' , lowerCamelCase_ )
trainer.save_metrics('eval' , lowerCamelCase_ )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
_lowercase : Any = predict_dataset.remove_columns('label' )
_lowercase : Optional[Any] = trainer.predict(lowerCamelCase_ , metric_key_prefix='predict' ).predictions
_lowercase : Union[str, Any] = np.argmax(lowerCamelCase_ , axis=1 )
_lowercase : Dict = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(lowerCamelCase_ , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(lowerCamelCase_ ):
_lowercase : List[str] = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
_lowercase : str = {'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**lowerCamelCase_ )
else:
trainer.create_model_card(**lowerCamelCase_ )
def UpperCamelCase_( lowerCamelCase_ ) -> Dict:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 89 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_SCREAMING_SNAKE_CASE = 2_99_79_24_58
# Symbols
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = symbols("ct x y z")
def _snake_case (_snake_case : float) -> float:
if velocity > c:
raise ValueError('Speed must not exceed light speed 299,792,458 [m/s]!')
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError('Speed must be greater than or equal to 1!')
return velocity / c
def _snake_case (_snake_case : float) -> float:
return 1 / sqrt(1 - beta(_snake_case) ** 2)
def _snake_case (_snake_case : float) -> np.ndarray:
return np.array(
[
[gamma(_snake_case), -gamma(_snake_case) * beta(_snake_case), 0, 0],
[-gamma(_snake_case) * beta(_snake_case), gamma(_snake_case), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
])
def _snake_case (_snake_case : float , _snake_case : np.ndarray | None = None) -> np.ndarray:
# Ensure event is not empty
if event is None:
_lowercase =np.array([ct, x, y, z]) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(_snake_case) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
_SCREAMING_SNAKE_CASE = transform(29_97_92_45)
print("Example of four vector: ")
print(f'''ct\' = {four_vector[0]}''')
print(f'''x\' = {four_vector[1]}''')
print(f'''y\' = {four_vector[2]}''')
print(f'''z\' = {four_vector[3]}''')
# Substitute symbols with numerical values
_SCREAMING_SNAKE_CASE = {ct: c, x: 1, y: 1, z: 1}
_SCREAMING_SNAKE_CASE = [four_vector[i].subs(sub_dict) for i in range(4)]
print(f'''\n{numerical_vector}''')
| 181 | 0 |
from __future__ import annotations
def _UpperCAmelCase ( a : list[list[int]] ):
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(a ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 , len(a ) ):
for j in range(1 , len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] , matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 99 |
import comet # From: unbabel-comet
import torch
import datasets
a__ = datasets.logging.get_logger(__name__)
a__ = """\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
title = {Unbabel's Participation in the WMT20 Metrics Shared Task},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
month = {November},
year = {2020},
address = {Online},
publisher = {Association for Computational Linguistics},
pages = {909--918},
}
@inproceedings{rei-etal-2020-comet,
title = \"{COMET}: A Neural Framework for {MT} Evaluation\",
author = \"Rei, Ricardo and
Stewart, Craig and
Farinha, Ana C and
Lavie, Alon\",
booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",
month = nov,
year = \"2020\",
address = \"Online\",
publisher = \"Association for Computational Linguistics\",
url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",
pages = \"2685--2702\",
}
"""
a__ = """\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.
See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
"""
a__ = """
COMET score.
Args:
`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.
Returns:
`samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
`scores`: List of scores.
Examples:
>>> comet_metric = datasets.load_metric('comet')
>>> # comet_metric = load_metric('comet', 'wmt20-comet-da') # you can also choose which model to use
>>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]
>>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]
>>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]
>>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
>>> print([round(v, 2) for v in results[\"scores\"]])
[0.19, 0.92]
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def __magic_name__ ( self : Optional[Any]):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""https://unbabel.github.io/COMET/html/index.html""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""sources""": datasets.Value("""string""" , id="""sequence"""),
"""predictions""": datasets.Value("""string""" , id="""sequence"""),
"""references""": datasets.Value("""string""" , id="""sequence"""),
}) , codebase_urls=["""https://github.com/Unbabel/COMET"""] , reference_urls=[
"""https://github.com/Unbabel/COMET""",
"""https://www.aclweb.org/anthology/2020.emnlp-main.213/""",
"""http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6""",
] , )
def __magic_name__ ( self : str , UpperCamelCase__ : Dict):
'''simple docstring'''
if self.config_name == "default":
snake_case__ = comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da"""))
else:
snake_case__ = comet.load_from_checkpoint(comet.download_model(self.config_name))
def __magic_name__ ( self : List[str] , UpperCamelCase__ : str , UpperCamelCase__ : Tuple , UpperCamelCase__ : List[Any] , UpperCamelCase__ : int=None , UpperCamelCase__ : List[Any]=False):
'''simple docstring'''
if gpus is None:
snake_case__ = 1 if torch.cuda.is_available() else 0
snake_case__ = {"""src""": sources, """mt""": predictions, """ref""": references}
snake_case__ = [dict(zip(UpperCamelCase__ , UpperCamelCase__)) for t in zip(*data.values())]
snake_case__ , snake_case__ = self.scorer.predict(UpperCamelCase__ , gpus=UpperCamelCase__ , progress_bar=UpperCamelCase__)
return {"mean_score": mean_score, "scores": scores}
| 99 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : str = {
"""facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
"""facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
"""facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
"""facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
"""facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
"""facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
"""facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
"""facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
"""facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}
class __SCREAMING_SNAKE_CASE ( UpperCAmelCase_ ):
__a ='xmod'
def __init__( self , lowerCamelCase=3_0522 , lowerCamelCase=768 , lowerCamelCase=12 , lowerCamelCase=12 , lowerCamelCase=3072 , lowerCamelCase="gelu" , lowerCamelCase=0.1 , lowerCamelCase=0.1 , lowerCamelCase=512 , lowerCamelCase=2 , lowerCamelCase=0.02 , lowerCamelCase=1e-12 , lowerCamelCase=1 , lowerCamelCase=0 , lowerCamelCase=2 , lowerCamelCase="absolute" , lowerCamelCase=True , lowerCamelCase=None , lowerCamelCase=False , lowerCamelCase=2 , lowerCamelCase=False , lowerCamelCase=True , lowerCamelCase=True , lowerCamelCase=("en_XX",) , lowerCamelCase=None , **lowerCamelCase , ) ->int:
'''simple docstring'''
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
__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 = type_vocab_size
__a = initializer_range
__a = layer_norm_eps
__a = position_embedding_type
__a = use_cache
__a = classifier_dropout
__a = pre_norm
__a = adapter_reduction_factor
__a = adapter_layer_norm
__a = adapter_reuse_layer_norm
__a = ln_before_adapter
__a = list(SCREAMING_SNAKE_CASE_ )
__a = default_language
class __SCREAMING_SNAKE_CASE ( UpperCAmelCase_ ):
@property
def __UpperCamelCase ( self ) ->List[str]:
'''simple docstring'''
if self.task == "multiple-choice":
__a = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
__a = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] ) | 448 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
lowerCamelCase__ : Optional[Any] = logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase_ )
class _snake_case ( UpperCAmelCase_ ):
def __init__( self , **SCREAMING_SNAKE_CASE_):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_)
if self.framework == "tf":
raise ValueError(f'The {self.__class__} is only available in PyTorch.')
requires_backends(self , """vision""")
self.check_model_type(SCREAMING_SNAKE_CASE_)
def __call__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
'''simple docstring'''
if "text_queries" in kwargs:
lowercase__ : Any = kwargs.pop("""text_queries""")
if isinstance(SCREAMING_SNAKE_CASE_ , (str, Image.Image)):
lowercase__ : Optional[Any] = {"""image""": image, """candidate_labels""": candidate_labels}
else:
lowercase__ : int = image
lowercase__ : List[str] = super().__call__(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_)
return results
def lowercase__ ( self , **SCREAMING_SNAKE_CASE_):
'''simple docstring'''
lowercase__ : Tuple = {}
if "threshold" in kwargs:
lowercase__ : List[Any] = kwargs["""threshold"""]
if "top_k" in kwargs:
lowercase__ : int = kwargs["""top_k"""]
return {}, {}, postprocess_params
def lowercase__ ( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
lowercase__ : str = load_image(inputs["""image"""])
lowercase__ : Any = inputs["""candidate_labels"""]
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_):
lowercase__ : List[str] = candidate_labels.split(""",""")
lowercase__ : Tuple = torch.tensor([[image.height, image.width]] , dtype=torch.intaa)
for i, candidate_label in enumerate(SCREAMING_SNAKE_CASE_):
lowercase__ : Optional[Any] = self.tokenizer(SCREAMING_SNAKE_CASE_ , return_tensors=self.framework)
lowercase__ : Union[str, Any] = self.image_processor(SCREAMING_SNAKE_CASE_ , return_tensors=self.framework)
yield {
"is_last": i == len(SCREAMING_SNAKE_CASE_) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def lowercase__ ( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
lowercase__ : str = model_inputs.pop("""target_size""")
lowercase__ : Optional[int] = model_inputs.pop("""candidate_label""")
lowercase__ : Dict = model_inputs.pop("""is_last""")
lowercase__ : Union[str, Any] = self.model(**SCREAMING_SNAKE_CASE_)
lowercase__ : Union[str, Any] = {"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
return model_outputs
def lowercase__ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=None):
'''simple docstring'''
lowercase__ : Union[str, Any] = []
for model_output in model_outputs:
lowercase__ : Optional[int] = model_output["""candidate_label"""]
lowercase__ : Tuple = BaseModelOutput(SCREAMING_SNAKE_CASE_)
lowercase__ : List[str] = self.image_processor.post_process_object_detection(
outputs=SCREAMING_SNAKE_CASE_ , threshold=SCREAMING_SNAKE_CASE_ , target_sizes=model_output["""target_size"""])[0]
for index in outputs["scores"].nonzero():
lowercase__ : Optional[Any] = outputs["""scores"""][index].item()
lowercase__ : Optional[Any] = self._get_bounding_box(outputs["""boxes"""][index][0])
lowercase__ : Tuple = {"""score""": score, """label""": label, """box""": box}
results.append(SCREAMING_SNAKE_CASE_)
lowercase__ : int = sorted(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_: x["score"] , reverse=SCREAMING_SNAKE_CASE_)
if top_k:
lowercase__ : Any = results[:top_k]
return results
def lowercase__ ( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
if self.framework != "pt":
raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch.""")
lowercase__ , lowercase__ , lowercase__ , lowercase__ : List[Any] = box.int().tolist()
lowercase__ : Optional[int] = {
"""xmin""": xmin,
"""ymin""": ymin,
"""xmax""": xmax,
"""ymax""": ymax,
}
return bbox
| 12 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
A_ : Optional[Any] = logging.get_logger(__name__)
class __snake_case ( _A ):
'''simple docstring'''
def __init__( self , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ):
warnings.warn(
"""The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use LayoutLMv2ImageProcessor instead.""" , UpperCamelCase__ , )
super().__init__(*UpperCamelCase__ , **UpperCamelCase__ )
| 710 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class __snake_case :
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE = 6 ):
snake_case__ : Node | None = None
snake_case__ : Node | None = None
self.create_linked_list(__SCREAMING_SNAKE_CASE )
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE ):
snake_case__ : Union[str, Any] = Node()
snake_case__ : Any = current_node
snake_case__ : Dict = current_node
snake_case__ : Union[str, Any] = current_node
for _ in range(1 , __SCREAMING_SNAKE_CASE ):
snake_case__ : List[str] = Node()
snake_case__ : Dict = current_node
snake_case__ : List[Any] = previous_node
snake_case__ : Optional[Any] = current_node
snake_case__ : List[Any] = self.front
snake_case__ : Union[str, Any] = previous_node
def __UpperCamelCase ( self ):
return (
self.front == self.rear
and self.front is not None
and self.front.data is None
)
def __UpperCamelCase ( self ):
self.check_can_perform_operation()
return self.front.data if self.front else None
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE ):
if self.rear is None:
return
self.check_is_full()
if not self.is_empty():
snake_case__ : List[str] = self.rear.next
if self.rear:
snake_case__ : List[Any] = data
def __UpperCamelCase ( self ):
self.check_can_perform_operation()
if self.rear is None or self.front is None:
return None
if self.front == self.rear:
snake_case__ : Tuple = self.front.data
snake_case__ : List[str] = None
return data
snake_case__ : Optional[Any] = self.front
snake_case__ : Any = old_front.next
snake_case__ : int = old_front.data
snake_case__ : Any = None
return data
def __UpperCamelCase ( self ):
if self.is_empty():
raise Exception("""Empty Queue""" )
def __UpperCamelCase ( self ):
if self.rear and self.rear.next == self.front:
raise Exception("""Full Queue""" )
class __snake_case :
'''simple docstring'''
def __init__( self ):
snake_case__ : Any | None = None
snake_case__ : Node | None = None
snake_case__ : Node | None = None
if __name__ == "__main__":
import doctest
doctest.testmod()
| 419 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class __a ( UpperCAmelCase__ ):
UpperCamelCase_ : Dict = """upernet"""
def __init__( self : Union[str, Any] , UpperCAmelCase_ : List[str]=None , UpperCAmelCase_ : Dict=512 , UpperCAmelCase_ : List[str]=0.02 , UpperCAmelCase_ : List[Any]=[1, 2, 3, 6] , UpperCAmelCase_ : str=True , UpperCAmelCase_ : List[str]=0.4 , UpperCAmelCase_ : List[str]=384 , UpperCAmelCase_ : Optional[int]=256 , UpperCAmelCase_ : Optional[Any]=1 , UpperCAmelCase_ : Dict=False , UpperCAmelCase_ : List[str]=255 , **UpperCAmelCase_ : Any , )-> str:
"""simple docstring"""
super().__init__(**SCREAMING_SNAKE_CASE__ )
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(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
UpperCamelCase = backbone_config.get("model_type" )
UpperCamelCase = CONFIG_MAPPING[backbone_model_type]
UpperCamelCase = config_class.from_dict(SCREAMING_SNAKE_CASE__ )
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 _SCREAMING_SNAKE_CASE ( self : Optional[int] )-> List[str]:
"""simple docstring"""
UpperCamelCase = copy.deepcopy(self.__dict__ )
UpperCamelCase = self.backbone_config.to_dict()
UpperCamelCase = self.__class__.model_type
return output
| 554 | '''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
_A : Any = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction. The statistic
is also known as the phi coefficient. [source: Wikipedia]
'''
_A : Optional[int] = '''
Args:
predictions (list of int): Predicted labels, as returned by a model.
references (list of int): Ground truth labels.
sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
matthews_correlation (dict containing float): Matthews correlation.
Examples:
Example 1, a basic example with only predictions and references as inputs:
>>> matthews_metric = datasets.load_metric("matthews_correlation")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3])
>>> print(round(results[\'matthews_correlation\'], 2))
0.54
Example 2, the same example as above, but also including sample weights:
>>> matthews_metric = datasets.load_metric("matthews_correlation")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 3, 1, 1, 1, 2])
>>> print(round(results[\'matthews_correlation\'], 2))
0.1
Example 3, the same example as above, but with sample weights that cause a negative correlation:
>>> matthews_metric = datasets.load_metric("matthews_correlation")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 1, 0, 0, 0, 1])
>>> print(round(results[\'matthews_correlation\'], 2))
-0.25
'''
_A : Any = '''\
@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 _lowercase ( datasets.Metric ):
'''simple docstring'''
def a ( self : Any ) -> List[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def a ( self : List[Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int=None ) -> Union[str, Any]:
return {
"matthews_correlation": float(matthews_corrcoef(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , sample_weight=SCREAMING_SNAKE_CASE__ ) ),
}
| 427 | 0 |
"""simple docstring"""
from typing import Any
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCamelCase__ : Any ):
A__ : int =data
A__ : List[str] =None
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Dict ):
A__ : str =None
def _UpperCAmelCase ( self : Optional[int] ):
A__ : str =self.head
while temp is not None:
print(temp.data , end=" " )
A__ : List[Any] =temp.next
print()
def _UpperCAmelCase ( self : Optional[int] , UpperCamelCase__ : Any ):
A__ : Dict =Node(a_ )
A__ : str =self.head
A__ : List[Any] =new_node
def _UpperCAmelCase ( self : Optional[Any] , UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any] ):
if node_data_a == node_data_a:
return
else:
A__ : List[Any] =self.head
while node_a is not None and node_a.data != node_data_a:
A__ : Optional[Any] =node_a.next
A__ : str =self.head
while node_a is not None and node_a.data != node_data_a:
A__ : Union[str, Any] =node_a.next
if node_a is None or node_a is None:
return
A__ : Dict =node_a.data, node_a.data
if __name__ == "__main__":
__A : Any = LinkedList()
for i in range(5, 0, -1):
ll.push(i)
ll.print_list()
ll.swap_nodes(1, 4)
print("After swapping")
ll.print_list()
| 706 | """simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__A : List[Any] = logging.get_logger(__name__)
__A : str = {
"b0": efficientnet.EfficientNetBa,
"b1": efficientnet.EfficientNetBa,
"b2": efficientnet.EfficientNetBa,
"b3": efficientnet.EfficientNetBa,
"b4": efficientnet.EfficientNetBa,
"b5": efficientnet.EfficientNetBa,
"b6": efficientnet.EfficientNetBa,
"b7": efficientnet.EfficientNetBa,
}
__A : List[Any] = {
"b0": {
"hidden_dim": 1_280,
"width_coef": 1.0,
"depth_coef": 1.0,
"image_size": 224,
"dropout_rate": 0.2,
"dw_padding": [],
},
"b1": {
"hidden_dim": 1_280,
"width_coef": 1.0,
"depth_coef": 1.1,
"image_size": 240,
"dropout_rate": 0.2,
"dw_padding": [16],
},
"b2": {
"hidden_dim": 1_408,
"width_coef": 1.1,
"depth_coef": 1.2,
"image_size": 260,
"dropout_rate": 0.3,
"dw_padding": [5, 8, 16],
},
"b3": {
"hidden_dim": 1_536,
"width_coef": 1.2,
"depth_coef": 1.4,
"image_size": 300,
"dropout_rate": 0.3,
"dw_padding": [5, 18],
},
"b4": {
"hidden_dim": 1_792,
"width_coef": 1.4,
"depth_coef": 1.8,
"image_size": 380,
"dropout_rate": 0.4,
"dw_padding": [6],
},
"b5": {
"hidden_dim": 2_048,
"width_coef": 1.6,
"depth_coef": 2.2,
"image_size": 456,
"dropout_rate": 0.4,
"dw_padding": [13, 27],
},
"b6": {
"hidden_dim": 2_304,
"width_coef": 1.8,
"depth_coef": 2.6,
"image_size": 528,
"dropout_rate": 0.5,
"dw_padding": [31],
},
"b7": {
"hidden_dim": 2_560,
"width_coef": 2.0,
"depth_coef": 3.1,
"image_size": 600,
"dropout_rate": 0.5,
"dw_padding": [18],
},
}
def lowercase ( UpperCamelCase : Dict ):
"""simple docstring"""
A__ : int =EfficientNetConfig()
A__ : Optional[int] =CONFIG_MAP[model_name]["hidden_dim"]
A__ : List[Any] =CONFIG_MAP[model_name]["width_coef"]
A__ : Tuple =CONFIG_MAP[model_name]["depth_coef"]
A__ : Union[str, Any] =CONFIG_MAP[model_name]["image_size"]
A__ : Dict =CONFIG_MAP[model_name]["dropout_rate"]
A__ : Any =CONFIG_MAP[model_name]["dw_padding"]
A__ : Tuple ="huggingface/label-files"
A__ : Tuple ="imagenet-1k-id2label.json"
A__ : Optional[int] =1000
A__ : List[str] =json.load(open(hf_hub_download(UpperCamelCase , UpperCamelCase , repo_type="dataset" ) , "r" ) )
A__ : Any ={int(UpperCamelCase ): v for k, v in idalabel.items()}
A__ : List[str] =idalabel
A__ : Optional[Any] ={v: k for k, v in idalabel.items()}
return config
def lowercase ( ):
"""simple docstring"""
A__ : List[str] ="http://images.cocodataset.org/val2017/000000039769.jpg"
A__ : int =Image.open(requests.get(UpperCamelCase , stream=UpperCamelCase ).raw )
return im
def lowercase ( UpperCamelCase : Optional[int] ):
"""simple docstring"""
A__ : List[Any] =CONFIG_MAP[model_name]["image_size"]
A__ : List[str] =EfficientNetImageProcessor(
size={"height": size, "width": size} , image_mean=[0.4_85, 0.4_56, 0.4_06] , image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63] , do_center_crop=UpperCamelCase , )
return preprocessor
def lowercase ( UpperCamelCase : Dict ):
"""simple docstring"""
A__ : List[str] =[v.split("_" )[0].split("block" )[1] for v in original_param_names if v.startswith("block" )]
A__ : Optional[Any] =sorted(set(UpperCamelCase ) )
A__ : List[Any] =len(UpperCamelCase )
A__ : int ={b: str(UpperCamelCase ) for b, i in zip(UpperCamelCase , range(UpperCamelCase ) )}
A__ : List[Any] =[]
rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight") )
rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight") )
rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias") )
rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean") )
rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var") )
for b in block_names:
A__ : List[Any] =block_name_mapping[b]
rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''') )
rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''') )
rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''') )
rename_keys.append(
(F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''') )
rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''') )
rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''') )
rename_keys.append(
(F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''') )
rename_keys.append(
(F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''') )
rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''') )
rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''') )
rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''') )
rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''') )
rename_keys.append(
(F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''') )
rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''') )
rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''') )
rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight") )
rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight") )
rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias") )
rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean") )
rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var") )
A__ : List[str] ={}
for item in rename_keys:
if item[0] in original_param_names:
A__ : Union[str, Any] ="efficientnet." + item[1]
A__ : str ="classifier.weight"
A__ : Tuple ="classifier.bias"
return key_mapping
def lowercase ( UpperCamelCase : Any , UpperCamelCase : Any , UpperCamelCase : int ):
"""simple docstring"""
for key, value in tf_params.items():
if "normalization" in key:
continue
A__ : str =key_mapping[key]
if "_conv" in key and "kernel" in key:
A__ : Optional[int] =torch.from_numpy(UpperCamelCase ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
A__ : Optional[int] =torch.from_numpy(UpperCamelCase ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
A__ : str =torch.from_numpy(np.transpose(UpperCamelCase ) )
else:
A__ : Optional[int] =torch.from_numpy(UpperCamelCase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(UpperCamelCase )
@torch.no_grad()
def lowercase ( UpperCamelCase : Tuple , UpperCamelCase : List[Any] , UpperCamelCase : Tuple , UpperCamelCase : str ):
"""simple docstring"""
A__ : Union[str, Any] =model_classes[model_name](
include_top=UpperCamelCase , weights="imagenet" , input_tensor=UpperCamelCase , input_shape=UpperCamelCase , pooling=UpperCamelCase , classes=1000 , classifier_activation="softmax" , )
A__ : Union[str, Any] =original_model.trainable_variables
A__ : str =original_model.non_trainable_variables
A__ : Any ={param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
A__ : int =param.numpy()
A__ : Optional[Any] =list(tf_params.keys() )
# Load HuggingFace model
A__ : Optional[Any] =get_efficientnet_config(UpperCamelCase )
A__ : List[str] =EfficientNetForImageClassification(UpperCamelCase ).eval()
A__ : Union[str, Any] =hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("Converting parameters..." )
A__ : List[Any] =rename_keys(UpperCamelCase )
replace_params(UpperCamelCase , UpperCamelCase , UpperCamelCase )
# Initialize preprocessor and preprocess input image
A__ : int =convert_image_processor(UpperCamelCase )
A__ : List[str] =preprocessor(images=prepare_img() , return_tensors="pt" )
# HF model inference
hf_model.eval()
with torch.no_grad():
A__ : Any =hf_model(**UpperCamelCase )
A__ : Union[str, Any] =outputs.logits.detach().numpy()
# Original model inference
A__ : Union[str, Any] =False
A__ : Tuple =CONFIG_MAP[model_name]["image_size"]
A__ : int =prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
A__ : Any =image.img_to_array(UpperCamelCase )
A__ : Dict =np.expand_dims(UpperCamelCase , axis=0 )
A__ : int =original_model.predict(UpperCamelCase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(UpperCamelCase , UpperCamelCase , atol=1E-3 ), "The predicted logits are not the same."
print("Model outputs match!" )
if save_model:
# Create folder to save model
if not os.path.isdir(UpperCamelCase ):
os.mkdir(UpperCamelCase )
# Save converted model and image processor
hf_model.save_pretrained(UpperCamelCase )
preprocessor.save_pretrained(UpperCamelCase )
if push_to_hub:
# Push model and image processor to hub
print(F'''Pushing converted {model_name} to the hub...''' )
A__ : Tuple =F'''efficientnet-{model_name}'''
preprocessor.push_to_hub(UpperCamelCase )
hf_model.push_to_hub(UpperCamelCase )
if __name__ == "__main__":
__A : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="b0",
type=str,
help="Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="hf_model",
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument("--save_model", action="store_true", help="Save model to local")
parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
__A : Optional[int] = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 595 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A ( __lowercase ):
_snake_case =42
_snake_case =42
def __init__( self: Tuple , _lowerCAmelCase: UNetaDModel , _lowerCAmelCase: ScoreSdeVeScheduler ) -> Optional[int]:
'''simple docstring'''
super().__init__()
self.register_modules(unet=_lowerCAmelCase , scheduler=_lowerCAmelCase )
@torch.no_grad()
def __call__( self: int , _lowerCAmelCase: int = 1 , _lowerCAmelCase: int = 2000 , _lowerCAmelCase: Optional[Union[torch.Generator, List[torch.Generator]]] = None , _lowerCAmelCase: Optional[str] = "pil" , _lowerCAmelCase: bool = True , **_lowerCAmelCase: Tuple , ) -> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
UpperCAmelCase_ =self.unet.config.sample_size
UpperCAmelCase_ =(batch_size, 3, img_size, img_size)
UpperCAmelCase_ =self.unet
UpperCAmelCase_ =randn_tensor(_lowerCAmelCase , generator=_lowerCAmelCase ) * self.scheduler.init_noise_sigma
UpperCAmelCase_ =sample.to(self.device )
self.scheduler.set_timesteps(_lowerCAmelCase )
self.scheduler.set_sigmas(_lowerCAmelCase )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
UpperCAmelCase_ =self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
UpperCAmelCase_ =self.unet(_lowerCAmelCase , _lowerCAmelCase ).sample
UpperCAmelCase_ =self.scheduler.step_correct(_lowerCAmelCase , _lowerCAmelCase , generator=_lowerCAmelCase ).prev_sample
# prediction step
UpperCAmelCase_ =model(_lowerCAmelCase , _lowerCAmelCase ).sample
UpperCAmelCase_ =self.scheduler.step_pred(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , generator=_lowerCAmelCase )
UpperCAmelCase_ , UpperCAmelCase_ =output.prev_sample, output.prev_sample_mean
UpperCAmelCase_ =sample_mean.clamp(0 , 1 )
UpperCAmelCase_ =sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase_ =self.numpy_to_pil(_lowerCAmelCase )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=_lowerCAmelCase )
| 54 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase : Optional[int] ="""\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
and Jeffrey Dean},
year={2016},
eprint={1609.08144},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
__lowercase : Dict ="""\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the 'GLEU score'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score's range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
"""
__lowercase : List[str] ="""\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.
Args:
predictions (list of str): list of translations to score.
Each translation should be tokenized into a list of tokens.
references (list of list of str): list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.
Returns:
'google_bleu': google_bleu score
Examples:
Example 1:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results[\"google_bleu\"], 2))
0.44
Example 2:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
... 'heed', 'the', 'cat', 'commands']
>>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results[\"google_bleu\"], 2))
0.61
Example 3:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
... 'heed', 'the', 'cat', 'commands']
>>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
>>> print(round(results[\"google_bleu\"], 2))
0.53
Example 4:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
... 'heed', 'the', 'cat', 'commands']
>>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
>>> print(round(results[\"google_bleu\"], 2))
0.4
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A ( datasets.Metric ):
def lowerCAmelCase__ ( self: int ) -> MetricInfo:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ),
"references": datasets.Sequence(
datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ) , id="references" ),
} ) , )
def lowerCAmelCase__ ( self: List[str] , _lowerCAmelCase: List[List[List[str]]] , _lowerCAmelCase: List[List[str]] , _lowerCAmelCase: int = 1 , _lowerCAmelCase: int = 4 , ) -> Dict[str, float]:
'''simple docstring'''
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=_lowerCAmelCase , hypotheses=_lowerCAmelCase , min_len=_lowerCAmelCase , max_len=_lowerCAmelCase )
}
| 54 | 1 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
lowerCAmelCase = """__DUMMY_TRANSFORMERS_USER__"""
lowerCAmelCase = """Dummy User"""
lowerCAmelCase = """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"""
lowerCAmelCase = """https://hub-ci.huggingface.co"""
lowerCAmelCase = CI_HUB_ENDPOINT + """/datasets/{repo_id}/resolve/{revision}/{path}"""
lowerCAmelCase = CI_HUB_ENDPOINT + """/{repo_id}/resolve/{revision}/{filename}"""
lowerCAmelCase = Path("""~/.huggingface/hub_ci_token""").expanduser()
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> Tuple:
'''simple docstring'''
monkeypatch.setattr(
'''huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE''' , lowercase_ )
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> List[Any]:
'''simple docstring'''
monkeypatch.setattr('''datasets.config.HF_ENDPOINT''' , lowercase_ )
monkeypatch.setattr('''datasets.config.HUB_DATASETS_URL''' , lowercase_ )
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
'''simple docstring'''
monkeypatch.setattr('''huggingface_hub.hf_api.HfFolder.path_token''' , lowercase_ )
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Dict:
'''simple docstring'''
HfFolder.save_token(lowercase_ )
yield
HfFolder.delete_token()
@pytest.fixture(scope='''session''' )
def __SCREAMING_SNAKE_CASE ( ) -> Optional[Any]:
'''simple docstring'''
return HfApi(endpoint=lowercase_ )
@pytest.fixture(scope='''session''' )
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
'''simple docstring'''
__UpperCAmelCase : int = HfFolder.get_token()
HfFolder.save_token(lowercase_ )
yield CI_HUB_USER_TOKEN
if previous_token is not None:
HfFolder.save_token(lowercase_ )
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> Union[str, Any]:
'''simple docstring'''
def _cleanup_repo(lowercase_ ):
hf_api.delete_repo(lowercase_ , token=lowercase_ , repo_type='''dataset''' )
return _cleanup_repo
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
'''simple docstring'''
@contextmanager
def _temporary_repo(lowercase_ ):
try:
yield repo_id
finally:
cleanup_repo(lowercase_ )
return _temporary_repo
@pytest.fixture(scope='''session''' )
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase : List[str] = f"repo_txt_data-{int(time.time() * 10e3 )}"
__UpperCAmelCase : Dict = f"{CI_HUB_USER}/{repo_name}"
hf_api.create_repo(lowercase_ , token=lowercase_ , repo_type='''dataset''' , private=lowercase_ )
hf_api.upload_file(
token=lowercase_ , path_or_fileobj=str(lowercase_ ) , path_in_repo='''data/text_data.txt''' , repo_id=lowercase_ , repo_type='''dataset''' , )
yield repo_id
try:
hf_api.delete_repo(lowercase_ , token=lowercase_ , repo_type='''dataset''' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> List[str]:
'''simple docstring'''
return hf_private_dataset_repo_txt_data_
@pytest.fixture(scope='''session''' )
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> Dict:
'''simple docstring'''
__UpperCAmelCase : Any = f"repo_zipped_txt_data-{int(time.time() * 10e3 )}"
__UpperCAmelCase : Tuple = f"{CI_HUB_USER}/{repo_name}"
hf_api.create_repo(lowercase_ , token=lowercase_ , repo_type='''dataset''' , private=lowercase_ )
hf_api.upload_file(
token=lowercase_ , path_or_fileobj=str(lowercase_ ) , path_in_repo='''data.zip''' , repo_id=lowercase_ , repo_type='''dataset''' , )
yield repo_id
try:
hf_api.delete_repo(lowercase_ , token=lowercase_ , repo_type='''dataset''' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> int:
'''simple docstring'''
return hf_private_dataset_repo_zipped_txt_data_
@pytest.fixture(scope='''session''' )
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> Optional[Any]:
'''simple docstring'''
__UpperCAmelCase : int = f"repo_zipped_img_data-{int(time.time() * 10e3 )}"
__UpperCAmelCase : Dict = f"{CI_HUB_USER}/{repo_name}"
hf_api.create_repo(lowercase_ , token=lowercase_ , repo_type='''dataset''' , private=lowercase_ )
hf_api.upload_file(
token=lowercase_ , path_or_fileobj=str(lowercase_ ) , path_in_repo='''data.zip''' , repo_id=lowercase_ , repo_type='''dataset''' , )
yield repo_id
try:
hf_api.delete_repo(lowercase_ , token=lowercase_ , repo_type='''dataset''' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
return hf_private_dataset_repo_zipped_img_data_
| 675 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
lowerCAmelCase = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def __SCREAMING_SNAKE_CASE ( lowercase_ = "mumbai" ) -> Generator[tuple[str, str], None, None]:
'''simple docstring'''
__UpperCAmelCase : List[Any] = BeautifulSoup(requests.get(url + location ).content , '''html.parser''' )
# This attribute finds out all the specifics listed in a job
for job in soup.find_all('''div''' , attrs={'''data-tn-component''': '''organicJob'''} ):
__UpperCAmelCase : str = job.find('''a''' , attrs={'''data-tn-element''': '''jobTitle'''} ).text.strip()
__UpperCAmelCase : List[str] = job.find('''span''' , {'''class''': '''company'''} ).text.strip()
yield job_title, company_name
if __name__ == "__main__":
for i, job in enumerate(fetch_jobs("""Bangalore"""), 1):
print(F'Job {i:>2} is {job[0]} at {job[1]}')
| 675 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCAmelCase = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mask2FormerConfig',
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ['Mask2FormerImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'Mask2FormerForUniversalSegmentation',
'Mask2FormerModel',
'Mask2FormerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_maskaformer import MaskaFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskaformer import (
MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskaFormerForUniversalSegmentation,
MaskaFormerModel,
MaskaFormerPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 65 |
"""simple docstring"""
__UpperCAmelCase = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
__UpperCAmelCase = frozenset(['prompt', 'negative_prompt'])
__UpperCAmelCase = frozenset([])
__UpperCAmelCase = frozenset(['image'])
__UpperCAmelCase = frozenset(
[
'image',
'height',
'width',
'guidance_scale',
]
)
__UpperCAmelCase = frozenset(['image'])
__UpperCAmelCase = frozenset(
[
'prompt',
'image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
__UpperCAmelCase = frozenset(['prompt', 'image', 'negative_prompt'])
__UpperCAmelCase = frozenset(
[
# Text guided image variation with an image mask
'prompt',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
__UpperCAmelCase = frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])
__UpperCAmelCase = frozenset(
[
# image variation with an image mask
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
__UpperCAmelCase = frozenset(['image', 'mask_image'])
__UpperCAmelCase = frozenset(
[
'example_image',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
__UpperCAmelCase = frozenset(['example_image', 'image', 'mask_image'])
__UpperCAmelCase = frozenset(['class_labels'])
__UpperCAmelCase = frozenset(['class_labels'])
__UpperCAmelCase = frozenset(['batch_size'])
__UpperCAmelCase = frozenset([])
__UpperCAmelCase = frozenset(['batch_size'])
__UpperCAmelCase = frozenset([])
__UpperCAmelCase = frozenset(
[
'prompt',
'audio_length_in_s',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
__UpperCAmelCase = frozenset(['prompt', 'negative_prompt'])
__UpperCAmelCase = frozenset(['input_tokens'])
__UpperCAmelCase = frozenset(['input_tokens'])
| 65 | 1 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
UpperCamelCase = re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
UpperCamelCase = 10
UpperCamelCase = 256
def lowerCamelCase_ ( _lowercase ) -> Optional[MinHash]:
if len(_lowercase ) < MIN_NUM_TOKENS:
return None
__A : Optional[int] = MinHash(num_perm=_lowercase )
for token in set(_lowercase ):
min_hash.update(token.encode() )
return min_hash
def lowerCamelCase_ ( _lowercase ) -> Set[str]:
return {t for t in NON_ALPHA.split(_lowercase ) if len(t.strip() ) > 0}
class _a :
'''simple docstring'''
def __init__( self , *,
__UpperCAmelCase = 0.85 , ):
__A : str = duplication_jaccard_threshold
__A : Dict = NUM_PERM
__A : Union[str, Any] = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
__A : List[Any] = defaultdict(__UpperCAmelCase )
def __UpperCAmelCase( self , __UpperCAmelCase , __UpperCAmelCase ):
__A : Any = self._index.query(__UpperCAmelCase )
if code_key in self._index.keys:
print(F"Duplicate key {code_key}" )
return
self._index.insert(__UpperCAmelCase , __UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(__UpperCAmelCase )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(__UpperCAmelCase )
def __UpperCAmelCase( self ):
__A : List[str] = []
for base, duplicates in self._duplicate_clusters.items():
__A : str = [base] + list(__UpperCAmelCase )
# reformat the cluster to be a list of dict
__A : Optional[Any] = [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
duplicate_clusters.append(__UpperCAmelCase )
return duplicate_clusters
def __UpperCAmelCase( self , __UpperCAmelCase ):
__A : Tuple = self.get_duplicate_clusters()
with open(__UpperCAmelCase , "w" ) as f:
json.dump(__UpperCAmelCase , __UpperCAmelCase )
def lowerCamelCase_ ( _lowercase ) -> Tuple:
__A : Any = element
__A : Optional[int] = get_min_hash([t for t in NON_ALPHA.split(data["content"] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def lowerCamelCase_ ( _lowercase ) -> List[Any]:
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(_lowercase , max_queue_size=10_000 ) , chunksize=100 , ):
if data is not None:
yield data
def lowerCamelCase_ ( _lowercase , _lowercase ) -> str:
__A : Dict = DuplicationIndex(duplication_jaccard_threshold=_lowercase )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_lowercase ) ) , max_queue_size=100 ) ):
di.add(_lowercase , _lowercase )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def lowerCamelCase_ ( _lowercase , _lowercase ) -> float:
__A : Optional[Any] = get_tokens(_lowercase )
__A : List[str] = get_tokens(_lowercase )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
UpperCamelCase = None
def lowerCamelCase_ ( _lowercase , _lowercase ) -> Tuple:
__A : List[Any] = []
for elementa in cluster:
__A : Any = _shared_dataset[elementa["base_index"]]["content"]
for elementa in extremes:
__A : Optional[Any] = _shared_dataset[elementa["base_index"]]["content"]
if jaccard_similarity(_lowercase , _lowercase ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
__A : Optional[int] = 1
extremes.append(_lowercase )
return extremes
def lowerCamelCase_ ( _lowercase , _lowercase , _lowercase ) -> Optional[int]:
global _shared_dataset
__A : Union[str, Any] = dataset
__A : Any = []
__A : Optional[Any] = partial(_find_cluster_extremes_shared , jaccard_threshold=_lowercase )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
_lowercase , _lowercase , ) , total=len(_lowercase ) , ):
extremes_list.append(_lowercase )
return extremes_list
def lowerCamelCase_ ( _lowercase , _lowercase = 0.85 ) -> Tuple[Type[Dataset], List[List[Dict]]]:
__A : Optional[int] = make_duplicate_clusters(_lowercase , _lowercase )
__A : int = {x["base_index"] for cluster in duplicate_clusters for x in cluster}
__A : Optional[Any] = {}
__A : Tuple = find_extremes(_lowercase , _lowercase , _lowercase )
for extremes in extremes_clusters:
for element in extremes:
__A : List[str] = element
__A : Dict = duplicate_indices - set(extreme_dict.keys() )
__A : Dict = dataset.filter(lambda _lowercase , _lowercase : idx not in remove_indices , with_indices=_lowercase )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
__A : Tuple = element["base_index"] in extreme_dict
if element["is_extreme"]:
__A : int = extreme_dict[element["base_index"]]["copies"]
print(F"Original dataset size: {len(_lowercase )}" )
print(F"Number of duplicate clusters: {len(_lowercase )}" )
print(F"Files in duplicate cluster: {len(_lowercase )}" )
print(F"Unique files in duplicate cluster: {len(_lowercase )}" )
print(F"Filtered dataset size: {len(_lowercase )}" )
return ds_filter, duplicate_clusters
| 714 | import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging
UpperCamelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class _a ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
super().__init__()
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(
speech_model=__UpperCAmelCase , speech_processor=__UpperCAmelCase , vae=__UpperCAmelCase , text_encoder=__UpperCAmelCase , tokenizer=__UpperCAmelCase , unet=__UpperCAmelCase , scheduler=__UpperCAmelCase , feature_extractor=__UpperCAmelCase , )
def __UpperCAmelCase( self , __UpperCAmelCase = "auto" ):
if slice_size == "auto":
__A : int = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__UpperCAmelCase )
def __UpperCAmelCase( self ):
self.enable_attention_slicing(__UpperCAmelCase )
@torch.no_grad()
def __call__( self , __UpperCAmelCase , __UpperCAmelCase=16_000 , __UpperCAmelCase = 512 , __UpperCAmelCase = 512 , __UpperCAmelCase = 50 , __UpperCAmelCase = 7.5 , __UpperCAmelCase = None , __UpperCAmelCase = 1 , __UpperCAmelCase = 0.0 , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = "pil" , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = 1 , **__UpperCAmelCase , ):
__A : List[str] = self.speech_processor.feature_extractor(
__UpperCAmelCase , return_tensors="pt" , sampling_rate=__UpperCAmelCase ).input_features.to(self.device )
__A : Any = self.speech_model.generate(__UpperCAmelCase , max_length=480_000 )
__A : List[str] = self.speech_processor.tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase , normalize=__UpperCAmelCase )[
0
]
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
__A : Optional[Any] = 1
elif isinstance(__UpperCAmelCase , __UpperCAmelCase ):
__A : Dict = len(__UpperCAmelCase )
else:
raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(__UpperCAmelCase )}" )
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 (callback_steps is None) or (
callback_steps is not None and (not isinstance(__UpperCAmelCase , __UpperCAmelCase ) or callback_steps <= 0)
):
raise ValueError(
F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
F" {type(__UpperCAmelCase )}." )
# get prompt text embeddings
__A : Optional[int] = self.tokenizer(
__UpperCAmelCase , padding="max_length" , max_length=self.tokenizer.model_max_length , return_tensors="pt" , )
__A : int = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
__A : List[str] = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
F" {self.tokenizer.model_max_length} tokens: {removed_text}" )
__A : Dict = text_input_ids[:, : self.tokenizer.model_max_length]
__A : int = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
__A , __A , __A : str = text_embeddings.shape
__A : Optional[int] = text_embeddings.repeat(1 , __UpperCAmelCase , 1 )
__A : List[str] = text_embeddings.view(bs_embed * num_images_per_prompt , __UpperCAmelCase , -1 )
# 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.
__A : Dict = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
__A : List[str]
if negative_prompt is None:
__A : Dict = [""] * batch_size
elif type(__UpperCAmelCase ) is not type(__UpperCAmelCase ):
raise TypeError(
F"`negative_prompt` should be the same type to `prompt`, but got {type(__UpperCAmelCase )} !="
F" {type(__UpperCAmelCase )}." )
elif isinstance(__UpperCAmelCase , __UpperCAmelCase ):
__A : Any = [negative_prompt]
elif batch_size != len(__UpperCAmelCase ):
raise ValueError(
F"`negative_prompt`: {negative_prompt} has batch size {len(__UpperCAmelCase )}, but `prompt`:"
F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`." )
else:
__A : int = negative_prompt
__A : int = text_input_ids.shape[-1]
__A : Any = self.tokenizer(
__UpperCAmelCase , padding="max_length" , max_length=__UpperCAmelCase , truncation=__UpperCAmelCase , return_tensors="pt" , )
__A : int = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
__A : Union[str, Any] = uncond_embeddings.shape[1]
__A : List[str] = uncond_embeddings.repeat(1 , __UpperCAmelCase , 1 )
__A : int = uncond_embeddings.view(batch_size * num_images_per_prompt , __UpperCAmelCase , -1 )
# 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
__A : Optional[int] = 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`.
__A : Dict = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
__A : Any = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
__A : Tuple = torch.randn(__UpperCAmelCase , generator=__UpperCAmelCase , device="cpu" , dtype=__UpperCAmelCase ).to(
self.device )
else:
__A : List[Any] = torch.randn(__UpperCAmelCase , generator=__UpperCAmelCase , device=self.device , dtype=__UpperCAmelCase )
else:
if latents.shape != latents_shape:
raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}" )
__A : Tuple = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(__UpperCAmelCase )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
__A : Optional[int] = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
__A : Tuple = 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]
__A : Any = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
__A : List[str] = {}
if accepts_eta:
__A : Tuple = eta
for i, t in enumerate(self.progress_bar(__UpperCAmelCase ) ):
# expand the latents if we are doing classifier free guidance
__A : Union[str, Any] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
__A : Dict = self.scheduler.scale_model_input(__UpperCAmelCase , __UpperCAmelCase )
# predict the noise residual
__A : List[Any] = self.unet(__UpperCAmelCase , __UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase ).sample
# perform guidance
if do_classifier_free_guidance:
__A , __A : str = noise_pred.chunk(2 )
__A : str = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
__A : Union[str, Any] = self.scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
__A : int = 1 / 0.1_82_15 * latents
__A : Union[str, Any] = self.vae.decode(__UpperCAmelCase ).sample
__A : Tuple = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
__A : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
__A : List[str] = self.numpy_to_pil(__UpperCAmelCase )
if not return_dict:
return image
return StableDiffusionPipelineOutput(images=__UpperCAmelCase , nsfw_content_detected=__UpperCAmelCase )
| 387 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
UpperCAmelCase_ = None
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
UpperCAmelCase_ = {
"""vocab_file""": {
"""facebook/nllb-200-distilled-600M""": (
"""https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
),
},
"""tokenizer_file""": {
"""facebook/nllb-200-distilled-600M""": (
"""https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
),
},
}
UpperCAmelCase_ = {
"""facebook/nllb-large-en-ro""": 1_0_2_4,
"""facebook/nllb-200-distilled-600M""": 1_0_2_4,
}
# fmt: off
UpperCAmelCase_ = ["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : Tuple = VOCAB_FILES_NAMES
a__ : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
a__ : Union[str, Any] = ["input_ids", "attention_mask"]
a__ : List[Any] = NllbTokenizer
a__ : List[int] = []
a__ : List[int] = []
def __init__( self : List[str] , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : str=None , __lowerCAmelCase : Dict="<s>" , __lowerCAmelCase : Optional[Any]="</s>" , __lowerCAmelCase : Optional[int]="</s>" , __lowerCAmelCase : Optional[int]="<s>" , __lowerCAmelCase : Optional[int]="<unk>" , __lowerCAmelCase : Dict="<pad>" , __lowerCAmelCase : List[str]="<mask>" , __lowerCAmelCase : str=None , __lowerCAmelCase : List[Any]=None , __lowerCAmelCase : int=None , __lowerCAmelCase : Union[str, Any]=False , **__lowerCAmelCase : int , ) -> Dict:
# Mask token behave like a normal word, i.e. include the space before it
_A = AddedToken(__lowerCAmelCase , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else mask_token
_A = legacy_behaviour
super().__init__(
vocab_file=__lowerCAmelCase , tokenizer_file=__lowerCAmelCase , bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , sep_token=__lowerCAmelCase , cls_token=__lowerCAmelCase , unk_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , mask_token=__lowerCAmelCase , src_lang=__lowerCAmelCase , tgt_lang=__lowerCAmelCase , additional_special_tokens=__lowerCAmelCase , legacy_behaviour=__lowerCAmelCase , **__lowerCAmelCase , )
_A = vocab_file
_A = False if not self.vocab_file else True
_A = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens} )
_A = {
lang_code: self.convert_tokens_to_ids(__lowerCAmelCase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
_A = src_lang if src_lang is not None else '''eng_Latn'''
_A = self.convert_tokens_to_ids(self._src_lang )
_A = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def snake_case_ ( self : Any ) -> str:
return self._src_lang
@src_lang.setter
def snake_case_ ( self : int , __lowerCAmelCase : str ) -> None:
_A = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def snake_case_ ( self : str , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def snake_case_ ( self : Union[str, Any] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def snake_case_ ( self : Optional[int] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : str , __lowerCAmelCase : Optional[str] , __lowerCAmelCase : Optional[str] , **__lowerCAmelCase : Dict ) -> Optional[int]:
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
_A = src_lang
_A = self(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase , return_tensors=__lowerCAmelCase , **__lowerCAmelCase )
_A = self.convert_tokens_to_ids(__lowerCAmelCase )
_A = tgt_lang_id
return inputs
def snake_case_ ( self : str , __lowerCAmelCase : List[str] , __lowerCAmelCase : str = "eng_Latn" , __lowerCAmelCase : Optional[List[str]] = None , __lowerCAmelCase : str = "fra_Latn" , **__lowerCAmelCase : str , ) -> BatchEncoding:
_A = src_lang
_A = tgt_lang
return super().prepare_seqaseq_batch(__lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
def snake_case_ ( self : Dict ) -> Dict:
return self.set_src_lang_special_tokens(self.src_lang )
def snake_case_ ( self : Any ) -> Dict:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def snake_case_ ( self : str , __lowerCAmelCase : Dict ) -> None:
_A = self.convert_tokens_to_ids(__lowerCAmelCase )
if self.legacy_behaviour:
_A = []
_A = [self.eos_token_id, self.cur_lang_code]
else:
_A = [self.cur_lang_code]
_A = [self.eos_token_id]
_A = self.convert_ids_to_tokens(self.prefix_tokens )
_A = self.convert_ids_to_tokens(self.suffix_tokens )
_A = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def snake_case_ ( self : List[str] , __lowerCAmelCase : str ) -> None:
_A = self.convert_tokens_to_ids(__lowerCAmelCase )
if self.legacy_behaviour:
_A = []
_A = [self.eos_token_id, self.cur_lang_code]
else:
_A = [self.cur_lang_code]
_A = [self.eos_token_id]
_A = self.convert_ids_to_tokens(self.prefix_tokens )
_A = self.convert_ids_to_tokens(self.suffix_tokens )
_A = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def snake_case_ ( self : int , __lowerCAmelCase : str , __lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(__lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
return
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCAmelCase ):
copyfile(self.vocab_file , __lowerCAmelCase )
return (out_vocab_file,)
| 2 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = """▁"""
UpperCAmelCase_ = {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}
UpperCAmelCase_ = {
"""vocab_file""": {
"""vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
},
"""monolingual_vocab_file""": {
"""vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
},
}
UpperCAmelCase_ = {"""vinai/bartpho-syllable""": 1_0_2_4}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : int = VOCAB_FILES_NAMES
a__ : Tuple = PRETRAINED_VOCAB_FILES_MAP
a__ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ : Tuple = ["input_ids", "attention_mask"]
def __init__( self : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any]="<s>" , __lowerCAmelCase : Dict="</s>" , __lowerCAmelCase : List[Any]="</s>" , __lowerCAmelCase : Optional[Any]="<s>" , __lowerCAmelCase : Tuple="<unk>" , __lowerCAmelCase : int="<pad>" , __lowerCAmelCase : Optional[Any]="<mask>" , __lowerCAmelCase : Optional[Dict[str, Any]] = None , **__lowerCAmelCase : Tuple , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
_A = AddedToken(__lowerCAmelCase , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else mask_token
_A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , unk_token=__lowerCAmelCase , sep_token=__lowerCAmelCase , cls_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , mask_token=__lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **__lowerCAmelCase , )
_A = vocab_file
_A = monolingual_vocab_file
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__lowerCAmelCase ) )
# Load the reduced vocab
# Keep order of special tokens for backward compatibility
_A = {}
_A = 0
for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
if str(__lowerCAmelCase ) not in self.fairseq_tokens_to_ids:
_A = cnt
cnt += 1
with open(__lowerCAmelCase , '''r''' , encoding='''utf-8''' ) as f:
for line in f.readlines():
_A = line.strip().split()[0]
_A = len(self.fairseq_tokens_to_ids )
if str(__lowerCAmelCase ) not in self.fairseq_tokens_to_ids:
_A = len(self.fairseq_tokens_to_ids )
_A = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : Any ) -> List[Any]:
_A = self.__dict__.copy()
_A = None
_A = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Union[str, Any] , __lowerCAmelCase : Dict ) -> List[Any]:
_A = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_A = {}
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def snake_case_ ( self : Optional[Any] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_A = [self.cls_token_id]
_A = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def snake_case_ ( self : List[Any] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None , __lowerCAmelCase : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCAmelCase , token_ids_a=__lowerCAmelCase , already_has_special_tokens=__lowerCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(__lowerCAmelCase )) + [1]
return [1] + ([0] * len(__lowerCAmelCase )) + [1, 1] + ([0] * len(__lowerCAmelCase )) + [1]
def snake_case_ ( self : Any , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def snake_case_ ( self : Optional[int] ) -> Union[str, Any]:
return len(self.fairseq_ids_to_tokens )
def snake_case_ ( self : Dict ) -> Optional[Any]:
_A = {self.convert_ids_to_tokens(__lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def snake_case_ ( self : List[str] , __lowerCAmelCase : str ) -> List[str]:
return self.sp_model.encode(__lowerCAmelCase , out_type=__lowerCAmelCase )
def snake_case_ ( self : str , __lowerCAmelCase : Optional[Any] ) -> Dict:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
else:
return self.unk_token_id
def snake_case_ ( self : int , __lowerCAmelCase : Optional[int] ) -> List[str]:
return self.fairseq_ids_to_tokens[index]
def snake_case_ ( self : List[str] , __lowerCAmelCase : Union[str, Any] ) -> Tuple:
_A = ''''''.join(__lowerCAmelCase ).replace(__lowerCAmelCase , ''' ''' ).strip()
return out_string
def snake_case_ ( self : str , __lowerCAmelCase : str , __lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file'''] , )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__lowerCAmelCase , '''wb''' ) as fi:
_A = self.sp_model.serialized_model_proto()
fi.write(__lowerCAmelCase )
if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
__lowerCAmelCase ) and os.path.isfile(self.monolingual_vocab_file ):
copyfile(self.monolingual_vocab_file , __lowerCAmelCase )
elif not os.path.isfile(self.monolingual_vocab_file ):
with open(__lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as fp:
for token in self.fairseq_tokens_to_ids:
if token not in self.all_special_tokens:
fp.write(f'''{str(__lowerCAmelCase )} \n''' )
return out_vocab_file, out_monolingual_vocab_file
| 2 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ : Dict = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Dict = [
"NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
"NllbMoeForConditionalGeneration",
"NllbMoeModel",
"NllbMoePreTrainedModel",
"NllbMoeTop2Router",
"NllbMoeSparseMLP",
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
snake_case_ : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 715 |
'''simple docstring'''
snake_case_ : int = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "huggingface-hub>=0.13.2",
"requests-mock": "requests-mock==1.10.0",
"importlib_metadata": "importlib_metadata",
"invisible-watermark": "invisible-watermark",
"isort": "isort>=5.5.4",
"jax": "jax>=0.2.8,!=0.3.2",
"jaxlib": "jaxlib>=0.1.65",
"Jinja2": "Jinja2",
"k-diffusion": "k-diffusion>=0.0.12",
"torchsde": "torchsde",
"note_seq": "note_seq",
"librosa": "librosa",
"numpy": "numpy",
"omegaconf": "omegaconf",
"parameterized": "parameterized",
"protobuf": "protobuf>=3.20.3,<4",
"pytest": "pytest",
"pytest-timeout": "pytest-timeout",
"pytest-xdist": "pytest-xdist",
"ruff": "ruff>=0.0.241",
"safetensors": "safetensors",
"sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
"scipy": "scipy",
"onnx": "onnx",
"regex": "regex!=2019.12.17",
"requests": "requests",
"tensorboard": "tensorboard",
"torch": "torch>=1.4",
"torchvision": "torchvision",
"transformers": "transformers>=4.25.1",
"urllib3": "urllib3<=2.0.0",
}
| 644 | 0 |
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():
__a : Optional[Any] = get_tests_dir('''fixtures/test_sentencepiece.model''')
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
__a : Tuple = 1_2_8_0_2_2
__a : str = 1_2_8_0_2_8
@require_sentencepiece
class UpperCAmelCase( snake_case_ , unittest.TestCase ):
"""simple docstring"""
a : List[Any] = MaMaaaTokenizer
a : Optional[int] = False
a : Dict = False
a : Dict = True
def __a ( self ) -> List[Any]:
"""simple docstring"""
super().setUp()
lowercase__ : Any = ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
lowercase__ : List[str] = dict(zip(lowerCamelCase , range(len(lowerCamelCase ) ) ) )
lowercase__ : Optional[int] = Path(self.tmpdirname )
save_json(lowerCamelCase , save_dir / VOCAB_FILES_NAMES["vocab_file"] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(lowerCamelCase , save_dir / VOCAB_FILES_NAMES["spm_file"] )
lowercase__ : Union[str, Any] = MaMaaaTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def __a ( self , **lowerCamelCase ) -> Any:
"""simple docstring"""
return MaMaaaTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase )
def __a ( self , lowerCamelCase ) -> Optional[int]:
"""simple docstring"""
return (
"This is a test",
"This is a test",
)
def __a ( self ) -> List[str]:
"""simple docstring"""
lowercase__ : List[Any] = "</s>"
lowercase__ : Any = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase ) , lowerCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase ) , lowerCamelCase )
def __a ( self ) -> Dict:
"""simple docstring"""
lowercase__ : Dict = self.get_tokenizer()
lowercase__ : Optional[int] = 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(lowerCamelCase ) , tokenizer.vocab_size + len(tokenizer.get_added_vocab() ) )
@unittest.skip("Skip this test while all models are still to be uploaded." )
def __a ( self ) -> List[Any]:
"""simple docstring"""
pass
def __a ( self ) -> List[str]:
"""simple docstring"""
lowercase__ : Optional[int] = self.get_tokenizer()
lowercase__ : Union[str, Any] = tokenizer.tokenize("This is a test" )
self.assertListEqual(lowerCamelCase , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCamelCase ) , [2, 3, 4, 5, 6] , )
lowercase__ : Optional[int] = tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6] )
self.assertListEqual(lowerCamelCase , ["▁This", "▁is", "▁a", "▁t", "est"] )
lowercase__ : Optional[int] = tokenizer.convert_tokens_to_string(lowerCamelCase )
self.assertEqual(lowerCamelCase , "This is a test" )
@slow
def __a ( self ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Dict = {"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=lowerCamelCase , model_name="facebook/m2m100_418M" , revision="c168bae485c864188cf9aa0e4108b0b6934dc91e" , )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase( unittest.TestCase ):
"""simple docstring"""
a : str = """facebook/m2m100_418M"""
a : Optional[int] = [
"""In my opinion, there are two levels of response from the French government.""",
"""NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
]
a : Tuple = [
"""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
a : Optional[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 __a ( cls ) -> Tuple:
"""simple docstring"""
lowercase__ : MaMaaaTokenizer = MaMaaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="en" , tgt_lang="fr" )
lowercase__ : Tuple = 1
return cls
def __a ( self ) -> Optional[int]:
"""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 __a ( self ) -> str:
"""simple docstring"""
lowercase__ : str = self.tokenizer.get_vocab()
self.assertEqual(len(lowerCamelCase ) , self.tokenizer.vocab_size )
self.assertEqual(vocab["<unk>"] , 3 )
self.assertIn(self.tokenizer.get_lang_token("en" ) , lowerCamelCase )
def __a ( self ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : str = "en"
lowercase__ : str = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , lowerCamelCase )
def __a ( self ) -> Tuple:
"""simple docstring"""
self.assertIn(lowerCamelCase , self.tokenizer.all_special_ids )
# fmt: off
lowercase__ : Optional[int] = [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
# fmt: on
lowercase__ : Optional[int] = self.tokenizer.decode(lowerCamelCase , skip_special_tokens=lowerCamelCase )
lowercase__ : Dict = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=lowerCamelCase )
self.assertEqual(lowerCamelCase , lowerCamelCase )
self.assertNotIn(self.tokenizer.eos_token , lowerCamelCase )
def __a ( self ) -> List[str]:
"""simple docstring"""
lowercase__ : List[Any] = tempfile.mkdtemp()
lowercase__ : int = self.tokenizer.lang_token_to_id
self.tokenizer.save_pretrained(lowerCamelCase )
lowercase__ : Union[str, Any] = MaMaaaTokenizer.from_pretrained(lowerCamelCase )
self.assertDictEqual(new_tok.lang_token_to_id , lowerCamelCase )
@require_torch
def __a ( self ) -> str:
"""simple docstring"""
lowercase__ : Any = "en"
lowercase__ : List[str] = "fr"
lowercase__ : Union[str, Any] = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=lowerCamelCase , return_tensors="pt" )
lowercase__ : Optional[Any] = shift_tokens_right(
batch["labels"] , self.tokenizer.pad_token_id , self.tokenizer.eos_token_id )
for k in batch:
lowercase__ : Dict = batch[k].tolist()
# batch = {k: v.tolist() for k,v in batch.items()}
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
# batch.decoder_inputs_ids[0][0] ==
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == FR_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
@require_torch
def __a ( self ) -> Optional[int]:
"""simple docstring"""
lowercase__ : Tuple = "mr"
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("mr" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
lowercase__ : Tuple = "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 __a ( self ) -> Tuple:
"""simple docstring"""
lowercase__ : str = "mr"
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("mr" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
lowercase__ : Any = "zh"
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("zh" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
@require_torch
def __a ( self ) -> Dict:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.tokenizer._build_translation_inputs("A test" , return_tensors="pt" , src_lang="en" , tgt_lang="ar" )
self.assertEqual(
nested_simplify(lowerCamelCase ) , {
# en_XX, A, test, EOS
"input_ids": [[128022, 58, 4183, 2]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 128006,
} , ) | 397 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase( snake_case_ ):
"""simple docstring"""
a : List[str] = (DEISMultistepScheduler,)
a : Tuple = (("""num_inference_steps""", 2_5),)
def __a ( self , **lowerCamelCase ) -> str:
"""simple docstring"""
lowercase__ : Optional[Any] = {
"num_train_timesteps": 1000,
"beta_start": 0.00_01,
"beta_end": 0.02,
"beta_schedule": "linear",
"solver_order": 2,
}
config.update(**lowerCamelCase )
return config
def __a ( self , lowerCamelCase=0 , **lowerCamelCase ) -> List[str]:
"""simple docstring"""
lowercase__ : int = dict(self.forward_default_kwargs )
lowercase__ : Union[str, Any] = kwargs.pop("num_inference_steps" , lowerCamelCase )
lowercase__ : Optional[int] = self.dummy_sample
lowercase__ : Any = 0.1 * sample
lowercase__ : List[Any] = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
lowercase__ : Optional[int] = self.get_scheduler_config(**lowerCamelCase )
lowercase__ : Union[str, Any] = scheduler_class(**lowerCamelCase )
scheduler.set_timesteps(lowerCamelCase )
# copy over dummy past residuals
lowercase__ : Dict = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCamelCase )
lowercase__ : Optional[int] = scheduler_class.from_pretrained(lowerCamelCase )
new_scheduler.set_timesteps(lowerCamelCase )
# copy over dummy past residuals
lowercase__ : str = dummy_past_residuals[: new_scheduler.config.solver_order]
lowercase__ , lowercase__ : Tuple = sample, sample
for t in range(lowerCamelCase , time_step + scheduler.config.solver_order + 1 ):
lowercase__ : str = scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase ).prev_sample
lowercase__ : Optional[Any] = new_scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def __a ( self ) -> str:
"""simple docstring"""
pass
def __a ( self , lowerCamelCase=0 , **lowerCamelCase ) -> str:
"""simple docstring"""
lowercase__ : int = dict(self.forward_default_kwargs )
lowercase__ : Optional[Any] = kwargs.pop("num_inference_steps" , lowerCamelCase )
lowercase__ : Any = self.dummy_sample
lowercase__ : Dict = 0.1 * sample
lowercase__ : str = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
lowercase__ : Any = self.get_scheduler_config()
lowercase__ : Union[str, Any] = scheduler_class(**lowerCamelCase )
scheduler.set_timesteps(lowerCamelCase )
# copy over dummy past residuals (must be after setting timesteps)
lowercase__ : List[Any] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCamelCase )
lowercase__ : str = scheduler_class.from_pretrained(lowerCamelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowerCamelCase )
# copy over dummy past residual (must be after setting timesteps)
lowercase__ : List[Any] = dummy_past_residuals[: new_scheduler.config.solver_order]
lowercase__ : Tuple = scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase ).prev_sample
lowercase__ : List[Any] = new_scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def __a ( self , lowerCamelCase=None , **lowerCamelCase ) -> Optional[Any]:
"""simple docstring"""
if scheduler is None:
lowercase__ : int = self.scheduler_classes[0]
lowercase__ : Dict = self.get_scheduler_config(**lowerCamelCase )
lowercase__ : Optional[Any] = scheduler_class(**lowerCamelCase )
lowercase__ : Optional[Any] = self.scheduler_classes[0]
lowercase__ : Tuple = self.get_scheduler_config(**lowerCamelCase )
lowercase__ : int = scheduler_class(**lowerCamelCase )
lowercase__ : Union[str, Any] = 10
lowercase__ : Dict = self.dummy_model()
lowercase__ : Optional[Any] = self.dummy_sample_deter
scheduler.set_timesteps(lowerCamelCase )
for i, t in enumerate(scheduler.timesteps ):
lowercase__ : int = model(lowerCamelCase , lowerCamelCase )
lowercase__ : Any = scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase ).prev_sample
return sample
def __a ( self ) -> List[Any]:
"""simple docstring"""
lowercase__ : List[str] = dict(self.forward_default_kwargs )
lowercase__ : int = kwargs.pop("num_inference_steps" , lowerCamelCase )
for scheduler_class in self.scheduler_classes:
lowercase__ : List[str] = self.get_scheduler_config()
lowercase__ : Optional[int] = scheduler_class(**lowerCamelCase )
lowercase__ : Optional[Any] = self.dummy_sample
lowercase__ : int = 0.1 * sample
if num_inference_steps is not None and hasattr(lowerCamelCase , "set_timesteps" ):
scheduler.set_timesteps(lowerCamelCase )
elif num_inference_steps is not None and not hasattr(lowerCamelCase , "set_timesteps" ):
lowercase__ : str = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
lowercase__ : str = [residual + 0.2, residual + 0.15, residual + 0.10]
lowercase__ : Optional[Any] = dummy_past_residuals[: scheduler.config.solver_order]
lowercase__ : Dict = scheduler.timesteps[5]
lowercase__ : str = scheduler.timesteps[6]
lowercase__ : List[Any] = scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase ).prev_sample
lowercase__ : int = scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def __a ( self ) -> List[Any]:
"""simple docstring"""
lowercase__ : List[str] = DEISMultistepScheduler(**self.get_scheduler_config() )
lowercase__ : Union[str, Any] = self.full_loop(scheduler=lowerCamelCase )
lowercase__ : Tuple = torch.mean(torch.abs(lowerCamelCase ) )
assert abs(result_mean.item() - 0.2_39_16 ) < 1E-3
lowercase__ : Tuple = DPMSolverSinglestepScheduler.from_config(scheduler.config )
lowercase__ : List[str] = DPMSolverMultistepScheduler.from_config(scheduler.config )
lowercase__ : List[Any] = UniPCMultistepScheduler.from_config(scheduler.config )
lowercase__ : Union[str, Any] = DEISMultistepScheduler.from_config(scheduler.config )
lowercase__ : int = self.full_loop(scheduler=lowerCamelCase )
lowercase__ : List[str] = torch.mean(torch.abs(lowerCamelCase ) )
assert abs(result_mean.item() - 0.2_39_16 ) < 1E-3
def __a ( self ) -> Dict:
"""simple docstring"""
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=lowerCamelCase )
def __a ( self ) -> Optional[int]:
"""simple docstring"""
self.check_over_configs(thresholding=lowerCamelCase )
for order in [1, 2, 3]:
for solver_type in ["logrho"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=lowerCamelCase , prediction_type=lowerCamelCase , sample_max_value=lowerCamelCase , algorithm_type="deis" , solver_order=lowerCamelCase , solver_type=lowerCamelCase , )
def __a ( self ) -> str:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCamelCase )
def __a ( self ) -> Optional[int]:
"""simple docstring"""
for algorithm_type in ["deis"]:
for solver_type in ["logrho"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=lowerCamelCase , solver_type=lowerCamelCase , prediction_type=lowerCamelCase , algorithm_type=lowerCamelCase , )
lowercase__ : Dict = self.full_loop(
solver_order=lowerCamelCase , solver_type=lowerCamelCase , prediction_type=lowerCamelCase , algorithm_type=lowerCamelCase , )
assert not torch.isnan(lowerCamelCase ).any(), "Samples have nan numbers"
def __a ( self ) -> List[str]:
"""simple docstring"""
self.check_over_configs(lower_order_final=lowerCamelCase )
self.check_over_configs(lower_order_final=lowerCamelCase )
def __a ( self ) -> List[Any]:
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=lowerCamelCase , time_step=0 )
def __a ( self ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.full_loop()
lowercase__ : Optional[int] = torch.mean(torch.abs(lowerCamelCase ) )
assert abs(result_mean.item() - 0.2_39_16 ) < 1E-3
def __a ( self ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : List[Any] = self.full_loop(prediction_type="v_prediction" )
lowercase__ : Union[str, Any] = torch.mean(torch.abs(lowerCamelCase ) )
assert abs(result_mean.item() - 0.0_91 ) < 1E-3
def __a ( self ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Dict = self.scheduler_classes[0]
lowercase__ : Union[str, Any] = self.get_scheduler_config(thresholding=lowerCamelCase , dynamic_thresholding_ratio=0 )
lowercase__ : Union[str, Any] = scheduler_class(**lowerCamelCase )
lowercase__ : Dict = 10
lowercase__ : List[str] = self.dummy_model()
lowercase__ : Tuple = self.dummy_sample_deter.half()
scheduler.set_timesteps(lowerCamelCase )
for i, t in enumerate(scheduler.timesteps ):
lowercase__ : Optional[int] = model(lowerCamelCase , lowerCamelCase )
lowercase__ : int = scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase ).prev_sample
assert sample.dtype == torch.floataa | 397 | 1 |
'''simple docstring'''
from __future__ import annotations
def A_( A : Union[str, Any] , A : Optional[Any]):
UpperCamelCase , UpperCamelCase = set(_SCREAMING_SNAKE_CASE), [start]
while stack:
UpperCamelCase = stack.pop()
explored.add(_SCREAMING_SNAKE_CASE)
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v]):
if adj not in explored:
stack.append(_SCREAMING_SNAKE_CASE)
return explored
lowerCAmelCase : List[str] = {
'A': ['B', 'C', 'D'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F'],
'D': ['B', 'D'],
'E': ['B', 'F'],
'F': ['C', 'E', 'G'],
'G': ['F'],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, 'A'))
| 702 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class SCREAMING_SNAKE_CASE__ ( snake_case_):
lowerCAmelCase_ = 42
lowerCAmelCase_ = 42
lowerCAmelCase_ = None
class SCREAMING_SNAKE_CASE__ ( snake_case_ , snake_case_):
lowerCAmelCase_ = 2
@register_to_config
def __init__( self , A_ = 0.02 , A_ = 100 , A_ = 1.007 , A_ = 80 , A_ = 0.05 , A_ = 50 , )-> List[Any]:
'''simple docstring'''
UpperCamelCase = sigma_max
# setable values
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None # sigma(t_i)
def UpperCAmelCase_ ( self , A_ , A_ = None )-> torch.FloatTensor:
'''simple docstring'''
return sample
def UpperCAmelCase_ ( self , A_ , A_ = None )-> Optional[int]:
'''simple docstring'''
UpperCamelCase = num_inference_steps
UpperCamelCase = np.arange(0 , self.num_inference_steps )[::-1].copy()
UpperCamelCase = torch.from_numpy(A_ ).to(A_ )
UpperCamelCase = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
UpperCamelCase = torch.tensor(A_ , dtype=torch.floataa , device=A_ )
def UpperCAmelCase_ ( self , A_ , A_ , A_ = None )-> Tuple[torch.FloatTensor, float]:
'''simple docstring'''
if self.config.s_min <= sigma <= self.config.s_max:
UpperCamelCase = min(self.config.s_churn / self.num_inference_steps , 2**0.5 - 1 )
else:
UpperCamelCase = 0
# sample eps ~ N(0, S_noise^2 * I)
UpperCamelCase = self.config.s_noise * randn_tensor(sample.shape , generator=A_ ).to(sample.device )
UpperCamelCase = sigma + gamma * sigma
UpperCamelCase = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def UpperCAmelCase_ ( self , A_ , A_ , A_ , A_ , A_ = True , )-> Union[KarrasVeOutput, Tuple]:
'''simple docstring'''
UpperCamelCase = sample_hat + sigma_hat * model_output
UpperCamelCase = (sample_hat - pred_original_sample) / sigma_hat
UpperCamelCase = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=A_ , derivative=A_ , pred_original_sample=A_ )
def UpperCAmelCase_ ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ = True , )-> Union[KarrasVeOutput, Tuple]:
'''simple docstring'''
UpperCamelCase = sample_prev + sigma_prev * model_output
UpperCamelCase = (sample_prev - pred_original_sample) / sigma_prev
UpperCamelCase = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=A_ , derivative=A_ , pred_original_sample=A_ )
def UpperCAmelCase_ ( self , A_ , A_ , A_ )-> Tuple:
'''simple docstring'''
raise NotImplementedError()
| 432 | 0 |
'''simple docstring'''
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
snake_case = {
'''facebook/mask2former-swin-small-coco-instance''': (
'''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
snake_case = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( __a ):
"""simple docstring"""
__A = "mask2former"
__A = ["swin"]
__A = {"hidden_size": "hidden_dim"}
def __init__( self : Any , __lowerCAmelCase : Optional[Dict] = None , __lowerCAmelCase : int = 256 , __lowerCAmelCase : int = 256 , __lowerCAmelCase : int = 256 , __lowerCAmelCase : int = 1024 , __lowerCAmelCase : str = "relu" , __lowerCAmelCase : int = 6 , __lowerCAmelCase : int = 10 , __lowerCAmelCase : int = 8 , __lowerCAmelCase : float = 0.0 , __lowerCAmelCase : int = 2048 , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , __lowerCAmelCase : int = 4 , __lowerCAmelCase : int = 255 , __lowerCAmelCase : int = 100 , __lowerCAmelCase : float = 0.1 , __lowerCAmelCase : float = 2.0 , __lowerCAmelCase : float = 5.0 , __lowerCAmelCase : float = 5.0 , __lowerCAmelCase : int = 1_2544 , __lowerCAmelCase : float = 3.0 , __lowerCAmelCase : float = 0.7_5 , __lowerCAmelCase : float = 0.0_2 , __lowerCAmelCase : float = 1.0 , __lowerCAmelCase : bool = True , __lowerCAmelCase : List[int] = [4, 8, 16, 32] , __lowerCAmelCase : bool = None , **__lowerCAmelCase : str , ):
"""simple docstring"""
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.' )
_lowerCAmelCase = CONFIG_MAPPING['swin'](
image_size=224 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=__lowerCAmelCase , out_features=['stage1', 'stage2', 'stage3', 'stage4'] , )
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
_lowerCAmelCase = backbone_config.pop('model_type' )
_lowerCAmelCase = CONFIG_MAPPING[backbone_model_type]
_lowerCAmelCase = config_class.from_dict(__lowerCAmelCase )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. "
F"Supported model types: {','.join(self.backbones_supported )}" )
_lowerCAmelCase = backbone_config
_lowerCAmelCase = feature_size
_lowerCAmelCase = mask_feature_size
_lowerCAmelCase = hidden_dim
_lowerCAmelCase = encoder_feedforward_dim
_lowerCAmelCase = activation_function
_lowerCAmelCase = encoder_layers
_lowerCAmelCase = decoder_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = dropout
_lowerCAmelCase = dim_feedforward
_lowerCAmelCase = pre_norm
_lowerCAmelCase = enforce_input_projection
_lowerCAmelCase = common_stride
_lowerCAmelCase = ignore_value
_lowerCAmelCase = num_queries
_lowerCAmelCase = no_object_weight
_lowerCAmelCase = class_weight
_lowerCAmelCase = mask_weight
_lowerCAmelCase = dice_weight
_lowerCAmelCase = train_num_points
_lowerCAmelCase = oversample_ratio
_lowerCAmelCase = importance_sample_ratio
_lowerCAmelCase = init_std
_lowerCAmelCase = init_xavier_std
_lowerCAmelCase = use_auxiliary_loss
_lowerCAmelCase = feature_strides
_lowerCAmelCase = output_auxiliary_logits
_lowerCAmelCase = decoder_layers
super().__init__(**__lowerCAmelCase )
@classmethod
def a ( cls : Optional[Any] , __lowerCAmelCase : PretrainedConfig , **__lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
return cls(
backbone_config=__lowerCAmelCase , **__lowerCAmelCase , )
def a ( self : Tuple ):
"""simple docstring"""
_lowerCAmelCase = copy.deepcopy(self.__dict__ )
_lowerCAmelCase = self.backbone_config.to_dict()
_lowerCAmelCase = self.__class__.model_type
return output
| 309 | '''simple docstring'''
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
snake_case = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('''3.7'''):
raise ImportWarning(
'''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
)
if version.parse(pyarrow.__version__).major < 8:
raise ImportWarning(
'''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
'''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
)
del platform
del pyarrow
del version
from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
list_datasets,
list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
NamedSplit,
NamedSplitAll,
Split,
SplitBase,
SplitDict,
SplitGenerator,
SplitInfo,
SubSplitInfo,
percent,
)
from .tasks import *
from .utils import *
from .utils import logging
# deprecated modules
from datasets import arrow_dataset as _arrow_dataset # isort:skip
from datasets import utils as _utils # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager # isort:skip
snake_case = concatenate_datasets
snake_case = DownloadConfig
snake_case = DownloadManager
snake_case = DownloadMode
snake_case = DownloadConfig
snake_case = DownloadMode
snake_case = DownloadManager
del _arrow_dataset, _utils, _deprecated_download_manager
| 309 | 1 |
import re
def lowerCAmelCase__ ( _a : Optional[Any] ):
snake_case_ : List[str] = re.compile(R"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$" )
if match := re.search(__snake_case , __snake_case ):
return match.string == phone
return False
if __name__ == "__main__":
print(indian_phone_validator('''+918827897895'''))
| 705 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Optional[int] = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Optional[Any] = [
'''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''NllbMoeForConditionalGeneration''',
'''NllbMoeModel''',
'''NllbMoePreTrainedModel''',
'''NllbMoeTop2Router''',
'''NllbMoeSparseMLP''',
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
lowercase : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 114 | 0 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
_lowerCAmelCase = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("", "|", "|"),
datarow=DataRow("", "|", "|"),
padding=1,
with_header_hide=None,
)
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = {"type": "section", "text": {"type": "plain_text", "text": "No failed tests! 🤗", "emoji": True}}
_lowerCAmelCase = [
{
"type": "header",
"text": {
"type": "plain_text",
"text": f'🤗 Accelerate nightly {os.environ.get("TEST_TYPE", "")} test results',
"emoji": True,
},
}
]
_lowerCAmelCase = 0
for log in Path().glob("*.log"):
_lowerCAmelCase = 0
with open(log, "r") as f:
for line in f:
_lowerCAmelCase = json.loads(line)
if line.get("nodeid", "") != "":
_lowerCAmelCase = line["nodeid"]
if line.get("duration", None) is not None:
_lowerCAmelCase = f'{line["duration"]:.4f}'
if line.get("outcome", "") == "failed":
section_num_failed += 1
failed.append([test, duration, log.name.split("_")[0]])
total_num_failed += 1
group_info.append([str(log), section_num_failed, failed])
_lowerCAmelCase = []
log.unlink()
_lowerCAmelCase = ""
_lowerCAmelCase = []
if total_num_failed > 0:
for name, num_failed, failed_tests in group_info:
if num_failed > 0:
if num_failed == 1:
message += f"*{name[1:]}: {num_failed} failed test*\n"
else:
message += f"*{name[1:]}: {num_failed} failed tests*\n"
_lowerCAmelCase = []
_lowerCAmelCase = {}
for test in failed_tests:
_lowerCAmelCase = test[0].split("::")
_lowerCAmelCase = data[0].split("/")[-1]
if data[0] not in filesafailed:
_lowerCAmelCase = [data[1:]]
else:
filesafailed[data[0]] += [data[1:]]
failed_table.append(data)
_lowerCAmelCase = [test[0] for test in failed_table]
_lowerCAmelCase = list(set(files))
# Count number of instances in failed_tests
_lowerCAmelCase = []
for file in individual_files:
table.append([file, len(filesafailed[file])])
_lowerCAmelCase = tabulate(
table,
headers=["Test Location", "Num Failed"],
tablefmt=hf_table_format,
stralign="right",
)
message += f"\n```\n{failed_table}\n```"
all_filesafailed.append(filesafailed)
if len(message) > 3_000:
_lowerCAmelCase = "Too many failed tests, please see the full report in the Action results."
_lowerCAmelCase = len(err) + 10
_lowerCAmelCase = message[: 3_000 - offset] + f'\n...\n```\n{err}'
print(f'### {message}')
else:
_lowerCAmelCase = "No failed tests! 🤗"
print(f'## {message}')
payload.append(no_error_payload)
if os.environ.get("TEST_TYPE", "") != "":
from slack_sdk import WebClient
_lowerCAmelCase = WebClient(token=os.environ["SLACK_API_TOKEN"])
if message != "No failed tests! 🤗":
_lowerCAmelCase = {
"type": "section",
"text": {
"type": "mrkdwn",
"text": message,
},
}
payload.append(md_report)
_lowerCAmelCase = {
"type": "section",
"text": {
"type": "mrkdwn",
"text": "*For more details:*",
},
"accessory": {
"type": "button",
"text": {
"type": "plain_text",
"text": "Check Action results",
"emoji": True,
},
"url": f'https://github.com/{os.environ["GITHUB_REPOSITORY"]}/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
},
}
payload.append(action_button)
_lowerCAmelCase = {
"type": "context",
"elements": [
{
"type": "plain_text",
"text": f'Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}',
}
],
}
payload.append(date_report)
_lowerCAmelCase = client.chat_postMessage(channel="#accelerate-ci-daily", text=message, blocks=payload)
_lowerCAmelCase = response.data["ts"]
for failed_file in all_filesafailed:
for test_location, test_failures in failed_file.items():
# Keep only the first instance of the test name
_lowerCAmelCase = ""
for i, row in enumerate(test_failures):
if row[0] != test_class:
_lowerCAmelCase = row[0]
else:
_lowerCAmelCase = ""
_lowerCAmelCase = {
"type": "section",
"text": {
"type": "mrkdwn",
"text": f'Test location: {test_location}\n```\n{tabulate(test_failures, headers=["Class", "Test"], tablefmt=hf_table_format, stralign="right")}\n```',
},
}
client.chat_postMessage(
channel="#accelerate-ci-daily",
thread_ts=ts,
blocks=[payload],
)
| 10 |
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
_lowerCAmelCase : Union[str, Any] = logging.getLogger(__name__)
require_version("pytorch_lightning>=1.0.4")
_lowerCAmelCase : List[str] = {
"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
_lowerCAmelCase : Tuple = {
"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
}
_lowerCAmelCase : Optional[Any] = sorted(arg_to_scheduler.keys())
_lowerCAmelCase : Optional[Any] = "{" + ", ".join(arg_to_scheduler_choices) + "}"
class __magic_name__ ( pl.LightningModule ):
def __init__( self , __snake_case , __snake_case=None , __snake_case="base" , __snake_case=None , __snake_case=None , __snake_case=None , **__snake_case , ) -> Union[str, Any]:
'''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(__snake_case )
__a =0
__a =Path(self.hparams.output_dir )
__a =self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
__a =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=__snake_case , **__snake_case , )
else:
__a =config
__a =('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
for p in extra_model_params:
if getattr(self.hparams , __snake_case , __snake_case ):
assert hasattr(self.config , __snake_case ), f'model config doesn\'t have a `{p}` attribute'
setattr(self.config , __snake_case , getattr(self.hparams , __snake_case ) )
if tokenizer is None:
__a =AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=__snake_case , )
else:
__a =tokenizer
__a =MODEL_MODES[mode]
if model is None:
__a =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=__snake_case , )
else:
__a =model
def __magic_name__ ( self , *__snake_case , **__snake_case ) -> int:
'''simple docstring'''
__a =self.model_type.from_pretrained(*__snake_case , **__snake_case )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =arg_to_scheduler[self.hparams.lr_scheduler]
__a =get_schedule_func(
self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps() )
__a ={'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
return scheduler
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =self.model
__a =['bias', 'LayerNorm.weight']
__a =[
{
'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:
__a =Adafactor(
__snake_case , lr=self.hparams.learning_rate , scale_parameter=__snake_case , relative_step=__snake_case )
else:
__a =AdamW(
__snake_case , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon )
__a =optimizer
__a =self.get_lr_scheduler()
return [optimizer], [scheduler]
def __magic_name__ ( self , __snake_case , __snake_case ) -> List[str]:
'''simple docstring'''
return self.validation_step(__snake_case , __snake_case )
def __magic_name__ ( self , __snake_case ) -> str:
'''simple docstring'''
return self.validation_end(__snake_case )
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =max(1 , self.hparams.gpus ) # TODO: consider num_tpu_cores
__a =self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def __magic_name__ ( self , __snake_case ) -> int:
'''simple docstring'''
if stage == "test":
__a =len(self.test_dataloader().dataset )
else:
__a =self.get_dataloader('train' , self.hparams.train_batch_size , shuffle=__snake_case )
__a =len(self.train_dataloader().dataset )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case = False ) -> Optional[Any]:
'''simple docstring'''
raise NotImplementedError('You must implement this for your task' )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
return self.train_loader
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return self.get_dataloader('dev' , self.hparams.eval_batch_size , shuffle=__snake_case )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
return self.get_dataloader('test' , self.hparams.eval_batch_size , shuffle=__snake_case )
def __magic_name__ ( self , __snake_case ) -> List[Any]:
'''simple docstring'''
return os.path.join(
self.hparams.data_dir , 'cached_{}_{}_{}'.format(
__snake_case , list(filter(__snake_case , self.hparams.model_name_or_path.split('/' ) ) ).pop() , str(self.hparams.max_seq_length ) , ) , )
@pl.utilities.rank_zero_only
def __magic_name__ ( self , __snake_case ) -> None:
'''simple docstring'''
__a =self.output_dir.joinpath('best_tfmr' )
__a =self.step_count
self.model.save_pretrained(__snake_case )
self.tokenizer.save_pretrained(__snake_case )
@staticmethod
def __magic_name__ ( __snake_case , __snake_case ) -> Union[str, Any]:
'''simple docstring'''
parser.add_argument(
'--model_name_or_path' , default=__snake_case , type=__snake_case , required=__snake_case , help='Path to pretrained model or model identifier from huggingface.co/models' , )
parser.add_argument(
'--config_name' , default='' , type=__snake_case , help='Pretrained config name or path if not the same as model_name' )
parser.add_argument(
'--tokenizer_name' , default=__snake_case , type=__snake_case , help='Pretrained tokenizer name or path if not the same as model_name' , )
parser.add_argument(
'--cache_dir' , default=str(Path(__snake_case ).parent / 'test_run' / 'cache' ) , type=__snake_case , help='Where do you want to store the pre-trained models downloaded from huggingface.co' , )
parser.add_argument(
'--encoder_layerdrop' , type=__snake_case , help='Encoder layer dropout probability (Optional). Goes into model.config' , )
parser.add_argument(
'--decoder_layerdrop' , type=__snake_case , help='Decoder layer dropout probability (Optional). Goes into model.config' , )
parser.add_argument(
'--dropout' , type=__snake_case , help='Dropout probability (Optional). Goes into model.config' , )
parser.add_argument(
'--attention_dropout' , type=__snake_case , help='Attention dropout probability (Optional). Goes into model.config' , )
parser.add_argument('--learning_rate' , default=5e-5 , type=__snake_case , help='The initial learning rate for Adam.' )
parser.add_argument(
'--lr_scheduler' , default='linear' , choices=__snake_case , metavar=__snake_case , type=__snake_case , help='Learning rate scheduler' , )
parser.add_argument('--weight_decay' , default=0.0 , type=__snake_case , help='Weight decay if we apply some.' )
parser.add_argument('--adam_epsilon' , default=1e-8 , type=__snake_case , help='Epsilon for Adam optimizer.' )
parser.add_argument('--warmup_steps' , default=0 , type=__snake_case , help='Linear warmup over warmup_steps.' )
parser.add_argument('--num_workers' , default=4 , type=__snake_case , help='kwarg passed to DataLoader' )
parser.add_argument('--num_train_epochs' , dest='max_epochs' , default=3 , type=__snake_case )
parser.add_argument('--train_batch_size' , default=32 , type=__snake_case )
parser.add_argument('--eval_batch_size' , default=32 , type=__snake_case )
parser.add_argument('--adafactor' , action='store_true' )
class __magic_name__ ( pl.Callback ):
def __magic_name__ ( self , __snake_case , __snake_case ) -> str:
'''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 __magic_name__ ( pl.Callback ):
def __magic_name__ ( self , __snake_case , __snake_case ) -> Any:
'''simple docstring'''
# print(pl_module.model.rag)
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(__snake_case )
class __magic_name__ ( pl.Callback ):
def __magic_name__ ( self , __snake_case , __snake_case ) -> int:
'''simple docstring'''
__a =trainer.lr_schedulers[0]['scheduler']
__a ={f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
pl_module.logger.log_metrics(__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case ) -> List[str]:
'''simple docstring'''
rank_zero_info('***** Validation results *****' )
__a =trainer.callback_metrics
# Log results
for key in sorted(__snake_case ):
if key not in ["log", "progress_bar"]:
rank_zero_info('{} = {}\n'.format(__snake_case , str(metrics[key] ) ) )
def __magic_name__ ( self , __snake_case , __snake_case ) -> List[str]:
'''simple docstring'''
rank_zero_info('***** Test results *****' )
__a =trainer.callback_metrics
# Log and save results to file
__a =os.path.join(pl_module.hparams.output_dir , 'test_results.txt' )
with open(__snake_case , 'w' ) as writer:
for key in sorted(__snake_case ):
if key not in ["log", "progress_bar"]:
rank_zero_info('{} = {}\n'.format(__snake_case , str(metrics[key] ) ) )
writer.write('{} = {}\n'.format(__snake_case , str(metrics[key] ) ) )
def UpperCamelCase_( _snake_case : str , _snake_case : int ):
"""simple docstring"""
parser.add_argument(
'--output_dir' , default=str(Path(_snake_case ).parent / 'test_run' / 'model_checkpoints' ) , type=_snake_case , 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=_snake_case , 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=_snake_case )
parser.add_argument('--max_grad_norm' , dest='gradient_clip_val' , default=1.0 , type=_snake_case , 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=_snake_case , default=1 , help='Number of updates steps to accumulate before performing a backward/update pass.' , )
parser.add_argument('--seed' , type=_snake_case , default=42 , help='random seed for initialization' )
parser.add_argument(
'--data_dir' , default=str(Path(_snake_case ).parent / 'test_run' / 'dummy-train-data' ) , type=_snake_case , help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.' , )
def UpperCamelCase_( _snake_case : BaseTransformer , _snake_case : argparse.Namespace , _snake_case : Union[str, Any]=None , _snake_case : List[Any]=True , _snake_case : Dict=[] , _snake_case : List[str]=None , _snake_case : Any=None , **_snake_case : str , ):
"""simple docstring"""
pl.seed_everything(args.seed )
# init model
__a =Path(model.hparams.output_dir )
odir.mkdir(exist_ok=_snake_case )
# add custom checkpoints
if checkpoint_callback is None:
__a =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(_snake_case )
if logging_callback is None:
__a =LoggingCallback()
__a ={}
if args.fpaa:
__a =16
if args.gpus > 1:
__a ='auto'
__a ='ddp'
__a =args.accumulate_grad_batches
__a =None
__a ='auto'
__a =pl.Trainer.from_argparse_args(
_snake_case , weights_summary=_snake_case , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=_snake_case , val_check_interval=1 , num_sanity_val_steps=2 , **_snake_case , )
if args.do_train:
trainer.fit(_snake_case )
else:
print('RAG modeling tests with new set functions successfuly executed!' )
return trainer
| 242 | 0 |
"""simple docstring"""
import numpy as np
def A( snake_case_ ):
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 120 |
"""simple docstring"""
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class _a ( lowercase_ , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase__ = FlaxAutoencoderKL
@property
def __lowercase ( self) -> Dict:
'''simple docstring'''
lowercase__: Dict = 4
lowercase__: str = 3
lowercase__: List[Any] = (32, 32)
lowercase__: str = jax.random.PRNGKey(0)
lowercase__: Tuple = jax.random.uniform(UpperCAmelCase_ , ((batch_size, num_channels) + sizes))
return {"sample": image, "prng_key": prng_key}
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
lowercase__: Any = {
"block_out_channels": [32, 64],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 4,
}
lowercase__: Any = self.dummy_input
return init_dict, inputs_dict
| 120 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'facebook/data2vec-vision-base-ft': (
'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
),
}
class __A ( SCREAMING_SNAKE_CASE_ ):
UpperCAmelCase__ = "data2vec-vision"
def __init__( self : int , __snake_case : List[str]=7_6_8 , __snake_case : Dict=1_2 , __snake_case : Union[str, Any]=1_2 , __snake_case : int=3_0_7_2 , __snake_case : List[Any]="gelu" , __snake_case : Optional[int]=0.0 , __snake_case : Optional[Any]=0.0 , __snake_case : List[Any]=0.02 , __snake_case : List[str]=1E-12 , __snake_case : Dict=2_2_4 , __snake_case : List[Any]=1_6 , __snake_case : str=3 , __snake_case : Union[str, Any]=False , __snake_case : Dict=False , __snake_case : Any=False , __snake_case : List[str]=False , __snake_case : List[str]=0.1 , __snake_case : Tuple=0.1 , __snake_case : List[str]=True , __snake_case : str=[3, 5, 7, 1_1] , __snake_case : Any=[1, 2, 3, 6] , __snake_case : Dict=True , __snake_case : Union[str, Any]=0.4 , __snake_case : Union[str, Any]=2_5_6 , __snake_case : Union[str, Any]=1 , __snake_case : Dict=False , __snake_case : Tuple=2_5_5 , **__snake_case : List[str] , ) -> Optional[Any]:
super().__init__(**__snake_case )
__magic_name__: List[Any] = hidden_size
__magic_name__: Any = num_hidden_layers
__magic_name__: List[str] = num_attention_heads
__magic_name__: Optional[int] = intermediate_size
__magic_name__: Optional[int] = hidden_act
__magic_name__: List[str] = hidden_dropout_prob
__magic_name__: Tuple = attention_probs_dropout_prob
__magic_name__: Tuple = initializer_range
__magic_name__: Dict = layer_norm_eps
__magic_name__: List[str] = image_size
__magic_name__: Optional[int] = patch_size
__magic_name__: Tuple = num_channels
__magic_name__: List[str] = use_mask_token
__magic_name__: List[str] = use_absolute_position_embeddings
__magic_name__: List[str] = use_relative_position_bias
__magic_name__: Optional[Any] = use_shared_relative_position_bias
__magic_name__: List[Any] = layer_scale_init_value
__magic_name__: Tuple = drop_path_rate
__magic_name__: int = use_mean_pooling
# decode head attributes (semantic segmentation)
__magic_name__: Union[str, Any] = out_indices
__magic_name__: List[Any] = pool_scales
# auxiliary head attributes (semantic segmentation)
__magic_name__: Dict = use_auxiliary_head
__magic_name__: str = auxiliary_loss_weight
__magic_name__: Tuple = auxiliary_channels
__magic_name__: List[str] = auxiliary_num_convs
__magic_name__: List[str] = auxiliary_concat_input
__magic_name__: int = semantic_loss_ignore_index
class __A ( SCREAMING_SNAKE_CASE_ ):
UpperCAmelCase__ = version.parse("1.11" )
@property
def lowerCamelCase__ ( self : Optional[int] ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def lowerCamelCase__ ( self : str ) -> float:
return 1E-4
| 96 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
def UpperCAmelCase_ ( __a : Optional[Any] ):
'''simple docstring'''
_lowerCamelCase : int = DPTConfig()
if "large" in checkpoint_url:
_lowerCamelCase : Optional[Any] = 10_24
_lowerCamelCase : List[str] = 40_96
_lowerCamelCase : Union[str, Any] = 24
_lowerCamelCase : Any = 16
_lowerCamelCase : Union[str, Any] = [5, 11, 17, 23]
_lowerCamelCase : Optional[int] = [2_56, 5_12, 10_24, 10_24]
_lowerCamelCase : List[str] = (1, 3_84, 3_84)
if "ade" in checkpoint_url:
_lowerCamelCase : Any = True
_lowerCamelCase : List[str] = 1_50
_lowerCamelCase : int = 'huggingface/label-files'
_lowerCamelCase : Union[str, Any] = 'ade20k-id2label.json'
_lowerCamelCase : Union[str, Any] = json.load(open(cached_download(hf_hub_url(__a , __a , repo_type='dataset' ) ) , 'r' ) )
_lowerCamelCase : Optional[Any] = {int(__a ): v for k, v in idalabel.items()}
_lowerCamelCase : int = idalabel
_lowerCamelCase : Any = {v: k for k, v in idalabel.items()}
_lowerCamelCase : List[str] = [1, 1_50, 4_80, 4_80]
return config, expected_shape
def UpperCAmelCase_ ( __a : Tuple ):
'''simple docstring'''
_lowerCamelCase : int = ['pretrained.model.head.weight', 'pretrained.model.head.bias']
for k in ignore_keys:
state_dict.pop(__a , __a )
def UpperCAmelCase_ ( __a : Dict ):
'''simple docstring'''
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
_lowerCamelCase : Optional[Any] = name.replace('pretrained.model' , 'dpt.encoder' )
if "pretrained.model" in name:
_lowerCamelCase : Optional[int] = name.replace('pretrained.model' , 'dpt.embeddings' )
if "patch_embed" in name:
_lowerCamelCase : Any = name.replace('patch_embed' , 'patch_embeddings' )
if "pos_embed" in name:
_lowerCamelCase : str = name.replace('pos_embed' , 'position_embeddings' )
if "attn.proj" in name:
_lowerCamelCase : int = name.replace('attn.proj' , 'attention.output.dense' )
if "proj" in name and "project" not in name:
_lowerCamelCase : Tuple = name.replace('proj' , 'projection' )
if "blocks" in name:
_lowerCamelCase : Optional[int] = name.replace('blocks' , 'layer' )
if "mlp.fc1" in name:
_lowerCamelCase : Union[str, Any] = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
_lowerCamelCase : Optional[int] = name.replace('mlp.fc2' , 'output.dense' )
if "norm1" in name:
_lowerCamelCase : Optional[Any] = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
_lowerCamelCase : str = name.replace('norm2' , 'layernorm_after' )
if "scratch.output_conv" in name:
_lowerCamelCase : Optional[int] = name.replace('scratch.output_conv' , 'head' )
if "scratch" in name:
_lowerCamelCase : Dict = name.replace('scratch' , 'neck' )
if "layer1_rn" in name:
_lowerCamelCase : Tuple = name.replace('layer1_rn' , 'convs.0' )
if "layer2_rn" in name:
_lowerCamelCase : Tuple = name.replace('layer2_rn' , 'convs.1' )
if "layer3_rn" in name:
_lowerCamelCase : Tuple = name.replace('layer3_rn' , 'convs.2' )
if "layer4_rn" in name:
_lowerCamelCase : List[Any] = name.replace('layer4_rn' , 'convs.3' )
if "refinenet" in name:
_lowerCamelCase : str = int(name[len('neck.refinenet' ) : len('neck.refinenet' ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
_lowerCamelCase : Union[str, Any] = name.replace(f"refinenet{layer_idx}" , f"fusion_stage.layers.{abs(layer_idx-4 )}" )
if "out_conv" in name:
_lowerCamelCase : Optional[Any] = name.replace('out_conv' , 'projection' )
if "resConfUnit1" in name:
_lowerCamelCase : str = name.replace('resConfUnit1' , 'residual_layer1' )
if "resConfUnit2" in name:
_lowerCamelCase : List[str] = name.replace('resConfUnit2' , 'residual_layer2' )
if "conv1" in name:
_lowerCamelCase : List[Any] = name.replace('conv1' , 'convolution1' )
if "conv2" in name:
_lowerCamelCase : Optional[Any] = name.replace('conv2' , 'convolution2' )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
_lowerCamelCase : Union[str, Any] = name.replace('pretrained.act_postprocess1.0.project.0' , 'neck.reassemble_stage.readout_projects.0.0' )
if "pretrained.act_postprocess2.0.project.0" in name:
_lowerCamelCase : Tuple = name.replace('pretrained.act_postprocess2.0.project.0' , 'neck.reassemble_stage.readout_projects.1.0' )
if "pretrained.act_postprocess3.0.project.0" in name:
_lowerCamelCase : List[str] = name.replace('pretrained.act_postprocess3.0.project.0' , 'neck.reassemble_stage.readout_projects.2.0' )
if "pretrained.act_postprocess4.0.project.0" in name:
_lowerCamelCase : Dict = name.replace('pretrained.act_postprocess4.0.project.0' , 'neck.reassemble_stage.readout_projects.3.0' )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
_lowerCamelCase : Optional[int] = name.replace('pretrained.act_postprocess1.3' , 'neck.reassemble_stage.layers.0.projection' )
if "pretrained.act_postprocess1.4" in name:
_lowerCamelCase : List[str] = name.replace('pretrained.act_postprocess1.4' , 'neck.reassemble_stage.layers.0.resize' )
if "pretrained.act_postprocess2.3" in name:
_lowerCamelCase : Union[str, Any] = name.replace('pretrained.act_postprocess2.3' , 'neck.reassemble_stage.layers.1.projection' )
if "pretrained.act_postprocess2.4" in name:
_lowerCamelCase : str = name.replace('pretrained.act_postprocess2.4' , 'neck.reassemble_stage.layers.1.resize' )
if "pretrained.act_postprocess3.3" in name:
_lowerCamelCase : List[Any] = name.replace('pretrained.act_postprocess3.3' , 'neck.reassemble_stage.layers.2.projection' )
if "pretrained.act_postprocess4.3" in name:
_lowerCamelCase : List[str] = name.replace('pretrained.act_postprocess4.3' , 'neck.reassemble_stage.layers.3.projection' )
if "pretrained.act_postprocess4.4" in name:
_lowerCamelCase : Tuple = name.replace('pretrained.act_postprocess4.4' , 'neck.reassemble_stage.layers.3.resize' )
if "pretrained" in name:
_lowerCamelCase : Any = name.replace('pretrained' , 'dpt' )
if "bn" in name:
_lowerCamelCase : Tuple = name.replace('bn' , 'batch_norm' )
if "head" in name:
_lowerCamelCase : str = name.replace('head' , 'head.head' )
if "encoder.norm" in name:
_lowerCamelCase : Union[str, Any] = name.replace('encoder.norm' , 'layernorm' )
if "auxlayer" in name:
_lowerCamelCase : Union[str, Any] = name.replace('auxlayer' , 'auxiliary_head.head' )
return name
def UpperCAmelCase_ ( __a : Tuple , __a : Any ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_lowerCamelCase : Any = state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight" )
_lowerCamelCase : Dict = state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : Tuple = in_proj_weight[: config.hidden_size, :]
_lowerCamelCase : Optional[int] = in_proj_bias[: config.hidden_size]
_lowerCamelCase : Optional[Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_lowerCamelCase : List[str] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_lowerCamelCase : Any = in_proj_weight[
-config.hidden_size :, :
]
_lowerCamelCase : str = in_proj_bias[-config.hidden_size :]
def UpperCAmelCase_ ( ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = 'http://images.cocodataset.org/val2017/000000039769.jpg'
_lowerCamelCase : Dict = Image.open(requests.get(__a , stream=__a ).raw )
return im
@torch.no_grad()
def UpperCAmelCase_ ( __a : Optional[int] , __a : int , __a : str , __a : List[str] ):
'''simple docstring'''
_lowerCamelCase , _lowerCamelCase : List[str] = get_dpt_config(__a )
# load original state_dict from URL
_lowerCamelCase : List[Any] = torch.hub.load_state_dict_from_url(__a , map_location='cpu' )
# remove certain keys
remove_ignore_keys_(__a )
# rename keys
for key in state_dict.copy().keys():
_lowerCamelCase : str = state_dict.pop(__a )
_lowerCamelCase : str = val
# read in qkv matrices
read_in_q_k_v(__a , __a )
# load HuggingFace model
_lowerCamelCase : List[str] = DPTForSemanticSegmentation(__a ) if 'ade' in checkpoint_url else DPTForDepthEstimation(__a )
model.load_state_dict(__a )
model.eval()
# Check outputs on an image
_lowerCamelCase : str = 4_80 if 'ade' in checkpoint_url else 3_84
_lowerCamelCase : Optional[Any] = DPTImageProcessor(size=__a )
_lowerCamelCase : List[str] = prepare_img()
_lowerCamelCase : str = image_processor(__a , return_tensors='pt' )
# forward pass
_lowerCamelCase : Tuple = model(**__a ).logits if 'ade' in checkpoint_url else model(**__a ).predicted_depth
# Assert logits
_lowerCamelCase : Dict = torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]] )
if "ade" in checkpoint_url:
_lowerCamelCase : List[Any] = torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]] )
assert outputs.shape == torch.Size(__a )
assert (
torch.allclose(outputs[0, 0, :3, :3] , __a , atol=1E-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3] , __a )
)
Path(__a ).mkdir(exist_ok=__a )
print(f"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(__a )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(__a )
if push_to_hub:
print('Pushing model to hub...' )
model.push_to_hub(
repo_path_or_name=Path(__a , __a ) , organization='nielsr' , commit_message='Add model' , use_temp_dir=__a , )
image_processor.push_to_hub(
repo_path_or_name=Path(__a , __a ) , organization='nielsr' , commit_message='Add image processor' , use_temp_dir=__a , )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
type=str,
help="""URL of the original DPT checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
)
parser.add_argument(
"""--model_name""",
default="""dpt-large""",
type=str,
help="""Name of the model, in case you're pushing to the hub.""",
)
a_ = parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 437 | 0 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed
_lowerCamelCase =logging.getLogger(__name__)
def snake_case__ ( lowerCAmelCase_=2, lowerCAmelCase_=3, lowerCAmelCase_=16, lowerCAmelCase_ = 10, lowerCAmelCase_ = 2 ):
"""simple docstring"""
def get_dataset(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE =torch.randn(batch_size * n_batches, 1 )
return TensorDataset(lowerCAmelCase_, a * x + b + 0.1 * torch.randn(batch_size * n_batches, 1 ) )
SCREAMING_SNAKE_CASE =get_dataset(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =get_dataset(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =DataLoader(lowerCAmelCase_, shuffle=lowerCAmelCase_, batch_size=lowerCAmelCase_, num_workers=4 )
SCREAMING_SNAKE_CASE =DataLoader(lowerCAmelCase_, shuffle=lowerCAmelCase_, batch_size=lowerCAmelCase_, num_workers=4 )
return (train_dataloader, valid_dataloader)
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_=None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =[]
for epoch in range(lowerCAmelCase_ ):
# Train quickly
model.train()
for batch in dataloader:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =batch
SCREAMING_SNAKE_CASE =model(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =torch.nn.functional.mse_loss(lowerCAmelCase_, lowerCAmelCase_ )
accelerator.backward(lowerCAmelCase_ )
optimizer.step()
optimizer.zero_grad()
rands.append(random.random() ) # Introduce some randomness
if scheduler is not None:
scheduler.step()
return rands
class a_ ( nn.Module ):
"""simple docstring"""
def __init__( self : List[str] ):
super().__init__()
SCREAMING_SNAKE_CASE =nn.Parameter(torch.randn(1 ) )
SCREAMING_SNAKE_CASE =nn.Parameter(torch.randn(1 ) )
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Tuple ):
return x * self.a + self.b
class a_ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : List[str] ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
SCREAMING_SNAKE_CASE =DummyModel()
SCREAMING_SNAKE_CASE =torch.optim.Adam(params=model.parameters() ,lr=1e-3 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =dummy_dataloaders()
SCREAMING_SNAKE_CASE =ProjectConfiguration(total_limit=1 ,project_dir=snake_case ,automatic_checkpoint_naming=snake_case )
# Train baseline
SCREAMING_SNAKE_CASE =Accelerator(project_config=snake_case )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.prepare(
snake_case ,snake_case ,snake_case ,snake_case )
# Save initial
accelerator.save_state()
# Save second state
accelerator.save_state()
self.assertEqual(len(os.listdir(accelerator.project_dir ) ) ,1 )
def _lowerCAmelCase ( self : int ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
SCREAMING_SNAKE_CASE =DummyModel()
SCREAMING_SNAKE_CASE =torch.optim.Adam(params=model.parameters() ,lr=1e-3 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =dummy_dataloaders()
# Train baseline
SCREAMING_SNAKE_CASE =Accelerator()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.prepare(
snake_case ,snake_case ,snake_case ,snake_case )
# Save initial
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'initial' )
accelerator.save_state(snake_case )
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) =model.a.item(), model.b.item()
SCREAMING_SNAKE_CASE =optimizer.state_dict()
SCREAMING_SNAKE_CASE =train(3 ,snake_case ,snake_case ,snake_case ,snake_case )
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) =model.a.item(), model.b.item()
SCREAMING_SNAKE_CASE =optimizer.state_dict()
# Train partially
set_seed(42 )
SCREAMING_SNAKE_CASE =DummyModel()
SCREAMING_SNAKE_CASE =torch.optim.Adam(params=model.parameters() ,lr=1e-3 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =dummy_dataloaders()
SCREAMING_SNAKE_CASE =Accelerator()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.prepare(
snake_case ,snake_case ,snake_case ,snake_case )
accelerator.load_state(snake_case )
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) =model.a.item(), model.b.item()
SCREAMING_SNAKE_CASE =optimizer.state_dict()
self.assertEqual(snake_case ,snake_case )
self.assertEqual(snake_case ,snake_case )
self.assertEqual(snake_case ,snake_case )
SCREAMING_SNAKE_CASE =train(2 ,snake_case ,snake_case ,snake_case ,snake_case )
# Save everything
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'checkpoint' )
accelerator.save_state(snake_case )
# Load everything back in and make sure all states work
accelerator.load_state(snake_case )
test_rands += train(1 ,snake_case ,snake_case ,snake_case ,snake_case )
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) =model.a.item(), model.b.item()
SCREAMING_SNAKE_CASE =optimizer.state_dict()
self.assertEqual(snake_case ,snake_case )
self.assertEqual(snake_case ,snake_case )
self.assertEqual(snake_case ,snake_case )
self.assertEqual(snake_case ,snake_case )
def _lowerCAmelCase ( self : Optional[Any] ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
SCREAMING_SNAKE_CASE =DummyModel()
SCREAMING_SNAKE_CASE =torch.optim.Adam(params=model.parameters() ,lr=1e-3 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =dummy_dataloaders()
SCREAMING_SNAKE_CASE =ProjectConfiguration(automatic_checkpoint_naming=snake_case )
# Train baseline
SCREAMING_SNAKE_CASE =Accelerator(project_dir=snake_case ,project_config=snake_case )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.prepare(
snake_case ,snake_case ,snake_case ,snake_case )
# Save initial
accelerator.save_state()
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) =model.a.item(), model.b.item()
SCREAMING_SNAKE_CASE =optimizer.state_dict()
SCREAMING_SNAKE_CASE =train(3 ,snake_case ,snake_case ,snake_case ,snake_case )
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) =model.a.item(), model.b.item()
SCREAMING_SNAKE_CASE =optimizer.state_dict()
# Train partially
set_seed(42 )
SCREAMING_SNAKE_CASE =DummyModel()
SCREAMING_SNAKE_CASE =torch.optim.Adam(params=model.parameters() ,lr=1e-3 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =dummy_dataloaders()
SCREAMING_SNAKE_CASE =ProjectConfiguration(iteration=1 ,automatic_checkpoint_naming=snake_case )
SCREAMING_SNAKE_CASE =Accelerator(project_dir=snake_case ,project_config=snake_case )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.prepare(
snake_case ,snake_case ,snake_case ,snake_case )
accelerator.load_state(os.path.join(snake_case ,'checkpoints' ,'checkpoint_0' ) )
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) =model.a.item(), model.b.item()
SCREAMING_SNAKE_CASE =optimizer.state_dict()
self.assertEqual(snake_case ,snake_case )
self.assertEqual(snake_case ,snake_case )
self.assertEqual(snake_case ,snake_case )
SCREAMING_SNAKE_CASE =train(2 ,snake_case ,snake_case ,snake_case ,snake_case )
# Save everything
accelerator.save_state()
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(snake_case ,'checkpoints' ,'checkpoint_1' ) )
test_rands += train(1 ,snake_case ,snake_case ,snake_case ,snake_case )
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) =model.a.item(), model.b.item()
SCREAMING_SNAKE_CASE =optimizer.state_dict()
self.assertEqual(snake_case ,snake_case )
self.assertEqual(snake_case ,snake_case )
self.assertEqual(snake_case ,snake_case )
self.assertEqual(snake_case ,snake_case )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =torch.tensor([1, 2, 3] )
SCREAMING_SNAKE_CASE =torch.tensor([2, 3, 4] )
SCREAMING_SNAKE_CASE =DummyModel()
SCREAMING_SNAKE_CASE =torch.optim.Adam(net.parameters() )
SCREAMING_SNAKE_CASE =Accelerator()
with self.assertRaises(snake_case ) as ve:
accelerator.register_for_checkpointing(snake_case ,snake_case ,snake_case ,snake_case )
SCREAMING_SNAKE_CASE =str(ve.exception )
self.assertTrue('Item at index 0' in message )
self.assertTrue('Item at index 1' in message )
self.assertFalse('Item at index 2' in message )
self.assertFalse('Item at index 3' in message )
def _lowerCAmelCase ( self : Optional[Any] ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
SCREAMING_SNAKE_CASE =DummyModel()
SCREAMING_SNAKE_CASE =torch.optim.Adam(params=model.parameters() ,lr=1e-3 )
SCREAMING_SNAKE_CASE =torch.optim.lr_scheduler.StepLR(snake_case ,step_size=1 ,gamma=0.99 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =dummy_dataloaders()
SCREAMING_SNAKE_CASE =ProjectConfiguration(automatic_checkpoint_naming=snake_case )
# Train baseline
SCREAMING_SNAKE_CASE =Accelerator(project_dir=snake_case ,project_config=snake_case )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.prepare(
snake_case ,snake_case ,snake_case ,snake_case ,snake_case )
# Save initial
accelerator.save_state()
SCREAMING_SNAKE_CASE =scheduler.state_dict()
train(3 ,snake_case ,snake_case ,snake_case ,snake_case ,snake_case )
self.assertNotEqual(snake_case ,scheduler.state_dict() )
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(snake_case ,'checkpoints' ,'checkpoint_0' ) )
self.assertEqual(snake_case ,scheduler.state_dict() )
def _lowerCAmelCase ( self : List[str] ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
SCREAMING_SNAKE_CASE =DummyModel()
SCREAMING_SNAKE_CASE =ProjectConfiguration(automatic_checkpoint_naming=snake_case ,total_limit=2 )
# Train baseline
SCREAMING_SNAKE_CASE =Accelerator(project_dir=snake_case ,project_config=snake_case )
SCREAMING_SNAKE_CASE =accelerator.prepare(snake_case )
# Save 3 states:
for _ in range(11 ):
accelerator.save_state()
self.assertTrue(not os.path.exists(os.path.join(snake_case ,'checkpoints' ,'checkpoint_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(snake_case ,'checkpoints' ,'checkpoint_9' ) ) )
self.assertTrue(os.path.exists(os.path.join(snake_case ,'checkpoints' ,'checkpoint_10' ) ) )
@require_cuda
def _lowerCAmelCase ( self : str ):
SCREAMING_SNAKE_CASE =['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__ )]
execute_subprocess_async(snake_case ,env=os.environ.copy() )
if __name__ == "__main__":
_lowerCamelCase ="/tmp/accelerate/state_checkpointing"
_lowerCamelCase =DummyModel()
_lowerCamelCase =torch.optim.Adam(params=model.parameters(), lr=1e-3)
_lowerCamelCase =torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
_lowerCamelCase , _lowerCamelCase =dummy_dataloaders()
_lowerCamelCase =ProjectConfiguration(automatic_checkpoint_naming=True)
# Train baseline
_lowerCamelCase =Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="no")
if accelerator.process_index == 0:
if os.path.exists(savedir):
shutil.rmtree(savedir)
os.makedirs(savedir)
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase =accelerator.prepare(
model, optimizer, train_dataloader, valid_dataloader, scheduler
)
_lowerCamelCase , _lowerCamelCase =accelerator.prepare(model, optimizer)
train(3, model, train_dataloader, optimizer, accelerator, scheduler)
# Check that the intial optimizer is loaded on the GPU
for group in optimizer.param_groups:
_lowerCamelCase =group["params"][0].device
break
assert param_device.type == accelerator.device.type
_lowerCamelCase =model.cpu()
accelerator.wait_for_everyone()
accelerator.save_state()
accelerator.wait_for_everyone()
# Check CPU state
accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="cpu")
for group in optimizer.param_groups:
_lowerCamelCase =group["params"][0].device
break
assert (
param_device.type == torch.device("cpu").type
), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"
# Check device state
model.to(accelerator.device)
accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="on_device")
for group in optimizer.param_groups:
_lowerCamelCase =group["params"][0].device
break
assert (
param_device.type == accelerator.device.type
), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"
# Check error
with pytest.raises(TypeError, match="Unsupported optimizer map location passed"):
accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="invalid")
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
shutil.rmtree(savedir)
accelerator.wait_for_everyone()
| 709 |
def snake_case__ ( lowerCAmelCase_ = 1000000 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =limit + 1
SCREAMING_SNAKE_CASE =[0] * limit
for first_term in range(1, lowerCAmelCase_ ):
for n in range(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE =first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
SCREAMING_SNAKE_CASE =sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f'{solution() = }')
| 252 | 0 |
def UpperCAmelCase_ ( __UpperCamelCase, __UpperCamelCase, __UpperCamelCase, __UpperCamelCase ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ =len(__UpperCamelCase ), len(grid[0] )
if (
min(__UpperCamelCase, __UpperCamelCase ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
SCREAMING_SNAKE_CASE__ =0
count += depth_first_search(__UpperCamelCase, row + 1, __UpperCamelCase, __UpperCamelCase )
count += depth_first_search(__UpperCamelCase, row - 1, __UpperCamelCase, __UpperCamelCase )
count += depth_first_search(__UpperCamelCase, __UpperCamelCase, col + 1, __UpperCamelCase )
count += depth_first_search(__UpperCamelCase, __UpperCamelCase, col - 1, __UpperCamelCase )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 151 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowerCamelCase_ = logging.getLogger(__name__)
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser(
description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
)
parser.add_argument(
"--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
)
parser.add_argument(
"--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
)
parser.add_argument("--vocab_size", default=30522, type=int)
lowerCamelCase_ = parser.parse_args()
logger.info(f"""Loading data from {args.data_file}""")
with open(args.data_file, "rb") as fp:
lowerCamelCase_ = pickle.load(fp)
logger.info("Counting occurrences for MLM.")
lowerCamelCase_ = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowerCamelCase_ = [0] * args.vocab_size
for k, v in counter.items():
lowerCamelCase_ = v
logger.info(f"""Dump to {args.token_counts_dump}""")
with open(args.token_counts_dump, "wb") as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 151 | 1 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger(__name__)
def lowerCamelCase_ ( UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Tuple ) -> str:
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = []
for i in range(encoder_config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'''encoder.deit.blocks.{i}.norm1.weight''', F'''encoder.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''encoder.deit.blocks.{i}.norm1.bias''', F'''encoder.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(F'''encoder.deit.blocks.{i}.attn.proj.weight''', F'''encoder.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(F'''encoder.deit.blocks.{i}.attn.proj.bias''', F'''encoder.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append(
(F'''encoder.deit.blocks.{i}.norm2.weight''', F'''encoder.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''encoder.deit.blocks.{i}.norm2.bias''', F'''encoder.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append(
(F'''encoder.deit.blocks.{i}.mlp.fc1.weight''', F'''encoder.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append(
(F'''encoder.deit.blocks.{i}.mlp.fc1.bias''', F'''encoder.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append(
(F'''encoder.deit.blocks.{i}.mlp.fc2.weight''', F'''encoder.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''encoder.deit.blocks.{i}.mlp.fc2.bias''', F'''encoder.encoder.layer.{i}.output.dense.bias''') )
# cls token, position embeddings and patch embeddings of encoder
rename_keys.extend(
[
('encoder.deit.cls_token', 'encoder.embeddings.cls_token'),
('encoder.deit.pos_embed', 'encoder.embeddings.position_embeddings'),
('encoder.deit.patch_embed.proj.weight', 'encoder.embeddings.patch_embeddings.projection.weight'),
('encoder.deit.patch_embed.proj.bias', 'encoder.embeddings.patch_embeddings.projection.bias'),
('encoder.deit.norm.weight', 'encoder.layernorm.weight'),
('encoder.deit.norm.bias', 'encoder.layernorm.bias'),
] )
return rename_keys
def lowerCamelCase_ ( UpperCAmelCase_ : Any , UpperCAmelCase_ : int ) -> Tuple:
'''simple docstring'''
for i in range(encoder_config.num_hidden_layers ):
# queries, keys and values (only weights, no biases)
_UpperCamelCase : Dict = state_dict.pop(F'''encoder.deit.blocks.{i}.attn.qkv.weight''' )
_UpperCamelCase : List[Any] = in_proj_weight[
: encoder_config.hidden_size, :
]
_UpperCamelCase : Optional[Any] = in_proj_weight[
encoder_config.hidden_size : encoder_config.hidden_size * 2, :
]
_UpperCamelCase : Union[str, Any] = in_proj_weight[
-encoder_config.hidden_size :, :
]
def lowerCamelCase_ ( UpperCAmelCase_ : int , UpperCAmelCase_ : Any , UpperCAmelCase_ : int ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase : Optional[Any] = dct.pop(UpperCAmelCase_ )
_UpperCamelCase : int = val
def lowerCamelCase_ ( UpperCAmelCase_ : Dict ) -> str:
'''simple docstring'''
if "handwritten" in checkpoint_url:
_UpperCamelCase : str = 'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg' # industry
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" #
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
_UpperCamelCase : List[Any] = 'https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'
_UpperCamelCase : Optional[Any] = Image.open(requests.get(UpperCAmelCase_ , stream=UpperCAmelCase_ ).raw ).convert('RGB' )
return im
@torch.no_grad()
def lowerCamelCase_ ( UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : int ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase : Tuple = ViTConfig(image_size=3_8_4 , qkv_bias=UpperCAmelCase_ )
_UpperCamelCase : Optional[int] = TrOCRConfig()
# size of the architecture
if "base" in checkpoint_url:
_UpperCamelCase : Optional[Any] = 7_6_8
elif "large" in checkpoint_url:
# use ViT-large encoder
_UpperCamelCase : Any = 1_0_2_4
_UpperCamelCase : Union[str, Any] = 4_0_9_6
_UpperCamelCase : int = 2_4
_UpperCamelCase : List[str] = 1_6
_UpperCamelCase : Optional[Any] = 1_0_2_4
else:
raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL' )
# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
_UpperCamelCase : Optional[Any] = False
_UpperCamelCase : List[Any] = 'relu'
_UpperCamelCase : List[Any] = 1_0_2_4
_UpperCamelCase : str = True
_UpperCamelCase : Dict = False
_UpperCamelCase : Any = False
# load HuggingFace model
_UpperCamelCase : str = ViTModel(UpperCAmelCase_ , add_pooling_layer=UpperCAmelCase_ )
_UpperCamelCase : Union[str, Any] = TrOCRForCausalLM(UpperCAmelCase_ )
_UpperCamelCase : Dict = VisionEncoderDecoderModel(encoder=UpperCAmelCase_ , decoder=UpperCAmelCase_ )
model.eval()
# load state_dict of original model, rename some keys
_UpperCamelCase : int = torch.hub.load_state_dict_from_url(UpperCAmelCase_ , map_location='cpu' , check_hash=UpperCAmelCase_ )['model']
_UpperCamelCase : List[Any] = create_rename_keys(UpperCAmelCase_ , UpperCAmelCase_ )
for src, dest in rename_keys:
rename_key(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
read_in_q_k_v(UpperCAmelCase_ , UpperCAmelCase_ )
# remove parameters we don't need
del state_dict["encoder.deit.head.weight"]
del state_dict["encoder.deit.head.bias"]
del state_dict["decoder.version"]
# add prefix to decoder keys
for key, val in state_dict.copy().items():
_UpperCamelCase : List[Any] = state_dict.pop(UpperCAmelCase_ )
if key.startswith('decoder' ) and "output_projection" not in key:
_UpperCamelCase : List[str] = val
else:
_UpperCamelCase : Optional[Any] = val
# load state dict
model.load_state_dict(UpperCAmelCase_ )
# Check outputs on an image
_UpperCamelCase : List[Any] = ViTImageProcessor(size=encoder_config.image_size )
_UpperCamelCase : Optional[Any] = RobertaTokenizer.from_pretrained('roberta-large' )
_UpperCamelCase : Optional[Any] = TrOCRProcessor(UpperCAmelCase_ , UpperCAmelCase_ )
_UpperCamelCase : Dict = processor(images=prepare_img(UpperCAmelCase_ ) , return_tensors='pt' ).pixel_values
# verify logits
_UpperCamelCase : int = torch.tensor([[model.config.decoder.decoder_start_token_id]] )
_UpperCamelCase : Tuple = model(pixel_values=UpperCAmelCase_ , decoder_input_ids=UpperCAmelCase_ )
_UpperCamelCase : Any = outputs.logits
_UpperCamelCase : Optional[Any] = torch.Size([1, 1, 5_0_2_6_5] )
if "trocr-base-handwritten" in checkpoint_url:
_UpperCamelCase : Any = torch.tensor(
[-1.4_5_0_2, -4.6_6_8_3, -0.5_3_4_7, -2.9_2_9_1, 9.1_4_3_5, -3.0_5_7_1, 8.9_7_6_4, 1.7_5_6_0, 8.7_3_5_8, -1.5_3_1_1] )
elif "trocr-large-handwritten" in checkpoint_url:
_UpperCamelCase : Dict = torch.tensor(
[-2.6_4_3_7, -1.3_1_2_9, -2.2_5_9_6, -5.3_4_5_5, 6.3_5_3_9, 1.7_6_0_4, 5.4_9_9_1, 1.4_7_0_2, 5.6_1_1_3, 2.0_1_7_0] )
elif "trocr-base-printed" in checkpoint_url:
_UpperCamelCase : List[str] = torch.tensor(
[-5.6_8_1_6, -5.8_3_8_8, 1.1_3_9_8, -6.9_0_3_4, 6.8_5_0_5, -2.4_3_9_3, 1.2_2_8_4, -1.0_2_3_2, -1.9_6_6_1, -3.9_2_1_0] )
elif "trocr-large-printed" in checkpoint_url:
_UpperCamelCase : Optional[Any] = torch.tensor(
[-6.0_1_6_2, -7.0_9_5_9, 4.4_1_5_5, -5.1_0_6_3, 7.0_4_6_8, -3.1_6_3_1, 2.6_4_6_6, -0.3_0_8_1, -0.8_1_0_6, -1.7_5_3_5] )
if "stage1" not in checkpoint_url:
assert logits.shape == expected_shape, "Shape of logits not as expected"
assert torch.allclose(logits[0, 0, :1_0] , UpperCAmelCase_ , atol=1e-3 ), "First elements of logits not as expected"
Path(UpperCAmelCase_ ).mkdir(exist_ok=UpperCAmelCase_ )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase_ )
print(F'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(UpperCAmelCase_ )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt""",
type=str,
help="""URL to the original PyTorch checkpoint (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
lowerCAmelCase__ = parser.parse_args()
convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 707 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase__ = {
"""configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
"""tokenization_canine""": ["""CanineTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CanineForMultipleChoice""",
"""CanineForQuestionAnswering""",
"""CanineForSequenceClassification""",
"""CanineForTokenClassification""",
"""CanineLayer""",
"""CanineModel""",
"""CaninePreTrainedModel""",
"""load_tf_weights_in_canine""",
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 648 | 0 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipaConfig,
BlipaForConditionalGeneration,
BlipaProcessor,
BlipaVisionConfig,
BlipImageProcessor,
OPTConfig,
TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def lowerCamelCase( ):
_SCREAMING_SNAKE_CASE ='''https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'''
_SCREAMING_SNAKE_CASE =Image.open(requests.get(A_ ,stream=A_).raw).convert('''RGB''')
return image
def lowerCamelCase( a__):
_SCREAMING_SNAKE_CASE =[]
# fmt: off
# vision encoder
rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding'''))
rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding'''))
rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight'''))
rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias'''))
rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight'''))
rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias'''))
for i in range(config.vision_config.num_hidden_layers):
rename_keys.append((f"visual_encoder.blocks.{i}.norm1.weight", f"vision_model.encoder.layers.{i}.layer_norm1.weight"))
rename_keys.append((f"visual_encoder.blocks.{i}.norm1.bias", f"vision_model.encoder.layers.{i}.layer_norm1.bias"))
rename_keys.append((f"visual_encoder.blocks.{i}.norm2.weight", f"vision_model.encoder.layers.{i}.layer_norm2.weight"))
rename_keys.append((f"visual_encoder.blocks.{i}.norm2.bias", f"vision_model.encoder.layers.{i}.layer_norm2.bias"))
rename_keys.append((f"visual_encoder.blocks.{i}.attn.qkv.weight", f"vision_model.encoder.layers.{i}.self_attn.qkv.weight"))
rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.weight", f"vision_model.encoder.layers.{i}.self_attn.projection.weight",))
rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.bias", f"vision_model.encoder.layers.{i}.self_attn.projection.bias"))
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.weight", f"vision_model.encoder.layers.{i}.mlp.fc1.weight"))
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.bias", f"vision_model.encoder.layers.{i}.mlp.fc1.bias"))
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.weight", f"vision_model.encoder.layers.{i}.mlp.fc2.weight"))
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.bias", f"vision_model.encoder.layers.{i}.mlp.fc2.bias"))
# QFormer
rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.layernorm.weight'''))
rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.layernorm.bias'''))
# fmt: on
return rename_keys
def lowerCamelCase( a__ ,a__ ,a__):
_SCREAMING_SNAKE_CASE =dct.pop(A_)
_SCREAMING_SNAKE_CASE =val
def lowerCamelCase( a__ ,a__):
for i in range(config.vision_config.num_hidden_layers):
# read in original q and v biases
_SCREAMING_SNAKE_CASE =state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias")
_SCREAMING_SNAKE_CASE =state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias")
# next, set bias in the state dict
_SCREAMING_SNAKE_CASE =torch.cat((q_bias, torch.zeros_like(A_ ,requires_grad=A_), v_bias))
_SCREAMING_SNAKE_CASE =qkv_bias
def lowerCamelCase( a__ ,a__):
_SCREAMING_SNAKE_CASE =364 if '''coco''' in model_name else 224
_SCREAMING_SNAKE_CASE =BlipaVisionConfig(image_size=A_).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "opt-2.7b" in model_name:
_SCREAMING_SNAKE_CASE =OPTConfig.from_pretrained('''facebook/opt-2.7b''' ,eos_token_id=A_).to_dict()
elif "opt-6.7b" in model_name:
_SCREAMING_SNAKE_CASE =OPTConfig.from_pretrained('''facebook/opt-6.7b''' ,eos_token_id=A_).to_dict()
elif "t5-xl" in model_name:
_SCREAMING_SNAKE_CASE =TaConfig.from_pretrained('''google/flan-t5-xl''' ,dense_act_fn='''gelu''' ,bos_token_id=1).to_dict()
elif "t5-xxl" in model_name:
_SCREAMING_SNAKE_CASE =TaConfig.from_pretrained('''google/flan-t5-xxl''' ,dense_act_fn='''gelu''' ,bos_token_id=1).to_dict()
_SCREAMING_SNAKE_CASE =BlipaConfig(vision_config=A_ ,text_config=A_)
return config, image_size
@torch.no_grad()
def lowerCamelCase( a__ ,a__=None ,a__=False):
_SCREAMING_SNAKE_CASE =(
AutoTokenizer.from_pretrained('''facebook/opt-2.7b''')
if '''opt''' in model_name
else AutoTokenizer.from_pretrained('''google/flan-t5-xl''')
)
_SCREAMING_SNAKE_CASE =tokenizer('''\n''' ,add_special_tokens=A_).input_ids[0]
_SCREAMING_SNAKE_CASE =get_blipa_config(A_ ,eos_token_id=A_)
_SCREAMING_SNAKE_CASE =BlipaForConditionalGeneration(A_).eval()
_SCREAMING_SNAKE_CASE ={
'''blip2-opt-2.7b''': ('''blip2_opt''', '''pretrain_opt2.7b'''),
'''blip2-opt-6.7b''': ('''blip2_opt''', '''pretrain_opt6.7b'''),
'''blip2-opt-2.7b-coco''': ('''blip2_opt''', '''caption_coco_opt2.7b'''),
'''blip2-opt-6.7b-coco''': ('''blip2_opt''', '''caption_coco_opt6.7b'''),
'''blip2-flan-t5-xl''': ('''blip2_t5''', '''pretrain_flant5xl'''),
'''blip2-flan-t5-xl-coco''': ('''blip2_t5''', '''caption_coco_flant5xl'''),
'''blip2-flan-t5-xxl''': ('''blip2_t5''', '''pretrain_flant5xxl'''),
}
_SCREAMING_SNAKE_CASE =model_name_to_original[model_name]
# load original model
print('''Loading original model...''')
_SCREAMING_SNAKE_CASE ='''cuda''' if torch.cuda.is_available() else '''cpu'''
_SCREAMING_SNAKE_CASE =load_model_and_preprocess(
name=A_ ,model_type=A_ ,is_eval=A_ ,device=A_)
original_model.eval()
print('''Done!''')
# update state dict keys
_SCREAMING_SNAKE_CASE =original_model.state_dict()
_SCREAMING_SNAKE_CASE =create_rename_keys(A_)
for src, dest in rename_keys:
rename_key(A_ ,A_ ,A_)
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
_SCREAMING_SNAKE_CASE =state_dict.pop(A_)
if key.startswith('''Qformer.bert'''):
_SCREAMING_SNAKE_CASE =key.replace('''Qformer.bert''' ,'''qformer''')
if "attention.self" in key:
_SCREAMING_SNAKE_CASE =key.replace('''self''' ,'''attention''')
if "opt_proj" in key:
_SCREAMING_SNAKE_CASE =key.replace('''opt_proj''' ,'''language_projection''')
if "t5_proj" in key:
_SCREAMING_SNAKE_CASE =key.replace('''t5_proj''' ,'''language_projection''')
if key.startswith('''opt'''):
_SCREAMING_SNAKE_CASE =key.replace('''opt''' ,'''language''')
if key.startswith('''t5'''):
_SCREAMING_SNAKE_CASE =key.replace('''t5''' ,'''language''')
_SCREAMING_SNAKE_CASE =val
# read in qv biases
read_in_q_v_bias(A_ ,A_)
_SCREAMING_SNAKE_CASE =hf_model.load_state_dict(A_ ,strict=A_)
assert len(A_) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
_SCREAMING_SNAKE_CASE =load_demo_image()
_SCREAMING_SNAKE_CASE =vis_processors['''eval'''](A_).unsqueeze(0).to(A_)
_SCREAMING_SNAKE_CASE =tokenizer(['''\n'''] ,return_tensors='''pt''').input_ids.to(A_)
# create processor
_SCREAMING_SNAKE_CASE =BlipImageProcessor(
size={'''height''': image_size, '''width''': image_size} ,image_mean=A_ ,image_std=A_)
_SCREAMING_SNAKE_CASE =BlipaProcessor(image_processor=A_ ,tokenizer=A_)
_SCREAMING_SNAKE_CASE =processor(images=A_ ,return_tensors='''pt''').pixel_values.to(A_)
# make sure processor creates exact same pixel values
assert torch.allclose(A_ ,A_)
original_model.to(A_)
hf_model.to(A_)
with torch.no_grad():
if "opt" in model_name:
_SCREAMING_SNAKE_CASE =original_model({'''image''': original_pixel_values, '''text_input''': ['''''']}).logits
_SCREAMING_SNAKE_CASE =hf_model(A_ ,A_).logits
else:
_SCREAMING_SNAKE_CASE =original_model(
{'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']}).logits
_SCREAMING_SNAKE_CASE =input_ids.masked_fill(input_ids == tokenizer.pad_token_id ,-100)
_SCREAMING_SNAKE_CASE =hf_model(A_ ,A_ ,labels=A_).logits
assert original_logits.shape == logits.shape
print('''First values of original logits:''' ,original_logits[0, :3, :3])
print('''First values of HF logits:''' ,logits[0, :3, :3])
# assert values
if model_name == "blip2-flan-t5-xl":
_SCREAMING_SNAKE_CASE =torch.tensor(
[[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]] ,device=A_)
assert torch.allclose(logits[0, :3, :3] ,A_ ,atol=1e-4)
elif model_name == "blip2-flan-t5-xl-coco":
_SCREAMING_SNAKE_CASE =torch.tensor(
[[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]] ,device=A_)
else:
# cast to same type
_SCREAMING_SNAKE_CASE =logits.dtype
assert torch.allclose(original_logits.to(A_) ,A_ ,atol=1e-2)
print('''Looks ok!''')
print('''Generating a caption...''')
_SCREAMING_SNAKE_CASE =''''''
_SCREAMING_SNAKE_CASE =tokenizer(A_ ,return_tensors='''pt''').input_ids.to(A_)
_SCREAMING_SNAKE_CASE =original_model.generate({'''image''': original_pixel_values})
_SCREAMING_SNAKE_CASE =hf_model.generate(
A_ ,A_ ,do_sample=A_ ,num_beams=5 ,max_length=30 ,min_length=1 ,top_p=0.9 ,repetition_penalty=1.0 ,length_penalty=1.0 ,temperature=1 ,)
print('''Original generation:''' ,A_)
_SCREAMING_SNAKE_CASE =input_ids.shape[1]
_SCREAMING_SNAKE_CASE =processor.batch_decode(outputs[:, prompt_length:] ,skip_special_tokens=A_)
_SCREAMING_SNAKE_CASE =[text.strip() for text in output_text]
print('''HF generation:''' ,A_)
if pytorch_dump_folder_path is not None:
processor.save_pretrained(A_)
hf_model.save_pretrained(A_)
if push_to_hub:
processor.push_to_hub(f"nielsr/{model_name}")
hf_model.push_to_hub(f"nielsr/{model_name}")
if __name__ == "__main__":
snake_case_ : Optional[Any] = argparse.ArgumentParser()
snake_case_ : Tuple = [
'''blip2-opt-2.7b''',
'''blip2-opt-6.7b''',
'''blip2-opt-2.7b-coco''',
'''blip2-opt-6.7b-coco''',
'''blip2-flan-t5-xl''',
'''blip2-flan-t5-xl-coco''',
'''blip2-flan-t5-xxl''',
]
parser.add_argument(
'''--model_name''',
default='''blip2-opt-2.7b''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model and processor to the hub after converting''',
)
snake_case_ : Optional[Any] = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) | 691 |
"""simple docstring"""
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_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Any ,lowercase_ : Dict ,lowercase_ : List[str]=7 ,lowercase_ : Tuple=3 ,lowercase_ : List[str]=1_8 ,lowercase_ : Optional[Any]=3_0 ,lowercase_ : List[Any]=4_0_0 ,lowercase_ : List[Any]=True ,lowercase_ : Any=None ,lowercase_ : Optional[Any]=True ,lowercase_ : str=None ,lowercase_ : List[Any]=True ,lowercase_ : Dict=[0.5, 0.5, 0.5] ,lowercase_ : Dict=[0.5, 0.5, 0.5] ,):
lowerCAmelCase__ : Any = size if size is not None else {'''shortest_edge''': 1_8}
lowerCAmelCase__ : Union[str, Any] = crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
lowerCAmelCase__ : Dict = parent
lowerCAmelCase__ : Dict = batch_size
lowerCAmelCase__ : List[str] = num_channels
lowerCAmelCase__ : Any = image_size
lowerCAmelCase__ : Union[str, Any] = min_resolution
lowerCAmelCase__ : Dict = max_resolution
lowerCAmelCase__ : List[str] = do_resize
lowerCAmelCase__ : Optional[Any] = size
lowerCAmelCase__ : Tuple = do_center_crop
lowerCAmelCase__ : Optional[int] = crop_size
lowerCAmelCase__ : List[str] = do_normalize
lowerCAmelCase__ : Tuple = image_mean
lowerCAmelCase__ : int = image_std
def __lowerCAmelCase ( self : List[str] ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( a_ , unittest.TestCase ):
"""simple docstring"""
lowercase__ = LevitImageProcessor if is_vision_available() else None
def __lowerCAmelCase ( self : List[str] ):
lowerCAmelCase__ : Optional[Any] = LevitImageProcessingTester(self )
@property
def __lowerCAmelCase ( self : Any ):
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCAmelCase ( self : str ):
lowerCAmelCase__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase_ ,'''image_mean''' ) )
self.assertTrue(hasattr(lowercase_ ,'''image_std''' ) )
self.assertTrue(hasattr(lowercase_ ,'''do_normalize''' ) )
self.assertTrue(hasattr(lowercase_ ,'''do_resize''' ) )
self.assertTrue(hasattr(lowercase_ ,'''do_center_crop''' ) )
self.assertTrue(hasattr(lowercase_ ,'''size''' ) )
def __lowerCAmelCase ( self : Any ):
lowerCAmelCase__ : int = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{'''shortest_edge''': 1_8} )
self.assertEqual(image_processor.crop_size ,{'''height''': 1_8, '''width''': 1_8} )
lowerCAmelCase__ : List[str] = self.image_processing_class.from_dict(self.image_processor_dict ,size=4_2 ,crop_size=8_4 )
self.assertEqual(image_processor.size ,{'''shortest_edge''': 4_2} )
self.assertEqual(image_processor.crop_size ,{'''height''': 8_4, '''width''': 8_4} )
def __lowerCAmelCase ( self : Union[str, Any] ):
pass
def __lowerCAmelCase ( self : Optional[int] ):
# Initialize image_processing
lowerCAmelCase__ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase__ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester ,equal_resolution=lowercase_ )
for image in image_inputs:
self.assertIsInstance(lowercase_ ,Image.Image )
# Test not batched input
lowerCAmelCase__ : Any = image_processing(image_inputs[0] ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) ,)
# Test batched
lowerCAmelCase__ : Optional[int] = image_processing(lowercase_ ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) ,)
def __lowerCAmelCase ( self : Union[str, Any] ):
# Initialize image_processing
lowerCAmelCase__ : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase__ : Optional[Any] = prepare_image_inputs(self.image_processor_tester ,equal_resolution=lowercase_ ,numpify=lowercase_ )
for image in image_inputs:
self.assertIsInstance(lowercase_ ,np.ndarray )
# Test not batched input
lowerCAmelCase__ : Tuple = image_processing(image_inputs[0] ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) ,)
# Test batched
lowerCAmelCase__ : Union[str, Any] = image_processing(lowercase_ ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) ,)
def __lowerCAmelCase ( self : int ):
# Initialize image_processing
lowerCAmelCase__ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase__ : Dict = prepare_image_inputs(self.image_processor_tester ,equal_resolution=lowercase_ ,torchify=lowercase_ )
for image in image_inputs:
self.assertIsInstance(lowercase_ ,torch.Tensor )
# Test not batched input
lowerCAmelCase__ : Dict = image_processing(image_inputs[0] ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) ,)
# Test batched
lowerCAmelCase__ : Optional[int] = image_processing(lowercase_ ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) ,)
| 450 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
lowerCamelCase__ : List[Any] = get_activation('gelu' )
self.assertTrue(torch.allclose(gelu_python(lowerCamelCase_ ), torch_builtin(lowerCamelCase_ ) ) )
self.assertFalse(torch.allclose(gelu_python(lowerCamelCase_ ), gelu_new(lowerCamelCase_ ) ) )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
lowerCamelCase__ : str = get_activation('gelu' )
lowerCamelCase__ : int = get_activation('gelu_10' )
lowerCamelCase__ : Optional[int] = torch_builtin(lowerCamelCase_ )
lowerCamelCase__ : Tuple = geluaa(lowerCamelCase_ )
lowerCamelCase__ : Dict = torch.where(y_gelu_aa < 10.0, 1, 0 )
self.assertTrue(torch.max(lowerCamelCase_ ).item() == 10.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask, y_gelu_aa * clipped_mask ) )
def a__ (self ):
'''simple docstring'''
get_activation('gelu' )
get_activation('gelu_10' )
get_activation('gelu_fast' )
get_activation('gelu_new' )
get_activation('gelu_python' )
get_activation('gelu_pytorch_tanh' )
get_activation('linear' )
get_activation('mish' )
get_activation('quick_gelu' )
get_activation('relu' )
get_activation('sigmoid' )
get_activation('silu' )
get_activation('swish' )
get_activation('tanh' )
with self.assertRaises(lowerCamelCase_ ):
get_activation('bogus' )
with self.assertRaises(lowerCamelCase_ ):
get_activation(lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = get_activation('gelu' )
lowerCamelCase__ : List[str] = 1
lowerCamelCase__ : Optional[Any] = get_activation('gelu' )
self.assertEqual(acta.a, 1 )
with self.assertRaises(lowerCamelCase_ ):
lowerCamelCase__ : str = acta.a
| 704 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, 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 (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_=1_3, lowerCamelCase_=7, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=9_9, lowerCamelCase_=6_4, lowerCamelCase_=5, lowerCamelCase_=4, lowerCamelCase_=3_7, lowerCamelCase_="gelu", lowerCamelCase_=0.1, lowerCamelCase_=0.1, lowerCamelCase_=5_1_2, lowerCamelCase_=1_6, lowerCamelCase_=2, lowerCamelCase_=0.02, lowerCamelCase_=3, lowerCamelCase_=4, lowerCamelCase_=None, ):
'''simple docstring'''
lowerCamelCase__ : Dict = parent
lowerCamelCase__ : Tuple = batch_size
lowerCamelCase__ : List[Any] = seq_length
lowerCamelCase__ : List[Any] = is_training
lowerCamelCase__ : str = use_input_mask
lowerCamelCase__ : Optional[Any] = use_token_type_ids
lowerCamelCase__ : Any = use_labels
lowerCamelCase__ : Optional[int] = vocab_size
lowerCamelCase__ : int = hidden_size
lowerCamelCase__ : Optional[int] = num_hidden_layers
lowerCamelCase__ : List[Any] = num_attention_heads
lowerCamelCase__ : Union[str, Any] = intermediate_size
lowerCamelCase__ : List[str] = hidden_act
lowerCamelCase__ : Union[str, Any] = hidden_dropout_prob
lowerCamelCase__ : Optional[int] = attention_probs_dropout_prob
lowerCamelCase__ : Dict = max_position_embeddings
lowerCamelCase__ : Dict = type_vocab_size
lowerCamelCase__ : Union[str, Any] = type_sequence_label_size
lowerCamelCase__ : List[Any] = initializer_range
lowerCamelCase__ : List[Any] = num_labels
lowerCamelCase__ : Union[str, Any] = num_choices
lowerCamelCase__ : List[str] = scope
lowerCamelCase__ : Dict = vocab_size - 1
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
lowerCamelCase__ : Optional[Any] = None
if self.use_input_mask:
lowerCamelCase__ : Any = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase__ : Any = None
if self.use_labels:
lowerCamelCase__ : Tuple = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
lowerCamelCase__ : str = self.get_config()
return config, input_ids, input_mask, token_labels
def a__ (self ):
'''simple docstring'''
return GPTNeoXConfig(
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=lowerCamelCase_, initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : List[str] = self.prepare_config_and_inputs()
lowerCamelCase__ : Optional[Any] = True
return config, input_ids, input_mask, token_labels
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = GPTNeoXModel(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : List[Any] = model(lowerCamelCase_, attention_mask=lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = model(lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : List[str] = True
lowerCamelCase__ : int = GPTNeoXModel(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Dict = model(lowerCamelCase_, attention_mask=lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = GPTNeoXForCausalLM(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : int = model(lowerCamelCase_, attention_mask=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.num_labels
lowerCamelCase__ : Optional[Any] = GPTNeoXForQuestionAnswering(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : str = model(lowerCamelCase_, attention_mask=lowerCamelCase_ )
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : str = self.num_labels
lowerCamelCase__ : Optional[int] = GPTNeoXForSequenceClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Dict = ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCamelCase__ : str = model(lowerCamelCase_, attention_mask=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.num_labels
lowerCamelCase__ : List[Any] = GPTNeoXForTokenClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Tuple = model(lowerCamelCase_, attention_mask=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = True
lowerCamelCase__ : List[str] = GPTNeoXForCausalLM(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
# first forward pass
lowerCamelCase__ : Optional[int] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, use_cache=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowerCamelCase__ : str = ids_tensor((self.batch_size, 3), config.vocab_size )
lowerCamelCase__ : List[Any] = ids_tensor((self.batch_size, 3), vocab_size=2 )
# append to next input_ids and
lowerCamelCase__ : Tuple = torch.cat([input_ids, next_tokens], dim=-1 )
lowerCamelCase__ : Tuple = torch.cat([input_mask, next_mask], dim=-1 )
lowerCamelCase__ : List[str] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, output_hidden_states=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = output_from_no_past['hidden_states'][0]
lowerCamelCase__ : Optional[Any] = model(
lowerCamelCase_, attention_mask=lowerCamelCase_, past_key_values=lowerCamelCase_, output_hidden_states=lowerCamelCase_, )['hidden_states'][0]
# select random slice
lowerCamelCase__ : Dict = ids_tensor((1,), output_from_past.shape[-1] ).item()
lowerCamelCase__ : Optional[Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCamelCase__ : Optional[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-3 ) )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = self.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Dict = config_and_inputs
lowerCamelCase__ : List[str] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a_ ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCamelCase__ : int = (GPTNeoXForCausalLM,) if is_torch_available() else ()
lowerCamelCase__ : Dict = (
{
'feature-extraction': GPTNeoXModel,
'question-answering': GPTNeoXForQuestionAnswering,
'text-classification': GPTNeoXForSequenceClassification,
'text-generation': GPTNeoXForCausalLM,
'token-classification': GPTNeoXForTokenClassification,
'zero-shot': GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ : Dict = False
lowerCamelCase__ : Optional[int] = False
lowerCamelCase__ : Any = False
lowerCamelCase__ : Dict = False
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = GPTNeoXModelTester(self )
lowerCamelCase__ : Union[str, Any] = ConfigTester(self, config_class=lowerCamelCase_, hidden_size=6_4, num_attention_heads=8 )
def a__ (self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : str = self.model_tester.prepare_config_and_inputs_for_decoder()
lowerCamelCase__ : Optional[Any] = None
self.model_tester.create_and_check_model_as_decoder(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_ )
@unittest.skip(reason='Feed forward chunking is not implemented' )
def a__ (self ):
'''simple docstring'''
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ : Optional[Any] = ids_tensor([1, 1_0], config.vocab_size )
lowerCamelCase__ : Tuple = ids_tensor([1, int(config.max_position_embeddings * 1.5 )], config.vocab_size )
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
lowerCamelCase__ : Any = GPTNeoXModel(lowerCamelCase_ )
original_model.to(lowerCamelCase_ )
original_model.eval()
lowerCamelCase__ : List[Any] = original_model(lowerCamelCase_ ).last_hidden_state
lowerCamelCase__ : Optional[int] = original_model(lowerCamelCase_ ).last_hidden_state
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
lowerCamelCase__ : Optional[int] = {'type': scaling_type, 'factor': 10.0}
lowerCamelCase__ : int = GPTNeoXModel(lowerCamelCase_ )
scaled_model.to(lowerCamelCase_ )
scaled_model.eval()
lowerCamelCase__ : Tuple = scaled_model(lowerCamelCase_ ).last_hidden_state
lowerCamelCase__ : Optional[int] = scaled_model(lowerCamelCase_ ).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(lowerCamelCase_, lowerCamelCase_, atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-5 ) )
@require_torch
class a_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped' )
for checkpointing in [True, False]:
lowerCamelCase__ : Optional[Any] = GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped' )
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = tokenizer('My favorite food is', return_tensors='pt' ).to(lowerCamelCase_ )
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
lowerCamelCase__ : Dict = 'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'
lowerCamelCase__ : Dict = model.generate(**lowerCamelCase_, do_sample=lowerCamelCase_, max_new_tokens=2_0 )
lowerCamelCase__ : Optional[Any] = tokenizer.batch_decode(lowerCamelCase_ )[0]
self.assertEqual(lowerCamelCase_, lowerCamelCase_ )
| 696 | 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 __snake_case ( lowerCAmelCase , unittest.TestCase ):
_a : str= BarthezTokenizer
_a : Optional[int]= BarthezTokenizerFast
_a : List[str]= True
_a : str= True
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
super().setUp()
lowercase : Tuple = BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez""" )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname ,legacy_format=snake_case )
lowercase : Any = tokenizer
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : List[Any] = """<pad>"""
lowercase : Any = 1
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 _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : List[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(snake_case ) ,101122 )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size ,101122 )
@require_torch
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : str = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
lowercase : Dict = [0, 57, 3018, 70307, 91, 2]
lowercase : Optional[int] = self.tokenizer(
snake_case ,max_length=len(snake_case ) ,padding=snake_case ,truncation=snake_case ,return_tensors="""pt""" )
self.assertIsInstance(snake_case ,snake_case )
self.assertEqual((2, 6) ,batch.input_ids.shape )
self.assertEqual((2, 6) ,batch.attention_mask.shape )
lowercase : int = batch.input_ids.tolist()[0]
self.assertListEqual(snake_case ,snake_case )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
lowercase : Tuple = self.get_tokenizer()
lowercase : Any = self.get_rust_tokenizer()
lowercase : str = """I was born in 92000, and this is falsé."""
lowercase : Union[str, Any] = tokenizer.tokenize(snake_case )
lowercase : Optional[int] = rust_tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case ,snake_case )
lowercase : str = tokenizer.encode(snake_case ,add_special_tokens=snake_case )
lowercase : Dict = rust_tokenizer.encode(snake_case ,add_special_tokens=snake_case )
self.assertListEqual(snake_case ,snake_case )
lowercase : Dict = self.get_rust_tokenizer()
lowercase : List[Any] = tokenizer.encode(snake_case )
lowercase : Any = rust_tokenizer.encode(snake_case )
self.assertListEqual(snake_case ,snake_case )
@slow
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : Dict = {"""input_ids""": [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 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.
lowercase : Dict = [
"""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=snake_case ,model_name="""moussaKam/mbarthez""" ,revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6""" ,sequences=snake_case ,)
| 336 |
# 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 __snake_case ( lowerCAmelCase ):
_a : Optional[int]= "openai/whisper-base"
_a : int= (
"This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
"transcribed text."
)
_a : int= "transcriber"
_a : List[str]= WhisperProcessor
_a : Optional[int]= WhisperForConditionalGeneration
_a : List[str]= ["audio"]
_a : Any= ["text"]
def _SCREAMING_SNAKE_CASE ( self ,snake_case ):
'''simple docstring'''
return self.pre_processor(snake_case ,return_tensors="""pt""" ).input_features
def _SCREAMING_SNAKE_CASE ( self ,snake_case ):
'''simple docstring'''
return self.model.generate(inputs=snake_case )
def _SCREAMING_SNAKE_CASE ( self ,snake_case ):
'''simple docstring'''
return self.pre_processor.batch_decode(snake_case ,skip_special_tokens=snake_case )[0]
| 336 | 1 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> list:
def merge(UpperCamelCase_ , UpperCamelCase_ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(UpperCamelCase_ ) <= 1:
return collection
UpperCamelCase_ = len(UpperCamelCase_ ) // 2
return merge(merge_sort(collection[:mid] ) , merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
_UpperCAmelCase = input('Enter numbers separated by a comma:\n').strip()
_UpperCAmelCase = [int(item) for item in user_input.split(',')]
print(*merge_sort(unsorted), sep=',')
| 371 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = '▁'
_UpperCAmelCase = {'vocab_file': 'sentencepiece.bpe.model'}
_UpperCAmelCase = {
'vocab_file': {
'facebook/nllb-200-distilled-600M': (
'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
),
}
}
_UpperCAmelCase = {
'facebook/nllb-200-distilled-600M': 1_0_2_4,
}
# fmt: off
_UpperCAmelCase = ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : Dict = VOCAB_FILES_NAMES
_UpperCamelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase : Tuple = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : Union[str, Any] = ['''input_ids''', '''attention_mask''']
_UpperCamelCase : List[int] = []
_UpperCamelCase : List[int] = []
def __init__( self: Tuple , _SCREAMING_SNAKE_CASE: Union[str, Any] , _SCREAMING_SNAKE_CASE: List[str]="<s>" , _SCREAMING_SNAKE_CASE: Optional[int]="</s>" , _SCREAMING_SNAKE_CASE: int="</s>" , _SCREAMING_SNAKE_CASE: Union[str, Any]="<s>" , _SCREAMING_SNAKE_CASE: Any="<unk>" , _SCREAMING_SNAKE_CASE: Union[str, Any]="<pad>" , _SCREAMING_SNAKE_CASE: int="<mask>" , _SCREAMING_SNAKE_CASE: Dict=None , _SCREAMING_SNAKE_CASE: Dict=None , _SCREAMING_SNAKE_CASE: int=None , _SCREAMING_SNAKE_CASE: Optional[Dict[str, Any]] = None , _SCREAMING_SNAKE_CASE: int=None , _SCREAMING_SNAKE_CASE: Tuple=False , **_SCREAMING_SNAKE_CASE: List[str] , ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else mask_token
UpperCamelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs
UpperCamelCase_ = legacy_behaviour
super().__init__(
bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , tokenizer_file=_SCREAMING_SNAKE_CASE , src_lang=_SCREAMING_SNAKE_CASE , tgt_lang=_SCREAMING_SNAKE_CASE , additional_special_tokens=_SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , legacy_behaviour=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
UpperCamelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase_ = 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>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
# spm | '<unk>' | '<s>' | '</s>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'
# Mimic fairseq token-to-id alignment for the first 4 token
UpperCamelCase_ = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
UpperCamelCase_ = 1
UpperCamelCase_ = len(self.sp_model )
UpperCamelCase_ = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_SCREAMING_SNAKE_CASE )
}
UpperCamelCase_ = {v: k for k, v in self.lang_code_to_id.items()}
UpperCamelCase_ = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
UpperCamelCase_ = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
UpperCamelCase_ = list(self.lang_code_to_id.keys() )
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens] )
UpperCamelCase_ = src_lang if src_lang is not None else "eng_Latn"
UpperCamelCase_ = self.lang_code_to_id[self._src_lang]
UpperCamelCase_ = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self: Any ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ = self.__dict__.copy()
UpperCamelCase_ = None
UpperCamelCase_ = self.sp_model.serialized_model_proto()
return state
def __setstate__( self: List[Any] , _SCREAMING_SNAKE_CASE: Optional[Any] ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
UpperCamelCase_ = {}
UpperCamelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
@property
def lowercase ( self: Union[str, Any] ) -> Dict:
"""simple docstring"""
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def lowercase ( self: Union[str, Any] ) -> str:
"""simple docstring"""
return self._src_lang
@src_lang.setter
def lowercase ( self: Tuple , _SCREAMING_SNAKE_CASE: str ) -> None:
"""simple docstring"""
UpperCamelCase_ = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: List[int] , _SCREAMING_SNAKE_CASE: Optional[List[int]] = None , _SCREAMING_SNAKE_CASE: bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_SCREAMING_SNAKE_CASE , token_ids_a=_SCREAMING_SNAKE_CASE , already_has_special_tokens=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = [1] * len(self.prefix_tokens )
UpperCamelCase_ = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE )) + suffix_ones
return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE )) + ([0] * len(_SCREAMING_SNAKE_CASE )) + suffix_ones
def lowercase ( self: Tuple , _SCREAMING_SNAKE_CASE: List[int] , _SCREAMING_SNAKE_CASE: Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: List[int] , _SCREAMING_SNAKE_CASE: Optional[List[int]] = 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 + sep + token_ids_a + sep ) * [0]
def lowercase ( self: Tuple , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: Optional[str] , _SCREAMING_SNAKE_CASE: Optional[str] , **_SCREAMING_SNAKE_CASE: Tuple ) -> int:
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
UpperCamelCase_ = src_lang
UpperCamelCase_ = self(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = tgt_lang_id
return inputs
def lowercase ( self: Tuple ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ = {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: str ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(_SCREAMING_SNAKE_CASE , out_type=_SCREAMING_SNAKE_CASE )
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: str ) -> Optional[int]:
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
UpperCamelCase_ = self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE )
# 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: int , _SCREAMING_SNAKE_CASE: Union[str, Any] ) -> Any:
"""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 , _SCREAMING_SNAKE_CASE: Optional[int] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = "".join(_SCREAMING_SNAKE_CASE ).replace(_SCREAMING_SNAKE_CASE , " " ).strip()
return out_string
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCamelCase_ = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(_SCREAMING_SNAKE_CASE , "wb" ) as fi:
UpperCamelCase_ = self.sp_model.serialized_model_proto()
fi.write(_SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: str = "eng_Latn" , _SCREAMING_SNAKE_CASE: Optional[List[str]] = None , _SCREAMING_SNAKE_CASE: str = "fra_Latn" , **_SCREAMING_SNAKE_CASE: List[str] , ) -> BatchEncoding:
"""simple docstring"""
UpperCamelCase_ = src_lang
UpperCamelCase_ = tgt_lang
return super().prepare_seqaseq_batch(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def lowercase ( self: Any ) -> Optional[int]:
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def lowercase ( self: Dict ) -> Optional[int]:
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def lowercase ( self: List[str] , _SCREAMING_SNAKE_CASE: Any ) -> None:
"""simple docstring"""
UpperCamelCase_ = self.lang_code_to_id[src_lang]
if self.legacy_behaviour:
UpperCamelCase_ = []
UpperCamelCase_ = [self.eos_token_id, self.cur_lang_code]
else:
UpperCamelCase_ = [self.cur_lang_code]
UpperCamelCase_ = [self.eos_token_id]
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: str ) -> None:
"""simple docstring"""
UpperCamelCase_ = self.lang_code_to_id[lang]
if self.legacy_behaviour:
UpperCamelCase_ = []
UpperCamelCase_ = [self.eos_token_id, self.cur_lang_code]
else:
UpperCamelCase_ = [self.cur_lang_code]
UpperCamelCase_ = [self.eos_token_id]
| 371 | 1 |
'''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def UpperCAmelCase_ ( lowerCAmelCase_ = "isbn/0140328726" ):
"""simple docstring"""
lowercase = olid.strip().strip("/" ) # Remove leading/trailing whitespace & slashes
if new_olid.count("/" ) != 1:
lowercase = f'{olid} is not a valid Open Library olid'
raise ValueError(lowerCAmelCase_ )
return requests.get(f'https://openlibrary.org/{new_olid}.json' ).json()
def UpperCAmelCase_ ( lowerCAmelCase_ ):
"""simple docstring"""
lowercase = {
"title": "Title",
"publish_date": "Publish date",
"authors": "Authors",
"number_of_pages": "Number of pages:",
"first_sentence": "First sentence",
"isbn_10": "ISBN (10)",
"isbn_13": "ISBN (13)",
}
lowercase = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
lowercase = [
get_openlibrary_data(author["key"] )["name"] for author in data["Authors"]
]
lowercase = data["First sentence"]["value"]
for key, value in data.items():
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
lowercase = ", ".join(lowerCAmelCase_ )
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
__lowerCamelCase : Any = input("\nEnter the ISBN code to search (or 'quit' to stop): ").strip()
if isbn.lower() in ("", "q", "quit", "exit", "stop"):
break
if len(isbn) not in (10, 13) or not isbn.isdigit():
print(f"Sorry, {isbn} is not a valid ISBN. Please, input a valid ISBN.")
continue
print(f"\nSearching Open Library for ISBN: {isbn}...\n")
try:
__lowerCamelCase : List[str] = summarize_book(get_openlibrary_data(f"isbn/{isbn}"))
print("\n".join(f"{key}: {value}" for key, value in book_summary.items()))
except JSONDecodeError: # Workaround for requests.exceptions.RequestException:
print(f"Sorry, there are no results for ISBN: {isbn}.")
| 310 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__lowerCamelCase : Dict = pytest.mark.integration
@pytest.mark.parametrize("path" , ["paws", "csv"] )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
inspect_dataset(lowerCAmelCase_ , lowerCAmelCase_ )
lowercase = path + ".py"
assert script_name in os.listdir(lowerCAmelCase_ )
assert "__pycache__" not in os.listdir(lowerCAmelCase_ )
@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning" )
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning" )
@pytest.mark.parametrize("path" , ["accuracy"] )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
inspect_metric(lowerCAmelCase_ , lowerCAmelCase_ )
lowercase = path + ".py"
assert script_name in os.listdir(lowerCAmelCase_ )
assert "__pycache__" not in os.listdir(lowerCAmelCase_ )
@pytest.mark.parametrize(
"path, config_name, expected_splits" , [
("squad", "plain_text", ["train", "validation"]),
("dalle-mini/wit", "dalle-mini--wit", ["train"]),
("paws", "labeled_final", ["train", "test", "validation"]),
] , )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
lowercase = get_dataset_config_info(lowerCAmelCase_ , config_name=lowerCAmelCase_ )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"path, config_name, expected_exception" , [
("paws", None, ValueError),
] , )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
with pytest.raises(lowerCAmelCase_ ):
get_dataset_config_info(lowerCAmelCase_ , config_name=lowerCAmelCase_ )
@pytest.mark.parametrize(
"path, expected" , [
("squad", "plain_text"),
("acronym_identification", "default"),
("lhoestq/squad", "plain_text"),
("lhoestq/test", "default"),
("lhoestq/demo1", "lhoestq--demo1"),
("dalle-mini/wit", "dalle-mini--wit"),
] , )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
lowercase = get_dataset_config_names(lowerCAmelCase_ )
assert expected in config_names
@pytest.mark.parametrize(
"path, expected_configs, expected_splits_in_first_config" , [
("squad", ["plain_text"], ["train", "validation"]),
("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
] , )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
lowercase = get_dataset_infos(lowerCAmelCase_ )
assert list(infos.keys() ) == expected_configs
lowercase = expected_configs[0]
assert expected_config in infos
lowercase = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
"path, expected_config, expected_splits" , [
("squad", "plain_text", ["train", "validation"]),
("dalle-mini/wit", "dalle-mini--wit", ["train"]),
("paws", "labeled_final", ["train", "test", "validation"]),
] , )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
lowercase = get_dataset_infos(lowerCAmelCase_ )
assert expected_config in infos
lowercase = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"path, config_name, expected_exception" , [
("paws", None, ValueError),
] , )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
with pytest.raises(lowerCAmelCase_ ):
get_dataset_split_names(lowerCAmelCase_ , config_name=lowerCAmelCase_ )
| 310 | 1 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_A = get_tests_dir('fixtures/test_sentencepiece_no_bos.model')
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase__ ( A_ , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Dict = PegasusTokenizer
UpperCAmelCase__ : Union[str, Any] = PegasusTokenizerFast
UpperCAmelCase__ : List[str] = True
UpperCAmelCase__ : List[str] = True
def _a ( self ) -> Dict:
super().setUp()
# We have a SentencePiece fixture for testing
__UpperCamelCase =PegasusTokenizer(A_ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _a ( self ) -> int:
return PegasusTokenizer.from_pretrained('google/pegasus-large' )
def _a ( self , **A_ ) -> PegasusTokenizer:
return PegasusTokenizer.from_pretrained(self.tmpdirname , **A_ )
def _a ( self , A_ ) -> Optional[Any]:
return ("This is a test", "This is a test")
def _a ( self ) -> List[Any]:
__UpperCamelCase ='</s>'
__UpperCamelCase =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_ ) , A_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_ ) , A_ )
def _a ( self ) -> Union[str, Any]:
__UpperCamelCase =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<pad>' )
self.assertEqual(vocab_keys[1] , '</s>' )
self.assertEqual(vocab_keys[-1] , 'v' )
self.assertEqual(len(A_ ) , 1103 )
def _a ( self ) -> Union[str, Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 1103 )
def _a ( self ) -> Dict:
__UpperCamelCase =self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
__UpperCamelCase =self.tokenizer_class.from_pretrained(self.tmpdirname )
__UpperCamelCase =(
'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
' </s> <pad> <pad> <pad>'
)
__UpperCamelCase =rust_tokenizer([raw_input_str] , return_tensors=A_ , add_special_tokens=A_ ).input_ids[0]
__UpperCamelCase =py_tokenizer([raw_input_str] , return_tensors=A_ , add_special_tokens=A_ ).input_ids[0]
self.assertListEqual(A_ , A_ )
def _a ( self ) -> Tuple:
__UpperCamelCase =self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
__UpperCamelCase ='<mask_1> To ensure a <mask_2> flow of bank resolutions.'
__UpperCamelCase =[2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
__UpperCamelCase =tokenizer([raw_input_str] , return_tensors=A_ ).input_ids[0]
self.assertListEqual(A_ , A_ )
def _a ( self ) -> Optional[Any]:
__UpperCamelCase =self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 96103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1024
__UpperCamelCase ='To ensure a smooth flow of bank resolutions.'
__UpperCamelCase =[413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
__UpperCamelCase =tokenizer([raw_input_str] , return_tensors=A_ ).input_ids[0]
self.assertListEqual(A_ , A_ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def _a ( self ) -> List[Any]:
__UpperCamelCase =['This is going to be way too long.' * 150, 'short example']
__UpperCamelCase =['not super long but more than 5 tokens', 'tiny']
__UpperCamelCase =self._large_tokenizer(A_ , padding=A_ , truncation=A_ , return_tensors='pt' )
__UpperCamelCase =self._large_tokenizer(
text_target=A_ , max_length=5 , padding=A_ , truncation=A_ , return_tensors='pt' )
assert batch.input_ids.shape == (2, 1024)
assert batch.attention_mask.shape == (2, 1024)
assert targets["input_ids"].shape == (2, 5)
assert len(A_ ) == 2 # input_ids, attention_mask.
@slow
def _a ( self ) -> Optional[int]:
# fmt: off
__UpperCamelCase ={'input_ids': [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 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]], '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=A_ , model_name='google/bigbird-pegasus-large-arxiv' , revision='ba85d0851d708441f91440d509690f1ab6353415' , )
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase__ ( A_ , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Dict = PegasusTokenizer
UpperCAmelCase__ : Union[str, Any] = PegasusTokenizerFast
UpperCAmelCase__ : Dict = True
UpperCAmelCase__ : int = True
def _a ( self ) -> List[str]:
super().setUp()
# We have a SentencePiece fixture for testing
__UpperCamelCase =PegasusTokenizer(A_ , offset=0 , mask_token_sent=A_ , mask_token='[MASK]' )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _a ( self ) -> Union[str, Any]:
return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv' )
def _a ( self , **A_ ) -> PegasusTokenizer:
return PegasusTokenizer.from_pretrained(self.tmpdirname , **A_ )
def _a ( self , A_ ) -> Optional[int]:
return ("This is a test", "This is a test")
def _a ( self ) -> List[str]:
__UpperCamelCase =self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
__UpperCamelCase =self.tokenizer_class.from_pretrained(self.tmpdirname )
__UpperCamelCase =(
'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
' <pad> <pad> <pad>'
)
__UpperCamelCase =rust_tokenizer([raw_input_str] , return_tensors=A_ , add_special_tokens=A_ ).input_ids[0]
__UpperCamelCase =py_tokenizer([raw_input_str] , return_tensors=A_ , add_special_tokens=A_ ).input_ids[0]
self.assertListEqual(A_ , A_ )
@require_torch
def _a ( self ) -> Dict:
__UpperCamelCase =['This is going to be way too long.' * 1000, 'short example']
__UpperCamelCase =['not super long but more than 5 tokens', 'tiny']
__UpperCamelCase =self._large_tokenizer(A_ , padding=A_ , truncation=A_ , return_tensors='pt' )
__UpperCamelCase =self._large_tokenizer(
text_target=A_ , max_length=5 , padding=A_ , truncation=A_ , return_tensors='pt' )
assert batch.input_ids.shape == (2, 4096)
assert batch.attention_mask.shape == (2, 4096)
assert targets["input_ids"].shape == (2, 5)
assert len(A_ ) == 2 # input_ids, attention_mask.
def _a ( self ) -> Any:
__UpperCamelCase =(
'This is an example string that is used to test the original TF implementation against the HF'
' implementation'
)
__UpperCamelCase =self._large_tokenizer(A_ ).input_ids
self.assertListEqual(
A_ , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1] , )
| 717 |
import math
from collections.abc import Callable
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Callable[[float], float] , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float ):
__UpperCamelCase =xa
__UpperCamelCase =xa
while True:
if x_n == x_na or function(SCREAMING_SNAKE_CASE__ ) == function(SCREAMING_SNAKE_CASE__ ):
raise ZeroDivisionError('float division by zero, could not find root' )
__UpperCamelCase =x_na - (
function(SCREAMING_SNAKE_CASE__ ) / ((function(SCREAMING_SNAKE_CASE__ ) - function(SCREAMING_SNAKE_CASE__ )) / (x_na - x_n))
)
if abs(x_na - x_na ) < 10**-5:
return x_na
__UpperCamelCase =x_na
__UpperCamelCase =x_na
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : float ):
return math.pow(SCREAMING_SNAKE_CASE__ , 3 ) - (2 * x) - 5
if __name__ == "__main__":
print(intersection(f, 3, 3.5))
| 682 | 0 |
'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
UpperCAmelCase__ = logging.get_logger(__name__)
def UpperCAmelCase__( _SCREAMING_SNAKE_CASE : np.ndarray,_SCREAMING_SNAKE_CASE : Union[int, Iterable[int]],_SCREAMING_SNAKE_CASE : bool,_SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
def constraint_to_multiple_of(_SCREAMING_SNAKE_CASE : str,_SCREAMING_SNAKE_CASE : List[Any],_SCREAMING_SNAKE_CASE : List[str]=0,_SCREAMING_SNAKE_CASE : List[str]=None ):
__A= round(val / multiple ) * multiple
if max_val is not None and x > max_val:
__A= math.floor(val / multiple ) * multiple
if x < min_val:
__A= math.ceil(val / multiple ) * multiple
return x
__A= (output_size, output_size) if isinstance(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE ) else output_size
__A, __A= get_image_size(_SCREAMING_SNAKE_CASE )
__A, __A= output_size
# determine new height and width
__A= output_height / input_height
__A= output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
__A= scale_width
else:
# fit height
__A= scale_height
__A= constraint_to_multiple_of(scale_height * input_height,multiple=_SCREAMING_SNAKE_CASE )
__A= constraint_to_multiple_of(scale_width * input_width,multiple=_SCREAMING_SNAKE_CASE )
return (new_height, new_width)
class a__ ( a_ ):
'''simple docstring'''
A : Optional[Any] = ['''pixel_values''']
def __init__( self : Union[str, Any] , lowerCAmelCase_ : bool = True , lowerCAmelCase_ : Dict[str, int] = None , lowerCAmelCase_ : PILImageResampling = PILImageResampling.BILINEAR , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : int = 1 , lowerCAmelCase_ : bool = True , lowerCAmelCase_ : Union[int, float] = 1 / 255 , lowerCAmelCase_ : bool = True , lowerCAmelCase_ : Optional[Union[float, List[float]]] = None , lowerCAmelCase_ : Optional[Union[float, List[float]]] = None , **lowerCAmelCase_ : int , ) -> None:
super().__init__(**lowerCAmelCase_ )
__A= size if size is not None else {'height': 384, 'width': 384}
__A= get_size_dict(lowerCAmelCase_ )
__A= do_resize
__A= size
__A= keep_aspect_ratio
__A= ensure_multiple_of
__A= resample
__A= do_rescale
__A= rescale_factor
__A= do_normalize
__A= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__A= image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCAmelCase ( self : Optional[int] , lowerCAmelCase_ : np.ndarray , lowerCAmelCase_ : Dict[str, int] , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : int = 1 , lowerCAmelCase_ : PILImageResampling = PILImageResampling.BICUBIC , lowerCAmelCase_ : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase_ : List[str] , ) -> np.ndarray:
__A= get_size_dict(lowerCAmelCase_ )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
__A= get_resize_output_image_size(
lowerCAmelCase_ , output_size=(size['height'], size['width']) , keep_aspect_ratio=lowerCAmelCase_ , multiple=lowerCAmelCase_ , )
return resize(lowerCAmelCase_ , size=lowerCAmelCase_ , resample=lowerCAmelCase_ , data_format=lowerCAmelCase_ , **lowerCAmelCase_ )
def lowerCAmelCase ( self : Optional[int] , lowerCAmelCase_ : np.ndarray , lowerCAmelCase_ : Union[int, float] , lowerCAmelCase_ : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase_ : Dict , ) -> List[Any]:
return rescale(lowerCAmelCase_ , scale=lowerCAmelCase_ , data_format=lowerCAmelCase_ , **lowerCAmelCase_ )
def lowerCAmelCase ( self : str , lowerCAmelCase_ : np.ndarray , lowerCAmelCase_ : Union[float, List[float]] , lowerCAmelCase_ : Union[float, List[float]] , lowerCAmelCase_ : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase_ : int , ) -> np.ndarray:
return normalize(lowerCAmelCase_ , mean=lowerCAmelCase_ , std=lowerCAmelCase_ , data_format=lowerCAmelCase_ , **lowerCAmelCase_ )
def lowerCAmelCase ( self : List[str] , lowerCAmelCase_ : ImageInput , lowerCAmelCase_ : bool = None , lowerCAmelCase_ : int = None , lowerCAmelCase_ : bool = None , lowerCAmelCase_ : int = None , lowerCAmelCase_ : PILImageResampling = None , lowerCAmelCase_ : bool = None , lowerCAmelCase_ : float = None , lowerCAmelCase_ : bool = None , lowerCAmelCase_ : Optional[Union[float, List[float]]] = None , lowerCAmelCase_ : Optional[Union[float, List[float]]] = None , lowerCAmelCase_ : Optional[Union[str, TensorType]] = None , lowerCAmelCase_ : ChannelDimension = ChannelDimension.FIRST , **lowerCAmelCase_ : List[Any] , ) -> PIL.Image.Image:
__A= do_resize if do_resize is not None else self.do_resize
__A= size if size is not None else self.size
__A= get_size_dict(lowerCAmelCase_ )
__A= keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
__A= ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
__A= resample if resample is not None else self.resample
__A= do_rescale if do_rescale is not None else self.do_rescale
__A= rescale_factor if rescale_factor is not None else self.rescale_factor
__A= do_normalize if do_normalize is not None else self.do_normalize
__A= image_mean if image_mean is not None else self.image_mean
__A= image_std if image_std is not None else self.image_std
__A= make_list_of_images(lowerCAmelCase_ )
if not valid_images(lowerCAmelCase_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
__A= [to_numpy_array(lowerCAmelCase_ ) for image in images]
if do_resize:
__A= [self.resize(image=lowerCAmelCase_ , size=lowerCAmelCase_ , resample=lowerCAmelCase_ ) for image in images]
if do_rescale:
__A= [self.rescale(image=lowerCAmelCase_ , scale=lowerCAmelCase_ ) for image in images]
if do_normalize:
__A= [self.normalize(image=lowerCAmelCase_ , mean=lowerCAmelCase_ , std=lowerCAmelCase_ ) for image in images]
__A= [to_channel_dimension_format(lowerCAmelCase_ , lowerCAmelCase_ ) for image in images]
__A= {'pixel_values': images}
return BatchFeature(data=lowerCAmelCase_ , tensor_type=lowerCAmelCase_ )
def lowerCAmelCase ( self : Tuple , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[Tuple] = None ) -> List[str]:
__A= outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(lowerCAmelCase_ ) != len(lowerCAmelCase_ ):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits' )
if is_torch_tensor(lowerCAmelCase_ ):
__A= target_sizes.numpy()
__A= []
for idx in range(len(lowerCAmelCase_ ) ):
__A= torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='bilinear' , align_corners=lowerCAmelCase_ )
__A= resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(lowerCAmelCase_ )
else:
__A= logits.argmax(dim=1 )
__A= [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 186 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {'''vocab_file''': '''spiece.model'''}
UpperCAmelCase__ = {
'''vocab_file''': {
'''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
}
}
UpperCAmelCase__ = {
'''AI-Sweden/gpt-sw3-126m''': 2_0_4_8,
'''AI-Sweden/gpt-sw3-350m''': 2_0_4_8,
'''AI-Sweden/gpt-sw3-1.6b''': 2_0_4_8,
'''AI-Sweden/gpt-sw3-6.7b''': 2_0_4_8,
'''AI-Sweden/gpt-sw3-20b''': 2_0_4_8,
}
class a__ ( a_ ):
'''simple docstring'''
A : int = VOCAB_FILES_NAMES
A : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
A : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A : int = ['''input_ids''', '''attention_mask''']
def __init__( self : int , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Union[str, Any]=False , lowerCAmelCase_ : List[str]=False , lowerCAmelCase_ : List[Any]=False , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : Optional[Dict[str, Any]] = None , **lowerCAmelCase_ : Optional[Any] , ) -> None:
__A= {} if sp_model_kwargs is None else sp_model_kwargs
__A= kwargs.get('name_or_path' )
if name_or_path is None:
logger.warning(
'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
' you are testing the model, this can safely be ignored' )
__A= 'None'
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
__A= '<|endoftext|>' if eos_token is None else eos_token
__A= '<unk>' if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
__A= unk_token if pad_token is None else pad_token
__A= eos_token if bos_token is None else bos_token
else:
__A= '<pad>' if pad_token is None else pad_token
__A= '<s>' if bos_token is None else bos_token
super().__init__(
do_lower_case=lowerCAmelCase_ , remove_space=lowerCAmelCase_ , keep_accents=lowerCAmelCase_ , bos_token=lowerCAmelCase_ , eos_token=lowerCAmelCase_ , unk_token=lowerCAmelCase_ , pad_token=lowerCAmelCase_ , sp_model_kwargs=self.sp_model_kwargs , **lowerCAmelCase_ , )
__A= do_lower_case
__A= remove_space
__A= keep_accents
__A= vocab_file
__A= spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowerCAmelCase_ )
# Used for whitespace normalization in input texts
# fmt : off
__A= {' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '', ''}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
__A= re.compile(
F"""[{"".join(map(lowerCAmelCase_ , list(range(0 , 9 ) ) + list(range(11 , 32 ) ) + list(range(127 , 160 ) ) + [160, 173, 8_203] ) )}]""" )
def __getstate__( self : Optional[int] ) -> Tuple:
__A= self.__dict__.copy()
__A= None
return state
def __setstate__( self : int , lowerCAmelCase_ : int ) -> Tuple:
__A= d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
__A= {}
__A= spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def lowerCAmelCase ( self : Tuple ) -> int:
return len(self.sp_model )
def lowerCAmelCase ( self : int , lowerCAmelCase_ : str ) -> str:
__A= self.non_printing_characters_re.sub('' , lowerCAmelCase_ )
# Normalize whitespaces
__A= ''.join([char if char not in self.whitespaces else ' ' for char in text] )
# NFC Unicode normalization
__A= unicodedata.normalize('NFC' , lowerCAmelCase_ )
return text
def lowerCAmelCase ( self : Tuple , lowerCAmelCase_ : str , **lowerCAmelCase_ : Optional[Any] ) -> List[str]:
__A= self.preprocess_text(lowerCAmelCase_ )
return self.sp_model.encode(lowerCAmelCase_ , out_type=lowerCAmelCase_ )
def lowerCAmelCase ( self : Any , lowerCAmelCase_ : str ) -> int:
return self.sp_model.PieceToId(lowerCAmelCase_ )
def lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : int ) -> str:
return self.sp_model.IdToPiece(lowerCAmelCase_ )
@staticmethod
def lowerCAmelCase ( lowerCAmelCase_ : str ) -> str:
return out_string
def lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : List[str] ) -> str:
__A= []
__A= ''
__A= False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowerCAmelCase_ ) + token
__A= True
__A= []
else:
current_sub_tokens.append(lowerCAmelCase_ )
__A= False
out_string += self.sp_model.decode(lowerCAmelCase_ )
return out_string
def lowerCAmelCase ( self : List[Any] ) -> Dict[str, int]:
__A= {self.convert_ids_to_tokens(lowerCAmelCase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCAmelCase ( self : Dict , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(lowerCAmelCase_ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
__A= os.path.join(
lowerCAmelCase_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowerCAmelCase_ )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCAmelCase_ , 'wb' ) as fi:
__A= self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase_ )
return (out_vocab_file,)
def lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Union[str, List[str]] , lowerCAmelCase_ : Union[str, bool] = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
__A= self.preprocess_text(lowerCAmelCase_ )
__A= self.sp_model.encode(lowerCAmelCase_ )
else:
__A= [self.preprocess_text(lowerCAmelCase_ ) for t in text]
__A= self.sp_model.encode(lowerCAmelCase_ )
if return_tensors is True or return_tensors == "pt":
__A= torch.tensor(lowerCAmelCase_ )
return token_ids
def lowerCAmelCase ( self : Tuple , lowerCAmelCase_ : Union[int, List[int]] ) -> str:
return self.sp_model.decode(lowerCAmelCase_ )
def lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : "Conversation" ) -> List[int]:
__A= [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
__A= (
F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(lowerCAmelCase_ ) + F"""{self.bos_token}Bot:"""
)
return self.encode(text=lowerCAmelCase_ )
| 186 | 1 |
def __SCREAMING_SNAKE_CASE ( lowercase_ = 4000000 ) -> int:
'''simple docstring'''
__UpperCAmelCase : int = []
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(lowercase_ )
__UpperCAmelCase , __UpperCAmelCase : Dict = b, a + b
return sum(lowercase_ )
if __name__ == "__main__":
print(F'{solution() = }')
| 675 |
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__UpperCAmelCase : Dict = str(bin(lowercase_ ) )[2:] # remove the leading "0b"
__UpperCAmelCase : List[Any] = str(bin(lowercase_ ) )[2:]
__UpperCAmelCase : List[Any] = max(len(lowercase_ ) , len(lowercase_ ) )
return "0b" + "".join(
str(int('''1''' in (char_a, char_b) ) )
for char_a, char_b in zip(a_binary.zfill(lowercase_ ) , b_binary.zfill(lowercase_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 675 | 1 |
'''simple docstring'''
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
UpperCamelCase_ = yaml.safe_load(
"""\
name: \"\"
allow_empty: false
allow_empty_text: true
subsections:
- name: \"Dataset Card for X\" # First-level markdown heading
allow_empty: false
allow_empty_text: true
subsections:
- name: \"Table of Contents\"
allow_empty: false
allow_empty_text: false
subsections: null
- name: \"Dataset Description\"
allow_empty: false
allow_empty_text: false
subsections:
- name: \"Dataset Summary\"
allow_empty: false
allow_empty_text: false
subsections: null
- name: \"Supported Tasks and Leaderboards\"
allow_empty: true
allow_empty_text: true
subsections: null
- name: Languages
allow_empty: false
allow_empty_text: true
subsections: null
"""
)
UpperCamelCase_ = {
"""name""": """root""",
"""text""": """""",
"""is_empty_text""": True,
"""subsections""": [
{
"""name""": """Dataset Card for My Dataset""",
"""text""": """""",
"""is_empty_text""": True,
"""subsections""": [
{"""name""": """Table of Contents""", """text""": """Some text here.""", """is_empty_text""": False, """subsections""": []},
{
"""name""": """Dataset Description""",
"""text""": """Some text here.""",
"""is_empty_text""": False,
"""subsections""": [
{
"""name""": """Dataset Summary""",
"""text""": """Some text here.""",
"""is_empty_text""": False,
"""subsections""": [],
},
{
"""name""": """Supported Tasks and Leaderboards""",
"""text""": """""",
"""is_empty_text""": True,
"""subsections""": [],
},
{"""name""": """Languages""", """text""": """Language Text""", """is_empty_text""": False, """subsections""": []},
],
},
],
}
],
}
UpperCamelCase_ = """\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
UpperCamelCase_ = """\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
#### Extra Ignored Subsection
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
UpperCamelCase_ = {
"""name""": """root""",
"""text""": """""",
"""is_empty_text""": True,
"""subsections""": [
{
"""name""": """Dataset Card for My Dataset""",
"""text""": """""",
"""is_empty_text""": True,
"""subsections""": [
{"""name""": """Table of Contents""", """text""": """Some text here.""", """is_empty_text""": False, """subsections""": []},
{
"""name""": """Dataset Description""",
"""text""": """Some text here.""",
"""is_empty_text""": False,
"""subsections""": [
{
"""name""": """Dataset Summary""",
"""text""": """Some text here.""",
"""is_empty_text""": False,
"""subsections""": [
{
"""name""": """Extra Ignored Subsection""",
"""text""": """""",
"""is_empty_text""": True,
"""subsections""": [],
}
],
},
{
"""name""": """Supported Tasks and Leaderboards""",
"""text""": """""",
"""is_empty_text""": True,
"""subsections""": [],
},
{"""name""": """Languages""", """text""": """Language Text""", """is_empty_text""": False, """subsections""": []},
],
},
],
}
],
}
UpperCamelCase_ = """\
---
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
UpperCamelCase_ = (
"""The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README."""
)
UpperCamelCase_ = """\
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
UpperCamelCase_ = (
"""The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README."""
)
UpperCamelCase_ = """\
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
UpperCamelCase_ = """The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README."""
UpperCamelCase_ = """\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
UpperCamelCase_ = """The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored)."""
UpperCamelCase_ = """\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
"""
UpperCamelCase_ = """The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Card for My Dataset` but it is empty.\n-\tSection `Dataset Card for My Dataset` expected the following subsections: `Table of Contents`, `Dataset Description`. Found 'None'."""
UpperCamelCase_ = """\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Languages
Language Text
"""
UpperCamelCase_ = """The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`."""
UpperCamelCase_ = """\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
"""
UpperCamelCase_ = """The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty."""
UpperCamelCase_ = """\
---
language:
- zh
- en
---
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
UpperCamelCase_ = """The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README."""
UpperCamelCase_ = """\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
# Dataset Card My Dataset
"""
UpperCamelCase_ = """The following issues were found for the README at `{path}`:\n-\tThe README has several first-level headings: `Dataset Card for My Dataset`, `Dataset Card My Dataset`. Only one heading is expected. Skipping further validation for this README."""
UpperCamelCase_ = """\
---
language:
- zh
- en
---
# Dataset Card My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
UpperCamelCase_ = """The following issues were found for the README at `{path}`:\n-\tNo first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README."""
UpperCamelCase_ = """"""
UpperCamelCase_ = """The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.\n-\tNo YAML markers are present in the README."""
UpperCamelCase_ = """\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
UpperCamelCase_ = """The following issues were found while parsing the README at `{path}`:\n-\tMultiple sections with the same heading `Dataset Card for My Dataset` have been found. Please keep only one of these sections."""
@pytest.mark.parametrize(
"""readme_md, expected_dict""" , [
(README_CORRECT, CORRECT_DICT),
(README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
] , )
def _UpperCAmelCase ( _lowerCamelCase : List[str] , _lowerCamelCase : List[str] ) -> Union[str, Any]:
assert ReadMe.from_string(_lowerCamelCase , _lowerCamelCase ).to_dict() == expected_dict
@pytest.mark.parametrize(
"""readme_md, expected_error""" , [
(README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
(README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
(README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
(README_EMPTY, EXPECTED_ERROR_README_EMPTY),
(README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
(README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
(README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
(README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
(README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
(README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
(README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
] , )
def _UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : Tuple ) -> Optional[Any]:
with pytest.raises(_lowerCamelCase , match=re.escape(expected_error.format(path="""root""" ) ) ):
_lowerCAmelCase : Optional[int] = ReadMe.from_string(_lowerCamelCase , _lowerCamelCase )
readme.validate()
@pytest.mark.parametrize(
"""readme_md, expected_error""" , [
(README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
] , )
def _UpperCAmelCase ( _lowerCamelCase : List[str] , _lowerCamelCase : str ) -> Union[str, Any]:
with pytest.raises(_lowerCamelCase , match=re.escape(expected_error.format(path="""root""" ) ) ):
ReadMe.from_string(_lowerCamelCase , _lowerCamelCase )
@pytest.mark.parametrize(
"""readme_md,""" , [
(README_MULTIPLE_SAME_HEADING_1),
] , )
def _UpperCAmelCase ( _lowerCamelCase : List[Any] ) -> Tuple:
ReadMe.from_string(_lowerCamelCase , _lowerCamelCase , suppress_parsing_errors=_lowerCamelCase )
@pytest.mark.parametrize(
"""readme_md, expected_dict""" , [
(README_CORRECT, CORRECT_DICT),
(README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
] , )
def _UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : Optional[int] ) -> Tuple:
with tempfile.TemporaryDirectory() as tmp_dir:
_lowerCAmelCase : Optional[Any] = Path(_lowerCamelCase ) / """README.md"""
with open(_lowerCamelCase , """w+""" ) as readme_file:
readme_file.write(_lowerCamelCase )
_lowerCAmelCase : Union[str, Any] = ReadMe.from_readme(_lowerCamelCase , _lowerCamelCase ).to_dict()
assert out["name"] == path
assert out["text"] == ""
assert out["is_empty_text"]
assert out["subsections"] == expected_dict["subsections"]
@pytest.mark.parametrize(
"""readme_md, expected_error""" , [
(README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
(README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
(README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
(README_EMPTY, EXPECTED_ERROR_README_EMPTY),
(README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
(README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
(README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
(README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
(README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
(README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
(README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
] , )
def _UpperCAmelCase ( _lowerCamelCase : str , _lowerCamelCase : Dict ) -> int:
with tempfile.TemporaryDirectory() as tmp_dir:
_lowerCAmelCase : Any = Path(_lowerCamelCase ) / """README.md"""
with open(_lowerCamelCase , """w+""" ) as readme_file:
readme_file.write(_lowerCamelCase )
_lowerCAmelCase : Optional[int] = expected_error.format(path=_lowerCamelCase )
with pytest.raises(_lowerCamelCase , match=re.escape(_lowerCamelCase ) ):
_lowerCAmelCase : Any = ReadMe.from_readme(_lowerCamelCase , _lowerCamelCase )
readme.validate()
@pytest.mark.parametrize(
"""readme_md, expected_error""" , [
(README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
] , )
def _UpperCAmelCase ( _lowerCamelCase : List[str] , _lowerCamelCase : List[str] ) -> Union[str, Any]:
with tempfile.TemporaryDirectory() as tmp_dir:
_lowerCAmelCase : int = Path(_lowerCamelCase ) / """README.md"""
with open(_lowerCamelCase , """w+""" ) as readme_file:
readme_file.write(_lowerCamelCase )
_lowerCAmelCase : Dict = expected_error.format(path=_lowerCamelCase )
with pytest.raises(_lowerCamelCase , match=re.escape(_lowerCamelCase ) ):
ReadMe.from_readme(_lowerCamelCase , _lowerCamelCase )
@pytest.mark.parametrize(
"""readme_md,""" , [
(README_MULTIPLE_SAME_HEADING_1),
] , )
def _UpperCAmelCase ( _lowerCamelCase : Optional[int] ) -> Tuple:
with tempfile.TemporaryDirectory() as tmp_dir:
_lowerCAmelCase : List[Any] = Path(_lowerCamelCase ) / """README.md"""
with open(_lowerCamelCase , """w+""" ) as readme_file:
readme_file.write(_lowerCamelCase )
ReadMe.from_readme(_lowerCamelCase , _lowerCamelCase , suppress_parsing_errors=_lowerCamelCase )
| 384 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {}
class a_ (_a ):
__lowerCAmelCase : int = """llama"""
__lowerCAmelCase : Tuple = ["""past_key_values"""]
def __init__( self , snake_case_=3_2_0_0_0 , snake_case_=4_0_9_6 , snake_case_=1_1_0_0_8 , snake_case_=3_2 , snake_case_=3_2 , snake_case_=None , snake_case_="silu" , snake_case_=2_0_4_8 , snake_case_=0.02 , snake_case_=1E-6 , snake_case_=True , snake_case_=0 , snake_case_=1 , snake_case_=2 , snake_case_=1 , snake_case_=False , snake_case_=None , **snake_case_ , ):
_lowerCAmelCase : Union[str, Any] = vocab_size
_lowerCAmelCase : Any = max_position_embeddings
_lowerCAmelCase : Any = hidden_size
_lowerCAmelCase : List[Any] = intermediate_size
_lowerCAmelCase : Optional[Any] = num_hidden_layers
_lowerCAmelCase : Any = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
_lowerCAmelCase : List[Any] = num_attention_heads
_lowerCAmelCase : Optional[Any] = num_key_value_heads
_lowerCAmelCase : List[str] = hidden_act
_lowerCAmelCase : str = initializer_range
_lowerCAmelCase : Optional[int] = rms_norm_eps
_lowerCAmelCase : Dict = pretraining_tp
_lowerCAmelCase : Any = use_cache
_lowerCAmelCase : Optional[int] = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ , tie_word_embeddings=snake_case_ , **snake_case_ , )
def __UpperCamelCase ( self ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , snake_case_ ) or len(self.rope_scaling ) != 2:
raise ValueError(
"""`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
f'got {self.rope_scaling}' )
_lowerCAmelCase : Optional[Any] = self.rope_scaling.get("""type""" , snake_case_ )
_lowerCAmelCase : List[Any] = self.rope_scaling.get("""factor""" , snake_case_ )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}' )
if rope_scaling_factor is None or not isinstance(snake_case_ , snake_case_ ) or rope_scaling_factor <= 1.0:
raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )
| 384 | 1 |
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_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowercase__ ( unittest.TestCase ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=3 , __UpperCAmelCase=224 , __UpperCAmelCase=30 , __UpperCAmelCase=400 , __UpperCAmelCase=True , __UpperCAmelCase=None , __UpperCAmelCase=True , __UpperCAmelCase=[0.5, 0.5, 0.5] , __UpperCAmelCase=[0.5, 0.5, 0.5] , )-> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase__ = size if size is not None else {"height": 18, "width": 18}
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = image_size
lowerCAmelCase__ = min_resolution
lowerCAmelCase__ = max_resolution
lowerCAmelCase__ = do_resize
lowerCAmelCase__ = size
lowerCAmelCase__ = do_normalize
lowerCAmelCase__ = image_mean
lowerCAmelCase__ = image_std
def UpperCAmelCase ( self )-> List[str]:
'''simple docstring'''
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class lowercase__ ( _UpperCAmelCase, unittest.TestCase ):
a_ =ViTImageProcessor if is_vision_available() else None
def UpperCAmelCase ( self )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = EfficientFormerImageProcessorTester(self )
@property
def UpperCAmelCase ( self )-> Any:
'''simple docstring'''
return self.image_proc_tester.prepare_image_processor_dict()
def UpperCAmelCase ( self )-> str:
'''simple docstring'''
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__UpperCAmelCase , "image_mean" ) )
self.assertTrue(hasattr(__UpperCAmelCase , "image_std" ) )
self.assertTrue(hasattr(__UpperCAmelCase , "do_normalize" ) )
self.assertTrue(hasattr(__UpperCAmelCase , "do_resize" ) )
self.assertTrue(hasattr(__UpperCAmelCase , "size" ) )
def UpperCAmelCase ( self )-> Optional[Any]:
'''simple docstring'''
pass
def UpperCAmelCase ( self )-> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase__ = prepare_image_inputs(self.image_proc_tester , equal_resolution=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , Image.Image )
# Test not batched input
lowerCAmelCase__ = image_processor(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
lowerCAmelCase__ = image_processor(__UpperCAmelCase , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def UpperCAmelCase ( self )-> Dict:
'''simple docstring'''
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase__ = prepare_image_inputs(self.image_proc_tester , equal_resolution=__UpperCAmelCase , numpify=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , np.ndarray )
# Test not batched input
lowerCAmelCase__ = image_processor(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
lowerCAmelCase__ = image_processor(__UpperCAmelCase , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def UpperCAmelCase ( self )-> str:
'''simple docstring'''
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase__ = prepare_image_inputs(self.image_proc_tester , equal_resolution=__UpperCAmelCase , torchify=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
# Test not batched input
lowerCAmelCase__ = image_processor(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
lowerCAmelCase__ = image_processor(__UpperCAmelCase , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
| 115 |
def _a ( UpperCamelCase_ : list , UpperCamelCase_ : list ) -> float:
"""simple docstring"""
_validate_point(UpperCamelCase_ )
_validate_point(UpperCamelCase_ )
if len(UpperCamelCase_ ) != len(UpperCamelCase_ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(UpperCamelCase_ , UpperCamelCase_ ) ) )
def _a ( UpperCamelCase_ : list[float] ) -> None:
"""simple docstring"""
if point:
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
for item in point:
if not isinstance(UpperCamelCase_ , (int, float) ):
lowerCAmelCase__ = (
"Expected a list of numbers as input, found "
F"{type(UpperCamelCase_ ).__name__}"
)
raise TypeError(UpperCamelCase_ )
else:
lowerCAmelCase__ = F"Expected a list of numbers as input, found {type(UpperCamelCase_ ).__name__}"
raise TypeError(UpperCamelCase_ )
else:
raise ValueError("Missing an input" )
def _a ( UpperCamelCase_ : list , UpperCamelCase_ : list ) -> float:
"""simple docstring"""
_validate_point(UpperCamelCase_ )
_validate_point(UpperCamelCase_ )
if len(UpperCamelCase_ ) != len(UpperCamelCase_ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(x - y ) for x, y in zip(UpperCamelCase_ , UpperCamelCase_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 115 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
lowercase__ = logging.get_logger(__name__)
class lowerCAmelCase__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCamelCase__ = ["""input_features""", """attention_mask"""]
def __init__( self , lowercase=80 , lowercase=16000 , lowercase=0.0 , lowercase=10 , lowercase=25 , lowercase="hamming_window" , lowercase=32768.0 , lowercase=0.97 , lowercase=1.0 , lowercase=True , lowercase=True , lowercase=False , **lowercase , ):
super().__init__(feature_size=UpperCAmelCase_ , sampling_rate=UpperCAmelCase_ , padding_value=UpperCAmelCase_ , **UpperCAmelCase_ )
_lowerCamelCase : Tuple = feature_size
_lowerCamelCase : str = sampling_rate
_lowerCamelCase : Tuple = padding_value
_lowerCamelCase : Optional[Any] = hop_length
_lowerCamelCase : List[str] = win_length
_lowerCamelCase : Dict = frame_signal_scale
_lowerCamelCase : Optional[int] = preemphasis_coeff
_lowerCamelCase : int = mel_floor
_lowerCamelCase : Optional[int] = normalize_means
_lowerCamelCase : Optional[int] = normalize_vars
_lowerCamelCase : List[Any] = win_function
_lowerCamelCase : int = return_attention_mask
_lowerCamelCase : Union[str, Any] = win_length * sampling_rate // 1000
_lowerCamelCase : List[Any] = hop_length * sampling_rate // 1000
_lowerCamelCase : Tuple = optimal_fft_length(self.sample_size )
_lowerCamelCase : Optional[Any] = (self.n_fft // 2) + 1
def A_ ( self , lowercase ):
if self.win_function == "hamming_window":
_lowerCamelCase : Tuple = window_function(window_length=self.sample_size , name=self.win_function , periodic=UpperCAmelCase_ )
else:
_lowerCamelCase : int = window_function(window_length=self.sample_size , name=self.win_function )
_lowerCamelCase : int = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.feature_size , min_frequency=0.0 , max_frequency=self.sampling_rate / 2.0 , sampling_rate=self.sampling_rate , )
_lowerCamelCase : Dict = spectrogram(
one_waveform * self.frame_signal_scale , window=UpperCAmelCase_ , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , center=UpperCAmelCase_ , preemphasis=self.preemphasis_coeff , mel_filters=UpperCAmelCase_ , mel_floor=self.mel_floor , log_mel='log' , )
return msfc_features.T
def A_ ( self , lowercase , lowercase , lowercase ):
if self.normalize_means:
_lowerCamelCase : str = x[:input_length].mean(axis=0 )
_lowerCamelCase : Optional[Any] = np.subtract(UpperCAmelCase_ , UpperCAmelCase_ )
if self.normalize_vars:
_lowerCamelCase : Tuple = x[:input_length].std(axis=0 )
_lowerCamelCase : Union[str, Any] = np.divide(UpperCAmelCase_ , UpperCAmelCase_ )
if input_length < x.shape[0]:
_lowerCamelCase : List[str] = padding_value
# make sure array is in float32
_lowerCamelCase : Union[str, Any] = x.astype(np.floataa )
return x
def A_ ( self , lowercase , lowercase = None ):
_lowerCamelCase : List[str] = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(UpperCAmelCase_ , UpperCAmelCase_ , self.padding_value ) for x, n in zip(UpperCAmelCase_ , UpperCAmelCase_ )]
def __call__( self , lowercase , lowercase = False , lowercase = None , lowercase = False , lowercase = None , lowercase = None , lowercase = None , lowercase = None , **lowercase , ):
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
F''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
_lowerCamelCase : Optional[Any] = isinstance(UpperCAmelCase_ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
_lowerCamelCase : Optional[Any] = is_batched_numpy or (
isinstance(UpperCAmelCase_ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_lowerCamelCase : Tuple = [np.asarray(UpperCAmelCase_ , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(UpperCAmelCase_ , np.ndarray ):
_lowerCamelCase : str = np.asarray(UpperCAmelCase_ , dtype=np.floataa )
elif isinstance(UpperCAmelCase_ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_lowerCamelCase : Optional[Any] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_lowerCamelCase : Optional[int] = [raw_speech]
# extract fbank features
_lowerCamelCase : List[Any] = [self._extract_mfsc_features(UpperCAmelCase_ ) for one_waveform in raw_speech]
# convert into correct format for padding
_lowerCamelCase : Dict = BatchFeature({'input_features': features} )
_lowerCamelCase : Any = self.pad(
UpperCAmelCase_ , padding=UpperCAmelCase_ , max_length=UpperCAmelCase_ , truncation=UpperCAmelCase_ , pad_to_multiple_of=UpperCAmelCase_ , return_attention_mask=UpperCAmelCase_ , **UpperCAmelCase_ , )
# make sure list is in array format
_lowerCamelCase : Optional[Any] = padded_inputs.get('input_features' )
if isinstance(input_features[0] , UpperCAmelCase_ ):
_lowerCamelCase : Any = [np.asarray(UpperCAmelCase_ , dtype=np.floataa ) for feature in input_features]
_lowerCamelCase : Dict = padded_inputs.get('attention_mask' )
if attention_mask is not None:
_lowerCamelCase : str = [np.asarray(UpperCAmelCase_ , dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
_lowerCamelCase : str = (
np.array(UpperCAmelCase_ , dtype=np.intaa )
if self._get_padding_strategies(UpperCAmelCase_ , max_length=UpperCAmelCase_ ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
_lowerCamelCase : Optional[Any] = self.normalize(
padded_inputs['input_features'] , attention_mask=UpperCAmelCase_ )
if return_tensors is not None:
_lowerCamelCase : int = padded_inputs.convert_to_tensors(UpperCAmelCase_ )
return padded_inputs | 630 |
import operator as op
def lowerCAmelCase_ ( __a ) -> Tuple:
"""simple docstring"""
lowerCamelCase__: Optional[Any] =[]
lowerCamelCase__: Tuple =lambda __a , __a : int(x / y ) # noqa: E731 integer division operation
lowerCamelCase__: Tuple ={
"^": op.pow,
"*": op.mul,
"/": div,
"+": op.add,
"-": op.sub,
} # operators & their respective operation
# print table header
print("Symbol".center(8 ) , "Action".center(12 ) , "Stack" , sep=" | " )
print("-" * (30 + len(__a )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(__a ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ("push(" + x + ")").ljust(12 ) , ",".join(__a ) , sep=" | " )
else:
lowerCamelCase__: List[Any] =stack.pop() # pop stack
# output in tabular format
print("".rjust(8 ) , ("pop(" + b + ")").ljust(12 ) , ",".join(__a ) , sep=" | " )
lowerCamelCase__: Optional[Any] =stack.pop() # pop stack
# output in tabular format
print("".rjust(8 ) , ("pop(" + a + ")").ljust(12 ) , ",".join(__a ) , sep=" | " )
stack.append(
str(opr[x](int(__a ) , int(__a ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ("push(" + a + x + b + ")").ljust(12 ) , ",".join(__a ) , sep=" | " , )
return int(stack[0] )
if __name__ == "__main__":
__A = input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
print("\n\tResult = ", solve(Postfix))
| 59 | 0 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score # noqa: E402 # isort:skip
snake_case_ = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def A__ ( SCREAMING_SNAKE_CASE_ ) -> Tuple:
if "token" in model_name_or_path:
return "rag_token"
if "sequence" in model_name_or_path:
return "rag_sequence"
if "bart" in model_name_or_path:
return "bart"
return None
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
return max(metric_fn(__UpperCamelCase , __UpperCamelCase ) for gt in ground_truths )
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
lowerCamelCase : List[str] =[line.strip() for line in open(__UpperCamelCase , '''r''' ).readlines()]
lowerCamelCase : Union[str, Any] =[]
if args.gold_data_mode == "qa":
lowerCamelCase : Tuple =pd.read_csv(__UpperCamelCase , sep='''\t''' , header=__UpperCamelCase )
for answer_list in data[1]:
lowerCamelCase : Optional[Any] =ast.literal_eval(__UpperCamelCase )
answers.append(__UpperCamelCase )
else:
lowerCamelCase : Dict =[line.strip() for line in open(__UpperCamelCase , '''r''' ).readlines()]
lowerCamelCase : List[Any] =[[reference] for reference in references]
lowerCamelCase : Optional[int] =0
for prediction, ground_truths in zip(__UpperCamelCase , __UpperCamelCase ):
total += 1
em += metric_max_over_ground_truths(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
fa += metric_max_over_ground_truths(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
lowerCamelCase : List[str] =1_0_0.0 * em / total
lowerCamelCase : Tuple =1_0_0.0 * fa / total
logger.info(F"F1: {fa:.2f}" )
logger.info(F"EM: {em:.2f}" )
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
lowerCamelCase : str =args.k
lowerCamelCase : Dict =[line.strip() for line in open(__UpperCamelCase , '''r''' ).readlines()]
lowerCamelCase : List[str] =[line.strip() for line in open(__UpperCamelCase , '''r''' ).readlines()]
lowerCamelCase : int =0
for hypo, reference in zip(__UpperCamelCase , __UpperCamelCase ):
lowerCamelCase : Tuple =set(hypo.split('''\t''' )[:k] )
lowerCamelCase : str =set(reference.split('''\t''' ) )
total += 1
em += len(hypo_provenance & ref_provenance ) / k
lowerCamelCase : Tuple =1_0_0.0 * em / total
logger.info(F"Precision@{k}: {em: .2f}" )
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
def strip_title(SCREAMING_SNAKE_CASE_ ):
if title.startswith('''"''' ):
lowerCamelCase : List[Any] =title[1:]
if title.endswith('''"''' ):
lowerCamelCase : Union[str, Any] =title[:-1]
return title
lowerCamelCase : Tuple =rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
__UpperCamelCase , return_tensors='''pt''' , padding=__UpperCamelCase , truncation=__UpperCamelCase , )['''input_ids'''].to(args.device )
lowerCamelCase : List[Any] =rag_model.rag.question_encoder(__UpperCamelCase )
lowerCamelCase : List[Any] =question_enc_outputs[0]
lowerCamelCase : Any =rag_model.retriever(
__UpperCamelCase , question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy() , prefix=rag_model.rag.generator.config.prefix , n_docs=rag_model.config.n_docs , return_tensors='''pt''' , )
lowerCamelCase : List[Any] =rag_model.retriever.index.get_doc_dicts(result.doc_ids )
lowerCamelCase : Optional[Any] =[]
for docs in all_docs:
lowerCamelCase : Optional[int] =[strip_title(__UpperCamelCase ) for title in docs['''title''']]
provenance_strings.append('''\t'''.join(__UpperCamelCase ) )
return provenance_strings
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
with torch.no_grad():
lowerCamelCase : int =rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
__UpperCamelCase , return_tensors='''pt''' , padding=__UpperCamelCase , truncation=__UpperCamelCase )
lowerCamelCase : Union[str, Any] =inputs_dict.input_ids.to(args.device )
lowerCamelCase : Optional[Any] =inputs_dict.attention_mask.to(args.device )
lowerCamelCase : Dict =rag_model.generate( # rag_model overwrites generate
__UpperCamelCase , attention_mask=__UpperCamelCase , num_beams=args.num_beams , min_length=args.min_length , max_length=args.max_length , early_stopping=__UpperCamelCase , num_return_sequences=1 , bad_words_ids=[[0, 0]] , )
lowerCamelCase : List[Any] =rag_model.retriever.generator_tokenizer.batch_decode(__UpperCamelCase , skip_special_tokens=__UpperCamelCase )
if args.print_predictions:
for q, a in zip(__UpperCamelCase , __UpperCamelCase ):
logger.info('''Q: {} - A: {}'''.format(__UpperCamelCase , __UpperCamelCase ) )
return answers
def A__ ( ) -> Any:
lowerCamelCase : Union[str, Any] =argparse.ArgumentParser()
parser.add_argument(
'''--model_type''' , choices=['''rag_sequence''', '''rag_token''', '''bart'''] , type=__UpperCamelCase , help=(
'''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
''' model_name_or_path'''
) , )
parser.add_argument(
'''--index_name''' , default=__UpperCamelCase , choices=['''exact''', '''compressed''', '''legacy'''] , type=__UpperCamelCase , help='''RAG model retriever type''' , )
parser.add_argument(
'''--index_path''' , default=__UpperCamelCase , type=__UpperCamelCase , help='''Path to the retrieval index''' , )
parser.add_argument('''--n_docs''' , default=5 , type=__UpperCamelCase , help='''Number of retrieved docs''' )
parser.add_argument(
'''--model_name_or_path''' , default=__UpperCamelCase , type=__UpperCamelCase , required=__UpperCamelCase , help='''Path to pretrained checkpoints or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--eval_mode''' , choices=['''e2e''', '''retrieval'''] , default='''e2e''' , type=__UpperCamelCase , help=(
'''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
''' precision@k.'''
) , )
parser.add_argument('''--k''' , default=1 , type=__UpperCamelCase , help='''k for the precision@k calculation''' )
parser.add_argument(
'''--evaluation_set''' , default=__UpperCamelCase , type=__UpperCamelCase , required=__UpperCamelCase , help='''Path to a file containing evaluation samples''' , )
parser.add_argument(
'''--gold_data_path''' , default=__UpperCamelCase , type=__UpperCamelCase , required=__UpperCamelCase , help='''Path to a tab-separated file with gold samples''' , )
parser.add_argument(
'''--gold_data_mode''' , default='''qa''' , type=__UpperCamelCase , choices=['''qa''', '''ans'''] , help=(
'''Format of the gold data file'''
'''qa - a single line in the following format: question [tab] answer_list'''
'''ans - a single line of the gold file contains the expected answer string'''
) , )
parser.add_argument(
'''--predictions_path''' , type=__UpperCamelCase , default='''predictions.txt''' , help='''Name of the predictions file, to be stored in the checkpoints directory''' , )
parser.add_argument(
'''--eval_all_checkpoints''' , action='''store_true''' , help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number''' , )
parser.add_argument(
'''--eval_batch_size''' , default=8 , type=__UpperCamelCase , help='''Batch size per GPU/CPU for evaluation.''' , )
parser.add_argument(
'''--recalculate''' , help='''Recalculate predictions even if the prediction file exists''' , action='''store_true''' , )
parser.add_argument(
'''--num_beams''' , default=4 , type=__UpperCamelCase , help='''Number of beams to be used when generating answers''' , )
parser.add_argument('''--min_length''' , default=1 , type=__UpperCamelCase , help='''Min length of the generated answers''' )
parser.add_argument('''--max_length''' , default=5_0 , type=__UpperCamelCase , help='''Max length of the generated answers''' )
parser.add_argument(
'''--print_predictions''' , action='''store_true''' , help='''If True, prints predictions while evaluating.''' , )
parser.add_argument(
'''--print_docs''' , action='''store_true''' , help='''If True, prints docs retried while generating.''' , )
lowerCamelCase : str =parser.parse_args()
lowerCamelCase : Optional[int] =torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
return args
def A__ ( SCREAMING_SNAKE_CASE_ ) -> int:
lowerCamelCase : Dict ={}
if args.model_type is None:
lowerCamelCase : str =infer_model_type(args.model_name_or_path )
assert args.model_type is not None
if args.model_type.startswith('''rag''' ):
lowerCamelCase : List[Any] =RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
lowerCamelCase : List[str] =args.n_docs
if args.index_name is not None:
lowerCamelCase : Any =args.index_name
if args.index_path is not None:
lowerCamelCase : int =args.index_path
else:
lowerCamelCase : Dict =BartForConditionalGeneration
lowerCamelCase : List[Any] =(
[f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info('''Evaluate the following checkpoints: %s''' , __UpperCamelCase )
lowerCamelCase : Union[str, Any] =get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
lowerCamelCase : Union[str, Any] =evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path ) and (not args.recalculate):
logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path ) )
score_fn(__UpperCamelCase , args.predictions_path , args.gold_data_path )
continue
logger.info('''***** Running evaluation for {} *****'''.format(__UpperCamelCase ) )
logger.info(''' Batch size = %d''' , args.eval_batch_size )
logger.info(''' Predictions will be stored under {}'''.format(args.predictions_path ) )
if args.model_type.startswith('''rag''' ):
lowerCamelCase : Optional[int] =RagRetriever.from_pretrained(__UpperCamelCase , **__UpperCamelCase )
lowerCamelCase : int =model_class.from_pretrained(__UpperCamelCase , retriever=__UpperCamelCase , **__UpperCamelCase )
model.retriever.init_retrieval()
else:
lowerCamelCase : Optional[Any] =model_class.from_pretrained(__UpperCamelCase , **__UpperCamelCase )
model.to(args.device )
with open(args.evaluation_set , '''r''' ) as eval_file, open(args.predictions_path , '''w''' ) as preds_file:
lowerCamelCase : Union[str, Any] =[]
for line in tqdm(__UpperCamelCase ):
questions.append(line.strip() )
if len(__UpperCamelCase ) == args.eval_batch_size:
lowerCamelCase : List[str] =evaluate_batch_fn(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
preds_file.write('''\n'''.join(__UpperCamelCase ) + '''\n''' )
preds_file.flush()
lowerCamelCase : str =[]
if len(__UpperCamelCase ) > 0:
lowerCamelCase : int =evaluate_batch_fn(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
preds_file.write('''\n'''.join(__UpperCamelCase ) )
preds_file.flush()
score_fn(__UpperCamelCase , args.predictions_path , args.gold_data_path )
if __name__ == "__main__":
snake_case_ = get_args()
main(args)
| 708 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def A__ ( SCREAMING_SNAKE_CASE_ ) -> tuple:
return (data["data"], data["target"])
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> np.ndarray:
lowerCamelCase : Dict =XGBRegressor(verbosity=0 , random_state=4_2 )
xgb.fit(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Predict target for test data
lowerCamelCase : List[str] =xgb.predict(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : List[Any] =predictions.reshape(len(SCREAMING_SNAKE_CASE_ ) , 1 )
return predictions
def A__ ( ) -> None:
lowerCamelCase : Union[str, Any] =fetch_california_housing()
lowerCamelCase , lowerCamelCase : Optional[Any] =data_handling(SCREAMING_SNAKE_CASE_ )
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : str =train_test_split(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , test_size=0.2_5 , random_state=1 )
lowerCamelCase : str =xgboost(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Error printing
print(F"Mean Absolute Error : {mean_absolute_error(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )}" )
print(F"Mean Square Error : {mean_squared_error(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )}" )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 262 | 0 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
@slow
def _lowerCamelCase ( self ):
__a : Optional[Any] = FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''' )
__a : List[Any] = AutoTokenizer.from_pretrained('''google/mt5-small''' )
__a : List[str] = tokenizer('''Hello there''' , return_tensors='''np''' ).input_ids
__a : List[str] = tokenizer('''Hi I am''' , return_tensors='''np''' ).input_ids
__a : Union[str, Any] = shift_tokens_right(_UpperCAmelCase , model.config.pad_token_id , model.config.decoder_start_token_id )
__a : List[Any] = model(_UpperCAmelCase , decoder_input_ids=_UpperCAmelCase ).logits
__a : int = optax.softmax_cross_entropy(_UpperCAmelCase , onehot(_UpperCAmelCase , logits.shape[-1] ) ).mean()
__a : List[Any] = -(labels.shape[-1] * loss.item())
__a : List[str] = -8_4.9_1_2_7
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 ) | 52 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class __lowerCAmelCase (TensorFormatter[Mapping, """torch.Tensor""", Mapping] ):
'''simple docstring'''
def __init__(self : Optional[Any] , UpperCamelCase : Tuple=None , **UpperCamelCase : List[str] ):
'''simple docstring'''
super().__init__(features=UpperCamelCase )
lowercase__ = torch_tensor_kwargs
import torch # noqa import torch at initialization
def UpperCamelCase__ (self : List[str] , UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
import torch
if isinstance(UpperCamelCase , UpperCamelCase ) and column:
if all(
isinstance(UpperCamelCase , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(UpperCamelCase )
return column
def UpperCamelCase__ (self : List[Any] , UpperCamelCase : Optional[Any] ):
'''simple docstring'''
import torch
if isinstance(UpperCamelCase , (str, bytes, type(UpperCamelCase )) ):
return value
elif isinstance(UpperCamelCase , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
lowercase__ = {}
if isinstance(UpperCamelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
lowercase__ = {'''dtype''': torch.intaa}
elif isinstance(UpperCamelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
lowercase__ = {'''dtype''': torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(UpperCamelCase , PIL.Image.Image ):
lowercase__ = np.asarray(UpperCamelCase )
return torch.tensor(UpperCamelCase , **{**default_dtype, **self.torch_tensor_kwargs} )
def UpperCamelCase__ (self : Optional[int] , UpperCamelCase : Dict ):
'''simple docstring'''
import torch
# support for torch, tf, jax etc.
if hasattr(UpperCamelCase , '''__array__''' ) and not isinstance(UpperCamelCase , torch.Tensor ):
lowercase__ = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(UpperCamelCase , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(UpperCamelCase ) for substruct in data_struct] )
elif isinstance(UpperCamelCase , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(UpperCamelCase ) for substruct in data_struct] )
return self._tensorize(UpperCamelCase )
def UpperCamelCase__ (self : List[str] , UpperCamelCase : dict ):
'''simple docstring'''
return map_nested(self._recursive_tensorize , UpperCamelCase , map_list=UpperCamelCase )
def UpperCamelCase__ (self : Dict , UpperCamelCase : pa.Table ):
'''simple docstring'''
lowercase__ = self.numpy_arrow_extractor().extract_row(UpperCamelCase )
lowercase__ = self.python_features_decoder.decode_row(UpperCamelCase )
return self.recursive_tensorize(UpperCamelCase )
def UpperCamelCase__ (self : List[Any] , UpperCamelCase : pa.Table ):
'''simple docstring'''
lowercase__ = self.numpy_arrow_extractor().extract_column(UpperCamelCase )
lowercase__ = self.python_features_decoder.decode_column(UpperCamelCase , pa_table.column_names[0] )
lowercase__ = self.recursive_tensorize(UpperCamelCase )
lowercase__ = self._consolidate(UpperCamelCase )
return column
def UpperCamelCase__ (self : Tuple , UpperCamelCase : pa.Table ):
'''simple docstring'''
lowercase__ = self.numpy_arrow_extractor().extract_batch(UpperCamelCase )
lowercase__ = self.python_features_decoder.decode_batch(UpperCamelCase )
lowercase__ = self.recursive_tensorize(UpperCamelCase )
for column_name in batch:
lowercase__ = self._consolidate(batch[column_name] )
return batch
| 460 | 0 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
_A = parse(importlib.metadata.version("""torch"""))
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> str:
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(f"""`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys() )}, received {operation}""" )
lowerCAmelCase__ : Dict = STR_OPERATION_TO_FUNC[operation]
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ : Optional[int] = parse(importlib.metadata.version(__UpperCAmelCase ) )
return operation(__UpperCAmelCase , parse(__UpperCAmelCase ) )
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> List[str]:
return compare_versions(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
| 700 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_A = """src/transformers"""
# This is to make sure the transformers module imported is the one in the repo.
_A = direct_transformers_import(PATH_TO_TRANSFORMERS)
_A = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_A = re.compile(r"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")
_A = {
"""DecisionTransformerConfig""",
"""EncoderDecoderConfig""",
"""MusicgenConfig""",
"""RagConfig""",
"""SpeechEncoderDecoderConfig""",
"""TimmBackboneConfig""",
"""VisionEncoderDecoderConfig""",
"""VisionTextDualEncoderConfig""",
"""LlamaConfig""",
}
def lowercase_ ( __UpperCAmelCase ) -> str:
lowerCAmelCase__ : Union[str, Any] = None
# source code of `config_class`
lowerCAmelCase__ : List[Any] = inspect.getsource(__UpperCAmelCase )
lowerCAmelCase__ : Union[str, Any] = _re_checkpoint.findall(__UpperCAmelCase )
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
for ckpt_name, ckpt_link in checkpoints:
# allow the link to end with `/`
if ckpt_link.endswith("""/""" ):
lowerCAmelCase__ : Optional[Any] = ckpt_link[:-1]
# verify the checkpoint name corresponds to the checkpoint link
lowerCAmelCase__ : Dict = f"""https://huggingface.co/{ckpt_name}"""
if ckpt_link == ckpt_link_from_name:
lowerCAmelCase__ : Optional[Any] = ckpt_name
break
return checkpoint
def lowercase_ ( ) -> Dict:
lowerCAmelCase__ : Union[str, Any] = []
for config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in config_class.__module__:
continue
lowerCAmelCase__ : Dict = get_checkpoint_from_config_class(__UpperCAmelCase )
lowerCAmelCase__ : Union[str, Any] = config_class.__name__
if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(__UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
lowerCAmelCase__ : int = """\n""".join(sorted(__UpperCAmelCase ) )
raise ValueError(f"""The following configurations don't contain any valid checkpoint:\n{message}""" )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
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