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import itertools
import string
from collections.abc import Generator, Iterable
def A ( __UpperCamelCase , __UpperCamelCase ) -> Generator[tuple[str, ...], None, None]:
A__ = iter(__UpperCamelCase )
while True:
A__ = tuple(itertools.islice(__UpperCamelCase , __UpperCamelCase ) )
if not chunk:
return
yield chunk
def A ( __UpperCamelCase ) -> str:
A__ = ''.join([c.upper() for c in dirty if c in string.ascii_letters] )
A__ = ''
if len(__UpperCamelCase ) < 2:
return dirty
for i in range(len(__UpperCamelCase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(__UpperCamelCase ) & 1:
clean += "X"
return clean
def A ( __UpperCamelCase ) -> list[str]:
# I and J are used interchangeably to allow
# us to use a 5x5 table (25 letters)
A__ = 'ABCDEFGHIKLMNOPQRSTUVWXYZ'
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
A__ = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(__UpperCamelCase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(__UpperCamelCase )
return table
def A ( __UpperCamelCase , __UpperCamelCase ) -> str:
A__ = generate_table(__UpperCamelCase )
A__ = prepare_input(__UpperCamelCase )
A__ = ''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(__UpperCamelCase , 2 ):
A__ , A__ = divmod(table.index(__UpperCamelCase ) , 5 )
A__ , A__ = divmod(table.index(__UpperCamelCase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def A ( __UpperCamelCase , __UpperCamelCase ) -> str:
A__ = generate_table(__UpperCamelCase )
A__ = ''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(__UpperCamelCase , 2 ):
A__ , A__ = divmod(table.index(__UpperCamelCase ) , 5 )
A__ , A__ = divmod(table.index(__UpperCamelCase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 703
|
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE__ = {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MraForMaskedLM''',
'''MraForMultipleChoice''',
'''MraForQuestionAnswering''',
'''MraForSequenceClassification''',
'''MraForTokenClassification''',
'''MraLayer''',
'''MraModel''',
'''MraPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 52
| 0
|
from itertools import permutations
def A ( __UpperCamelCase ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
A__ = [7, 11, 13, 17]
for i, test in enumerate(__UpperCamelCase ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def A ( __UpperCamelCase = 10 ) -> int:
return sum(
int(''.join(map(__UpperCamelCase , __UpperCamelCase ) ) )
for num in permutations(range(__UpperCamelCase ) )
if is_substring_divisible(__UpperCamelCase ) )
if __name__ == "__main__":
print(f'{solution() = }')
| 704
|
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)
| 52
| 0
|
def A ( __UpperCamelCase = 10**12 ) -> int:
A__ = 1
A__ = 0
A__ = 1
A__ = 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() = }')
| 705
|
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __lowerCAmelCase ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
A__ : Any = IFInpaintingPipeline
A__ : Dict = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
A__ : List[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
A__ : Dict = PipelineTesterMixin.required_optional_params - {"latents"}
def _a ( self : Any ):
"""simple docstring"""
return self._get_dummy_components()
def _a ( self : Optional[int] , _snake_case : Any , _snake_case : str=0 ):
"""simple docstring"""
if str(_snake_case ).startswith('mps' ):
A__ = torch.manual_seed(_snake_case )
else:
A__ = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
A__ = floats_tensor((1, 3, 32, 32) , rng=random.Random(_snake_case ) ).to(_snake_case )
A__ = floats_tensor((1, 3, 32, 32) , rng=random.Random(_snake_case ) ).to(_snake_case )
A__ = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'mask_image': mask_image,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def _a ( self : Dict ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def _a ( self : int ):
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def _a ( self : Optional[int] ):
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1 )
def _a ( self : List[str] ):
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def _a ( self : Dict ):
"""simple docstring"""
self._test_save_load_local()
def _a ( self : Optional[int] ):
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 52
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
'''configuration_xlm_roberta''': [
'''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaConfig''',
'''XLMRobertaOnnxConfig''',
],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ['''XLMRobertaTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ['''XLMRobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLMRobertaForCausalLM''',
'''XLMRobertaForMaskedLM''',
'''XLMRobertaForMultipleChoice''',
'''XLMRobertaForQuestionAnswering''',
'''XLMRobertaForSequenceClassification''',
'''XLMRobertaForTokenClassification''',
'''XLMRobertaModel''',
'''XLMRobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLMRobertaForCausalLM''',
'''TFXLMRobertaForMaskedLM''',
'''TFXLMRobertaForMultipleChoice''',
'''TFXLMRobertaForQuestionAnswering''',
'''TFXLMRobertaForSequenceClassification''',
'''TFXLMRobertaForTokenClassification''',
'''TFXLMRobertaModel''',
'''TFXLMRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FlaxXLMRobertaForMaskedLM''',
'''FlaxXLMRobertaForCausalLM''',
'''FlaxXLMRobertaForMultipleChoice''',
'''FlaxXLMRobertaForQuestionAnswering''',
'''FlaxXLMRobertaForSequenceClassification''',
'''FlaxXLMRobertaForTokenClassification''',
'''FlaxXLMRobertaModel''',
'''FlaxXLMRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaConfig,
XLMRobertaOnnxConfig,
)
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta import XLMRobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaForCausalLM,
XLMRobertaForMaskedLM,
XLMRobertaForMultipleChoice,
XLMRobertaForQuestionAnswering,
XLMRobertaForSequenceClassification,
XLMRobertaForTokenClassification,
XLMRobertaModel,
XLMRobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm_roberta import (
TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMRobertaForCausalLM,
TFXLMRobertaForMaskedLM,
TFXLMRobertaForMultipleChoice,
TFXLMRobertaForQuestionAnswering,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TFXLMRobertaModel,
TFXLMRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xlm_roberta import (
FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxXLMRobertaForCausalLM,
FlaxXLMRobertaForMaskedLM,
FlaxXLMRobertaForMultipleChoice,
FlaxXLMRobertaForQuestionAnswering,
FlaxXLMRobertaForSequenceClassification,
FlaxXLMRobertaForTokenClassification,
FlaxXLMRobertaModel,
FlaxXLMRobertaPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 706
|
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
SCREAMING_SNAKE_CASE__ = get_logger(__name__)
SCREAMING_SNAKE_CASE__ = r'''
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs (`Dict[str, Any]`, *optional*):
Additional logits processor specific kwargs.
Return:
`jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.
'''
class __lowerCAmelCase :
"""simple docstring"""
@add_start_docstrings(_snake_case )
def __call__( self : Optional[int] , _snake_case : jnp.ndarray , _snake_case : jnp.ndarray ):
"""simple docstring"""
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class __lowerCAmelCase :
"""simple docstring"""
@add_start_docstrings(_snake_case )
def __call__( self : List[Any] , _snake_case : jnp.ndarray , _snake_case : jnp.ndarray ):
"""simple docstring"""
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
@add_start_docstrings(_snake_case )
def __call__( self : Any , _snake_case : jnp.ndarray , _snake_case : jnp.ndarray , _snake_case : int , **_snake_case : Optional[int] ):
"""simple docstring"""
for processor in self:
A__ = inspect.signature(processor.__call__ ).parameters
if len(_snake_case ) > 3:
if not all(arg in kwargs for arg in list(function_args.keys() )[2:] ):
raise ValueError(
F'''Make sure that all the required parameters: {list(function_args.keys() )} for '''
F'''{processor.__class__} are passed to the logits processor.''' )
A__ = processor(_snake_case , _snake_case , _snake_case , **_snake_case )
else:
A__ = processor(_snake_case , _snake_case , _snake_case )
return scores
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Any , _snake_case : float ):
"""simple docstring"""
if not isinstance(_snake_case , _snake_case ) or not (temperature > 0):
raise ValueError(F'''`temperature` has to be a strictly positive float, but is {temperature}''' )
A__ = temperature
def __call__( self : str , _snake_case : jnp.ndarray , _snake_case : jnp.ndarray , _snake_case : int ):
"""simple docstring"""
A__ = scores / self.temperature
return scores
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Optional[Any] , _snake_case : float , _snake_case : float = -float('Inf' ) , _snake_case : int = 1 ):
"""simple docstring"""
if not isinstance(_snake_case , _snake_case ) or (top_p < 0 or top_p > 1.0):
raise ValueError(F'''`top_p` has to be a float > 0 and < 1, but is {top_p}''' )
if not isinstance(_snake_case , _snake_case ) or (min_tokens_to_keep < 1):
raise ValueError(F'''`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}''' )
A__ = top_p
A__ = filter_value
A__ = min_tokens_to_keep
def __call__( self : str , _snake_case : jnp.ndarray , _snake_case : jnp.ndarray , _snake_case : int ):
"""simple docstring"""
A__ , A__ = lax.top_k(_snake_case , scores.shape[-1] )
A__ = jnp.full_like(_snake_case , self.filter_value )
A__ = jax.nn.softmax(_snake_case , axis=-1 ).cumsum(axis=-1 )
A__ = cumulative_probs < self.top_p
# include the token that is higher than top_p as well
A__ = jnp.roll(_snake_case , 1 )
score_mask |= score_mask.at[:, 0].set(_snake_case )
# min tokens to keep
A__ = score_mask.at[:, : self.min_tokens_to_keep].set(_snake_case )
A__ = jnp.where(_snake_case , _snake_case , _snake_case )
A__ = jax.lax.sort_key_val(_snake_case , _snake_case )[-1]
return next_scores
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , _snake_case : int , _snake_case : float = -float('Inf' ) , _snake_case : int = 1 ):
"""simple docstring"""
if not isinstance(_snake_case , _snake_case ) or top_k <= 0:
raise ValueError(F'''`top_k` has to be a strictly positive integer, but is {top_k}''' )
A__ = max(_snake_case , _snake_case )
A__ = filter_value
def __call__( self : Optional[Any] , _snake_case : jnp.ndarray , _snake_case : jnp.ndarray , _snake_case : int ):
"""simple docstring"""
A__ , A__ = scores.shape
A__ = jnp.full(batch_size * vocab_size , self.filter_value )
A__ = min(self.top_k , scores.shape[-1] ) # Safety check
A__ , A__ = lax.top_k(_snake_case , _snake_case )
A__ = jnp.broadcast_to((jnp.arange(_snake_case ) * vocab_size)[:, None] , (batch_size, topk) ).flatten()
A__ = topk_scores.flatten()
A__ = topk_indices.flatten() + shift
A__ = next_scores_flat.at[topk_indices_flat].set(_snake_case )
A__ = next_scores_flat.reshape(_snake_case , _snake_case )
return next_scores
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Any , _snake_case : int ):
"""simple docstring"""
A__ = bos_token_id
def __call__( self : Optional[int] , _snake_case : jnp.ndarray , _snake_case : jnp.ndarray , _snake_case : int ):
"""simple docstring"""
A__ = jnp.full(scores.shape , -float('inf' ) )
A__ = 1 - jnp.bool_(cur_len - 1 )
A__ = jnp.where(_snake_case , new_scores.at[:, self.bos_token_id].set(0 ) , _snake_case )
return scores
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Any , _snake_case : int , _snake_case : int ):
"""simple docstring"""
A__ = max_length
A__ = eos_token_id
def __call__( self : List[Any] , _snake_case : jnp.ndarray , _snake_case : jnp.ndarray , _snake_case : int ):
"""simple docstring"""
A__ = jnp.full(scores.shape , -float('inf' ) )
A__ = 1 - jnp.bool_(cur_len - self.max_length + 1 )
A__ = jnp.where(_snake_case , new_scores.at[:, self.eos_token_id].set(0 ) , _snake_case )
return scores
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Dict , _snake_case : int , _snake_case : int ):
"""simple docstring"""
if not isinstance(_snake_case , _snake_case ) or min_length < 0:
raise ValueError(F'''`min_length` has to be a positive integer, but is {min_length}''' )
if not isinstance(_snake_case , _snake_case ) or eos_token_id < 0:
raise ValueError(F'''`eos_token_id` has to be a positive integer, but is {eos_token_id}''' )
A__ = min_length
A__ = eos_token_id
def __call__( self : int , _snake_case : jnp.ndarray , _snake_case : jnp.ndarray , _snake_case : int ):
"""simple docstring"""
A__ = 1 - jnp.clip(cur_len - self.min_length , 0 , 1 )
A__ = jnp.where(_snake_case , scores.at[:, self.eos_token_id].set(-float('inf' ) ) , _snake_case )
return scores
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : int , _snake_case : Tuple , _snake_case : Union[str, Any] ):
"""simple docstring"""
A__ = list(_snake_case )
A__ = begin_index
def __call__( self : Union[str, Any] , _snake_case : Optional[Any] , _snake_case : str , _snake_case : int ):
"""simple docstring"""
A__ = 1 - jnp.bool_(cur_len - self.begin_index )
A__ = jnp.where(_snake_case , scores.at[:, self.begin_suppress_tokens].set(-float('inf' ) ) , _snake_case )
return scores
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : int , _snake_case : list ):
"""simple docstring"""
A__ = list(_snake_case )
def __call__( self : List[Any] , _snake_case : jnp.ndarray , _snake_case : jnp.ndarray , _snake_case : int ):
"""simple docstring"""
A__ = scores.at[..., self.suppress_tokens].set(-float('inf' ) )
return scores
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : List[str] , _snake_case : Optional[Any] ):
"""simple docstring"""
A__ = dict(_snake_case )
# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
# Indexes without forced tokens will have a negative value.
A__ = jnp.ones((max(force_token_map.keys() ) + 1) , dtype=jnp.intaa ) * -1
for index, token in force_token_map.items():
if token is not None:
A__ = force_token_array.at[index].set(_snake_case )
A__ = jnp.intaa(_snake_case )
def __call__( self : List[Any] , _snake_case : jnp.ndarray , _snake_case : jnp.ndarray , _snake_case : int ):
"""simple docstring"""
def _force_token(_snake_case : Dict ):
A__ = scores.shape[0]
A__ = self.force_token_array[generation_idx]
A__ = jnp.ones_like(_snake_case , dtype=scores.dtype ) * -float('inf' )
A__ = jnp.zeros((batch_size, 1) , dtype=scores.dtype )
A__ = lax.dynamic_update_slice(_snake_case , _snake_case , (0, current_token) )
return new_scores
A__ = lax.cond(
cur_len >= self.force_token_array.shape[0] , lambda: scores , lambda: lax.cond(
self.force_token_array[cur_len] >= 0 , lambda: _force_token(_snake_case ) , lambda: scores , ) , )
return scores
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , _snake_case : Union[str, Any] , _snake_case : Dict , _snake_case : List[Any] ):
"""simple docstring"""
A__ = generate_config.eos_token_id
A__ = generate_config.no_timestamps_token_id
A__ = generate_config.no_timestamps_token_id + 1
A__ = decoder_input_length + 1
if generate_config.is_multilingual:
# room for language token and task token
self.begin_index += 2
if hasattr(_snake_case , 'max_initial_timestamp_index' ):
A__ = generate_config.max_initial_timestamp_index
else:
A__ = model_config.vocab_size
if self.max_initial_timestamp_index is None:
A__ = model_config.vocab_size
def __call__( self : Tuple , _snake_case : List[Any] , _snake_case : Dict , _snake_case : Dict ):
"""simple docstring"""
A__ = scores.at[:, self.no_timestamps_token_id].set(-float('inf' ) )
def handle_pairs(_snake_case : Dict , _snake_case : str ):
A__ = jnp.where((cur_len - self.begin_index) >= 1 , _snake_case , _snake_case )
A__ = jnp.where(
input_ids_k[cur_len - 1] >= self.timestamp_begin , True and last_was_timestamp , _snake_case , )
A__ = jnp.where((cur_len - self.begin_index) < 2 , _snake_case , _snake_case )
A__ = jnp.where(
input_ids_k[cur_len - 2] >= self.timestamp_begin , _snake_case , _snake_case , )
return jnp.where(
_snake_case , jnp.where(
penultimate_was_timestamp > 0 , scores_k.at[self.timestamp_begin :].set(-float('inf' ) ) , scores_k.at[: self.eos_token_id].set(-float('inf' ) ) , ) , _snake_case , )
A__ = jax.vmap(_snake_case )(_snake_case , _snake_case )
A__ = jnp.where(cur_len == self.begin_index , _snake_case , _snake_case )
A__ = jnp.where(
self.max_initial_timestamp_index is not None , True and apply_max_initial_timestamp , _snake_case , )
A__ = self.timestamp_begin + self.max_initial_timestamp_index
A__ = jnp.where(
_snake_case , scores.at[:, last_allowed + 1 :].set(-float('inf' ) ) , _snake_case , )
# if sum of probability over timestamps is above any other token, sample timestamp
A__ = jax.nn.log_softmax(_snake_case , axis=-1 )
def handle_cumulative_probs(_snake_case : List[Any] , _snake_case : Union[str, Any] ):
A__ = jax.nn.logsumexp(logprobs_k[self.timestamp_begin :] , axis=-1 )
A__ = jnp.max(logprobs_k[: self.timestamp_begin] )
return jnp.where(
timestamp_logprob > max_text_token_logprob , scores_k.at[: self.timestamp_begin].set(-float('inf' ) ) , _snake_case , )
A__ = jax.vmap(_snake_case )(_snake_case , _snake_case )
return scores
| 52
| 0
|
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ = logging.get_logger('''transformers.models.encodec''')
SCREAMING_SNAKE_CASE__ = {
'''quantizer.vq.layers.*._codebook.inited''': '''quantizer.layers.*.codebook.inited''',
'''quantizer.vq.layers.*._codebook.cluster_size''': '''quantizer.layers.*.codebook.cluster_size''',
'''quantizer.vq.layers.*._codebook.embed''': '''quantizer.layers.*.codebook.embed''',
'''quantizer.vq.layers.*._codebook.embed_avg''': '''quantizer.layers.*.codebook.embed_avg''',
}
SCREAMING_SNAKE_CASE__ = {
'''encoder.model.0.conv.conv''': '''encoder.layers.0.conv''',
'''encoder.model.1.block.1.conv.conv''': '''encoder.layers.1.block.1.conv''',
'''encoder.model.1.block.3.conv.conv''': '''encoder.layers.1.block.3.conv''',
'''encoder.model.1.shortcut.conv.conv''': '''encoder.layers.1.shortcut.conv''',
'''encoder.model.3.conv.conv''': '''encoder.layers.3.conv''',
'''encoder.model.4.block.1.conv.conv''': '''encoder.layers.4.block.1.conv''',
'''encoder.model.4.block.3.conv.conv''': '''encoder.layers.4.block.3.conv''',
'''encoder.model.4.shortcut.conv.conv''': '''encoder.layers.4.shortcut.conv''',
'''encoder.model.6.conv.conv''': '''encoder.layers.6.conv''',
'''encoder.model.7.block.1.conv.conv''': '''encoder.layers.7.block.1.conv''',
'''encoder.model.7.block.3.conv.conv''': '''encoder.layers.7.block.3.conv''',
'''encoder.model.7.shortcut.conv.conv''': '''encoder.layers.7.shortcut.conv''',
'''encoder.model.9.conv.conv''': '''encoder.layers.9.conv''',
'''encoder.model.10.block.1.conv.conv''': '''encoder.layers.10.block.1.conv''',
'''encoder.model.10.block.3.conv.conv''': '''encoder.layers.10.block.3.conv''',
'''encoder.model.10.shortcut.conv.conv''': '''encoder.layers.10.shortcut.conv''',
'''encoder.model.12.conv.conv''': '''encoder.layers.12.conv''',
'''encoder.model.13.lstm''': '''encoder.layers.13.lstm''',
'''encoder.model.15.conv.conv''': '''encoder.layers.15.conv''',
}
SCREAMING_SNAKE_CASE__ = {
'''encoder.model.0.conv.norm''': '''encoder.layers.0.norm''',
'''encoder.model.1.block.1.conv.norm''': '''encoder.layers.1.block.1.norm''',
'''encoder.model.1.block.3.conv.norm''': '''encoder.layers.1.block.3.norm''',
'''encoder.model.1.shortcut.conv.norm''': '''encoder.layers.1.shortcut.norm''',
'''encoder.model.3.conv.norm''': '''encoder.layers.3.norm''',
'''encoder.model.4.block.1.conv.norm''': '''encoder.layers.4.block.1.norm''',
'''encoder.model.4.block.3.conv.norm''': '''encoder.layers.4.block.3.norm''',
'''encoder.model.4.shortcut.conv.norm''': '''encoder.layers.4.shortcut.norm''',
'''encoder.model.6.conv.norm''': '''encoder.layers.6.norm''',
'''encoder.model.7.block.1.conv.norm''': '''encoder.layers.7.block.1.norm''',
'''encoder.model.7.block.3.conv.norm''': '''encoder.layers.7.block.3.norm''',
'''encoder.model.7.shortcut.conv.norm''': '''encoder.layers.7.shortcut.norm''',
'''encoder.model.9.conv.norm''': '''encoder.layers.9.norm''',
'''encoder.model.10.block.1.conv.norm''': '''encoder.layers.10.block.1.norm''',
'''encoder.model.10.block.3.conv.norm''': '''encoder.layers.10.block.3.norm''',
'''encoder.model.10.shortcut.conv.norm''': '''encoder.layers.10.shortcut.norm''',
'''encoder.model.12.conv.norm''': '''encoder.layers.12.norm''',
'''encoder.model.15.conv.norm''': '''encoder.layers.15.norm''',
}
SCREAMING_SNAKE_CASE__ = {
'''decoder.model.0.conv.conv''': '''decoder.layers.0.conv''',
'''decoder.model.1.lstm''': '''decoder.layers.1.lstm''',
'''decoder.model.3.convtr.convtr''': '''decoder.layers.3.conv''',
'''decoder.model.4.block.1.conv.conv''': '''decoder.layers.4.block.1.conv''',
'''decoder.model.4.block.3.conv.conv''': '''decoder.layers.4.block.3.conv''',
'''decoder.model.4.shortcut.conv.conv''': '''decoder.layers.4.shortcut.conv''',
'''decoder.model.6.convtr.convtr''': '''decoder.layers.6.conv''',
'''decoder.model.7.block.1.conv.conv''': '''decoder.layers.7.block.1.conv''',
'''decoder.model.7.block.3.conv.conv''': '''decoder.layers.7.block.3.conv''',
'''decoder.model.7.shortcut.conv.conv''': '''decoder.layers.7.shortcut.conv''',
'''decoder.model.9.convtr.convtr''': '''decoder.layers.9.conv''',
'''decoder.model.10.block.1.conv.conv''': '''decoder.layers.10.block.1.conv''',
'''decoder.model.10.block.3.conv.conv''': '''decoder.layers.10.block.3.conv''',
'''decoder.model.10.shortcut.conv.conv''': '''decoder.layers.10.shortcut.conv''',
'''decoder.model.12.convtr.convtr''': '''decoder.layers.12.conv''',
'''decoder.model.13.block.1.conv.conv''': '''decoder.layers.13.block.1.conv''',
'''decoder.model.13.block.3.conv.conv''': '''decoder.layers.13.block.3.conv''',
'''decoder.model.13.shortcut.conv.conv''': '''decoder.layers.13.shortcut.conv''',
'''decoder.model.15.conv.conv''': '''decoder.layers.15.conv''',
}
SCREAMING_SNAKE_CASE__ = {
'''decoder.model.0.conv.norm''': '''decoder.layers.0.norm''',
'''decoder.model.3.convtr.norm''': '''decoder.layers.3.norm''',
'''decoder.model.4.block.1.conv.norm''': '''decoder.layers.4.block.1.norm''',
'''decoder.model.4.block.3.conv.norm''': '''decoder.layers.4.block.3.norm''',
'''decoder.model.4.shortcut.conv.norm''': '''decoder.layers.4.shortcut.norm''',
'''decoder.model.6.convtr.norm''': '''decoder.layers.6.norm''',
'''decoder.model.7.block.1.conv.norm''': '''decoder.layers.7.block.1.norm''',
'''decoder.model.7.block.3.conv.norm''': '''decoder.layers.7.block.3.norm''',
'''decoder.model.7.shortcut.conv.norm''': '''decoder.layers.7.shortcut.norm''',
'''decoder.model.9.convtr.norm''': '''decoder.layers.9.norm''',
'''decoder.model.10.block.1.conv.norm''': '''decoder.layers.10.block.1.norm''',
'''decoder.model.10.block.3.conv.norm''': '''decoder.layers.10.block.3.norm''',
'''decoder.model.10.shortcut.conv.norm''': '''decoder.layers.10.shortcut.norm''',
'''decoder.model.12.convtr.norm''': '''decoder.layers.12.norm''',
'''decoder.model.13.block.1.conv.norm''': '''decoder.layers.13.block.1.norm''',
'''decoder.model.13.block.3.conv.norm''': '''decoder.layers.13.block.3.norm''',
'''decoder.model.13.shortcut.conv.norm''': '''decoder.layers.13.shortcut.norm''',
'''decoder.model.15.conv.norm''': '''decoder.layers.15.norm''',
}
SCREAMING_SNAKE_CASE__ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
SCREAMING_SNAKE_CASE__ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = []
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Any:
for attribute in key.split('.' ):
A__ = getattr(__UpperCamelCase , __UpperCamelCase )
if weight_type is not None:
A__ = getattr(__UpperCamelCase , __UpperCamelCase ).shape
else:
A__ = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
f''' {value.shape} for {full_name}''' )
if weight_type == "weight":
A__ = value
elif weight_type == "weight_g":
A__ = value
elif weight_type == "weight_v":
A__ = value
elif weight_type == "bias":
A__ = value
elif weight_type == "running_mean":
A__ = value
elif weight_type == "running_var":
A__ = value
elif weight_type == "num_batches_tracked":
A__ = value
elif weight_type == "weight_ih_l0":
A__ = value
elif weight_type == "weight_hh_l0":
A__ = value
elif weight_type == "bias_ih_l0":
A__ = value
elif weight_type == "bias_hh_l0":
A__ = value
elif weight_type == "weight_ih_l1":
A__ = value
elif weight_type == "weight_hh_l1":
A__ = value
elif weight_type == "bias_ih_l1":
A__ = value
elif weight_type == "bias_hh_l1":
A__ = value
else:
A__ = value
logger.info(f'''{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.''' )
def A ( __UpperCamelCase , __UpperCamelCase ) -> Union[str, Any]:
for key in ignore_keys:
if key.endswith('.*' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
A__ , A__ = key.split('.*.' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Any:
A__ = []
if model_name == "encodec_24khz" or "encodec_32khz":
A__ = MAPPING_24K
elif model_name == "encodec_48khz":
A__ = MAPPING_48K
else:
raise ValueError(f'''Unsupported model: {model_name}''' )
for name, value in orig_dict.items():
if should_ignore(__UpperCamelCase , __UpperCamelCase ):
logger.info(f'''{name} was ignored''' )
continue
A__ = False
for key, mapped_key in MAPPING.items():
if "*" in key:
A__ , A__ = key.split('.*.' )
if prefix in name and suffix in name:
A__ = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith('embed' ) and name.endswith('embed_avg' ):
continue
A__ = True
if "*" in mapped_key:
A__ = name.split(__UpperCamelCase )[0].split('.' )[-2]
A__ = mapped_key.replace('*' , __UpperCamelCase )
if "weight_g" in name:
A__ = 'weight_g'
elif "weight_v" in name:
A__ = 'weight_v'
elif "weight_ih_l0" in name:
A__ = 'weight_ih_l0'
elif "weight_hh_l0" in name:
A__ = 'weight_hh_l0'
elif "bias_ih_l0" in name:
A__ = 'bias_ih_l0'
elif "bias_hh_l0" in name:
A__ = 'bias_hh_l0'
elif "weight_ih_l1" in name:
A__ = 'weight_ih_l1'
elif "weight_hh_l1" in name:
A__ = 'weight_hh_l1'
elif "bias_ih_l1" in name:
A__ = 'bias_ih_l1'
elif "bias_hh_l1" in name:
A__ = 'bias_hh_l1'
elif "bias" in name:
A__ = 'bias'
elif "weight" in name:
A__ = 'weight'
elif "running_mean" in name:
A__ = 'running_mean'
elif "running_var" in name:
A__ = 'running_var'
elif "num_batches_tracked" in name:
A__ = 'num_batches_tracked'
else:
A__ = None
set_recursively(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
continue
if not is_used:
unused_weights.append(__UpperCamelCase )
logger.warning(f'''Unused weights: {unused_weights}''' )
@torch.no_grad()
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None , ) -> int:
if config_path is not None:
A__ = EncodecConfig.from_pretrained(__UpperCamelCase )
else:
A__ = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
A__ = [8, 5, 4, 4]
A__ = [2.2]
A__ = 64
A__ = 32_000
A__ = 2_048
A__ = False
A__ = False
A__ = False
elif model_name == "encodec_48khz":
A__ = [8, 5, 4, 2]
A__ = [3.0, 6.0, 12.0, 24.0]
A__ = 48_000
A__ = 2
A__ = False
A__ = 'time_group_norm'
A__ = True
A__ = 1.0
A__ = 0.01
else:
raise ValueError(f'''Unknown model name: {model_name}''' )
A__ = EncodecModel(__UpperCamelCase )
A__ = EncodecFeatureExtractor(
feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , )
feature_extractor.save_pretrained(__UpperCamelCase )
A__ = torch.load(__UpperCamelCase )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
A__ = original_checkpoint['best_state']
recursively_load_weights(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
model.save_pretrained(__UpperCamelCase )
if repo_id:
print('Pushing to the hub...' )
feature_extractor.push_to_hub(__UpperCamelCase )
model.push_to_hub(__UpperCamelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parser.add_argument(
'''--model''',
default='''encodec_24khz''',
type=str,
help='''The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.''',
)
parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 707
|
import argparse
import struct
import unittest
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : List[str] , _snake_case : bytes ):
"""simple docstring"""
A__ = data
# Initialize hash values
A__ = [
0x6A09E667,
0xBB67AE85,
0x3C6EF372,
0xA54FF53A,
0x510E527F,
0x9B05688C,
0x1F83D9AB,
0x5BE0CD19,
]
# Initialize round constants
A__ = [
0x428A2F98,
0x71374491,
0xB5C0FBCF,
0xE9B5DBA5,
0x3956C25B,
0x59F111F1,
0x923F82A4,
0xAB1C5ED5,
0xD807AA98,
0x12835B01,
0x243185BE,
0x550C7DC3,
0x72BE5D74,
0x80DEB1FE,
0x9BDC06A7,
0xC19BF174,
0xE49B69C1,
0xEFBE4786,
0x0FC19DC6,
0x240CA1CC,
0x2DE92C6F,
0x4A7484AA,
0x5CB0A9DC,
0x76F988DA,
0x983E5152,
0xA831C66D,
0xB00327C8,
0xBF597FC7,
0xC6E00BF3,
0xD5A79147,
0x06CA6351,
0x14292967,
0x27B70A85,
0x2E1B2138,
0x4D2C6DFC,
0x53380D13,
0x650A7354,
0x766A0ABB,
0x81C2C92E,
0x92722C85,
0xA2BFE8A1,
0xA81A664B,
0xC24B8B70,
0xC76C51A3,
0xD192E819,
0xD6990624,
0xF40E3585,
0x106AA070,
0x19A4C116,
0x1E376C08,
0x2748774C,
0x34B0BCB5,
0x391C0CB3,
0x4ED8AA4A,
0x5B9CCA4F,
0x682E6FF3,
0x748F82EE,
0x78A5636F,
0x84C87814,
0x8CC70208,
0x90BEFFFA,
0xA4506CEB,
0xBEF9A3F7,
0xC67178F2,
]
A__ = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def _a ( _snake_case : bytes ):
"""simple docstring"""
A__ = B'\x80' + (B'\x00' * (63 - (len(_snake_case ) + 8) % 64))
A__ = struct.pack('>Q' , (len(_snake_case ) * 8) )
return data + padding + big_endian_integer
def _a ( self : Optional[int] ):
"""simple docstring"""
A__ = [
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
A__ = list(struct.unpack('>16L' , _snake_case ) )
# add 48 0-ed integers
words += [0] * 48
A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ = self.hashes
for index in range(0 , 64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
A__ = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
A__ = (
self.ror(words[index - 2] , 17 )
^ self.ror(words[index - 2] , 19 )
^ (words[index - 2] >> 10)
)
A__ = (
words[index - 16] + sa + words[index - 7] + sa
) % 0x100000000
# Compression
A__ = self.ror(_snake_case , 6 ) ^ self.ror(_snake_case , 11 ) ^ self.ror(_snake_case , 25 )
A__ = (e & f) ^ ((~e & 0xFFFFFFFF) & g)
A__ = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0x100000000
A__ = self.ror(_snake_case , 2 ) ^ self.ror(_snake_case , 13 ) ^ self.ror(_snake_case , 22 )
A__ = (a & b) ^ (a & c) ^ (b & c)
A__ = (sa + maj) % 0x100000000
A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ = (
g,
f,
e,
((d + tempa) % 0x100000000),
c,
b,
a,
((tempa + tempa) % 0x100000000),
)
A__ = [a, b, c, d, e, f, g, h]
# Modify final values
A__ = [
((element + mutated_hash_values[index]) % 0x100000000)
for index, element in enumerate(self.hashes )
]
A__ = ''.join([hex(_snake_case )[2:].zfill(8 ) for value in self.hashes] )
def _a ( self : Dict , _snake_case : int , _snake_case : int ):
"""simple docstring"""
return 0xFFFFFFFF & (value << (32 - rotations)) | (value >> rotations)
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def _a ( self : str ):
"""simple docstring"""
import hashlib
A__ = bytes('Test String' , 'utf-8' )
self.assertEqual(SHAaaa(_snake_case ).hash , hashlib.shaaaa(_snake_case ).hexdigest() )
def A ( ) -> None:
import doctest
doctest.testmod()
A__ = 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' )
A__ = parser.parse_args()
A__ = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , 'rb' ) as f:
A__ = f.read()
else:
A__ = bytes(__UpperCamelCase , 'utf-8' )
print(SHAaaa(__UpperCamelCase ).hash )
if __name__ == "__main__":
main()
| 52
| 0
|
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
def A ( __UpperCamelCase , __UpperCamelCase ) -> List[Any]:
return (preds == labels).mean()
@dataclass
class __lowerCAmelCase :
"""simple docstring"""
A__ : str = field(
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": "Pretrained tokenizer 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 huggingface.co"} , )
@dataclass
class __lowerCAmelCase :
"""simple docstring"""
A__ : str = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys() )} )
A__ : str = field(metadata={"help": "Should contain the data files for the task."} )
A__ : int = field(
default=1_28 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
A__ : bool = field(
default=UpperCAmelCase_ , metadata={"help": "Overwrite the cached training and evaluation sets"} )
def A ( ) -> Any:
# 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.
A__ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
A__ , A__ , A__ = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
' --overwrite_output_dir to overcome.' )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('Training/evaluation parameters %s' , __UpperCamelCase )
# Set seed
set_seed(training_args.seed )
try:
A__ = processors[data_args.task_name]()
A__ = processor.get_labels()
A__ = len(__UpperCamelCase )
except KeyError:
raise ValueError('Task not found: %s' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
A__ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__UpperCamelCase , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
A__ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
A__ = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=__UpperCamelCase , cache_dir=model_args.cache_dir , )
# Get datasets
A__ = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__UpperCamelCase , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
A__ = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__UpperCamelCase , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(__UpperCamelCase ) -> Dict:
A__ = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(__UpperCamelCase , p.label_ids )}
# Data collator
A__ = DataCollatorWithPadding(__UpperCamelCase , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
A__ = Trainer(
model=__UpperCamelCase , args=__UpperCamelCase , train_dataset=__UpperCamelCase , eval_dataset=__UpperCamelCase , compute_metrics=__UpperCamelCase , data_collator=__UpperCamelCase , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
A__ = {}
if training_args.do_eval:
logger.info('*** Evaluate ***' )
A__ = trainer.evaluate()
A__ = os.path.join(training_args.output_dir , 'eval_results.txt' )
if trainer.is_world_master():
with open(__UpperCamelCase , 'w' ) as writer:
logger.info('***** Eval results *****' )
for key, value in result.items():
logger.info(' %s = %s' , __UpperCamelCase , __UpperCamelCase )
writer.write('%s = %s\n' % (key, value) )
results.update(__UpperCamelCase )
return results
def A ( __UpperCamelCase ) -> List[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 708
|
import math
import random
def A ( __UpperCamelCase , __UpperCamelCase = False ) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
SCREAMING_SNAKE_CASE__ = 0.02
def A ( __UpperCamelCase , __UpperCamelCase ) -> float:
A__ = float(2 * (random.randint(1 , 100 )) - 1 )
for _ in range(__UpperCamelCase ):
# Forward propagation
A__ = sigmoid_function(INITIAL_VALUE * weight )
# How much did we miss?
A__ = (expected / 100) - layer_a
# Error delta
A__ = layer_1_error * sigmoid_function(__UpperCamelCase , __UpperCamelCase )
# Update weight
weight += INITIAL_VALUE * layer_1_delta
return layer_a * 100
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE__ = int(input('''Expected value: '''))
SCREAMING_SNAKE_CASE__ = int(input('''Number of propagations: '''))
print(forward_propagation(expected, number_propagations))
| 52
| 0
|
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .scheduling_consistency_models import CMStochasticIterativeScheduler
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddim_inverse import DDIMInverseScheduler
from .scheduling_ddim_parallel import DDIMParallelScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_ddpm_parallel import DDPMParallelScheduler
from .scheduling_deis_multistep import DEISMultistepScheduler
from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from .scheduling_euler_discrete import EulerDiscreteScheduler
from .scheduling_heun_discrete import HeunDiscreteScheduler
from .scheduling_ipndm import IPNDMScheduler
from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_repaint import RePaintScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_unclip import UnCLIPScheduler
from .scheduling_unipc_multistep import UniPCMultistepScheduler
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
from .scheduling_vq_diffusion import VQDiffusionScheduler
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_flax_objects import * # noqa F403
else:
from .scheduling_ddim_flax import FlaxDDIMScheduler
from .scheduling_ddpm_flax import FlaxDDPMScheduler
from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
from .scheduling_pndm_flax import FlaxPNDMScheduler
from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
from .scheduling_utils_flax import (
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
broadcast_to_shape_from_left,
)
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .scheduling_lms_discrete import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
| 709
|
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 __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def _a ( self : int ):
"""simple docstring"""
A__ = FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small' )
A__ = AutoTokenizer.from_pretrained('google/mt5-small' )
A__ = tokenizer('Hello there' , return_tensors='np' ).input_ids
A__ = tokenizer('Hi I am' , return_tensors='np' ).input_ids
A__ = shift_tokens_right(_snake_case , model.config.pad_token_id , model.config.decoder_start_token_id )
A__ = model(_snake_case , decoder_input_ids=_snake_case ).logits
A__ = optax.softmax_cross_entropy(_snake_case , onehot(_snake_case , logits.shape[-1] ) ).mean()
A__ = -(labels.shape[-1] * loss.item())
A__ = -84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 52
| 0
|
from __future__ import annotations
SCREAMING_SNAKE_CASE__ = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : List[Any] , _snake_case : dict[str, list[str]] , _snake_case : str ):
"""simple docstring"""
A__ = graph
# mapping node to its parent in resulting breadth first tree
A__ = {}
A__ = source_vertex
def _a ( self : Tuple ):
"""simple docstring"""
A__ = {self.source_vertex}
A__ = None
A__ = [self.source_vertex] # first in first out queue
while queue:
A__ = queue.pop(0 )
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(_snake_case )
A__ = vertex
queue.append(_snake_case )
def _a ( self : str , _snake_case : str ):
"""simple docstring"""
if target_vertex == self.source_vertex:
return self.source_vertex
A__ = self.parent.get(_snake_case )
if target_vertex_parent is None:
A__ = (
F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
)
raise ValueError(_snake_case )
return self.shortest_path(_snake_case ) + F'''->{target_vertex}'''
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = Graph(graph, '''G''')
g.breath_first_search()
print(g.shortest_path('''D'''))
print(g.shortest_path('''G'''))
print(g.shortest_path('''Foo'''))
| 710
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
'''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
'''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
'''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
'''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
'''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
A__ : List[str] = "roberta"
def __init__( self : List[str] , _snake_case : Union[str, Any]=5_02_65 , _snake_case : List[Any]=7_68 , _snake_case : List[str]=12 , _snake_case : List[str]=12 , _snake_case : Any=30_72 , _snake_case : Union[str, Any]="gelu" , _snake_case : int=0.1 , _snake_case : Union[str, Any]=0.1 , _snake_case : Tuple=5_12 , _snake_case : Union[str, Any]=2 , _snake_case : Any=0.02 , _snake_case : Any=1E-12 , _snake_case : List[Any]=1 , _snake_case : int=0 , _snake_case : Any=2 , _snake_case : Optional[Any]="absolute" , _snake_case : int=True , _snake_case : Any=None , **_snake_case : Any , ):
"""simple docstring"""
super().__init__(pad_token_id=_snake_case , bos_token_id=_snake_case , eos_token_id=_snake_case , **_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
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
@property
def _a ( self : Dict ):
"""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),
] )
| 52
| 0
|
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
SCREAMING_SNAKE_CASE__ = '''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 A ( __UpperCamelCase , __UpperCamelCase=None ) -> Dict:
require_version(deps[pkg] , __UpperCamelCase )
| 711
|
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __lowerCAmelCase ( UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
A__ : int = LongformerTokenizer
A__ : Optional[int] = True
A__ : Any = LongformerTokenizerFast
A__ : Dict = True
def _a ( self : int ):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
A__ = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
]
A__ = dict(zip(_snake_case , range(len(_snake_case ) ) ) )
A__ = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
A__ = {'unk_token': '<unk>'}
A__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
A__ = 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(_snake_case ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(_snake_case ) )
def _a ( self : int , **_snake_case : Union[str, Any] ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **_snake_case )
def _a ( self : Optional[int] , **_snake_case : List[Any] ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **_snake_case )
def _a ( self : Any , _snake_case : Optional[Any] ):
"""simple docstring"""
A__ = 'lower newer'
A__ = 'lower newer'
return input_text, output_text
def _a ( self : Any ):
"""simple docstring"""
A__ = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
A__ = 'lower newer'
A__ = ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
A__ = tokenizer.tokenize(_snake_case ) # , add_prefix_space=True)
self.assertListEqual(_snake_case , _snake_case )
A__ = tokens + [tokenizer.unk_token]
A__ = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case ) , _snake_case )
def _a ( self : List[str] ):
"""simple docstring"""
A__ = self.get_tokenizer()
self.assertListEqual(tokenizer.encode('Hello world!' , add_special_tokens=_snake_case ) , [0, 3_14_14, 2_32, 3_28, 2] )
self.assertListEqual(
tokenizer.encode('Hello world! cécé herlolip 418' , add_special_tokens=_snake_case ) , [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2] , )
@slow
def _a ( self : List[Any] ):
"""simple docstring"""
A__ = self.tokenizer_class.from_pretrained('allenai/longformer-base-4096' )
A__ = tokenizer.encode('sequence builders' , add_special_tokens=_snake_case )
A__ = tokenizer.encode('multi-sequence build' , add_special_tokens=_snake_case )
A__ = tokenizer.encode(
'sequence builders' , add_special_tokens=_snake_case , add_prefix_space=_snake_case )
A__ = tokenizer.encode(
'sequence builders' , 'multi-sequence build' , add_special_tokens=_snake_case , add_prefix_space=_snake_case )
A__ = tokenizer.build_inputs_with_special_tokens(_snake_case )
A__ = tokenizer.build_inputs_with_special_tokens(_snake_case , _snake_case )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def _a ( self : List[str] ):
"""simple docstring"""
A__ = self.get_tokenizer()
A__ = 'Encode this sequence.'
A__ = tokenizer.byte_encoder[' '.encode('utf-8' )[0]]
# Testing encoder arguments
A__ = tokenizer.encode(_snake_case , add_special_tokens=_snake_case , add_prefix_space=_snake_case )
A__ = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(_snake_case , _snake_case )
A__ = tokenizer.encode(_snake_case , add_special_tokens=_snake_case , add_prefix_space=_snake_case )
A__ = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(_snake_case , _snake_case )
tokenizer.add_special_tokens({'bos_token': '<s>'} )
A__ = tokenizer.encode(_snake_case , add_special_tokens=_snake_case )
A__ = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(_snake_case , _snake_case )
# Testing spaces after special tokens
A__ = '<mask>'
tokenizer.add_special_tokens(
{'mask_token': AddedToken(_snake_case , lstrip=_snake_case , rstrip=_snake_case )} ) # mask token has a left space
A__ = tokenizer.convert_tokens_to_ids(_snake_case )
A__ = 'Encode <mask> sequence'
A__ = 'Encode <mask>sequence'
A__ = tokenizer.encode(_snake_case )
A__ = encoded.index(_snake_case )
A__ = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(_snake_case , _snake_case )
A__ = tokenizer.encode(_snake_case )
A__ = encoded.index(_snake_case )
A__ = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(_snake_case , _snake_case )
def _a ( self : Dict ):
"""simple docstring"""
pass
def _a ( self : Union[str, Any] ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
A__ = self.rust_tokenizer_class.from_pretrained(_snake_case , **_snake_case )
A__ = self.tokenizer_class.from_pretrained(_snake_case , **_snake_case )
A__ = 'A, <mask> AllenNLP sentence.'
A__ = tokenizer_r.encode_plus(_snake_case , add_special_tokens=_snake_case , return_token_type_ids=_snake_case )
A__ = tokenizer_p.encode_plus(_snake_case , add_special_tokens=_snake_case , return_token_type_ids=_snake_case )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r['token_type_ids'] ) , sum(tokens_p['token_type_ids'] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] ) , sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] ) , )
A__ = tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
A__ = tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p['input_ids'] , [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r['input_ids'] , [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
_snake_case , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
self.assertSequenceEqual(
_snake_case , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
def _a ( self : List[Any] ):
"""simple docstring"""
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
A__ = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=_snake_case , add_prefix_space=_snake_case , trim_offsets=_snake_case )
A__ = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
A__ = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state['add_prefix_space'] , _snake_case )
self.assertEqual(post_processor_state['add_prefix_space'] , _snake_case )
self.assertEqual(post_processor_state['trim_offsets'] , _snake_case )
def _a ( self : Optional[Any] ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
A__ = 'hello' # `hello` is a token in the vocabulary of `pretrained_name`
A__ = F'''{text_of_1_token} {text_of_1_token}'''
A__ = self.rust_tokenizer_class.from_pretrained(
_snake_case , use_fast=_snake_case , add_prefix_space=_snake_case , trim_offsets=_snake_case )
A__ = tokenizer_r(_snake_case , return_offsets_mapping=_snake_case , add_special_tokens=_snake_case )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_snake_case )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_snake_case ) + 1, len(_snake_case ) + 1 + len(_snake_case )) , )
A__ = self.rust_tokenizer_class.from_pretrained(
_snake_case , use_fast=_snake_case , add_prefix_space=_snake_case , trim_offsets=_snake_case )
A__ = tokenizer_r(_snake_case , return_offsets_mapping=_snake_case , add_special_tokens=_snake_case )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_snake_case )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_snake_case ) + 1, len(_snake_case ) + 1 + len(_snake_case )) , )
A__ = self.rust_tokenizer_class.from_pretrained(
_snake_case , use_fast=_snake_case , add_prefix_space=_snake_case , trim_offsets=_snake_case )
A__ = tokenizer_r(_snake_case , return_offsets_mapping=_snake_case , add_special_tokens=_snake_case )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_snake_case )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_snake_case ), len(_snake_case ) + 1 + len(_snake_case )) , )
A__ = self.rust_tokenizer_class.from_pretrained(
_snake_case , use_fast=_snake_case , add_prefix_space=_snake_case , trim_offsets=_snake_case )
A__ = tokenizer_r(_snake_case , return_offsets_mapping=_snake_case , add_special_tokens=_snake_case )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_snake_case )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_snake_case ), len(_snake_case ) + 1 + len(_snake_case )) , )
A__ = F''' {text}'''
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
A__ = self.rust_tokenizer_class.from_pretrained(
_snake_case , use_fast=_snake_case , add_prefix_space=_snake_case , trim_offsets=_snake_case )
A__ = tokenizer_r(_snake_case , return_offsets_mapping=_snake_case , add_special_tokens=_snake_case )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(_snake_case )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(_snake_case ) + 1, 1 + len(_snake_case ) + 1 + len(_snake_case )) , )
A__ = self.rust_tokenizer_class.from_pretrained(
_snake_case , use_fast=_snake_case , add_prefix_space=_snake_case , trim_offsets=_snake_case )
A__ = tokenizer_r(_snake_case , return_offsets_mapping=_snake_case , add_special_tokens=_snake_case )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(_snake_case )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(_snake_case ), 1 + len(_snake_case ) + 1 + len(_snake_case )) , )
A__ = self.rust_tokenizer_class.from_pretrained(
_snake_case , use_fast=_snake_case , add_prefix_space=_snake_case , trim_offsets=_snake_case )
A__ = tokenizer_r(_snake_case , return_offsets_mapping=_snake_case , add_special_tokens=_snake_case )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(_snake_case )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(_snake_case ), 1 + len(_snake_case ) + 1 + len(_snake_case )) , )
| 52
| 0
|
'''simple docstring'''
from __future__ import annotations
from typing import Any
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : str , _snake_case : int , _snake_case : int , _snake_case : float = 0 ):
"""simple docstring"""
A__ , A__ = row, column
A__ = [[default_value for c in range(_snake_case )] for r in range(_snake_case )]
def __str__( self : List[Any] ):
"""simple docstring"""
A__ = F'''Matrix consist of {self.row} rows and {self.column} columns\n'''
# Make string identifier
A__ = 0
for row_vector in self.array:
for obj in row_vector:
A__ = max(_snake_case , len(str(_snake_case ) ) )
A__ = F'''%{max_element_length}s'''
# Make string and return
def single_line(_snake_case : list[float] ) -> str:
nonlocal string_format_identifier
A__ = '['
line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
line += "]"
return line
s += "\n".join(single_line(_snake_case ) for row_vector in self.array )
return s
def __repr__( self : Union[str, Any] ):
"""simple docstring"""
return str(self )
def _a ( self : List[Any] , _snake_case : tuple[int, int] ):
"""simple docstring"""
if not (isinstance(_snake_case , (list, tuple) ) and len(_snake_case ) == 2):
return False
elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
return False
else:
return True
def __getitem__( self : List[str] , _snake_case : tuple[int, int] ):
"""simple docstring"""
assert self.validate_indicies(_snake_case )
return self.array[loc[0]][loc[1]]
def __setitem__( self : Dict , _snake_case : tuple[int, int] , _snake_case : float ):
"""simple docstring"""
assert self.validate_indicies(_snake_case )
A__ = value
def __add__( self : Optional[int] , _snake_case : Matrix ):
"""simple docstring"""
assert isinstance(_snake_case , _snake_case )
assert self.row == another.row and self.column == another.column
# Add
A__ = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
A__ = self[r, c] + another[r, c]
return result
def __neg__( self : Tuple ):
"""simple docstring"""
A__ = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
A__ = -self[r, c]
return result
def __sub__( self : Tuple , _snake_case : Matrix ):
"""simple docstring"""
return self + (-another)
def __mul__( self : str , _snake_case : int | float | Matrix ):
"""simple docstring"""
if isinstance(_snake_case , (int, float) ): # Scalar multiplication
A__ = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
A__ = self[r, c] * another
return result
elif isinstance(_snake_case , _snake_case ): # Matrix multiplication
assert self.column == another.row
A__ = Matrix(self.row , another.column )
for r in range(self.row ):
for c in range(another.column ):
for i in range(self.column ):
result[r, c] += self[r, i] * another[i, c]
return result
else:
A__ = F'''Unsupported type given for another ({type(_snake_case )})'''
raise TypeError(_snake_case )
def _a ( self : List[Any] ):
"""simple docstring"""
A__ = Matrix(self.column , self.row )
for r in range(self.row ):
for c in range(self.column ):
A__ = self[r, c]
return result
def _a ( self : List[Any] , _snake_case : Matrix , _snake_case : Matrix ):
"""simple docstring"""
assert isinstance(_snake_case , _snake_case ) and isinstance(_snake_case , _snake_case )
assert self.row == self.column == u.row == v.row # u, v should be column vector
assert u.column == v.column == 1 # u, v should be column vector
# Calculate
A__ = v.transpose()
A__ = (v_t * self * u)[0, 0] + 1
if numerator_factor == 0:
return None # It's not invertable
return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))
# Testing
if __name__ == "__main__":
def A ( ) -> None:
# a^(-1)
A__ = Matrix(3 , 3 , 0 )
for i in range(3 ):
A__ = 1
print(f'''a^(-1) is {ainv}''' )
# u, v
A__ = Matrix(3 , 1 , 0 )
A__ , A__ , A__ = 1, 2, -3
A__ = Matrix(3 , 1 , 0 )
A__ , A__ , A__ = 4, -2, 5
print(f'''u is {u}''' )
print(f'''v is {v}''' )
print(f'''uv^T is {u * v.transpose()}''' )
# Sherman Morrison
print(f'''(a + uv^T)^(-1) is {ainv.sherman_morrison(__UpperCamelCase , __UpperCamelCase )}''' )
def A ( ) -> None:
import doctest
doctest.testmod()
testa()
| 712
|
import pytest
import datasets
# Import fixture modules as plugins
SCREAMING_SNAKE_CASE__ = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def A ( __UpperCamelCase , __UpperCamelCase ) -> Optional[int]:
# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
for item in items:
if any(marker in item.keywords for marker in ['integration', 'unit'] ):
continue
item.add_marker(pytest.mark.unit )
def A ( __UpperCamelCase ) -> str:
config.addinivalue_line('markers' , 'torchaudio_latest: mark test to run with torchaudio>=0.12' )
@pytest.fixture(autouse=__UpperCamelCase )
def A ( __UpperCamelCase , __UpperCamelCase ) -> Union[str, Any]:
# test_hf_cache_home = tmp_path_factory.mktemp("cache") # TODO: why a cache dir per test function does not work?
A__ = tmp_path_factory.getbasetemp() / 'cache'
A__ = test_hf_cache_home / 'datasets'
A__ = test_hf_cache_home / 'metrics'
A__ = test_hf_cache_home / 'modules'
monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE' , str(__UpperCamelCase ) )
monkeypatch.setattr('datasets.config.HF_METRICS_CACHE' , str(__UpperCamelCase ) )
monkeypatch.setattr('datasets.config.HF_MODULES_CACHE' , str(__UpperCamelCase ) )
A__ = test_hf_datasets_cache / 'downloads'
monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH' , str(__UpperCamelCase ) )
A__ = test_hf_datasets_cache / 'downloads' / 'extracted'
monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH' , str(__UpperCamelCase ) )
@pytest.fixture(autouse=__UpperCamelCase , scope='session' )
def A ( ) -> Union[str, Any]:
datasets.disable_progress_bar()
@pytest.fixture(autouse=__UpperCamelCase )
def A ( __UpperCamelCase ) -> int:
# don't take tests into account when counting downloads
monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS' , __UpperCamelCase )
@pytest.fixture
def A ( __UpperCamelCase ) -> Any:
# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
# To be removed once SQLAlchemy 2.0 supported
monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING' , __UpperCamelCase )
| 52
| 0
|
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 __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@property
def _a ( self : Optional[Any] ):
"""simple docstring"""
torch.manual_seed(0 )
A__ = 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 _a ( self : List[str] ):
"""simple docstring"""
A__ = self.dummy_uncond_unet
A__ = ScoreSdeVeScheduler()
A__ = ScoreSdeVePipeline(unet=_snake_case , scheduler=_snake_case )
sde_ve.to(_snake_case )
sde_ve.set_progress_bar_config(disable=_snake_case )
A__ = torch.manual_seed(0 )
A__ = sde_ve(num_inference_steps=2 , output_type='numpy' , generator=_snake_case ).images
A__ = torch.manual_seed(0 )
A__ = sde_ve(num_inference_steps=2 , output_type='numpy' , generator=_snake_case , return_dict=_snake_case )[
0
]
A__ = image[0, -3:, -3:, -1]
A__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A__ = 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 __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A__ = 'google/ncsnpp-church-256'
A__ = UNetaDModel.from_pretrained(_snake_case )
A__ = ScoreSdeVeScheduler.from_pretrained(_snake_case )
A__ = ScoreSdeVePipeline(unet=_snake_case , scheduler=_snake_case )
sde_ve.to(_snake_case )
sde_ve.set_progress_bar_config(disable=_snake_case )
A__ = torch.manual_seed(0 )
A__ = sde_ve(num_inference_steps=10 , output_type='numpy' , generator=_snake_case ).images
A__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
A__ = 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
| 713
|
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A ( __UpperCamelCase , __UpperCamelCase ) -> Tuple:
A__ = args.log_outputs
A__ = '_'.join(args.dataset.split('/' ) + [args.config, args.split] )
# load metric
A__ = load_metric('wer' )
A__ = load_metric('cer' )
# compute metrics
A__ = wer.compute(references=result['target'] , predictions=result['prediction'] )
A__ = cer.compute(references=result['target'] , predictions=result['prediction'] )
# print & log results
A__ = f'''WER: {wer_result}\nCER: {cer_result}'''
print(__UpperCamelCase )
with open(f'''{dataset_id}_eval_results.txt''' , 'w' ) as f:
f.write(__UpperCamelCase )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
A__ = f'''log_{dataset_id}_predictions.txt'''
A__ = f'''log_{dataset_id}_targets.txt'''
with open(__UpperCamelCase , 'w' ) as p, open(__UpperCamelCase , 'w' ) as t:
# mapping function to write output
def write_to_file(__UpperCamelCase , __UpperCamelCase ):
p.write(f'''{i}''' + '\n' )
p.write(batch['prediction'] + '\n' )
t.write(f'''{i}''' + '\n' )
t.write(batch['target'] + '\n' )
result.map(__UpperCamelCase , with_indices=__UpperCamelCase )
def A ( __UpperCamelCase ) -> str:
A__ = '[,?.!\-\;\:"“%‘”�—’…–]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
A__ = re.sub(__UpperCamelCase , '' , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
A__ = ['\n\n', '\n', ' ', ' ']
for t in token_sequences_to_ignore:
A__ = ' '.join(text.split(__UpperCamelCase ) )
return text
def A ( __UpperCamelCase ) -> Union[str, Any]:
# load dataset
A__ = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=__UpperCamelCase )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
A__ = AutoFeatureExtractor.from_pretrained(args.model_id )
A__ = feature_extractor.sampling_rate
# resample audio
A__ = dataset.cast_column('audio' , Audio(sampling_rate=__UpperCamelCase ) )
# load eval pipeline
if args.device is None:
A__ = 0 if torch.cuda.is_available() else -1
A__ = pipeline('automatic-speech-recognition' , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(__UpperCamelCase ):
A__ = asr(
batch['audio']['array'] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
A__ = prediction['text']
A__ = normalize_text(batch['sentence'] )
return batch
# run inference on all examples
A__ = dataset.map(__UpperCamelCase , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(__UpperCamelCase , __UpperCamelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
main(args)
| 52
| 0
|
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
SCREAMING_SNAKE_CASE__ = '''<<<<<<< This should probably be modified because it mentions: '''
SCREAMING_SNAKE_CASE__ = '''=======
>>>>>>>
'''
SCREAMING_SNAKE_CASE__ = [
'''TextEncoderConfig''',
'''ByteTextEncoder''',
'''SubwordTextEncoder''',
'''encoder_config''',
'''maybe_build_from_corpus''',
'''manual_dir''',
]
SCREAMING_SNAKE_CASE__ = [
# (pattern, replacement)
# Order is important here for some replacements
(r'''tfds\.core''', r'''datasets'''),
(r'''tf\.io\.gfile\.GFile''', r'''open'''),
(r'''tf\.([\w\d]+)''', r'''datasets.Value(\'\1\')'''),
(r'''tfds\.features\.Text\(\)''', r'''datasets.Value(\'string\')'''),
(r'''tfds\.features\.Text\(''', r'''datasets.Value(\'string\'),'''),
(r'''features\s*=\s*tfds.features.FeaturesDict\(''', r'''features=datasets.Features('''),
(r'''tfds\.features\.FeaturesDict\(''', r'''dict('''),
(r'''The TensorFlow Datasets Authors''', r'''The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'''),
(r'''tfds\.''', r'''datasets.'''),
(r'''dl_manager\.manual_dir''', r'''self.config.data_dir'''),
(r'''self\.builder_config''', r'''self.config'''),
]
def A ( __UpperCamelCase ) -> Optional[Any]:
return ConvertCommand(args.tfds_path , args.datasets_directory )
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
@staticmethod
def _a ( _snake_case : ArgumentParser ):
"""simple docstring"""
A__ = parser.add_parser(
'convert' , help='Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.' , )
train_parser.add_argument(
'--tfds_path' , type=_snake_case , required=_snake_case , help='Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.' , )
train_parser.add_argument(
'--datasets_directory' , type=_snake_case , required=_snake_case , help='Path to the HuggingFace Datasets folder.' )
train_parser.set_defaults(func=_snake_case )
def __init__( self : str , _snake_case : str , _snake_case : str , *_snake_case : Dict ):
"""simple docstring"""
A__ = get_logger('datasets-cli/converting' )
A__ = tfds_path
A__ = datasets_directory
def _a ( self : Dict ):
"""simple docstring"""
if os.path.isdir(self._tfds_path ):
A__ = os.path.abspath(self._tfds_path )
elif os.path.isfile(self._tfds_path ):
A__ = os.path.dirname(self._tfds_path )
else:
raise ValueError('--tfds_path is neither a directory nor a file. Please check path.' )
A__ = os.path.abspath(self._datasets_directory )
self._logger.info(F'''Converting datasets from {abs_tfds_path} to {abs_datasets_path}''' )
A__ = []
A__ = []
A__ = {}
if os.path.isdir(self._tfds_path ):
A__ = os.listdir(_snake_case )
else:
A__ = [os.path.basename(self._tfds_path )]
for f_name in file_names:
self._logger.info(F'''Looking at file {f_name}''' )
A__ = os.path.join(_snake_case , _snake_case )
A__ = os.path.join(_snake_case , _snake_case )
if not os.path.isfile(_snake_case ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
self._logger.info('Skipping file' )
continue
with open(_snake_case , encoding='utf-8' ) as f:
A__ = f.readlines()
A__ = []
A__ = False
A__ = False
A__ = []
for line in lines:
A__ = line
# Convert imports
if "import tensorflow.compat.v2 as tf" in out_line:
continue
elif "@tfds.core" in out_line:
continue
elif "builder=self" in out_line:
continue
elif "import tensorflow_datasets.public_api as tfds" in out_line:
A__ = 'import datasets\n'
elif "import tensorflow" in out_line:
# order is important here
A__ = ''
continue
elif "from absl import logging" in out_line:
A__ = 'from datasets import logging\n'
elif "getLogger" in out_line:
A__ = out_line.replace('getLogger' , 'get_logger' )
elif any(expression in out_line for expression in TO_HIGHLIGHT ):
A__ = True
A__ = list(filter(lambda _snake_case : e in out_line , _snake_case ) )
out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(_snake_case ) + '\n' )
out_lines.append(_snake_case )
out_lines.append(_snake_case )
continue
else:
for pattern, replacement in TO_CONVERT:
A__ = re.sub(_snake_case , _snake_case , _snake_case )
# Take care of saving utilities (to later move them together with main script)
if "tensorflow_datasets" in out_line:
A__ = re.match(R'from\stensorflow_datasets.*import\s([^\.\r\n]+)' , _snake_case )
tfds_imports.extend(imp.strip() for imp in match.group(1 ).split(',' ) )
A__ = 'from . import ' + match.group(1 )
# Check we have not forget anything
if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
raise ValueError(F'''Error converting {out_line.strip()}''' )
if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
A__ = True
out_lines.append(_snake_case )
if is_builder or "wmt" in f_name:
# We create a new directory for each dataset
A__ = f_name.replace('.py' , '' )
A__ = os.path.join(_snake_case , _snake_case )
A__ = os.path.join(_snake_case , _snake_case )
os.makedirs(_snake_case , exist_ok=_snake_case )
self._logger.info(F'''Adding directory {output_dir}''' )
imports_to_builder_map.update({imp: output_dir for imp in tfds_imports} )
else:
# Utilities will be moved at the end
utils_files.append(_snake_case )
if needs_manual_update:
with_manual_update.append(_snake_case )
with open(_snake_case , 'w' , encoding='utf-8' ) as f:
f.writelines(_snake_case )
self._logger.info(F'''Converted in {output_file}''' )
for utils_file in utils_files:
try:
A__ = os.path.basename(_snake_case )
A__ = imports_to_builder_map[f_name.replace('.py' , '' )]
self._logger.info(F'''Moving {dest_folder} to {utils_file}''' )
shutil.copy(_snake_case , _snake_case )
except KeyError:
self._logger.error(F'''Cannot find destination folder for {utils_file}. Please copy manually.''' )
if with_manual_update:
for file_path in with_manual_update:
self._logger.warning(
F'''You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.''' )
| 714
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
def A ( __UpperCamelCase ) -> YolosConfig:
A__ = YolosConfig()
# size of the architecture
if "yolos_ti" in yolos_name:
A__ = 192
A__ = 768
A__ = 12
A__ = 3
A__ = [800, 1_333]
A__ = False
elif yolos_name == "yolos_s_dWr":
A__ = 330
A__ = 14
A__ = 6
A__ = 1_320
elif "yolos_s" in yolos_name:
A__ = 384
A__ = 1_536
A__ = 12
A__ = 6
elif "yolos_b" in yolos_name:
A__ = [800, 1_344]
A__ = 91
A__ = 'huggingface/label-files'
A__ = 'coco-detection-id2label.json'
A__ = json.load(open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type='dataset' ) , 'r' ) )
A__ = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
A__ = idalabel
A__ = {v: k for k, v in idalabel.items()}
return config
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = False ) -> str:
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
A__ = state_dict.pop(f'''blocks.{i}.attn.qkv.weight''' )
A__ = state_dict.pop(f'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
A__ = in_proj_weight[: config.hidden_size, :]
A__ = in_proj_bias[: config.hidden_size]
A__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
A__ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
A__ = in_proj_weight[-config.hidden_size :, :]
A__ = in_proj_bias[-config.hidden_size :]
def A ( __UpperCamelCase ) -> str:
if "backbone" in name:
A__ = name.replace('backbone' , 'vit' )
if "cls_token" in name:
A__ = name.replace('cls_token' , 'embeddings.cls_token' )
if "det_token" in name:
A__ = name.replace('det_token' , 'embeddings.detection_tokens' )
if "mid_pos_embed" in name:
A__ = name.replace('mid_pos_embed' , 'encoder.mid_position_embeddings' )
if "pos_embed" in name:
A__ = name.replace('pos_embed' , 'embeddings.position_embeddings' )
if "patch_embed.proj" in name:
A__ = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "blocks" in name:
A__ = name.replace('blocks' , 'encoder.layer' )
if "attn.proj" in name:
A__ = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
A__ = name.replace('attn' , 'attention.self' )
if "norm1" in name:
A__ = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
A__ = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
A__ = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
A__ = name.replace('mlp.fc2' , 'output.dense' )
if "class_embed" in name:
A__ = name.replace('class_embed' , 'class_labels_classifier' )
if "bbox_embed" in name:
A__ = name.replace('bbox_embed' , 'bbox_predictor' )
if "vit.norm" in name:
A__ = name.replace('vit.norm' , 'vit.layernorm' )
return name
def A ( __UpperCamelCase , __UpperCamelCase ) -> dict:
for key in orig_state_dict.copy().keys():
A__ = orig_state_dict.pop(__UpperCamelCase )
if "qkv" in key:
A__ = key.split('.' )
A__ = int(key_split[2] )
A__ = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
if "weight" in key:
A__ = val[:dim, :]
A__ = val[
dim : dim * 2, :
]
A__ = val[-dim:, :]
else:
A__ = val[:dim]
A__ = val[dim : dim * 2]
A__ = val[-dim:]
else:
A__ = val
return orig_state_dict
def A ( ) -> torch.Tensor:
A__ = 'http://images.cocodataset.org/val2017/000000039769.jpg'
A__ = Image.open(requests.get(__UpperCamelCase , stream=__UpperCamelCase ).raw )
return im
@torch.no_grad()
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = False ) -> List[str]:
A__ = get_yolos_config(__UpperCamelCase )
# load original state_dict
A__ = torch.load(__UpperCamelCase , map_location='cpu' )['model']
# load 🤗 model
A__ = YolosForObjectDetection(__UpperCamelCase )
model.eval()
A__ = convert_state_dict(__UpperCamelCase , __UpperCamelCase )
model.load_state_dict(__UpperCamelCase )
# Check outputs on an image, prepared by YolosImageProcessor
A__ = 800 if yolos_name != 'yolos_ti' else 512
A__ = YolosImageProcessor(format='coco_detection' , size=__UpperCamelCase )
A__ = image_processor(images=prepare_img() , return_tensors='pt' )
A__ = model(**__UpperCamelCase )
A__ , A__ = outputs.logits, outputs.pred_boxes
A__ , A__ = None, None
if yolos_name == "yolos_ti":
A__ = torch.tensor(
[[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]] )
A__ = torch.tensor(
[[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]] )
elif yolos_name == "yolos_s_200_pre":
A__ = torch.tensor(
[[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]] )
A__ = torch.tensor(
[[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]] )
elif yolos_name == "yolos_s_300_pre":
A__ = torch.tensor(
[[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]] )
A__ = torch.tensor(
[[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]] )
elif yolos_name == "yolos_s_dWr":
A__ = torch.tensor(
[[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]] )
A__ = torch.tensor(
[[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]] )
elif yolos_name == "yolos_base":
A__ = torch.tensor(
[[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]] )
A__ = torch.tensor(
[[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]] )
else:
raise ValueError(f'''Unknown yolos_name: {yolos_name}''' )
assert torch.allclose(logits[0, :3, :3] , __UpperCamelCase , atol=1E-4 )
assert torch.allclose(pred_boxes[0, :3, :3] , __UpperCamelCase , atol=1E-4 )
Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
print(f'''Saving model {yolos_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(__UpperCamelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__UpperCamelCase )
if push_to_hub:
A__ = {
'yolos_ti': 'yolos-tiny',
'yolos_s_200_pre': 'yolos-small',
'yolos_s_300_pre': 'yolos-small-300',
'yolos_s_dWr': 'yolos-small-dwr',
'yolos_base': 'yolos-base',
}
print('Pushing to the hub...' )
A__ = model_mapping[yolos_name]
image_processor.push_to_hub(__UpperCamelCase , organization='hustvl' )
model.push_to_hub(__UpperCamelCase , organization='hustvl' )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--yolos_name''',
default='''yolos_s_200_pre''',
type=str,
help=(
'''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
),
)
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 52
| 0
|
def A ( __UpperCamelCase ) -> str:
A__ = 0
A__ = len(__UpperCamelCase )
for i in range(n - 1 ):
for j in range(i + 1 , __UpperCamelCase ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def A ( __UpperCamelCase ) -> Dict:
if len(__UpperCamelCase ) <= 1:
return arr, 0
A__ = len(__UpperCamelCase ) // 2
A__ = arr[0:mid]
A__ = arr[mid:]
A__ , A__ = count_inversions_recursive(__UpperCamelCase )
A__ , A__ = count_inversions_recursive(__UpperCamelCase )
A__ , A__ = _count_cross_inversions(__UpperCamelCase , __UpperCamelCase )
A__ = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def A ( __UpperCamelCase , __UpperCamelCase ) -> Optional[int]:
A__ = []
A__ = A__ = A__ = 0
while i < len(__UpperCamelCase ) and j < len(__UpperCamelCase ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(__UpperCamelCase ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(__UpperCamelCase ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def A ( ) -> List[str]:
A__ = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
A__ = count_inversions_bf(__UpperCamelCase )
A__ , A__ = count_inversions_recursive(__UpperCamelCase )
assert num_inversions_bf == num_inversions_recursive == 8
print('number of inversions = ' , __UpperCamelCase )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
A__ = count_inversions_bf(__UpperCamelCase )
A__ , A__ = count_inversions_recursive(__UpperCamelCase )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ' , __UpperCamelCase )
# an empty list should also have zero inversions
A__ = []
A__ = count_inversions_bf(__UpperCamelCase )
A__ , A__ = count_inversions_recursive(__UpperCamelCase )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ' , __UpperCamelCase )
if __name__ == "__main__":
main()
| 715
|
from typing import TYPE_CHECKING
from ..utils import _LazyModule
SCREAMING_SNAKE_CASE__ = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
'''PatchingSpec''',
],
'''convert''': ['''export''', '''validate_model_outputs'''],
'''features''': ['''FeaturesManager'''],
'''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}
if TYPE_CHECKING:
from .config import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
OnnxConfigWithPast,
OnnxSeqaSeqConfigWithPast,
PatchingSpec,
)
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import ParameterFormat, compute_serialized_parameters_size
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 52
| 0
|
from random import randint
from tempfile import TemporaryFile
import numpy as np
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> str:
A__ = 0
if start < end:
A__ = randint(__UpperCamelCase , __UpperCamelCase )
A__ = a[end]
A__ = a[pivot]
A__ = temp
A__ , A__ = _in_place_partition(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
count += _in_place_quick_sort(__UpperCamelCase , __UpperCamelCase , p - 1 )
count += _in_place_quick_sort(__UpperCamelCase , p + 1 , __UpperCamelCase )
return count
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> List[Any]:
A__ = 0
A__ = randint(__UpperCamelCase , __UpperCamelCase )
A__ = a[end]
A__ = a[pivot]
A__ = temp
A__ = start - 1
for index in range(__UpperCamelCase , __UpperCamelCase ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
A__ = new_pivot_index + 1
A__ = a[new_pivot_index]
A__ = a[index]
A__ = temp
A__ = a[new_pivot_index + 1]
A__ = a[end]
A__ = temp
return new_pivot_index + 1, count
SCREAMING_SNAKE_CASE__ = TemporaryFile()
SCREAMING_SNAKE_CASE__ = 1_0_0 # 1000 elements are to be sorted
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 0, 1 # mean and standard deviation
SCREAMING_SNAKE_CASE__ = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)
outfile.seek(0) # using the same array
SCREAMING_SNAKE_CASE__ = np.load(outfile)
SCREAMING_SNAKE_CASE__ = len(M) - 1
SCREAMING_SNAKE_CASE__ = _in_place_quick_sort(M, 0, r)
print(
'''No of Comparisons for 100 elements selected from a standard normal distribution'''
'''is :'''
)
print(z)
| 716
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_rembert import RemBertTokenizer
else:
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {'''vocab_file''': '''sentencepiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
SCREAMING_SNAKE_CASE__ = {
'''vocab_file''': {
'''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
},
'''tokenizer_file''': {
'''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/tokenizer.json''',
},
}
SCREAMING_SNAKE_CASE__ = {
'''google/rembert''': 2_5_6,
}
SCREAMING_SNAKE_CASE__ = '''▁'''
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
A__ : Any = VOCAB_FILES_NAMES
A__ : str = PRETRAINED_VOCAB_FILES_MAP
A__ : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ : int = RemBertTokenizer
def __init__( self : Union[str, Any] , _snake_case : Any=None , _snake_case : Optional[Any]=None , _snake_case : Any=True , _snake_case : Optional[int]=True , _snake_case : Dict=False , _snake_case : Dict="[CLS]" , _snake_case : List[Any]="[SEP]" , _snake_case : Union[str, Any]="<unk>" , _snake_case : List[str]="[SEP]" , _snake_case : List[str]="<pad>" , _snake_case : str="[CLS]" , _snake_case : Any="[MASK]" , **_snake_case : Any , ):
"""simple docstring"""
A__ = AddedToken(_snake_case , lstrip=_snake_case , rstrip=_snake_case ) if isinstance(_snake_case , _snake_case ) else mask_token
super().__init__(
_snake_case , tokenizer_file=_snake_case , do_lower_case=_snake_case , remove_space=_snake_case , keep_accents=_snake_case , bos_token=_snake_case , eos_token=_snake_case , unk_token=_snake_case , sep_token=_snake_case , pad_token=_snake_case , cls_token=_snake_case , mask_token=_snake_case , **_snake_case , )
A__ = do_lower_case
A__ = remove_space
A__ = keep_accents
A__ = vocab_file
A__ = False if not self.vocab_file else True
def _a ( self : Any , _snake_case : List[int] , _snake_case : Optional[List[int]] = None ):
"""simple docstring"""
A__ = [self.sep_token_id]
A__ = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def _a ( self : Tuple , _snake_case : List[int] , _snake_case : Optional[List[int]] = None , _snake_case : bool = False ):
"""simple docstring"""
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(_snake_case )) + [1] + ([0] * len(_snake_case )) + [1]
return [1] + ([0] * len(_snake_case )) + [1]
def _a ( self : Dict , _snake_case : List[int] , _snake_case : Optional[List[int]] = None ):
"""simple docstring"""
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 ) * [0] + len(token_ids_a + sep ) * [1]
def _a ( self : Any , _snake_case : str , _snake_case : Optional[str] = None ):
"""simple docstring"""
if not os.path.isdir(_snake_case ):
logger.error('Vocabulary path ({}) should be a directory'.format(_snake_case ) )
return
A__ = os.path.join(
_snake_case , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_snake_case ):
copyfile(self.vocab_file , _snake_case )
return (out_vocab_file,)
| 52
| 0
|
def A ( __UpperCamelCase = 600_851_475_143 ) -> int:
try:
A__ = int(__UpperCamelCase )
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.' )
A__ = 1
A__ = 2
while i * i <= n:
while n % i == 0:
A__ = i
n //= i
i += 1
if n > 1:
A__ = n
return int(__UpperCamelCase )
if __name__ == "__main__":
print(f'{solution() = }')
| 717
|
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
SCREAMING_SNAKE_CASE__ = '''sshleifer/bart-tiny-random'''
SCREAMING_SNAKE_CASE__ = '''patrickvonplaten/t5-tiny-random'''
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _a ( self : Optional[int] ):
"""simple docstring"""
return AutoConfig.from_pretrained(_snake_case )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A__ , *A__ = create_student_by_copying_alternating_layers(_snake_case , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.num_hidden_layers , 1 )
def _a ( self : Optional[int] ):
"""simple docstring"""
A__ , *A__ = create_student_by_copying_alternating_layers(_snake_case , tempfile.mkdtemp() , e=1 , d=_snake_case )
def _a ( self : int ):
"""simple docstring"""
A__ , *A__ = create_student_by_copying_alternating_layers(_snake_case , tempfile.mkdtemp() , e=1 , d=_snake_case )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , self.teacher_config.encoder_layers )
def _a ( self : str ):
"""simple docstring"""
A__ , *A__ = create_student_by_copying_alternating_layers(_snake_case , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , 1 )
def _a ( self : str ):
"""simple docstring"""
with self.assertRaises(_snake_case ):
create_student_by_copying_alternating_layers(_snake_case , tempfile.mkdtemp() , e=_snake_case , d=_snake_case )
| 52
| 0
|
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler
@dataclass
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
A__ : int = 0
A__ : bool = False
A__ : float = 3.0
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def _a ( self : Union[str, Any] ):
"""simple docstring"""
self.assertDictEqual(MockClass().to_kwargs() , {} )
self.assertDictEqual(MockClass(a=2 ).to_kwargs() , {'a': 2} )
self.assertDictEqual(MockClass(a=2 , b=_snake_case ).to_kwargs() , {'a': 2, 'b': True} )
self.assertDictEqual(MockClass(a=2 , c=2.25 ).to_kwargs() , {'a': 2, 'c': 2.25} )
@require_cuda
def _a ( self : Optional[int] ):
"""simple docstring"""
A__ = GradScalerKwargs(init_scale=10_24 , growth_factor=2 )
AcceleratorState._reset_state()
A__ = Accelerator(mixed_precision='fp16' , kwargs_handlers=[scaler_handler] )
print(accelerator.use_fpaa )
A__ = accelerator.scaler
# Check the kwargs have been applied
self.assertEqual(scaler._init_scale , 1024.0 )
self.assertEqual(scaler._growth_factor , 2.0 )
# Check the other values are at the default
self.assertEqual(scaler._backoff_factor , 0.5 )
self.assertEqual(scaler._growth_interval , 20_00 )
self.assertEqual(scaler._enabled , _snake_case )
@require_multi_gpu
def _a ( self : Dict ):
"""simple docstring"""
A__ = ['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__":
SCREAMING_SNAKE_CASE__ = DistributedDataParallelKwargs(bucket_cap_mb=1_5, find_unused_parameters=True)
SCREAMING_SNAKE_CASE__ = Accelerator(kwargs_handlers=[ddp_scaler])
SCREAMING_SNAKE_CASE__ = torch.nn.Linear(1_0_0, 2_0_0)
SCREAMING_SNAKE_CASE__ = accelerator.prepare(model)
# Check the values changed in kwargs
SCREAMING_SNAKE_CASE__ = ''''''
SCREAMING_SNAKE_CASE__ = model.bucket_bytes_cap // (1_0_2_4 * 1_0_2_4)
if observed_bucket_cap_map != 1_5:
error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
if model.find_unused_parameters is not True:
error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"
# Check the values of the defaults
if model.dim != 0:
error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
if model.broadcast_buffers is not True:
error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
if model.gradient_as_bucket_view is not False:
error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 718
|
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 __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
A__ : Union[str, Any] = ["image_processor", "tokenizer"]
A__ : Optional[Any] = "BridgeTowerImageProcessor"
A__ : List[Any] = ("RobertaTokenizer", "RobertaTokenizerFast")
def __init__( self : List[Any] , _snake_case : Optional[Any] , _snake_case : Optional[int] ):
"""simple docstring"""
super().__init__(_snake_case , _snake_case )
def __call__( self : List[Any] , _snake_case : int , _snake_case : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , _snake_case : bool = True , _snake_case : Union[bool, str, PaddingStrategy] = False , _snake_case : Union[bool, str, TruncationStrategy] = None , _snake_case : Optional[int] = None , _snake_case : int = 0 , _snake_case : Optional[int] = None , _snake_case : Optional[bool] = None , _snake_case : Optional[bool] = None , _snake_case : bool = False , _snake_case : bool = False , _snake_case : bool = False , _snake_case : bool = False , _snake_case : bool = True , _snake_case : Optional[Union[str, TensorType]] = None , **_snake_case : Optional[int] , ):
"""simple docstring"""
A__ = self.tokenizer(
text=_snake_case , add_special_tokens=_snake_case , padding=_snake_case , truncation=_snake_case , max_length=_snake_case , stride=_snake_case , pad_to_multiple_of=_snake_case , return_token_type_ids=_snake_case , return_attention_mask=_snake_case , return_overflowing_tokens=_snake_case , return_special_tokens_mask=_snake_case , return_offsets_mapping=_snake_case , return_length=_snake_case , verbose=_snake_case , return_tensors=_snake_case , **_snake_case , )
# add pixel_values + pixel_mask
A__ = self.image_processor(
_snake_case , return_tensors=_snake_case , do_normalize=_snake_case , do_center_crop=_snake_case , **_snake_case )
encoding.update(_snake_case )
return encoding
def _a ( self : Any , *_snake_case : Tuple , **_snake_case : List[Any] ):
"""simple docstring"""
return self.tokenizer.batch_decode(*_snake_case , **_snake_case )
def _a ( self : Dict , *_snake_case : Dict , **_snake_case : List[str] ):
"""simple docstring"""
return self.tokenizer.decode(*_snake_case , **_snake_case )
@property
def _a ( self : Tuple ):
"""simple docstring"""
A__ = self.tokenizer.model_input_names
A__ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 52
| 0
|
'''simple docstring'''
import argparse
import os
import re
import packaging.version
SCREAMING_SNAKE_CASE__ = '''examples/'''
SCREAMING_SNAKE_CASE__ = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(r'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
'''setup''': (re.compile(r'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), r'''\1version="VERSION",'''),
'''doc''': (re.compile(r'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
SCREAMING_SNAKE_CASE__ = {
'''init''': '''src/transformers/__init__.py''',
'''setup''': '''setup.py''',
}
SCREAMING_SNAKE_CASE__ = '''README.md'''
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> List[Any]:
with open(__UpperCamelCase , 'r' , encoding='utf-8' , newline='\n' ) as f:
A__ = f.read()
A__ , A__ = REPLACE_PATTERNS[pattern]
A__ = replace.replace('VERSION' , __UpperCamelCase )
A__ = re_pattern.sub(__UpperCamelCase , __UpperCamelCase )
with open(__UpperCamelCase , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.write(__UpperCamelCase )
def A ( __UpperCamelCase ) -> Any:
for folder, directories, fnames in os.walk(__UpperCamelCase ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('research_projects' )
if "legacy" in directories:
directories.remove('legacy' )
for fname in fnames:
if fname.endswith('.py' ):
update_version_in_file(os.path.join(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase , pattern='examples' )
def A ( __UpperCamelCase , __UpperCamelCase=False ) -> int:
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
if not patch:
update_version_in_examples(__UpperCamelCase )
def A ( ) -> Dict:
A__ = '🤗 Transformers currently provides the following architectures'
A__ = '1. Want to contribute a new model?'
with open(__UpperCamelCase , 'r' , encoding='utf-8' , newline='\n' ) as f:
A__ = f.readlines()
# Find the start of the list.
A__ = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
A__ = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('1.' ):
A__ = lines[index].replace(
'https://huggingface.co/docs/transformers/main/model_doc' , 'https://huggingface.co/docs/transformers/model_doc' , )
index += 1
with open(__UpperCamelCase , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(__UpperCamelCase )
def A ( ) -> Optional[Any]:
with open(REPLACE_FILES['init'] , 'r' ) as f:
A__ = f.read()
A__ = REPLACE_PATTERNS['init'][0].search(__UpperCamelCase ).groups()[0]
return packaging.version.parse(__UpperCamelCase )
def A ( __UpperCamelCase=False ) -> Tuple:
A__ = get_version()
if patch and default_version.is_devrelease:
raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!' )
if default_version.is_devrelease:
A__ = default_version.base_version
elif patch:
A__ = f'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
else:
A__ = f'''{default_version.major}.{default_version.minor + 1}.0'''
# Now let's ask nicely if that's the right one.
A__ = input(f'''Which version are you releasing? [{default_version}]''' )
if len(__UpperCamelCase ) == 0:
A__ = default_version
print(f'''Updating version to {version}.''' )
global_version_update(__UpperCamelCase , patch=__UpperCamelCase )
if not patch:
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
def A ( ) -> Union[str, Any]:
A__ = get_version()
A__ = f'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
A__ = current_version.base_version
# Check with the user we got that right.
A__ = input(f'''Which version are we developing now? [{dev_version}]''' )
if len(__UpperCamelCase ) == 0:
A__ = dev_version
print(f'''Updating version to {version}.''' )
global_version_update(__UpperCamelCase )
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
SCREAMING_SNAKE_CASE__ = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('''Nothing to do after a patch :-)''')
else:
post_release_work()
| 719
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
'''configuration_xlm_roberta''': [
'''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaConfig''',
'''XLMRobertaOnnxConfig''',
],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ['''XLMRobertaTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ['''XLMRobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLMRobertaForCausalLM''',
'''XLMRobertaForMaskedLM''',
'''XLMRobertaForMultipleChoice''',
'''XLMRobertaForQuestionAnswering''',
'''XLMRobertaForSequenceClassification''',
'''XLMRobertaForTokenClassification''',
'''XLMRobertaModel''',
'''XLMRobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLMRobertaForCausalLM''',
'''TFXLMRobertaForMaskedLM''',
'''TFXLMRobertaForMultipleChoice''',
'''TFXLMRobertaForQuestionAnswering''',
'''TFXLMRobertaForSequenceClassification''',
'''TFXLMRobertaForTokenClassification''',
'''TFXLMRobertaModel''',
'''TFXLMRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FlaxXLMRobertaForMaskedLM''',
'''FlaxXLMRobertaForCausalLM''',
'''FlaxXLMRobertaForMultipleChoice''',
'''FlaxXLMRobertaForQuestionAnswering''',
'''FlaxXLMRobertaForSequenceClassification''',
'''FlaxXLMRobertaForTokenClassification''',
'''FlaxXLMRobertaModel''',
'''FlaxXLMRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaConfig,
XLMRobertaOnnxConfig,
)
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta import XLMRobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaForCausalLM,
XLMRobertaForMaskedLM,
XLMRobertaForMultipleChoice,
XLMRobertaForQuestionAnswering,
XLMRobertaForSequenceClassification,
XLMRobertaForTokenClassification,
XLMRobertaModel,
XLMRobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm_roberta import (
TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMRobertaForCausalLM,
TFXLMRobertaForMaskedLM,
TFXLMRobertaForMultipleChoice,
TFXLMRobertaForQuestionAnswering,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TFXLMRobertaModel,
TFXLMRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xlm_roberta import (
FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxXLMRobertaForCausalLM,
FlaxXLMRobertaForMaskedLM,
FlaxXLMRobertaForMultipleChoice,
FlaxXLMRobertaForQuestionAnswering,
FlaxXLMRobertaForSequenceClassification,
FlaxXLMRobertaForTokenClassification,
FlaxXLMRobertaModel,
FlaxXLMRobertaPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 52
| 0
|
def A ( __UpperCamelCase ) -> int:
if not isinstance(__UpperCamelCase , __UpperCamelCase ) or number < 0:
raise ValueError('Input must be a non-negative integer' )
A__ = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 720
|
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
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def A ( __UpperCamelCase ) -> 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 ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> List[Any]:
return max(metric_fn(__UpperCamelCase , __UpperCamelCase ) for gt in ground_truths )
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> List[str]:
A__ = [line.strip() for line in open(__UpperCamelCase , 'r' ).readlines()]
A__ = []
if args.gold_data_mode == "qa":
A__ = pd.read_csv(__UpperCamelCase , sep='\t' , header=__UpperCamelCase )
for answer_list in data[1]:
A__ = ast.literal_eval(__UpperCamelCase )
answers.append(__UpperCamelCase )
else:
A__ = [line.strip() for line in open(__UpperCamelCase , 'r' ).readlines()]
A__ = [[reference] for reference in references]
A__ = A__ = A__ = 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 )
A__ = 100.0 * em / total
A__ = 100.0 * fa / total
logger.info(f'''F1: {fa:.2f}''' )
logger.info(f'''EM: {em:.2f}''' )
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Optional[int]:
A__ = args.k
A__ = [line.strip() for line in open(__UpperCamelCase , 'r' ).readlines()]
A__ = [line.strip() for line in open(__UpperCamelCase , 'r' ).readlines()]
A__ = A__ = 0
for hypo, reference in zip(__UpperCamelCase , __UpperCamelCase ):
A__ = set(hypo.split('\t' )[:k] )
A__ = set(reference.split('\t' ) )
total += 1
em += len(hypo_provenance & ref_provenance ) / k
A__ = 100.0 * em / total
logger.info(f'''Precision@{k}: {em: .2f}''' )
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Union[str, Any]:
def strip_title(__UpperCamelCase ):
if title.startswith('"' ):
A__ = title[1:]
if title.endswith('"' ):
A__ = title[:-1]
return title
A__ = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
__UpperCamelCase , return_tensors='pt' , padding=__UpperCamelCase , truncation=__UpperCamelCase , )['input_ids'].to(args.device )
A__ = rag_model.rag.question_encoder(__UpperCamelCase )
A__ = question_enc_outputs[0]
A__ = 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' , )
A__ = rag_model.retriever.index.get_doc_dicts(result.doc_ids )
A__ = []
for docs in all_docs:
A__ = [strip_title(__UpperCamelCase ) for title in docs['title']]
provenance_strings.append('\t'.join(__UpperCamelCase ) )
return provenance_strings
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Optional[Any]:
with torch.no_grad():
A__ = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
__UpperCamelCase , return_tensors='pt' , padding=__UpperCamelCase , truncation=__UpperCamelCase )
A__ = inputs_dict.input_ids.to(args.device )
A__ = inputs_dict.attention_mask.to(args.device )
A__ = 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]] , )
A__ = 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:
A__ = 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=50 , 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.' , )
A__ = parser.parse_args()
A__ = torch.device('cuda' if torch.cuda.is_available() else 'cpu' )
return args
def A ( __UpperCamelCase ) -> int:
A__ = {}
if args.model_type is None:
A__ = infer_model_type(args.model_name_or_path )
assert args.model_type is not None
if args.model_type.startswith('rag' ):
A__ = RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
A__ = args.n_docs
if args.index_name is not None:
A__ = args.index_name
if args.index_path is not None:
A__ = args.index_path
else:
A__ = BartForConditionalGeneration
A__ = (
[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 )
A__ = get_scores if args.eval_mode == 'e2e' else get_precision_at_k
A__ = 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' ):
A__ = RagRetriever.from_pretrained(__UpperCamelCase , **__UpperCamelCase )
A__ = model_class.from_pretrained(__UpperCamelCase , retriever=__UpperCamelCase , **__UpperCamelCase )
model.retriever.init_retrieval()
else:
A__ = 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:
A__ = []
for line in tqdm(__UpperCamelCase ):
questions.append(line.strip() )
if len(__UpperCamelCase ) == args.eval_batch_size:
A__ = evaluate_batch_fn(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
preds_file.write('\n'.join(__UpperCamelCase ) + '\n' )
preds_file.flush()
A__ = []
if len(__UpperCamelCase ) > 0:
A__ = 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__":
SCREAMING_SNAKE_CASE__ = get_args()
main(args)
| 52
| 0
|
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __get__( self : str , _snake_case : List[str] , _snake_case : List[Any]=None ):
"""simple docstring"""
if obj is None:
return self
if self.fget is None:
raise AttributeError('unreadable attribute' )
A__ = '__cached_' + self.fget.__name__
A__ = getattr(_snake_case , _snake_case , _snake_case )
if cached is None:
A__ = self.fget(_snake_case )
setattr(_snake_case , _snake_case , _snake_case )
return cached
def A ( __UpperCamelCase ) -> Optional[Any]:
A__ = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(f'''invalid truth value {val!r}''' )
def A ( __UpperCamelCase ) -> Any:
if is_torch_fx_proxy(__UpperCamelCase ):
return True
if is_torch_available():
import torch
if isinstance(__UpperCamelCase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(__UpperCamelCase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(__UpperCamelCase , (jnp.ndarray, Tracer) ):
return True
return isinstance(__UpperCamelCase , np.ndarray )
def A ( __UpperCamelCase ) -> Union[str, Any]:
return isinstance(__UpperCamelCase , np.ndarray )
def A ( __UpperCamelCase ) -> List[Any]:
return _is_numpy(__UpperCamelCase )
def A ( __UpperCamelCase ) -> str:
import torch
return isinstance(__UpperCamelCase , torch.Tensor )
def A ( __UpperCamelCase ) -> Any:
return False if not is_torch_available() else _is_torch(__UpperCamelCase )
def A ( __UpperCamelCase ) -> str:
import torch
return isinstance(__UpperCamelCase , torch.device )
def A ( __UpperCamelCase ) -> List[Any]:
return False if not is_torch_available() else _is_torch_device(__UpperCamelCase )
def A ( __UpperCamelCase ) -> List[Any]:
import torch
if isinstance(__UpperCamelCase , __UpperCamelCase ):
if hasattr(__UpperCamelCase , __UpperCamelCase ):
A__ = getattr(__UpperCamelCase , __UpperCamelCase )
else:
return False
return isinstance(__UpperCamelCase , torch.dtype )
def A ( __UpperCamelCase ) -> List[Any]:
return False if not is_torch_available() else _is_torch_dtype(__UpperCamelCase )
def A ( __UpperCamelCase ) -> Any:
import tensorflow as tf
return isinstance(__UpperCamelCase , tf.Tensor )
def A ( __UpperCamelCase ) -> List[Any]:
return False if not is_tf_available() else _is_tensorflow(__UpperCamelCase )
def A ( __UpperCamelCase ) -> Any:
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(__UpperCamelCase , 'is_symbolic_tensor' ):
return tf.is_symbolic_tensor(__UpperCamelCase )
return type(__UpperCamelCase ) == tf.Tensor
def A ( __UpperCamelCase ) -> Any:
return False if not is_tf_available() else _is_tf_symbolic_tensor(__UpperCamelCase )
def A ( __UpperCamelCase ) -> Optional[Any]:
import jax.numpy as jnp # noqa: F811
return isinstance(__UpperCamelCase , jnp.ndarray )
def A ( __UpperCamelCase ) -> str:
return False if not is_flax_available() else _is_jax(__UpperCamelCase )
def A ( __UpperCamelCase ) -> Any:
if isinstance(__UpperCamelCase , (dict, UserDict) ):
return {k: to_py_obj(__UpperCamelCase ) for k, v in obj.items()}
elif isinstance(__UpperCamelCase , (list, tuple) ):
return [to_py_obj(__UpperCamelCase ) for o in obj]
elif is_tf_tensor(__UpperCamelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(__UpperCamelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(__UpperCamelCase ):
return np.asarray(__UpperCamelCase ).tolist()
elif isinstance(__UpperCamelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def A ( __UpperCamelCase ) -> Dict:
if isinstance(__UpperCamelCase , (dict, UserDict) ):
return {k: to_numpy(__UpperCamelCase ) for k, v in obj.items()}
elif isinstance(__UpperCamelCase , (list, tuple) ):
return np.array(__UpperCamelCase )
elif is_tf_tensor(__UpperCamelCase ):
return obj.numpy()
elif is_torch_tensor(__UpperCamelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(__UpperCamelCase ):
return np.asarray(__UpperCamelCase )
else:
return obj
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def _a ( self : Tuple ):
"""simple docstring"""
A__ = fields(self )
# Safety and consistency checks
if not len(_snake_case ):
raise ValueError(F'''{self.__class__.__name__} has no fields.''' )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(F'''{self.__class__.__name__} should not have more than one required field.''' )
A__ = getattr(self , class_fields[0].name )
A__ = all(getattr(self , field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(_snake_case ):
if isinstance(_snake_case , _snake_case ):
A__ = first_field.items()
A__ = True
else:
try:
A__ = iter(_snake_case )
A__ = True
except TypeError:
A__ = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(_snake_case ):
if (
not isinstance(_snake_case , (list, tuple) )
or not len(_snake_case ) == 2
or not isinstance(element[0] , _snake_case )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
A__ = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
F'''Cannot set key/value for {element}. It needs to be a tuple (key, value).''' )
break
setattr(self , element[0] , element[1] )
if element[1] is not None:
A__ = element[1]
elif first_field is not None:
A__ = first_field
else:
for field in class_fields:
A__ = getattr(self , field.name )
if v is not None:
A__ = v
def __delitem__( self : List[Any] , *_snake_case : str , **_snake_case : Optional[Any] ):
"""simple docstring"""
raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.''' )
def _a ( self : Any , *_snake_case : List[str] , **_snake_case : List[str] ):
"""simple docstring"""
raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.''' )
def _a ( self : Optional[int] , *_snake_case : List[Any] , **_snake_case : str ):
"""simple docstring"""
raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.''' )
def _a ( self : Union[str, Any] , *_snake_case : Tuple , **_snake_case : Optional[int] ):
"""simple docstring"""
raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.''' )
def __getitem__( self : Optional[int] , _snake_case : List[Any] ):
"""simple docstring"""
if isinstance(_snake_case , _snake_case ):
A__ = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self : Union[str, Any] , _snake_case : Tuple , _snake_case : int ):
"""simple docstring"""
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(_snake_case , _snake_case )
super().__setattr__(_snake_case , _snake_case )
def __setitem__( self : List[str] , _snake_case : str , _snake_case : Any ):
"""simple docstring"""
super().__setitem__(_snake_case , _snake_case )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(_snake_case , _snake_case )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
return tuple(self[k] for k in self.keys() )
class __lowerCAmelCase ( UpperCAmelCase_ , UpperCAmelCase_ ):
"""simple docstring"""
@classmethod
def _a ( cls : Any , _snake_case : Optional[int] ):
"""simple docstring"""
raise ValueError(
F'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}''' )
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
A__ : Optional[Any] = "longest"
A__ : str = "max_length"
A__ : Dict = "do_not_pad"
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
A__ : int = "pt"
A__ : List[str] = "tf"
A__ : List[Any] = "np"
A__ : Any = "jax"
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : Optional[int] , _snake_case : List[ContextManager] ):
"""simple docstring"""
A__ = context_managers
A__ = ExitStack()
def __enter__( self : Tuple ):
"""simple docstring"""
for context_manager in self.context_managers:
self.stack.enter_context(_snake_case )
def __exit__( self : List[Any] , *_snake_case : Tuple , **_snake_case : str ):
"""simple docstring"""
self.stack.__exit__(*_snake_case , **_snake_case )
def A ( __UpperCamelCase ) -> List[Any]:
A__ = infer_framework(__UpperCamelCase )
if framework == "tf":
A__ = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
A__ = inspect.signature(model_class.forward ) # PyTorch models
else:
A__ = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def A ( __UpperCamelCase ) -> Dict:
A__ = model_class.__name__
A__ = infer_framework(__UpperCamelCase )
if framework == "tf":
A__ = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
A__ = inspect.signature(model_class.forward ) # PyTorch models
else:
A__ = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def A ( __UpperCamelCase , __UpperCamelCase = "" , __UpperCamelCase = "." ) -> Any:
def _flatten_dict(__UpperCamelCase , __UpperCamelCase="" , __UpperCamelCase="." ):
for k, v in d.items():
A__ = str(__UpperCamelCase ) + delimiter + str(__UpperCamelCase ) if parent_key else k
if v and isinstance(__UpperCamelCase , __UpperCamelCase ):
yield from flatten_dict(__UpperCamelCase , __UpperCamelCase , delimiter=__UpperCamelCase ).items()
else:
yield key, v
return dict(_flatten_dict(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) )
@contextmanager
def A ( __UpperCamelCase , __UpperCamelCase = False ) -> int:
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def A ( __UpperCamelCase , __UpperCamelCase=None ) -> List[Any]:
if is_numpy_array(__UpperCamelCase ):
return np.transpose(__UpperCamelCase , axes=__UpperCamelCase )
elif is_torch_tensor(__UpperCamelCase ):
return array.T if axes is None else array.permute(*__UpperCamelCase )
elif is_tf_tensor(__UpperCamelCase ):
import tensorflow as tf
return tf.transpose(__UpperCamelCase , perm=__UpperCamelCase )
elif is_jax_tensor(__UpperCamelCase ):
return jnp.transpose(__UpperCamelCase , axes=__UpperCamelCase )
else:
raise ValueError(f'''Type not supported for transpose: {type(__UpperCamelCase )}.''' )
def A ( __UpperCamelCase , __UpperCamelCase ) -> List[Any]:
if is_numpy_array(__UpperCamelCase ):
return np.reshape(__UpperCamelCase , __UpperCamelCase )
elif is_torch_tensor(__UpperCamelCase ):
return array.reshape(*__UpperCamelCase )
elif is_tf_tensor(__UpperCamelCase ):
import tensorflow as tf
return tf.reshape(__UpperCamelCase , __UpperCamelCase )
elif is_jax_tensor(__UpperCamelCase ):
return jnp.reshape(__UpperCamelCase , __UpperCamelCase )
else:
raise ValueError(f'''Type not supported for reshape: {type(__UpperCamelCase )}.''' )
def A ( __UpperCamelCase , __UpperCamelCase=None ) -> Tuple:
if is_numpy_array(__UpperCamelCase ):
return np.squeeze(__UpperCamelCase , axis=__UpperCamelCase )
elif is_torch_tensor(__UpperCamelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=__UpperCamelCase )
elif is_tf_tensor(__UpperCamelCase ):
import tensorflow as tf
return tf.squeeze(__UpperCamelCase , axis=__UpperCamelCase )
elif is_jax_tensor(__UpperCamelCase ):
return jnp.squeeze(__UpperCamelCase , axis=__UpperCamelCase )
else:
raise ValueError(f'''Type not supported for squeeze: {type(__UpperCamelCase )}.''' )
def A ( __UpperCamelCase , __UpperCamelCase ) -> List[Any]:
if is_numpy_array(__UpperCamelCase ):
return np.expand_dims(__UpperCamelCase , __UpperCamelCase )
elif is_torch_tensor(__UpperCamelCase ):
return array.unsqueeze(dim=__UpperCamelCase )
elif is_tf_tensor(__UpperCamelCase ):
import tensorflow as tf
return tf.expand_dims(__UpperCamelCase , axis=__UpperCamelCase )
elif is_jax_tensor(__UpperCamelCase ):
return jnp.expand_dims(__UpperCamelCase , axis=__UpperCamelCase )
else:
raise ValueError(f'''Type not supported for expand_dims: {type(__UpperCamelCase )}.''' )
def A ( __UpperCamelCase ) -> Optional[Any]:
if is_numpy_array(__UpperCamelCase ):
return np.size(__UpperCamelCase )
elif is_torch_tensor(__UpperCamelCase ):
return array.numel()
elif is_tf_tensor(__UpperCamelCase ):
import tensorflow as tf
return tf.size(__UpperCamelCase )
elif is_jax_tensor(__UpperCamelCase ):
return array.size
else:
raise ValueError(f'''Type not supported for expand_dims: {type(__UpperCamelCase )}.''' )
def A ( __UpperCamelCase , __UpperCamelCase ) -> Tuple:
for key, value in auto_map.items():
if isinstance(__UpperCamelCase , (tuple, list) ):
A__ = [f'''{repo_id}--{v}''' if (v is not None and '--' not in v) else v for v in value]
elif value is not None and "--" not in value:
A__ = f'''{repo_id}--{value}'''
return auto_map
def A ( __UpperCamelCase ) -> Optional[int]:
for base_class in inspect.getmro(__UpperCamelCase ):
A__ = base_class.__module__
A__ = base_class.__name__
if module.startswith('tensorflow' ) or module.startswith('keras' ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith('torch' ) or name == "PreTrainedModel":
return "pt"
elif module.startswith('flax' ) or module.startswith('jax' ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(f'''Could not infer framework from class {model_class}.''' )
| 721
|
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : List[Any] , _snake_case : Any , _snake_case : Optional[int]=13 , _snake_case : Optional[Any]=64 , _snake_case : List[str]=2 , _snake_case : Any=3 , _snake_case : Union[str, Any]=True , _snake_case : Dict=True , _snake_case : int=32 , _snake_case : int=5 , _snake_case : Union[str, Any]=4 , _snake_case : int=37 , _snake_case : Tuple="gelu" , _snake_case : Optional[int]=0.1 , _snake_case : Dict=0.1 , _snake_case : List[str]=10 , _snake_case : Union[str, Any]=0.02 , _snake_case : Dict=[1, 16, 4, 4] , _snake_case : Dict=None , ):
"""simple docstring"""
A__ = parent
A__ = batch_size
A__ = image_size
A__ = patch_size
A__ = num_channels
A__ = is_training
A__ = use_labels
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = type_sequence_label_size
A__ = initializer_range
A__ = scope
A__ = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
A__ = (self.image_size // 32) ** 2
A__ = num_patches + 1
def _a ( self : Any ):
"""simple docstring"""
A__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A__ = self.get_config()
return config, pixel_values, labels
def _a ( self : Tuple ):
"""simple docstring"""
A__ = {
'global_padding': 'same',
'layer_type': 'bottleneck',
'depths': [3, 4, 9],
'out_features': ['stage1', 'stage2', 'stage3'],
'embedding_dynamic_padding': True,
'hidden_sizes': [4, 8, 16, 32],
'num_groups': 2,
}
return ViTHybridConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_snake_case , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=_snake_case , )
def _a ( self : int , _snake_case : Optional[int] , _snake_case : Union[str, Any] , _snake_case : Optional[int] ):
"""simple docstring"""
A__ = ViTHybridModel(config=_snake_case )
model.to(_snake_case )
model.eval()
A__ = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : List[str] , _snake_case : str , _snake_case : Union[str, Any] , _snake_case : Any ):
"""simple docstring"""
A__ = self.type_sequence_label_size
A__ = ViTHybridForImageClassification(_snake_case )
model.to(_snake_case )
model.eval()
A__ = model(_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _a ( self : Dict ):
"""simple docstring"""
A__ = self.prepare_config_and_inputs()
A__ , A__ , A__ = config_and_inputs
A__ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
A__ : Union[str, Any] = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
A__ : str = (
{"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
if is_torch_available()
else {}
)
A__ : Union[str, Any] = False
A__ : Any = False
A__ : Union[str, Any] = False
def _a ( self : Dict ):
"""simple docstring"""
A__ = ViTHybridModelTester(self )
A__ = ConfigTester(self , config_class=_snake_case , has_text_modality=_snake_case , hidden_size=37 )
def _a ( self : int ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='ViT does not use inputs_embeds' )
def _a ( self : int ):
"""simple docstring"""
pass
def _a ( self : int ):
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(_snake_case )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_snake_case , nn.Linear ) )
def _a ( self : List[str] ):
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(_snake_case )
A__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A__ = [*signature.parameters.keys()]
A__ = ['pixel_values']
self.assertListEqual(arg_names[:1] , _snake_case )
def _a ( self : Any ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def _a ( self : str ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_snake_case )
def _a ( self : Any ):
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = _config_zero_init(_snake_case )
for model_class in self.all_model_classes:
A__ = model_class(config=_snake_case )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
A__ = [F'''{name}.{key}''' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@slow
def _a ( self : int ):
"""simple docstring"""
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = ViTHybridModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
def A ( ) -> Union[str, Any]:
A__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _a ( self : Tuple ):
"""simple docstring"""
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def _a ( self : Optional[Any] ):
"""simple docstring"""
A__ = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
_snake_case )
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(images=_snake_case , return_tensors='pt' ).to(_snake_case )
# forward pass
with torch.no_grad():
A__ = model(**_snake_case )
# verify the logits
A__ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , _snake_case )
A__ = torch.tensor([-1.9090, -0.4993, -0.2389] ).to(_snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _snake_case , atol=1E-4 ) )
@slow
@require_accelerate
def _a ( self : List[Any] ):
"""simple docstring"""
A__ = ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384' )
A__ = ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384' , device_map='auto' )
A__ = prepare_img()
A__ = image_processor(images=_snake_case , return_tensors='pt' )
A__ = model(**_snake_case )
A__ = outputs.logits
# model predicts one of the 1000 ImageNet classes
A__ = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx] , 'tabby, tabby cat' )
| 52
| 0
|
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = ["""a""", """b""", """c"""]
# Defaults to last layer if both are None
__lowercase , __lowercase = get_aligned_output_features_output_indices(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
self.assertEqual(_lowerCAmelCase , ["""c"""] )
self.assertEqual(_lowerCAmelCase , [2] )
# Out indices set to match out features
__lowercase , __lowercase = get_aligned_output_features_output_indices(["""a""", """c"""] , _lowerCAmelCase , _lowerCAmelCase )
self.assertEqual(_lowerCAmelCase , ["""a""", """c"""] )
self.assertEqual(_lowerCAmelCase , [0, 2] )
# Out features set to match out indices
__lowercase , __lowercase = get_aligned_output_features_output_indices(_lowerCAmelCase , [0, 2] , _lowerCAmelCase )
self.assertEqual(_lowerCAmelCase , ["""a""", """c"""] )
self.assertEqual(_lowerCAmelCase , [0, 2] )
# Out features selected from negative indices
__lowercase , __lowercase = get_aligned_output_features_output_indices(_lowerCAmelCase , [-3, -1] , _lowerCAmelCase )
self.assertEqual(_lowerCAmelCase , ["""a""", """c"""] )
self.assertEqual(_lowerCAmelCase , [-3, -1] )
def _a ( self : str ) -> int:
"""simple docstring"""
with self.assertRaises(_lowerCAmelCase ):
verify_out_features_out_indices(["""a""", """b"""] , (0, 1) , _lowerCAmelCase )
# Out features must be a list
with self.assertRaises(_lowerCAmelCase ):
verify_out_features_out_indices(("""a""", """b""") , (0, 1) , ["""a""", """b"""] )
# Out features must be a subset of stage names
with self.assertRaises(_lowerCAmelCase ):
verify_out_features_out_indices(["""a""", """b"""] , (0, 1) , ["""a"""] )
# Out indices must be a list or tuple
with self.assertRaises(_lowerCAmelCase ):
verify_out_features_out_indices(_lowerCAmelCase , 0 , ["""a""", """b"""] )
# Out indices must be a subset of stage names
with self.assertRaises(_lowerCAmelCase ):
verify_out_features_out_indices(_lowerCAmelCase , (0, 1) , ["""a"""] )
# Out features and out indices must be the same length
with self.assertRaises(_lowerCAmelCase ):
verify_out_features_out_indices(["""a""", """b"""] , (0,) , ["""a""", """b""", """c"""] )
# Out features should match out indices
with self.assertRaises(_lowerCAmelCase ):
verify_out_features_out_indices(["""a""", """b"""] , (0, 2) , ["""a""", """b""", """c"""] )
# Out features and out indices should be in order
with self.assertRaises(_lowerCAmelCase ):
verify_out_features_out_indices(["""b""", """a"""] , (0, 1) , ["""a""", """b"""] )
# Check passes with valid inputs
verify_out_features_out_indices(["""a""", """b""", """d"""] , (0, 1, -1) , ["""a""", """b""", """c""", """d"""] )
def _a ( self : Tuple ) -> List[Any]:
"""simple docstring"""
__lowercase = BackboneMixin()
__lowercase = ["""a""", """b""", """c"""]
__lowercase = ["""a""", """c"""]
__lowercase = [0, 2]
# Check that the output features and indices are set correctly
self.assertEqual(backbone.out_features , ["""a""", """c"""] )
self.assertEqual(backbone.out_indices , [0, 2] )
# Check out features and indices are updated correctly
__lowercase = ["""a""", """b"""]
self.assertEqual(backbone.out_features , ["""a""", """b"""] )
self.assertEqual(backbone.out_indices , [0, 1] )
__lowercase = [-3, -1]
self.assertEqual(backbone.out_features , ["""a""", """c"""] )
self.assertEqual(backbone.out_indices , [-3, -1] )
| 53
|
import heapq
import sys
import numpy as np
__UpperCamelCase : List[str] = tuple[int, int]
class __UpperCamelCase :
def __init__( self : Optional[int] ) -> Dict:
"""simple docstring"""
__lowercase = []
__lowercase = set()
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
if not self.empty():
return self.elements[0][0]
else:
return float("""inf""" )
def _a ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
return len(self.elements ) == 0
def _a ( self : str , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
if item not in self.set:
heapq.heappush(self.elements , (priority, item) )
self.set.add(_lowerCAmelCase )
else:
# update
# print("update", item)
__lowercase = []
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
while x != item:
temp.append((pri, x) )
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
temp.append((priority, item) )
for pro, xxx in temp:
heapq.heappush(self.elements , (pro, xxx) )
def _a ( self : List[str] , _lowerCAmelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
if item in self.set:
self.set.remove(_lowerCAmelCase )
__lowercase = []
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
while x != item:
temp.append((pro, x) )
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
for prito, yyy in temp:
heapq.heappush(self.elements , (prito, yyy) )
def _a ( self : Any ) -> List[Any]:
"""simple docstring"""
return self.elements[0][1]
def _a ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
self.set.remove(_lowerCAmelCase )
return (priority, item)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = np.array(lowerCamelCase )
__lowercase = np.array(lowerCamelCase )
return np.linalg.norm(a - b )
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return consistent_heuristic(lowerCamelCase , lowerCamelCase ) // t
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return abs(p[0] - goal[0] ) + abs(p[1] - goal[1] )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = g_function[start] + Wa * heuristics[i](lowerCamelCase , lowerCamelCase )
return ans
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = np.chararray((n, n) )
for i in range(lowerCamelCase ):
for j in range(lowerCamelCase ):
__lowercase = """*"""
for i in range(lowerCamelCase ):
for j in range(lowerCamelCase ):
if (j, (n - 1) - i) in blocks:
__lowercase = """#"""
__lowercase = """-"""
__lowercase = back_pointer[goal]
while x != start:
((__lowercase) , (__lowercase)) = x
# print(x)
__lowercase = """-"""
__lowercase = back_pointer[x]
__lowercase = """-"""
for i in range(lowerCamelCase ):
for j in range(lowerCamelCase ):
if (i, j) == (0, n - 1):
print(grid[i][j] , end=""" """ )
print("""<-- End position""" , end=""" """ )
else:
print(grid[i][j] , end=""" """ )
print()
print("""^""" )
print("""Start position""" )
print()
print("""# is an obstacle""" )
print("""- is the path taken by algorithm""" )
print("""PATH TAKEN BY THE ALGORITHM IS:-""" )
__lowercase = back_pointer[goal]
while x != start:
print(lowerCamelCase , end=""" """ )
__lowercase = back_pointer[x]
print(lowerCamelCase )
sys.exit()
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if p[0] < 0 or p[0] > n - 1:
return False
if p[1] < 0 or p[1] > n - 1:
return False
return True
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , ):
'''simple docstring'''
for itera in range(lowerCamelCase ):
open_list[itera].remove_element(lowerCamelCase )
# print("s", s)
# print("j", j)
((__lowercase) , (__lowercase)) = s
__lowercase = (x - 1, y)
__lowercase = (x + 1, y)
__lowercase = (x, y + 1)
__lowercase = (x, y - 1)
for neighbours in [left, right, up, down]:
if neighbours not in blocks:
if valid(lowerCamelCase ) and neighbours not in visited:
# print("neighbour", neighbours)
visited.add(lowerCamelCase )
__lowercase = -1
__lowercase = float("""inf""" )
if valid(lowerCamelCase ) and g_function[neighbours] > g_function[s] + 1:
__lowercase = g_function[s] + 1
__lowercase = s
if neighbours not in close_list_anchor:
open_list[0].put(lowerCamelCase , key(lowerCamelCase , 0 , lowerCamelCase , lowerCamelCase ) )
if neighbours not in close_list_inad:
for var in range(1 , lowerCamelCase ):
if key(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) <= Wa * key(
lowerCamelCase , 0 , lowerCamelCase , lowerCamelCase ):
open_list[j].put(
lowerCamelCase , key(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) )
def snake_case ( ):
'''simple docstring'''
__lowercase = []
for x in range(1 , 5 ):
for y in range(1 , 6 ):
some_list.append((x, y) )
for x in range(15 , 20 ):
some_list.append((x, 17) )
for x in range(10 , 19 ):
for y in range(1 , 15 ):
some_list.append((x, y) )
# L block
for x in range(1 , 4 ):
for y in range(12 , 19 ):
some_list.append((x, y) )
for x in range(3 , 13 ):
for y in range(16 , 19 ):
some_list.append((x, y) )
return some_list
__UpperCamelCase : Optional[int] = {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}
__UpperCamelCase : Optional[Any] = [
(0, 1),
(1, 1),
(2, 1),
(3, 1),
(4, 1),
(5, 1),
(6, 1),
(7, 1),
(8, 1),
(9, 1),
(10, 1),
(11, 1),
(12, 1),
(13, 1),
(14, 1),
(15, 1),
(16, 1),
(17, 1),
(18, 1),
(19, 1),
]
__UpperCamelCase : Optional[Any] = make_common_ground()
__UpperCamelCase : Dict = blocks_blk
# hyper parameters
__UpperCamelCase : Union[str, Any] = 1
__UpperCamelCase : Union[str, Any] = 1
__UpperCamelCase : Optional[int] = 20
__UpperCamelCase : List[str] = 3 # one consistent and two other inconsistent
# start and end destination
__UpperCamelCase : str = (0, 0)
__UpperCamelCase : str = (n - 1, n - 1)
__UpperCamelCase : Optional[Any] = 1
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = {start: 0, goal: float("""inf""" )}
__lowercase = {start: -1, goal: -1}
__lowercase = []
__lowercase = set()
for i in range(lowerCamelCase ):
open_list.append(PriorityQueue() )
open_list[i].put(lowerCamelCase , key(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) )
__lowercase = []
__lowercase = []
while open_list[0].minkey() < float("""inf""" ):
for i in range(1 , lowerCamelCase ):
# print(open_list[0].minkey(), open_list[i].minkey())
if open_list[i].minkey() <= Wa * open_list[0].minkey():
global t
t += 1
if g_function[goal] <= open_list[i].minkey():
if g_function[goal] < float("""inf""" ):
do_something(lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
__lowercase , __lowercase = open_list[i].top_show()
visited.add(lowerCamelCase )
expand_state(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , )
close_list_inad.append(lowerCamelCase )
else:
if g_function[goal] <= open_list[0].minkey():
if g_function[goal] < float("""inf""" ):
do_something(lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
__lowercase = open_list[0].top_show()
visited.add(lowerCamelCase )
expand_state(
lowerCamelCase , 0 , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , )
close_list_anchor.append(lowerCamelCase )
print("""No path found to goal""" )
print()
for i in range(n - 1 , -1 , -1 ):
for j in range(lowerCamelCase ):
if (j, i) in blocks:
print("""#""" , end=""" """ )
elif (j, i) in back_pointer:
if (j, i) == (n - 1, n - 1):
print("""*""" , end=""" """ )
else:
print("""-""" , end=""" """ )
else:
print("""*""" , end=""" """ )
if (j, i) == (n - 1, n - 1):
print("""<-- End position""" , end=""" """ )
print()
print("""^""" )
print("""Start position""" )
print()
print("""# is an obstacle""" )
print("""- is the path taken by algorithm""" )
if __name__ == "__main__":
multi_a_star(start, goal, n_heuristic)
| 53
| 1
|
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
__UpperCamelCase : int = logging.get_logger(__name__)
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : Any , *_lowerCAmelCase : Dict , **_lowerCAmelCase : Union[str, Any] ) -> None:
"""simple docstring"""
warnings.warn(
"""The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use FlavaImageProcessor instead.""" , _lowerCAmelCase , )
super().__init__(*_lowerCAmelCase , **_lowerCAmelCase )
| 53
|
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = SwinConfig.from_pretrained(
"""microsoft/swin-tiny-patch4-window7-224""" , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
__lowercase = MaskFormerConfig(backbone_config=lowerCamelCase )
__lowercase = """huggingface/label-files"""
if "ade20k-full" in model_name:
# this should be ok
__lowercase = 847
__lowercase = """maskformer-ade20k-full-id2label.json"""
elif "ade" in model_name:
# this should be ok
__lowercase = 150
__lowercase = """ade20k-id2label.json"""
elif "coco-stuff" in model_name:
# this should be ok
__lowercase = 171
__lowercase = """maskformer-coco-stuff-id2label.json"""
elif "coco" in model_name:
# TODO
__lowercase = 133
__lowercase = """coco-panoptic-id2label.json"""
elif "cityscapes" in model_name:
# this should be ok
__lowercase = 19
__lowercase = """cityscapes-id2label.json"""
elif "vistas" in model_name:
# this should be ok
__lowercase = 65
__lowercase = """mapillary-vistas-id2label.json"""
__lowercase = json.load(open(hf_hub_download(lowerCamelCase , lowerCamelCase , repo_type="""dataset""" ) , """r""" ) )
__lowercase = {int(lowerCamelCase ): v for k, v in idalabel.items()}
return config
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = []
# stem
# fmt: off
rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""") )
rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""") )
rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""") )
rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias') )
if i < 3:
rename_keys.append((F'backbone.layers.{i}.downsample.reduction.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight') )
rename_keys.append((F'backbone.layers.{i}.downsample.norm.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight') )
rename_keys.append((F'backbone.layers.{i}.downsample.norm.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias') )
rename_keys.append((F'backbone.norm{i}.weight', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight') )
rename_keys.append((F'backbone.norm{i}.bias', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias') )
# FPN
rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""") )
rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""") )
rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""") )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight') )
rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight') )
rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias') )
rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""") )
rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""") )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias') )
# cross-attention out projection
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias') )
# MLP 1
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias') )
# MLP 2
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias') )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias') )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias') )
# layernorm 3 (final layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias') )
rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""") )
rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""") )
# heads on top
rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""") )
rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""") )
rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""") )
rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""") )
rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""") )
for i in range(3 ):
rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight') )
rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias') )
# fmt: on
return rename_keys
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = dct.pop(lowerCamelCase )
__lowercase = val
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
__lowercase = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
__lowercase = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight' )
__lowercase = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
__lowercase = in_proj_weight[:dim, :]
__lowercase = in_proj_bias[: dim]
__lowercase = in_proj_weight[
dim : dim * 2, :
]
__lowercase = in_proj_bias[
dim : dim * 2
]
__lowercase = in_proj_weight[
-dim :, :
]
__lowercase = in_proj_bias[-dim :]
# fmt: on
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
__lowercase = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight' )
__lowercase = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
__lowercase = in_proj_weight[: hidden_size, :]
__lowercase = in_proj_bias[:config.hidden_size]
__lowercase = in_proj_weight[hidden_size : hidden_size * 2, :]
__lowercase = in_proj_bias[hidden_size : hidden_size * 2]
__lowercase = in_proj_weight[-hidden_size :, :]
__lowercase = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
__lowercase = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight' )
__lowercase = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
__lowercase = in_proj_weight[: hidden_size, :]
__lowercase = in_proj_bias[:config.hidden_size]
__lowercase = in_proj_weight[hidden_size : hidden_size * 2, :]
__lowercase = in_proj_bias[hidden_size : hidden_size * 2]
__lowercase = in_proj_weight[-hidden_size :, :]
__lowercase = in_proj_bias[-hidden_size :]
# fmt: on
def snake_case ( ):
'''simple docstring'''
__lowercase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__lowercase = Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase ).raw )
return im
@torch.no_grad()
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = False ):
'''simple docstring'''
__lowercase = get_maskformer_config(lowerCamelCase )
# load original state_dict
with open(lowerCamelCase , """rb""" ) as f:
__lowercase = pickle.load(lowerCamelCase )
__lowercase = data["""model"""]
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
__lowercase = create_rename_keys(lowerCamelCase )
for src, dest in rename_keys:
rename_key(lowerCamelCase , lowerCamelCase , lowerCamelCase )
read_in_swin_q_k_v(lowerCamelCase , config.backbone_config )
read_in_decoder_q_k_v(lowerCamelCase , lowerCamelCase )
# update to torch tensors
for key, value in state_dict.items():
__lowercase = torch.from_numpy(lowerCamelCase )
# load 🤗 model
__lowercase = MaskFormerForInstanceSegmentation(lowerCamelCase )
model.eval()
for name, param in model.named_parameters():
print(lowerCamelCase , param.shape )
__lowercase , __lowercase = model.load_state_dict(lowerCamelCase , strict=lowerCamelCase )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(lowerCamelCase ) == 0, F'Unexpected keys: {unexpected_keys}'
# verify results
__lowercase = prepare_img()
if "vistas" in model_name:
__lowercase = 65
elif "cityscapes" in model_name:
__lowercase = 65_535
else:
__lowercase = 255
__lowercase = True if """ade""" in model_name else False
__lowercase = MaskFormerImageProcessor(ignore_index=lowerCamelCase , reduce_labels=lowerCamelCase )
__lowercase = image_processor(lowerCamelCase , return_tensors="""pt""" )
__lowercase = model(**lowerCamelCase )
print("""Logits:""" , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
__lowercase = torch.tensor(
[[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , lowerCamelCase , atol=1e-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F'Saving model and image processor to {pytorch_dump_folder_path}' )
Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
model.save_pretrained(lowerCamelCase )
image_processor.save_pretrained(lowerCamelCase )
if push_to_hub:
print("""Pushing model and image processor to the hub...""" )
model.push_to_hub(F'nielsr/{model_name}' )
image_processor.push_to_hub(F'nielsr/{model_name}' )
if __name__ == "__main__":
__UpperCamelCase : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""maskformer-swin-tiny-ade""",
type=str,
help=("""Name of the MaskFormer model you'd like to convert""",),
)
parser.add_argument(
"""--checkpoint_path""",
default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
type=str,
help="""Path to the original state dict (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
__UpperCamelCase : List[Any] = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 53
| 1
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase : str = logging.get_logger(__name__)
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = """huggingface/label-files"""
__lowercase = """imagenet-1k-id2label.json"""
__lowercase = json.load(open(hf_hub_download(lowerCamelCase , lowerCamelCase , repo_type="""dataset""" ) , """r""" ) )
__lowercase = {int(lowerCamelCase ): v for k, v in idalabel.items()}
__lowercase = {v: k for k, v in idalabel.items()}
__lowercase = """std_conv""" if """bit""" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
__lowercase = BitConfig(
conv_layer=lowerCamelCase , num_labels=1_000 , idalabel=lowerCamelCase , labelaid=lowerCamelCase , )
return config
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if "stem.conv" in name:
__lowercase = name.replace("""stem.conv""" , """bit.embedder.convolution""" )
if "blocks" in name:
__lowercase = name.replace("""blocks""" , """layers""" )
if "head.fc" in name:
__lowercase = name.replace("""head.fc""" , """classifier.1""" )
if name.startswith("""norm""" ):
__lowercase = """bit.""" + name
if "bit" not in name and "classifier" not in name:
__lowercase = """bit.encoder.""" + name
return name
def snake_case ( ):
'''simple docstring'''
__lowercase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__lowercase = Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase ).raw )
return im
@torch.no_grad()
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase=False ):
'''simple docstring'''
__lowercase = get_config(lowerCamelCase )
# load original model from timm
__lowercase = create_model(lowerCamelCase , pretrained=lowerCamelCase )
timm_model.eval()
# load state_dict of original model
__lowercase = timm_model.state_dict()
for key in state_dict.copy().keys():
__lowercase = state_dict.pop(lowerCamelCase )
__lowercase = val.squeeze() if """head""" in key else val
# load HuggingFace model
__lowercase = BitForImageClassification(lowerCamelCase )
model.eval()
model.load_state_dict(lowerCamelCase )
# create image processor
__lowercase = create_transform(**resolve_data_config({} , model=lowerCamelCase ) )
__lowercase = transform.transforms
__lowercase = {
"""bilinear""": PILImageResampling.BILINEAR,
"""bicubic""": PILImageResampling.BICUBIC,
"""nearest""": PILImageResampling.NEAREST,
}
__lowercase = BitImageProcessor(
do_resize=lowerCamelCase , size={"""shortest_edge""": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=lowerCamelCase , crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]} , do_normalize=lowerCamelCase , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
__lowercase = prepare_img()
__lowercase = transform(lowerCamelCase ).unsqueeze(0 )
__lowercase = processor(lowerCamelCase , return_tensors="""pt""" ).pixel_values
# verify pixel values
assert torch.allclose(lowerCamelCase , lowerCamelCase )
# verify logits
with torch.no_grad():
__lowercase = model(lowerCamelCase )
__lowercase = outputs.logits
print("""Logits:""" , logits[0, :3] )
print("""Predicted class:""" , model.config.idalabel[logits.argmax(-1 ).item()] )
__lowercase = timm_model(lowerCamelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(lowerCamelCase , outputs.logits , atol=1e-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}' )
model.save_pretrained(lowerCamelCase )
processor.save_pretrained(lowerCamelCase )
if push_to_hub:
print(F'Pushing model {model_name} and processor to the hub' )
model.push_to_hub(F'ybelkada/{model_name}' )
processor.push_to_hub(F'ybelkada/{model_name}' )
if __name__ == "__main__":
__UpperCamelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""resnetv2_50x1_bitm""",
type=str,
help="""Name of the BiT timm model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether to push the model to the hub.""",
)
__UpperCamelCase : str = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 53
|
from math import sqrt
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(lowerCamelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def snake_case ( lowerCamelCase = 10_001 ):
'''simple docstring'''
__lowercase = 0
__lowercase = 1
while count != nth and number < 3:
number += 1
if is_prime(lowerCamelCase ):
count += 1
while count != nth:
number += 2
if is_prime(lowerCamelCase ):
count += 1
return number
if __name__ == "__main__":
print(F'''{solution() = }''')
| 53
| 1
|
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCamelCase : Dict = {"""vocab_file""": """vocab.txt""", """emoji_file""": """emoji.json"""}
__UpperCamelCase : str = {
"""vocab_file""": {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt""",
},
"""emoji_file""": {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json""",
},
}
__UpperCamelCase : Dict = {
"""abeja/gpt-neox-japanese-2.7b""": 2048,
}
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
with open(lowerCamelCase , """r""" , encoding="""utf-8""" ) as f:
__lowercase = json.loads(f.read() )
__lowercase = collections.OrderedDict()
__lowercase = collections.OrderedDict()
__lowercase = collections.OrderedDict()
with open(lowerCamelCase , """r""" , encoding="""utf-8""" ) as f:
__lowercase = f.readlines()
__lowercase = [[t.rstrip("""\n""" )] if (t == """,""" or """,""" not in t) else t.rstrip("""\n""" ).split(""",""" ) for t in token]
for idx, b in enumerate(lowerCamelCase ):
__lowercase = b
__lowercase = idx
for wd in b:
__lowercase = idx
return vocab, raw_vocab, ids_to_tokens, emoji
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Optional[int] = VOCAB_FILES_NAMES
__snake_case :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
__snake_case :List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case :Union[str, Any] = ['input_ids', 'attention_mask']
def __init__( self : Tuple , _lowerCAmelCase : int , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict="<|endoftext|>" , _lowerCAmelCase : Union[str, Any]="<|endoftext|>" , _lowerCAmelCase : Union[str, Any]="<|startoftext|>" , _lowerCAmelCase : Any="<|endoftext|>" , _lowerCAmelCase : Union[str, Any]=False , **_lowerCAmelCase : Dict , ) -> int:
"""simple docstring"""
super().__init__(
unk_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , do_clean_text=_lowerCAmelCase , **_lowerCAmelCase , )
if not os.path.isfile(_lowerCAmelCase ):
raise ValueError(
F'Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'
""" model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`""" )
if not os.path.isfile(_lowerCAmelCase ):
raise ValueError(
F'Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'
""" pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`""" )
__lowercase = do_clean_text
__lowercase , __lowercase , __lowercase , __lowercase = load_vocab_and_emoji(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = SubWordJapaneseTokenizer(
vocab=self.vocab , ids_to_tokens=self.ids_to_tokens , emoji=self.emoji )
@property
def _a ( self : List[str] ) -> Dict:
"""simple docstring"""
return len(self.raw_vocab )
def _a ( self : List[str] ) -> int:
"""simple docstring"""
return dict(self.raw_vocab , **self.added_tokens_encoder )
def _a ( self : Any , _lowerCAmelCase : List[Any] ) -> Any:
"""simple docstring"""
return self.subword_tokenizer.tokenize(_lowerCAmelCase , clean=self.do_clean_text )
def _a ( self : Tuple , _lowerCAmelCase : int ) -> List[Any]:
"""simple docstring"""
return self.vocab.get(_lowerCAmelCase , self.vocab.get(self.unk_token ) )
def _a ( self : str , _lowerCAmelCase : Union[str, Any] ) -> str:
"""simple docstring"""
return self.subword_tokenizer.convert_id_to_token(_lowerCAmelCase )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = """""".join(_lowerCAmelCase ).strip()
return out_string
def _a ( self : Tuple , _lowerCAmelCase : "Conversation" ) -> List[int]:
"""simple docstring"""
__lowercase = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(_lowerCAmelCase , add_special_tokens=_lowerCAmelCase ) + [self.eos_token_id] )
if len(_lowerCAmelCase ) > self.model_max_length:
__lowercase = input_ids[-self.model_max_length :]
return input_ids
def _a ( self : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
__lowercase = 0
if os.path.isdir(_lowerCAmelCase ):
__lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
__lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""emoji_file"""] )
else:
__lowercase = (
(filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""vocab_file"""]
)
__lowercase = (
(filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""emoji_file"""]
)
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as writer:
for token_index, token in self.ids_to_tokens.items():
if index != token_index:
logger.warning(
F'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
""" Please check that the vocabulary is not corrupted!""" )
__lowercase = token_index
writer.write(""",""".join(_lowerCAmelCase ) + """\n""" )
index += 1
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as writer:
json.dump(self.emoji , _lowerCAmelCase )
return vocab_file, emoji_file
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : Dict , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Any ) -> List[Any]:
"""simple docstring"""
__lowercase = vocab # same as swe
__lowercase = ids_to_tokens # same as bpe
__lowercase = emoji
__lowercase = np.max([len(_lowerCAmelCase ) for w in self.vocab.keys()] )
__lowercase = re.compile(r"""(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)""" )
__lowercase = re.compile(r"""[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*""" )
__lowercase = re.compile(r"""[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}""" )
__lowercase = re.compile(
r"""([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*""" )
__lowercase = re.compile(
r"""(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*""" )
__lowercase = re.compile(
r"""((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*""" )
__lowercase = """─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"""
__lowercase = """▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"""
__lowercase = str.maketrans({k: """<BLOCK>""" for k in keisen + blocks} )
def __len__( self : Any ) -> Optional[Any]:
"""simple docstring"""
return len(self.ids_to_tokens )
def _a ( self : Union[str, Any] , _lowerCAmelCase : List[str] ) -> int:
"""simple docstring"""
__lowercase = self.content_repattera.sub("""<URL>""" , _lowerCAmelCase )
__lowercase = self.content_repattera.sub("""<EMAIL>""" , _lowerCAmelCase )
__lowercase = self.content_repattera.sub("""<TEL>""" , _lowerCAmelCase )
__lowercase = self.content_repattera.sub("""<DATE>""" , _lowerCAmelCase )
__lowercase = self.content_repattera.sub("""<DATE>""" , _lowerCAmelCase )
__lowercase = self.content_repattera.sub("""<PRICE>""" , _lowerCAmelCase )
__lowercase = content.translate(self.content_transa )
while "<BLOCK><BLOCK>" in content:
__lowercase = content.replace("""<BLOCK><BLOCK>""" , """<BLOCK>""" )
return content
def _a ( self : List[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any]=False ) -> Optional[Any]:
"""simple docstring"""
__lowercase = text.replace(""" """ , """<SP>""" )
__lowercase = text.replace(""" """ , """<SP>""" )
__lowercase = text.replace("""\r\n""" , """<BR>""" )
__lowercase = text.replace("""\n""" , """<BR>""" )
__lowercase = text.replace("""\r""" , """<BR>""" )
__lowercase = text.replace("""\t""" , """<TAB>""" )
__lowercase = text.replace("""—""" , """ー""" )
__lowercase = text.replace("""−""" , """ー""" )
for k, v in self.emoji["emoji"].items():
if k in text:
__lowercase = text.replace(_lowerCAmelCase , _lowerCAmelCase )
if clean:
__lowercase = self.clean_text(_lowerCAmelCase )
def check_simbol(_lowerCAmelCase : Union[str, Any] ):
__lowercase = x.encode()
if len(_lowerCAmelCase ) == 1 and len(_lowerCAmelCase ) == 2:
__lowercase = (int(e[0] ) << 8) + int(e[1] )
if (
(c >= 0Xc2a1 and c <= 0Xc2bf)
or (c >= 0Xc780 and c <= 0Xc783)
or (c >= 0Xcab9 and c <= 0Xcbbf)
or (c >= 0Xcc80 and c <= 0Xcda2)
):
return True
return False
def checkuae(_lowerCAmelCase : Tuple ):
__lowercase = x.encode()
if len(_lowerCAmelCase ) == 1 and len(_lowerCAmelCase ) == 3:
__lowercase = (int(e[0] ) << 16) + (int(e[1] ) << 8) + int(e[2] )
if c >= 0Xe2_8080 and c <= 0Xe2_b07f:
return True
return False
__lowercase = 0
__lowercase = []
while pos < len(_lowerCAmelCase ):
__lowercase = min(len(_lowerCAmelCase ) , pos + self.maxlen + 1 ) if text[pos] == """<""" else pos + 3
__lowercase = [] # (token_id, token, pos)
for e in range(_lowerCAmelCase , _lowerCAmelCase , -1 ):
__lowercase = text[pos:e]
if wd in self.vocab:
if wd[0] == "<" and len(_lowerCAmelCase ) > 2:
__lowercase = [(self.vocab[wd], wd, e)]
break
else:
candidates.append((self.vocab[wd], wd, e) )
if len(_lowerCAmelCase ) > 0:
# the smallest token_id is adopted
__lowercase , __lowercase , __lowercase = sorted(_lowerCAmelCase , key=lambda _lowerCAmelCase : x[0] )[0]
result.append(_lowerCAmelCase )
__lowercase = e
else:
__lowercase = pos + 1
__lowercase = text[pos:end]
if check_simbol(_lowerCAmelCase ):
result.append("""<KIGOU>""" )
elif checkuae(_lowerCAmelCase ):
result.append("""<U2000U2BFF>""" )
else:
for i in wd.encode("""utf-8""" ):
result.append("""<|byte%d|>""" % i )
__lowercase = end
return result
def _a ( self : Union[str, Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int]="\n" ) -> Optional[int]:
"""simple docstring"""
__lowercase = []
__lowercase = []
__lowercase = self.ids_to_tokens[index][0]
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2] ) )
else:
if len(_lowerCAmelCase ) > 0:
words.append(bytearray(_lowerCAmelCase ).decode("""utf-8""" , errors="""replace""" ) )
__lowercase = []
if word[:7] == "<|emoji" and word[-2:] == "|>":
words.append(self.emoji["""emoji_inv"""][word] )
elif word == "<SP>":
words.append(""" """ )
elif word == "<BR>":
words.append(_lowerCAmelCase )
elif word == "<TAB>":
words.append("""\t""" )
elif word == "<BLOCK>":
words.append("""▀""" )
elif word == "<KIGOU>":
words.append("""ǀ""" )
elif word == "<U2000U2BFF>":
words.append("""‖""" )
else:
words.append(_lowerCAmelCase )
if len(_lowerCAmelCase ) > 0:
words.append(bytearray(_lowerCAmelCase ).decode("""utf-8""" , errors="""replace""" ) )
__lowercase = """""".join(_lowerCAmelCase )
return text
| 53
|
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if isinstance(lowerCamelCase , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class __UpperCamelCase :
def _a ( self : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
pass
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
pass
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
pass
def _a ( self : int , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Dict=None , **_lowerCAmelCase : int ) -> str:
"""simple docstring"""
__lowercase = VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) )
def _a ( self : str , _lowerCAmelCase : Tuple , _lowerCAmelCase : str , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : int=None , **_lowerCAmelCase : Dict ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase , text_model=_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def _a ( self : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Dict , _lowerCAmelCase : str , _lowerCAmelCase : Any=None , **_lowerCAmelCase : str ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = {"""vision_model""": vision_model, """text_model""": text_model}
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def _a ( self : Dict , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any]=None , **_lowerCAmelCase : Any ) -> Optional[int]:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase , text_model=_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
__lowercase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
__lowercase = after_output[0].numpy()
__lowercase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCAmelCase , 1e-5 )
def _a ( self : int , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int]=None , **_lowerCAmelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase , text_model=_lowerCAmelCase )
__lowercase = model(
input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase , output_attentions=_lowerCAmelCase )
__lowercase = output.vision_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
__lowercase = to_atuple(vision_model.config.image_size )
__lowercase = to_atuple(vision_model.config.patch_size )
__lowercase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowercase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__lowercase = output.text_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _a ( self : List[Any] , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : float ) -> Optional[int]:
"""simple docstring"""
__lowercase = np.abs((a - b) ).max()
self.assertLessEqual(_lowerCAmelCase , _lowerCAmelCase , F'Difference between torch and flax is {diff} (>= {tol}).' )
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**_lowerCAmelCase )
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> int:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_lowerCAmelCase )
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_save_load(**_lowerCAmelCase )
def _a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_lowerCAmelCase )
@slow
def _a ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.get_pretrained_model_and_inputs()
__lowercase = model_a(**_lowerCAmelCase )
__lowercase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase )
__lowercase = model_a(**_lowerCAmelCase )
__lowercase = after_outputs[0].numpy()
__lowercase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCAmelCase , 1e-5 )
@require_tf
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
def _a ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-random-bert""" )
__lowercase = 13
__lowercase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowercase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowercase = random_attention_mask([batch_size, 4] )
__lowercase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _a ( self : Tuple , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
__lowercase = TFViTModel(_lowerCAmelCase , name="""vision_model""" )
__lowercase = TFBertModel(_lowerCAmelCase , name="""text_model""" )
return vision_model, text_model
def _a ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = TFViTModelTester(self )
__lowercase = TFBertModelTester(self )
__lowercase = vit_model_tester.prepare_config_and_inputs()
__lowercase = bert_model_tester.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = vision_config_and_inputs
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-deit-tf""" , """hf-internal-testing/tiny-random-roberta""" )
__lowercase = 13
__lowercase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowercase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowercase = random_attention_mask([batch_size, 4] )
__lowercase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _a ( self : Optional[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : Tuple , _lowerCAmelCase : int=None , **_lowerCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase , text_model=_lowerCAmelCase )
__lowercase = model(
input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase , output_attentions=_lowerCAmelCase )
__lowercase = output.vision_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
__lowercase = to_atuple(vision_model.config.image_size )
__lowercase = to_atuple(vision_model.config.patch_size )
__lowercase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowercase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__lowercase = output.text_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _a ( self : Tuple , _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict ) -> int:
"""simple docstring"""
__lowercase = TFDeiTModel(_lowerCAmelCase , name="""vision_model""" )
__lowercase = TFRobertaModel(_lowerCAmelCase , name="""text_model""" )
return vision_model, text_model
def _a ( self : Tuple ) -> str:
"""simple docstring"""
__lowercase = TFDeiTModelTester(self )
__lowercase = TFRobertaModelTester(self )
__lowercase = vit_model_tester.prepare_config_and_inputs()
__lowercase = bert_model_tester.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = vision_config_and_inputs
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
def _a ( self : int ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-clip-tf""" , """hf-internal-testing/tiny-random-bert""" )
__lowercase = 13
__lowercase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowercase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowercase = random_attention_mask([batch_size, 4] )
__lowercase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _a ( self : Optional[int] , _lowerCAmelCase : Dict , _lowerCAmelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
__lowercase = TFCLIPVisionModel(_lowerCAmelCase , name="""vision_model""" )
__lowercase = TFBertModel(_lowerCAmelCase , name="""text_model""" )
return vision_model, text_model
def _a ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = TFCLIPVisionModelTester(self )
__lowercase = TFBertModelTester(self )
__lowercase = clip_model_tester.prepare_config_and_inputs()
__lowercase = bert_model_tester.prepare_config_and_inputs()
__lowercase , __lowercase = vision_config_and_inputs
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class __UpperCamelCase ( unittest.TestCase ):
@slow
def _a ( self : int ) -> Tuple:
"""simple docstring"""
__lowercase = TFVisionTextDualEncoderModel.from_pretrained(
"""clip-italian/clip-italian""" , logit_scale_init_value=1.0 , from_pt=_lowerCAmelCase )
__lowercase = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
__lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
__lowercase = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=_lowerCAmelCase , padding=_lowerCAmelCase , return_tensors="""np""" )
__lowercase = model(**_lowerCAmelCase )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
__lowercase = np.array([[1.2_284_727, 0.3_104_122]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , _lowerCAmelCase , atol=1e-3 ) )
| 53
| 1
|
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
__snake_case :List[Any] = DiTPipeline
__snake_case :Any = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
__snake_case :Tuple = PipelineTesterMixin.required_optional_params - {
'latents',
'num_images_per_prompt',
'callback',
'callback_steps',
}
__snake_case :str = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
__snake_case :List[Any] = False
def _a ( self : str ) -> Any:
"""simple docstring"""
torch.manual_seed(0 )
__lowercase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=_lowerCAmelCase , activation_fn="""gelu-approximate""" , num_embeds_ada_norm=1000 , norm_type="""ada_norm_zero""" , norm_elementwise_affine=_lowerCAmelCase , )
__lowercase = AutoencoderKL()
__lowercase = DDIMScheduler()
__lowercase = {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
return components
def _a ( self : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple=0 ) -> Optional[Any]:
"""simple docstring"""
if str(_lowerCAmelCase ).startswith("""mps""" ):
__lowercase = torch.manual_seed(_lowerCAmelCase )
else:
__lowercase = torch.Generator(device=_lowerCAmelCase ).manual_seed(_lowerCAmelCase )
__lowercase = {
"""class_labels""": [1],
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def _a ( self : str ) -> Any:
"""simple docstring"""
__lowercase = """cpu"""
__lowercase = self.get_dummy_components()
__lowercase = self.pipeline_class(**_lowerCAmelCase )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs(_lowerCAmelCase )
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
__lowercase = np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457] )
__lowercase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_lowerCAmelCase , 1e-3 )
def _a ( self : List[str] ) -> str:
"""simple docstring"""
self._test_inference_batch_single_identical(relax_max_difference=_lowerCAmelCase , expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def _a ( self : int ) -> Dict:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : int ) -> int:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__lowercase = torch.manual_seed(0 )
__lowercase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256""" )
pipe.to("""cuda""" )
__lowercase = ["""vase""", """umbrella""", """white shark""", """white wolf"""]
__lowercase = pipe.get_label_ids(_lowerCAmelCase )
__lowercase = pipe(_lowerCAmelCase , generator=_lowerCAmelCase , num_inference_steps=40 , output_type="""np""" ).images
for word, image in zip(_lowerCAmelCase , _lowerCAmelCase ):
__lowercase = load_numpy(
F'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
assert np.abs((expected_image - image).max() ) < 1e-2
def _a ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512""" )
__lowercase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("""cuda""" )
__lowercase = ["""vase""", """umbrella"""]
__lowercase = pipe.get_label_ids(_lowerCAmelCase )
__lowercase = torch.manual_seed(0 )
__lowercase = pipe(_lowerCAmelCase , generator=_lowerCAmelCase , num_inference_steps=25 , output_type="""np""" ).images
for word, image in zip(_lowerCAmelCase , _lowerCAmelCase ):
__lowercase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
F'/dit/{word}_512.npy' )
assert np.abs((expected_image - image).max() ) < 1e-1
| 53
|
from __future__ import annotations
from scipy.special import comb # type: ignore
class __UpperCamelCase :
def __init__( self : int , _lowerCAmelCase : list[tuple[float, float]] ) -> Any:
"""simple docstring"""
__lowercase = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
__lowercase = len(_lowerCAmelCase ) - 1
def _a ( self : Tuple , _lowerCAmelCase : float ) -> list[float]:
"""simple docstring"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__lowercase = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , _lowerCAmelCase ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(_lowerCAmelCase ) , 5 ) == 1
return output_values
def _a ( self : List[str] , _lowerCAmelCase : float ) -> tuple[float, float]:
"""simple docstring"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__lowercase = self.basis_function(_lowerCAmelCase )
__lowercase = 0.0
__lowercase = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def _a ( self : Optional[int] , _lowerCAmelCase : float = 0.01 ) -> Union[str, Any]:
"""simple docstring"""
from matplotlib import pyplot as plt # type: ignore
__lowercase = [] # x coordinates of points to plot
__lowercase = [] # y coordinates of points to plot
__lowercase = 0.0
while t <= 1:
__lowercase = self.bezier_curve_function(_lowerCAmelCase )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
__lowercase = [i[0] for i in self.list_of_points]
__lowercase = [i[1] for i in self.list_of_points]
plt.plot(
_lowerCAmelCase , _lowerCAmelCase , color="""blue""" , label="""Curve of Degree """ + str(self.degree ) , )
plt.scatter(_lowerCAmelCase , _lowerCAmelCase , color="""red""" , label="""Control Points""" )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 53
| 1
|
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if is_timm_available():
import timm
if is_torch_available():
from torch import Tensor
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ):
__snake_case :int = 'pixel_values'
__snake_case :Union[str, Any] = False
__snake_case :int = TimmBackboneConfig
def __init__( self : Tuple , _lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : Dict ) -> Tuple:
"""simple docstring"""
requires_backends(self , """timm""" )
super().__init__(_lowerCAmelCase )
__lowercase = config
if config.backbone is None:
raise ValueError("""backbone is not set in the config. Please set it to a timm model name.""" )
if config.backbone not in timm.list_models():
raise ValueError(F'backbone {config.backbone} is not supported by timm.' )
if hasattr(_lowerCAmelCase , """out_features""" ) and config.out_features is not None:
raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead.""" )
__lowercase = getattr(_lowerCAmelCase , """use_pretrained_backbone""" , _lowerCAmelCase )
if pretrained is None:
raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False.""" )
# We just take the final layer by default. This matches the default for the transformers models.
__lowercase = config.out_indices if getattr(_lowerCAmelCase , """out_indices""" , _lowerCAmelCase ) is not None else (-1,)
__lowercase = timm.create_model(
config.backbone , pretrained=_lowerCAmelCase , features_only=config.features_only , in_chans=config.num_channels , out_indices=_lowerCAmelCase , **_lowerCAmelCase , )
# These are used to control the output of the model when called. If output_hidden_states is True, then
# return_layers is modified to include all layers.
__lowercase = self._backbone.return_layers
__lowercase = {layer["""module"""]: str(_lowerCAmelCase ) for i, layer in enumerate(self._backbone.feature_info.info )}
super()._init_backbone(_lowerCAmelCase )
@classmethod
def _a ( cls : List[str] , _lowerCAmelCase : Optional[int] , *_lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : Any ) -> int:
"""simple docstring"""
requires_backends(cls , ["""vision""", """timm"""] )
from ...models.timm_backbone import TimmBackboneConfig
__lowercase = kwargs.pop("""config""" , TimmBackboneConfig() )
__lowercase = kwargs.pop("""use_timm_backbone""" , _lowerCAmelCase )
if not use_timm:
raise ValueError("""use_timm_backbone must be True for timm backbones""" )
__lowercase = kwargs.pop("""num_channels""" , config.num_channels )
__lowercase = kwargs.pop("""features_only""" , config.features_only )
__lowercase = kwargs.pop("""use_pretrained_backbone""" , config.use_pretrained_backbone )
__lowercase = kwargs.pop("""out_indices""" , config.out_indices )
__lowercase = TimmBackboneConfig(
backbone=_lowerCAmelCase , num_channels=_lowerCAmelCase , features_only=_lowerCAmelCase , use_pretrained_backbone=_lowerCAmelCase , out_indices=_lowerCAmelCase , )
return super()._from_config(_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self : str , _lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
pass
def _a ( self : Optional[int] , _lowerCAmelCase : str , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : str=None , _lowerCAmelCase : List[Any]=None , **_lowerCAmelCase : Union[str, Any] ) -> Union[BackboneOutput, Tuple[Tensor, ...]]:
"""simple docstring"""
__lowercase = return_dict if return_dict is not None else self.config.use_return_dict
__lowercase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__lowercase = output_attentions if output_attentions is not None else self.config.output_attentions
if output_attentions:
raise ValueError("""Cannot output attentions for timm backbones at the moment""" )
if output_hidden_states:
# We modify the return layers to include all the stages of the backbone
__lowercase = self._all_layers
__lowercase = self._backbone(_lowerCAmelCase , **_lowerCAmelCase )
__lowercase = self._return_layers
__lowercase = tuple(hidden_states[i] for i in self.out_indices )
else:
__lowercase = self._backbone(_lowerCAmelCase , **_lowerCAmelCase )
__lowercase = None
__lowercase = tuple(_lowerCAmelCase )
__lowercase = tuple(_lowerCAmelCase ) if hidden_states is not None else None
if not return_dict:
__lowercase = (feature_maps,)
if output_hidden_states:
__lowercase = output + (hidden_states,)
return output
return BackboneOutput(feature_maps=_lowerCAmelCase , hidden_states=_lowerCAmelCase , attentions=_lowerCAmelCase )
| 53
|
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
)
from transformers.models.efficientformer.modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_vision_available():
from PIL import Image
from transformers import EfficientFormerImageProcessor
class __UpperCamelCase :
def __init__( self : int , _lowerCAmelCase : Tuple , _lowerCAmelCase : int = 13 , _lowerCAmelCase : int = 64 , _lowerCAmelCase : int = 2 , _lowerCAmelCase : int = 3 , _lowerCAmelCase : int = 3 , _lowerCAmelCase : bool = True , _lowerCAmelCase : bool = True , _lowerCAmelCase : int = 128 , _lowerCAmelCase : Optional[int]=[16, 32, 64, 128] , _lowerCAmelCase : int = 7 , _lowerCAmelCase : int = 4 , _lowerCAmelCase : int = 37 , _lowerCAmelCase : str = "gelu" , _lowerCAmelCase : float = 0.1 , _lowerCAmelCase : float = 0.1 , _lowerCAmelCase : int = 10 , _lowerCAmelCase : float = 0.02 , _lowerCAmelCase : int = 2 , _lowerCAmelCase : int = 1 , _lowerCAmelCase : int = 128 , _lowerCAmelCase : List[int] = [2, 2, 2, 2] , _lowerCAmelCase : int = 2 , _lowerCAmelCase : int = 2 , ) -> Tuple:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = is_training
__lowercase = use_labels
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = encoder_stride
__lowercase = num_attention_outputs
__lowercase = embed_dim
__lowercase = embed_dim + 1
__lowercase = resolution
__lowercase = depths
__lowercase = hidden_sizes
__lowercase = dim
__lowercase = mlp_expansion_ratio
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
return EfficientFormerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_lowerCAmelCase , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , resolution=self.resolution , depths=self.depths , hidden_sizes=self.hidden_sizes , dim=self.dim , mlp_expansion_ratio=self.mlp_expansion_ratio , )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = TFEfficientFormerModel(config=_lowerCAmelCase )
__lowercase = model(_lowerCAmelCase , training=_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.type_sequence_label_size
__lowercase = TFEfficientFormerForImageClassification(_lowerCAmelCase )
__lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase , training=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__lowercase = 1
__lowercase = TFEfficientFormerForImageClassification(_lowerCAmelCase )
__lowercase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Any = (
(
TFEfficientFormerModel,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerForImageClassification,
)
if is_tf_available()
else ()
)
__snake_case :Any = (
{
'feature-extraction': TFEfficientFormerModel,
'image-classification': (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
),
}
if is_tf_available()
else {}
)
__snake_case :int = False
__snake_case :Optional[int] = False
__snake_case :int = False
__snake_case :Any = False
__snake_case :Any = False
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
__lowercase = TFEfficientFormerModelTester(self )
__lowercase = ConfigTester(
self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 )
def _a ( self : Optional[int] ) -> int:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""EfficientFormer does not use inputs_embeds""" )
def _a ( self : Optional[int] ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""EfficientFormer does not support input and output embeddings""" )
def _a ( self : int ) -> str:
"""simple docstring"""
pass
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
def check_hidden_states_output(_lowerCAmelCase : int , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] ):
__lowercase = model_class(_lowerCAmelCase )
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) , training=_lowerCAmelCase )
__lowercase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowercase = getattr(
self.model_tester , """expected_num_hidden_layers""" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
if hasattr(self.model_tester , """encoder_seq_length""" ):
__lowercase = self.model_tester.encoder_seq_length
if hasattr(self.model_tester , """chunk_length""" ) and self.model_tester.chunk_length > 1:
__lowercase = seq_length * self.model_tester.chunk_length
else:
__lowercase = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
if config.is_encoder_decoder:
__lowercase = outputs.decoder_hidden_states
self.asseretIsInstance(_lowerCAmelCase , (list, tuple) )
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """seq_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """decoder_seq_length""" , _lowerCAmelCase )
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [decoder_seq_length, self.model_tester.hidden_size] , )
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Optional[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any]=False ) -> Dict:
"""simple docstring"""
__lowercase = super()._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
if return_labels:
if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def _a ( self : int ) -> int:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet""" )
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase )
def _a ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@slow
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = TFEfficientFormerModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = True
__lowercase = getattr(self.model_tester , """seq_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """encoder_seq_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """key_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """chunk_length""" , _lowerCAmelCase )
if chunk_length is not None and hasattr(self.model_tester , """num_hashes""" ):
__lowercase = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
__lowercase = True
__lowercase = False
__lowercase = True
__lowercase = model_class(_lowerCAmelCase )
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) , training=_lowerCAmelCase )
__lowercase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_attention_outputs )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__lowercase = True
__lowercase = model_class(_lowerCAmelCase )
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) , training=_lowerCAmelCase )
__lowercase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_attention_outputs )
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length] , )
else:
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length] , )
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Prepare our model
__lowercase = model_class(_lowerCAmelCase )
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
__lowercase = {
key: tf.keras.Input(shape=val.shape[1:] , dtype=val.dtype , name=_lowerCAmelCase )
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
__lowercase = model(_lowerCAmelCase )
self.assertTrue(outputs_dict is not None )
def snake_case ( ):
'''simple docstring'''
__lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
return (
EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300""" )
if is_vision_available()
else None
)
@slow
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__lowercase = TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300""" )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""tf""" )
# forward pass
__lowercase = model(**_lowerCAmelCase , training=_lowerCAmelCase )
# verify the logits
__lowercase = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
__lowercase = tf.constant([-0.0_555, 0.4_825, -0.0_852] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
@slow
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__lowercase = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
"""snap-research/efficientformer-l1-300""" )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""tf""" )
# forward pass
__lowercase = model(**_lowerCAmelCase , training=_lowerCAmelCase )
# verify the logits
__lowercase = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
__lowercase = tf.constant([-0.1_312, 0.4_353, -1.0_499] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
| 53
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
__UpperCamelCase : Dict = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
"""SpeechT5Config""",
"""SpeechT5HifiGanConfig""",
],
"""feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
"""processing_speecht5""": ["""SpeechT5Processor"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[str] = ["""SpeechT5Tokenizer"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[Any] = [
"""SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SpeechT5ForSpeechToText""",
"""SpeechT5ForSpeechToSpeech""",
"""SpeechT5ForTextToSpeech""",
"""SpeechT5Model""",
"""SpeechT5PreTrainedModel""",
"""SpeechT5HifiGan""",
]
if TYPE_CHECKING:
from .configuration_speechta import (
SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
SpeechTaConfig,
SpeechTaHifiGanConfig,
)
from .feature_extraction_speechta import SpeechTaFeatureExtractor
from .processing_speechta import SpeechTaProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speechta import SpeechTaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speechta import (
SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaHifiGan,
SpeechTaModel,
SpeechTaPreTrainedModel,
)
else:
import sys
__UpperCamelCase : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 53
|
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
__UpperCamelCase : Tuple = 2
class __UpperCamelCase :
def __init__( self : List[str] , *, # begin keyword-only arguments
_lowerCAmelCase : Optional[int]="<s>" , _lowerCAmelCase : Optional[int]="<pad>" , _lowerCAmelCase : int="</s>" , _lowerCAmelCase : str="<unk>" , _lowerCAmelCase : List[str]=None , ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase , __lowercase , __lowercase = bos, unk, pad, eos
__lowercase = []
__lowercase = []
__lowercase = {}
__lowercase = self.add_symbol(_lowerCAmelCase )
__lowercase = self.add_symbol(_lowerCAmelCase )
__lowercase = self.add_symbol(_lowerCAmelCase )
__lowercase = self.add_symbol(_lowerCAmelCase )
if extra_special_symbols:
for s in extra_special_symbols:
self.add_symbol(_lowerCAmelCase )
__lowercase = len(self.symbols )
def __eq__( self : Dict , _lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
return self.indices == other.indices
def __getitem__( self : Any , _lowerCAmelCase : str ) -> Dict:
"""simple docstring"""
if idx < len(self.symbols ):
return self.symbols[idx]
return self.unk_word
def __len__( self : str ) -> List[str]:
"""simple docstring"""
return len(self.symbols )
def __contains__( self : Union[str, Any] , _lowerCAmelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
return sym in self.indices
@classmethod
def _a ( cls : Dict , _lowerCAmelCase : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = cls()
d.add_from_file(_lowerCAmelCase )
return d
def _a ( self : int , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any]=1 , _lowerCAmelCase : Optional[int]=False ) -> Union[str, Any]:
"""simple docstring"""
if word in self.indices and not overwrite:
__lowercase = self.indices[word]
__lowercase = self.count[idx] + n
return idx
else:
__lowercase = len(self.symbols )
__lowercase = idx
self.symbols.append(_lowerCAmelCase )
self.count.append(_lowerCAmelCase )
return idx
def _a ( self : List[str] , _lowerCAmelCase : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
return 0
def _a ( self : Optional[Any] , _lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
try:
with open(_lowerCAmelCase , """r""" , encoding="""utf-8""" ) as fd:
self.add_from_file(_lowerCAmelCase )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(_lowerCAmelCase ) )
return
__lowercase = f.readlines()
__lowercase = self._load_meta(_lowerCAmelCase )
for line in lines[indices_start_line:]:
try:
__lowercase , __lowercase = line.rstrip().rsplit(""" """ , 1 )
if field == "#fairseq:overwrite":
__lowercase = True
__lowercase , __lowercase = line.rsplit(""" """ , 1 )
else:
__lowercase = False
__lowercase = int(_lowerCAmelCase )
__lowercase = line
if word in self and not overwrite:
raise RuntimeError(
"""Duplicate word found when loading Dictionary: '{}'. """
"""Duplicate words can overwrite earlier ones by adding the """
"""#fairseq:overwrite flag at the end of the corresponding row """
"""in the dictionary file. If using the Camembert model, please """
"""download an updated copy of the model file.""".format(_lowerCAmelCase ) )
self.add_symbol(_lowerCAmelCase , n=_lowerCAmelCase , overwrite=_lowerCAmelCase )
except ValueError:
raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'""" )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = dict((re.sub(r"""@@$""" , """""" , lowerCamelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , lowerCamelCase ), v) for k, v in d.items() )
__lowercase = """<s> <pad> </s> <unk>""".split()
# restore the special tokens
for k in keep_keys:
del da[F'{k}</w>']
__lowercase = d[k] # restore
return da
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if not os.path.exists(lowerCamelCase ):
raise ValueError(F'path {biogpt_checkpoint_path} does not exist!' )
os.makedirs(lowerCamelCase , exist_ok=lowerCamelCase )
print(F'Writing results to {pytorch_dump_folder_path}' )
# handle various types of models
__lowercase = os.path.join(lowerCamelCase , """checkpoint.pt""" )
if not os.path.isfile(lowerCamelCase ):
raise ValueError(F'path to the file {checkpoint_file} does not exist!' )
__lowercase = torch.load(lowerCamelCase , map_location="""cpu""" )
__lowercase = chkpt["""cfg"""]["""model"""]
# dicts
__lowercase = os.path.join(lowerCamelCase , """dict.txt""" )
if not os.path.isfile(lowerCamelCase ):
raise ValueError(F'path to the file {dict_file} does not exist!' )
__lowercase = Dictionary.load(lowerCamelCase )
__lowercase = rewrite_dict_keys(src_dict.indices )
__lowercase = len(lowerCamelCase )
__lowercase = os.path.join(lowerCamelCase , VOCAB_FILES_NAMES["""vocab_file"""] )
print(F'Generating {src_vocab_file} of {src_vocab_size} records' )
with open(lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(lowerCamelCase , ensure_ascii=lowerCamelCase , indent=lowerCamelCase ) )
# merges_file (bpecodes)
__lowercase = os.path.join(lowerCamelCase , """bpecodes""" )
if not os.path.isfile(lowerCamelCase ):
raise ValueError(F'path to the file {bpecodes_file} does not exist!' )
__lowercase = os.path.join(lowerCamelCase , VOCAB_FILES_NAMES["""merges_file"""] )
shutil.copyfile(lowerCamelCase , lowerCamelCase )
# model config
__lowercase = os.path.join(lowerCamelCase , """config.json""" )
__lowercase = {
"""activation_dropout""": args["""activation_dropout"""],
"""architectures""": ["""BioGptForCausalLM"""],
"""attention_probs_dropout_prob""": args["""attention_dropout"""],
"""bos_token_id""": 0,
"""eos_token_id""": 2,
"""hidden_act""": args["""activation_fn"""],
"""hidden_dropout_prob""": args["""dropout"""],
"""hidden_size""": args["""decoder_embed_dim"""],
"""initializer_range""": 0.02,
"""intermediate_size""": args["""decoder_ffn_embed_dim"""],
"""layer_norm_eps""": 1e-12,
"""layerdrop""": args["""decoder_layerdrop"""],
"""max_position_embeddings""": args["""max_target_positions"""],
"""model_type""": """biogpt""",
"""num_attention_heads""": args["""decoder_attention_heads"""],
"""num_hidden_layers""": args["""decoder_layers"""],
"""pad_token_id""": 1,
"""scale_embedding""": not args["""no_scale_embedding"""],
"""tie_word_embeddings""": args["""share_decoder_input_output_embed"""],
"""vocab_size""": src_vocab_size,
}
# good hparam defaults to start with
print(F'Generating {biogpt_model_config_file}' )
with open(lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(lowerCamelCase , ensure_ascii=lowerCamelCase , indent=lowerCamelCase ) )
# tokenizer config
__lowercase = os.path.join(lowerCamelCase , lowerCamelCase )
__lowercase = {
"""bos_token""": """<s>""",
"""eos_token""": """</s>""",
"""model_max_length""": 1_024,
"""pad_token""": """<pad>""",
"""special_tokens_map_file""": None,
"""tokenizer_class""": """BioGptTokenizer""",
"""unk_token""": """<unk>""",
}
print(F'Generating {biogpt_tokenizer_config_file}' )
with open(lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(lowerCamelCase , ensure_ascii=lowerCamelCase , indent=lowerCamelCase ) )
# model
__lowercase = chkpt["""model"""]
# remove unneeded keys
__lowercase = [
"""decoder.version""",
]
for k in ignore_keys:
model_state_dict.pop(lowerCamelCase , lowerCamelCase )
__lowercase = list(model_state_dict.keys() )
for layer_name in layer_names:
if layer_name.endswith("""output_projection.weight""" ):
__lowercase = model_state_dict.pop(lowerCamelCase )
else:
__lowercase = model_state_dict.pop(lowerCamelCase )
__lowercase = BioGptConfig.from_pretrained(lowerCamelCase )
__lowercase = BioGptForCausalLM(lowerCamelCase )
# check that it loads ok
model_new.load_state_dict(lowerCamelCase )
# save
__lowercase = os.path.join(lowerCamelCase , lowerCamelCase )
print(F'Generating {pytorch_weights_dump_path}' )
torch.save(lowerCamelCase , lowerCamelCase )
print("""Conversion is done!""" )
if __name__ == "__main__":
__UpperCamelCase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--biogpt_checkpoint_path""",
default=None,
type=str,
required=True,
help=(
"""Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
""" bpecodes, etc."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
__UpperCamelCase : Optional[Any] = parser.parse_args()
convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
| 53
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__UpperCamelCase : Tuple = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormerConfig""",
"""SwiftFormerOnnxConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Optional[Any] = [
"""SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SwiftFormerForImageClassification""",
"""SwiftFormerModel""",
"""SwiftFormerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
__UpperCamelCase : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 53
|
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
__snake_case :Union[str, Any] = 'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
def _a ( self : Any , _lowerCAmelCase : str=0 ) -> str:
"""simple docstring"""
__lowercase = np.random.RandomState(_lowerCAmelCase )
__lowercase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.65_072, 0.58_492, 0.48_219, 0.55_521, 0.53_180, 0.55_939, 0.50_697, 0.39_800, 0.46_455] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.65_863, 0.59_425, 0.49_326, 0.56_313, 0.53_875, 0.56_627, 0.51_065, 0.39_777, 0.46_330] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Tuple ) -> int:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_817, 0.60_812, 0.47_384, 0.49_530, 0.51_894, 0.49_814, 0.47_984, 0.38_958, 0.44_271] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_895, 0.60_808, 0.47_933, 0.49_608, 0.51_886, 0.49_950, 0.48_053, 0.38_957, 0.44_200] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * [inputs["""prompt"""]]
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * [inputs.pop("""prompt""" )]
__lowercase = pipe.tokenizer(
_lowerCAmelCase , padding="""max_length""" , max_length=pipe.tokenizer.model_max_length , truncation=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = text_inputs["""input_ids"""]
__lowercase = pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]
__lowercase = prompt_embeds
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
def _a ( self : int ) -> str:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * ["""this is a negative prompt"""]
__lowercase = negative_prompt
__lowercase = 3 * [inputs["""prompt"""]]
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * [inputs.pop("""prompt""" )]
__lowercase = []
for p in [prompt, negative_prompt]:
__lowercase = pipe.tokenizer(
_lowerCAmelCase , padding="""max_length""" , max_length=pipe.tokenizer.model_max_length , truncation=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = text_inputs["""input_ids"""]
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )
__lowercase , __lowercase = embeds
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
@nightly
@require_onnxruntime
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
@property
def _a ( self : Dict ) -> str:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _a ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = ort.SessionOptions()
__lowercase = False
return options
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""onnx""" , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """A painting of a squirrel eating a burger"""
np.random.seed(0 )
__lowercase = sd_pipe([prompt] , guidance_scale=6.0 , num_inference_steps=10 , output_type="""np""" )
__lowercase = output.images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.0_452, 0.0_390, 0.0_087, 0.0_350, 0.0_617, 0.0_364, 0.0_544, 0.0_523, 0.0_720] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
__lowercase = DDIMScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """open neural network exchange"""
__lowercase = np.random.RandomState(0 )
__lowercase = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=_lowerCAmelCase , output_type="""np""" )
__lowercase = output.images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.2_867, 0.1_974, 0.1_481, 0.7_294, 0.7_251, 0.6_667, 0.4_194, 0.5_642, 0.6_486] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
__lowercase = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """open neural network exchange"""
__lowercase = np.random.RandomState(0 )
__lowercase = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=_lowerCAmelCase , output_type="""np""" )
__lowercase = output.images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.2_306, 0.1_959, 0.1_593, 0.6_549, 0.6_394, 0.5_408, 0.5_065, 0.6_010, 0.6_161] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : str ) -> List[str]:
"""simple docstring"""
__lowercase = 0
def test_callback_fn(_lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : np.ndarray ) -> None:
__lowercase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 64, 64)
__lowercase = latents[0, -3:, -3:, -1]
__lowercase = np.array(
[-0.6_772, -0.3_835, -1.2_456, 0.1_905, -1.0_974, 0.6_967, -1.9_353, 0.0_178, 1.0_167] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
elif step == 5:
assert latents.shape == (1, 4, 64, 64)
__lowercase = latents[0, -3:, -3:, -1]
__lowercase = np.array(
[-0.3_351, 0.2_241, -0.1_837, -0.2_325, -0.6_577, 0.3_393, -0.0_241, 0.5_899, 1.3_875] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
__lowercase = False
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """Andromeda galaxy in a bottle"""
__lowercase = np.random.RandomState(0 )
pipe(
prompt=_lowerCAmelCase , num_inference_steps=5 , guidance_scale=7.5 , generator=_lowerCAmelCase , callback=_lowerCAmelCase , callback_steps=1 , )
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
assert pipe.safety_checker is None
__lowercase = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_lowerCAmelCase )
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(_lowerCAmelCase )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
__lowercase = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
| 53
| 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 __UpperCamelCase ( _lowerCAmelCase ):
@slow
@require_torch
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" , """prajjwal1/bert-tiny""" )
__lowercase = BertTokenizer.from_pretrained("""bert-base-uncased""" )
__lowercase = bertabert.config.encoder.vocab_size
__lowercase = tokenizer.sep_token_id
__lowercase = tokenizer.cls_token_id
__lowercase = 128
__lowercase = datasets.load_dataset("""cnn_dailymail""" , """3.0.0""" , split="""train[:1%]""" )
__lowercase = datasets.load_dataset("""cnn_dailymail""" , """3.0.0""" , split="""validation[:1%]""" )
__lowercase = train_dataset.select(range(32 ) )
__lowercase = val_dataset.select(range(16 ) )
__lowercase = 4
def _map_to_encoder_decoder_inputs(_lowerCAmelCase : Any ):
# Tokenizer will automatically set [BOS] <text> [EOS]
__lowercase = tokenizer(batch["""article"""] , padding="""max_length""" , truncation=_lowerCAmelCase , max_length=512 )
__lowercase = tokenizer(batch["""highlights"""] , padding="""max_length""" , truncation=_lowerCAmelCase , max_length=128 )
__lowercase = inputs.input_ids
__lowercase = inputs.attention_mask
__lowercase = outputs.input_ids
__lowercase = outputs.input_ids.copy()
__lowercase = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
]
__lowercase = outputs.attention_mask
assert all(len(_lowerCAmelCase ) == 512 for x in inputs.input_ids )
assert all(len(_lowerCAmelCase ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(_lowerCAmelCase : Tuple ):
__lowercase = pred.label_ids
__lowercase = pred.predictions
# all unnecessary tokens are removed
__lowercase = tokenizer.batch_decode(_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase )
__lowercase = tokenizer.batch_decode(_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase )
__lowercase = sum([int(pred_str[i] == label_str[i] ) for i in range(len(_lowerCAmelCase ) )] ) / len(_lowerCAmelCase )
return {"accuracy": accuracy}
# map train dataset
__lowercase = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=_lowerCAmelCase , batch_size=_lowerCAmelCase , 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
__lowercase = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=_lowerCAmelCase , batch_size=_lowerCAmelCase , remove_columns=["""article""", """highlights"""] , )
val_dataset.set_format(
type="""torch""" , columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] , )
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = SeqaSeqTrainingArguments(
output_dir=_lowerCAmelCase , per_device_train_batch_size=_lowerCAmelCase , per_device_eval_batch_size=_lowerCAmelCase , predict_with_generate=_lowerCAmelCase , evaluation_strategy="""steps""" , do_train=_lowerCAmelCase , do_eval=_lowerCAmelCase , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
__lowercase = SeqaSeqTrainer(
model=_lowerCAmelCase , args=_lowerCAmelCase , compute_metrics=_compute_metrics , train_dataset=_lowerCAmelCase , eval_dataset=_lowerCAmelCase , tokenizer=_lowerCAmelCase , )
# start training
trainer.train()
| 53
|
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = """"""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
__lowercase = remove_duplicates(key.upper() )
__lowercase = len(lowerCamelCase )
# First fill cipher with key characters
__lowercase = {alphabet[i]: char for i, char in enumerate(lowerCamelCase )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(lowerCamelCase ) , 26 ):
__lowercase = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
__lowercase = alphabet[i - offset]
__lowercase = char
return cipher_alphabet
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return "".join(cipher_map.get(lowerCamelCase , lowerCamelCase ) for ch in message.upper() )
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(lowerCamelCase , lowerCamelCase ) for ch in message.upper() )
def snake_case ( ):
'''simple docstring'''
__lowercase = input("""Enter message to encode or decode: """ ).strip()
__lowercase = input("""Enter keyword: """ ).strip()
__lowercase = input("""Encipher or decipher? E/D:""" ).strip()[0].lower()
try:
__lowercase = {"""e""": encipher, """d""": decipher}[option]
except KeyError:
raise KeyError("""invalid input option""" )
__lowercase = create_cipher_map(lowerCamelCase )
print(func(lowerCamelCase , lowerCamelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 53
| 1
|
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoTokenizer,
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForWholeWordMask,
HfArgumentParser,
LineByLineTextDataset,
LineByLineWithRefDataset,
PreTrainedTokenizer,
TextDataset,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
__UpperCamelCase : List[Any] = logging.getLogger(__name__)
__UpperCamelCase : Optional[Any] = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
__UpperCamelCase : List[str] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class __UpperCamelCase :
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={
'help': (
'The model checkpoint for weights initialization. Leave None if you want to train a model from'
' scratch.'
)
} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_lowerCAmelCase )} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class __UpperCamelCase :
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'The input training data file (a text file).'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={
'help': (
'The input training data files (multiple files in glob format). '
'Very often splitting large files to smaller files can prevent tokenizer going out of memory'
)
} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'An optional input train ref data file for whole word mask in Chinese.'} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'An optional input eval ref data file for whole word mask in Chinese.'} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Whether distinct lines of text in the dataset are to be handled as distinct sequences.'} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Train with masked-language modeling loss instead of language modeling.'} )
__snake_case :bool = field(default=_lowerCAmelCase , metadata={'help': 'Whether ot not to use whole word mask.'} )
__snake_case :float = field(
default=0.15 , metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'} )
__snake_case :float = field(
default=1 / 6 , metadata={
'help': (
'Ratio of length of a span of masked tokens to surrounding context length for permutation language'
' modeling.'
)
} , )
__snake_case :int = field(
default=5 , metadata={'help': 'Maximum length of a span of masked tokens for permutation language modeling.'} )
__snake_case :int = field(
default=-1 , metadata={
'help': (
'Optional input sequence length after tokenization.'
'The training dataset will be truncated in block of this size for training.'
'Default to the model max input length for single sentence inputs (take into account special tokens).'
)
} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase = False , lowerCamelCase = None , ):
'''simple docstring'''
def _dataset(lowerCamelCase , lowerCamelCase=None ):
if args.line_by_line:
if ref_path is not None:
if not args.whole_word_mask or not args.mlm:
raise ValueError("""You need to set world whole masking and mlm to True for Chinese Whole Word Mask""" )
return LineByLineWithRefDataset(
tokenizer=lowerCamelCase , file_path=lowerCamelCase , block_size=args.block_size , ref_path=lowerCamelCase , )
return LineByLineTextDataset(tokenizer=lowerCamelCase , file_path=lowerCamelCase , block_size=args.block_size )
else:
return TextDataset(
tokenizer=lowerCamelCase , file_path=lowerCamelCase , block_size=args.block_size , overwrite_cache=args.overwrite_cache , cache_dir=lowerCamelCase , )
if evaluate:
return _dataset(args.eval_data_file , args.eval_ref_file )
elif args.train_data_files:
return ConcatDataset([_dataset(lowerCamelCase ) for f in glob(args.train_data_files )] )
else:
return _dataset(args.train_data_file , args.train_ref_file )
def snake_case ( ):
'''simple docstring'''
__lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
__lowercase , __lowercase , __lowercase = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_args.do_eval:
raise ValueError(
"""Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file """
"""or remove the --do_eval argument.""" )
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
""" --overwrite_output_dir to overcome.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , lowerCamelCase )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if model_args.config_name:
__lowercase = AutoConfig.from_pretrained(model_args.config_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
__lowercase = AutoConfig.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
__lowercase = CONFIG_MAPPING[model_args.model_type]()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.tokenizer_name:
__lowercase = AutoTokenizer.from_pretrained(model_args.tokenizer_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
__lowercase = AutoTokenizer.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
raise ValueError(
"""You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"""
""" script, save it,and load it from here, using --tokenizer_name""" )
if model_args.model_name_or_path:
__lowercase = AutoModelWithLMHead.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 , )
else:
logger.info("""Training new model from scratch""" )
__lowercase = AutoModelWithLMHead.from_config(lowerCamelCase )
model.resize_token_embeddings(len(lowerCamelCase ) )
if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
raise ValueError(
"""BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"""
"""--mlm flag (masked language modeling).""" )
if data_args.block_size <= 0:
__lowercase = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
__lowercase = min(data_args.block_size , tokenizer.max_len )
# Get datasets
__lowercase = (
get_dataset(lowerCamelCase , tokenizer=lowerCamelCase , cache_dir=model_args.cache_dir ) if training_args.do_train else None
)
__lowercase = (
get_dataset(lowerCamelCase , tokenizer=lowerCamelCase , evaluate=lowerCamelCase , cache_dir=model_args.cache_dir )
if training_args.do_eval
else None
)
if config.model_type == "xlnet":
__lowercase = DataCollatorForPermutationLanguageModeling(
tokenizer=lowerCamelCase , plm_probability=data_args.plm_probability , max_span_length=data_args.max_span_length , )
else:
if data_args.mlm and data_args.whole_word_mask:
__lowercase = DataCollatorForWholeWordMask(
tokenizer=lowerCamelCase , mlm_probability=data_args.mlm_probability )
else:
__lowercase = DataCollatorForLanguageModeling(
tokenizer=lowerCamelCase , mlm=data_args.mlm , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
__lowercase = Trainer(
model=lowerCamelCase , args=lowerCamelCase , data_collator=lowerCamelCase , train_dataset=lowerCamelCase , eval_dataset=lowerCamelCase , prediction_loss_only=lowerCamelCase , )
# Training
if training_args.do_train:
__lowercase = (
model_args.model_name_or_path
if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
else None
)
trainer.train(model_path=lowerCamelCase )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
__lowercase = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__lowercase = trainer.evaluate()
__lowercase = math.exp(eval_output["""eval_loss"""] )
__lowercase = {"""perplexity""": perplexity}
__lowercase = os.path.join(training_args.output_dir , """eval_results_lm.txt""" )
if trainer.is_world_master():
with open(lowerCamelCase , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key in sorted(result.keys() ):
logger.info(""" %s = %s""" , lowerCamelCase , str(result[key] ) )
writer.write("""%s = %s\n""" % (key, str(result[key] )) )
results.update(lowerCamelCase )
return results
def snake_case ( lowerCamelCase ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 53
|
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Any = IFInpaintingPipeline
__snake_case :str = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
__snake_case :Optional[int] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__snake_case :str = PipelineTesterMixin.required_optional_params - {'latents'}
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
return self._get_dummy_components()
def _a ( self : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict=0 ) -> Any:
"""simple docstring"""
if str(_lowerCAmelCase ).startswith("""mps""" ):
__lowercase = torch.manual_seed(_lowerCAmelCase )
else:
__lowercase = torch.Generator(device=_lowerCAmelCase ).manual_seed(_lowerCAmelCase )
__lowercase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_lowerCAmelCase ) ).to(_lowerCAmelCase )
__lowercase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_lowerCAmelCase ) ).to(_lowerCAmelCase )
__lowercase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""mask_image""": mask_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def _a ( self : Tuple ) -> Dict:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def _a ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""" )
def _a ( self : List[str] ) -> Tuple:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1e-1 )
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def _a ( self : str ) -> Optional[int]:
"""simple docstring"""
self._test_save_load_local()
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 53
| 1
|
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __UpperCamelCase :
def _a ( self : List[Any] , _lowerCAmelCase : Any ) -> str:
"""simple docstring"""
raise NotImplementedError()
def _a ( self : Dict ) -> int:
"""simple docstring"""
raise NotImplementedError()
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : Dict , _lowerCAmelCase : "AutoTokenizer" , _lowerCAmelCase : bool = False , **_lowerCAmelCase : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = tokenizer
__lowercase = skip_prompt
__lowercase = decode_kwargs
# variables used in the streaming process
__lowercase = []
__lowercase = 0
__lowercase = True
def _a ( self : Optional[int] , _lowerCAmelCase : List[Any] ) -> Optional[Any]:
"""simple docstring"""
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError("""TextStreamer only supports batch size 1""" )
elif len(value.shape ) > 1:
__lowercase = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
__lowercase = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
__lowercase = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith("""\n""" ):
__lowercase = text[self.print_len :]
__lowercase = []
__lowercase = 0
# If the last token is a CJK character, we print the characters.
elif len(_lowerCAmelCase ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
__lowercase = text[self.print_len :]
self.print_len += len(_lowerCAmelCase )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
__lowercase = text[self.print_len : text.rfind(""" """ ) + 1]
self.print_len += len(_lowerCAmelCase )
self.on_finalized_text(_lowerCAmelCase )
def _a ( self : int ) -> Optional[int]:
"""simple docstring"""
if len(self.token_cache ) > 0:
__lowercase = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
__lowercase = text[self.print_len :]
__lowercase = []
__lowercase = 0
else:
__lowercase = """"""
__lowercase = True
self.on_finalized_text(_lowerCAmelCase , stream_end=_lowerCAmelCase )
def _a ( self : int , _lowerCAmelCase : str , _lowerCAmelCase : bool = False ) -> str:
"""simple docstring"""
print(_lowerCAmelCase , flush=_lowerCAmelCase , end="""""" if not stream_end else None )
def _a ( self : Tuple , _lowerCAmelCase : Tuple ) -> int:
"""simple docstring"""
if (
(cp >= 0X4e00 and cp <= 0X9fff)
or (cp >= 0X3400 and cp <= 0X4dbf) #
or (cp >= 0X2_0000 and cp <= 0X2_a6df) #
or (cp >= 0X2_a700 and cp <= 0X2_b73f) #
or (cp >= 0X2_b740 and cp <= 0X2_b81f) #
or (cp >= 0X2_b820 and cp <= 0X2_ceaf) #
or (cp >= 0Xf900 and cp <= 0Xfaff)
or (cp >= 0X2_f800 and cp <= 0X2_fa1f) #
): #
return True
return False
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : Optional[int] , _lowerCAmelCase : "AutoTokenizer" , _lowerCAmelCase : bool = False , _lowerCAmelCase : Optional[float] = None , **_lowerCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
super().__init__(_lowerCAmelCase , _lowerCAmelCase , **_lowerCAmelCase )
__lowercase = Queue()
__lowercase = None
__lowercase = timeout
def _a ( self : Dict , _lowerCAmelCase : str , _lowerCAmelCase : bool = False ) -> int:
"""simple docstring"""
self.text_queue.put(_lowerCAmelCase , timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal , timeout=self.timeout )
def __iter__( self : Any ) -> Any:
"""simple docstring"""
return self
def _a ( self : Any ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 53
|
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :str = (UnCLIPScheduler,)
def _a ( self : Optional[int] , **_lowerCAmelCase : Any ) -> Tuple:
"""simple docstring"""
__lowercase = {
"""num_train_timesteps""": 1000,
"""variance_type""": """fixed_small_log""",
"""clip_sample""": True,
"""clip_sample_range""": 1.0,
"""prediction_type""": """epsilon""",
}
config.update(**_lowerCAmelCase )
return config
def _a ( self : Dict ) -> List[Any]:
"""simple docstring"""
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=_lowerCAmelCase )
def _a ( self : List[str] ) -> Tuple:
"""simple docstring"""
for variance in ["fixed_small_log", "learned_range"]:
self.check_over_configs(variance_type=_lowerCAmelCase )
def _a ( self : Any ) -> Any:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_lowerCAmelCase )
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
for clip_sample_range in [1, 5, 10, 20]:
self.check_over_configs(clip_sample_range=_lowerCAmelCase )
def _a ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(prediction_type=_lowerCAmelCase )
def _a ( self : str ) -> int:
"""simple docstring"""
for time_step in [0, 500, 999]:
for prev_timestep in [None, 5, 100, 250, 500, 750]:
if prev_timestep is not None and prev_timestep >= time_step:
continue
self.check_over_forward(time_step=_lowerCAmelCase , prev_timestep=_lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config(variance_type="""fixed_small_log""" )
__lowercase = scheduler_class(**_lowerCAmelCase )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.0000e-10 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_549_625 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.9_994_987 ) ) < 1e-5
def _a ( self : str ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config(variance_type="""learned_range""" )
__lowercase = scheduler_class(**_lowerCAmelCase )
__lowercase = 0.5
assert scheduler._get_variance(1 , predicted_variance=_lowerCAmelCase ) - -10.1_712_790 < 1e-5
assert scheduler._get_variance(487 , predicted_variance=_lowerCAmelCase ) - -5.7_998_052 < 1e-5
assert scheduler._get_variance(999 , predicted_variance=_lowerCAmelCase ) - -0.0_010_011 < 1e-5
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config()
__lowercase = scheduler_class(**_lowerCAmelCase )
__lowercase = scheduler.timesteps
__lowercase = self.dummy_model()
__lowercase = self.dummy_sample_deter
__lowercase = torch.manual_seed(0 )
for i, t in enumerate(_lowerCAmelCase ):
# 1. predict noise residual
__lowercase = model(_lowerCAmelCase , _lowerCAmelCase )
# 2. predict previous mean of sample x_t-1
__lowercase = scheduler.step(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , generator=_lowerCAmelCase ).prev_sample
__lowercase = pred_prev_sample
__lowercase = torch.sum(torch.abs(_lowerCAmelCase ) )
__lowercase = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 252.2_682_495 ) < 1e-2
assert abs(result_mean.item() - 0.3_284_743 ) < 1e-3
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config()
__lowercase = scheduler_class(**_lowerCAmelCase )
scheduler.set_timesteps(25 )
__lowercase = scheduler.timesteps
__lowercase = self.dummy_model()
__lowercase = self.dummy_sample_deter
__lowercase = torch.manual_seed(0 )
for i, t in enumerate(_lowerCAmelCase ):
# 1. predict noise residual
__lowercase = model(_lowerCAmelCase , _lowerCAmelCase )
if i + 1 == timesteps.shape[0]:
__lowercase = None
else:
__lowercase = timesteps[i + 1]
# 2. predict previous mean of sample x_t-1
__lowercase = scheduler.step(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , prev_timestep=_lowerCAmelCase , generator=_lowerCAmelCase ).prev_sample
__lowercase = pred_prev_sample
__lowercase = torch.sum(torch.abs(_lowerCAmelCase ) )
__lowercase = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 258.2_044_983 ) < 1e-2
assert abs(result_mean.item() - 0.3_362_038 ) < 1e-3
def _a ( self : str ) -> Union[str, Any]:
"""simple docstring"""
pass
def _a ( self : int ) -> List[str]:
"""simple docstring"""
pass
| 53
| 1
|
import numpy
# List of input, output pairs
__UpperCamelCase : List[str] = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
__UpperCamelCase : List[Any] = (((515, 22, 13), 555), ((61, 35, 49), 150))
__UpperCamelCase : List[Any] = [2, 4, 1, 5]
__UpperCamelCase : Union[str, Any] = len(train_data)
__UpperCamelCase : List[Any] = 0.0_0_9
def snake_case ( lowerCamelCase , lowerCamelCase="train" ):
'''simple docstring'''
return calculate_hypothesis_value(lowerCamelCase , lowerCamelCase ) - output(
lowerCamelCase , lowerCamelCase )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = 0
for i in range(len(lowerCamelCase ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def snake_case ( lowerCamelCase , lowerCamelCase=m ):
'''simple docstring'''
__lowercase = 0
for i in range(lowerCamelCase ):
if index == -1:
summation_value += _error(lowerCamelCase )
else:
summation_value += _error(lowerCamelCase ) * train_data[i][0][index]
return summation_value
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = summation_of_cost_derivative(lowerCamelCase , lowerCamelCase ) / m
return cost_derivative_value
def snake_case ( ):
'''simple docstring'''
global parameter_vector
# Tune these values to set a tolerance value for predicted output
__lowercase = 0.000002
__lowercase = 0
__lowercase = 0
while True:
j += 1
__lowercase = [0, 0, 0, 0]
for i in range(0 , len(lowerCamelCase ) ):
__lowercase = get_cost_derivative(i - 1 )
__lowercase = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
lowerCamelCase , lowerCamelCase , atol=lowerCamelCase , rtol=lowerCamelCase , ):
break
__lowercase = temp_parameter_vector
print(("""Number of iterations:""", j) )
def snake_case ( ):
'''simple docstring'''
for i in range(len(lowerCamelCase ) ):
print(("""Actual output value:""", output(lowerCamelCase , """test""" )) )
print(("""Hypothesis output:""", calculate_hypothesis_value(lowerCamelCase , """test""" )) )
if __name__ == "__main__":
run_gradient_descent()
print("""\nTesting gradient descent for a linear hypothesis function.\n""")
test_gradient_descent()
| 53
|
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
__UpperCamelCase : Any = logging.get_logger(__name__)
@dataclass
class __UpperCamelCase :
__snake_case :str = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys() )} )
__snake_case :str = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
__snake_case :int = field(
default=1_2_8 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def _a ( self : Dict ) -> List[Any]:
"""simple docstring"""
__lowercase = self.task_name.lower()
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Optional[int] = 'train'
__snake_case :int = 'dev'
__snake_case :Any = 'test'
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :GlueDataTrainingArguments
__snake_case :str
__snake_case :List[InputFeatures]
def __init__( self : Dict , _lowerCAmelCase : GlueDataTrainingArguments , _lowerCAmelCase : PreTrainedTokenizerBase , _lowerCAmelCase : Optional[int] = None , _lowerCAmelCase : Union[str, Split] = Split.train , _lowerCAmelCase : Optional[str] = None , ) -> List[Any]:
"""simple docstring"""
warnings.warn(
"""This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
"""library. You can have a look at this example script for pointers: """
"""https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py""" , _lowerCAmelCase , )
__lowercase = args
__lowercase = glue_processors[args.task_name]()
__lowercase = glue_output_modes[args.task_name]
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
try:
__lowercase = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
# Load data features from cache or dataset file
__lowercase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}' , )
__lowercase = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
__lowercase , __lowercase = label_list[2], label_list[1]
__lowercase = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowercase = cached_features_file + """.lock"""
with FileLock(_lowerCAmelCase ):
if os.path.exists(_lowerCAmelCase ) and not args.overwrite_cache:
__lowercase = time.time()
__lowercase = torch.load(_lowerCAmelCase )
logger.info(
F'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start )
else:
logger.info(F'Creating features from dataset file at {args.data_dir}' )
if mode == Split.dev:
__lowercase = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
__lowercase = self.processor.get_test_examples(args.data_dir )
else:
__lowercase = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
__lowercase = examples[:limit_length]
__lowercase = glue_convert_examples_to_features(
_lowerCAmelCase , _lowerCAmelCase , max_length=args.max_seq_length , label_list=_lowerCAmelCase , output_mode=self.output_mode , )
__lowercase = time.time()
torch.save(self.features , _lowerCAmelCase )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self : Dict ) -> Optional[int]:
"""simple docstring"""
return len(self.features )
def __getitem__( self : Tuple , _lowerCAmelCase : Optional[int] ) -> InputFeatures:
"""simple docstring"""
return self.features[i]
def _a ( self : str ) -> int:
"""simple docstring"""
return self.label_list
| 53
| 1
|
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
__UpperCamelCase : Optional[Any] = {
"""microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ):
__snake_case :Dict = 'resnet'
__snake_case :Optional[int] = ['basic', 'bottleneck']
def __init__( self : List[Any] , _lowerCAmelCase : Tuple=3 , _lowerCAmelCase : List[str]=64 , _lowerCAmelCase : str=[256, 512, 1024, 2048] , _lowerCAmelCase : Union[str, Any]=[3, 4, 6, 3] , _lowerCAmelCase : Union[str, Any]="bottleneck" , _lowerCAmelCase : List[Any]="relu" , _lowerCAmelCase : int=False , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : Optional[Any]=None , **_lowerCAmelCase : int , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**_lowerCAmelCase )
if layer_type not in self.layer_types:
raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types )}' )
__lowercase = num_channels
__lowercase = embedding_size
__lowercase = hidden_sizes
__lowercase = depths
__lowercase = layer_type
__lowercase = hidden_act
__lowercase = downsample_in_first_stage
__lowercase = ["""stem"""] + [F'stage{idx}' for idx in range(1 , len(_lowerCAmelCase ) + 1 )]
__lowercase , __lowercase = get_aligned_output_features_output_indices(
out_features=_lowerCAmelCase , out_indices=_lowerCAmelCase , stage_names=self.stage_names )
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :int = version.parse('1.11' )
@property
def _a ( 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 _a ( self : Any ) -> float:
"""simple docstring"""
return 1e-3
| 53
|
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoTokenizer,
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForWholeWordMask,
HfArgumentParser,
LineByLineTextDataset,
LineByLineWithRefDataset,
PreTrainedTokenizer,
TextDataset,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
__UpperCamelCase : List[Any] = logging.getLogger(__name__)
__UpperCamelCase : Optional[Any] = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
__UpperCamelCase : List[str] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class __UpperCamelCase :
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={
'help': (
'The model checkpoint for weights initialization. Leave None if you want to train a model from'
' scratch.'
)
} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_lowerCAmelCase )} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class __UpperCamelCase :
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'The input training data file (a text file).'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={
'help': (
'The input training data files (multiple files in glob format). '
'Very often splitting large files to smaller files can prevent tokenizer going out of memory'
)
} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'An optional input train ref data file for whole word mask in Chinese.'} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'An optional input eval ref data file for whole word mask in Chinese.'} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Whether distinct lines of text in the dataset are to be handled as distinct sequences.'} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Train with masked-language modeling loss instead of language modeling.'} )
__snake_case :bool = field(default=_lowerCAmelCase , metadata={'help': 'Whether ot not to use whole word mask.'} )
__snake_case :float = field(
default=0.15 , metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'} )
__snake_case :float = field(
default=1 / 6 , metadata={
'help': (
'Ratio of length of a span of masked tokens to surrounding context length for permutation language'
' modeling.'
)
} , )
__snake_case :int = field(
default=5 , metadata={'help': 'Maximum length of a span of masked tokens for permutation language modeling.'} )
__snake_case :int = field(
default=-1 , metadata={
'help': (
'Optional input sequence length after tokenization.'
'The training dataset will be truncated in block of this size for training.'
'Default to the model max input length for single sentence inputs (take into account special tokens).'
)
} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase = False , lowerCamelCase = None , ):
'''simple docstring'''
def _dataset(lowerCamelCase , lowerCamelCase=None ):
if args.line_by_line:
if ref_path is not None:
if not args.whole_word_mask or not args.mlm:
raise ValueError("""You need to set world whole masking and mlm to True for Chinese Whole Word Mask""" )
return LineByLineWithRefDataset(
tokenizer=lowerCamelCase , file_path=lowerCamelCase , block_size=args.block_size , ref_path=lowerCamelCase , )
return LineByLineTextDataset(tokenizer=lowerCamelCase , file_path=lowerCamelCase , block_size=args.block_size )
else:
return TextDataset(
tokenizer=lowerCamelCase , file_path=lowerCamelCase , block_size=args.block_size , overwrite_cache=args.overwrite_cache , cache_dir=lowerCamelCase , )
if evaluate:
return _dataset(args.eval_data_file , args.eval_ref_file )
elif args.train_data_files:
return ConcatDataset([_dataset(lowerCamelCase ) for f in glob(args.train_data_files )] )
else:
return _dataset(args.train_data_file , args.train_ref_file )
def snake_case ( ):
'''simple docstring'''
__lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
__lowercase , __lowercase , __lowercase = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_args.do_eval:
raise ValueError(
"""Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file """
"""or remove the --do_eval argument.""" )
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
""" --overwrite_output_dir to overcome.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , lowerCamelCase )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if model_args.config_name:
__lowercase = AutoConfig.from_pretrained(model_args.config_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
__lowercase = AutoConfig.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
__lowercase = CONFIG_MAPPING[model_args.model_type]()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.tokenizer_name:
__lowercase = AutoTokenizer.from_pretrained(model_args.tokenizer_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
__lowercase = AutoTokenizer.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
raise ValueError(
"""You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"""
""" script, save it,and load it from here, using --tokenizer_name""" )
if model_args.model_name_or_path:
__lowercase = AutoModelWithLMHead.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 , )
else:
logger.info("""Training new model from scratch""" )
__lowercase = AutoModelWithLMHead.from_config(lowerCamelCase )
model.resize_token_embeddings(len(lowerCamelCase ) )
if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
raise ValueError(
"""BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"""
"""--mlm flag (masked language modeling).""" )
if data_args.block_size <= 0:
__lowercase = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
__lowercase = min(data_args.block_size , tokenizer.max_len )
# Get datasets
__lowercase = (
get_dataset(lowerCamelCase , tokenizer=lowerCamelCase , cache_dir=model_args.cache_dir ) if training_args.do_train else None
)
__lowercase = (
get_dataset(lowerCamelCase , tokenizer=lowerCamelCase , evaluate=lowerCamelCase , cache_dir=model_args.cache_dir )
if training_args.do_eval
else None
)
if config.model_type == "xlnet":
__lowercase = DataCollatorForPermutationLanguageModeling(
tokenizer=lowerCamelCase , plm_probability=data_args.plm_probability , max_span_length=data_args.max_span_length , )
else:
if data_args.mlm and data_args.whole_word_mask:
__lowercase = DataCollatorForWholeWordMask(
tokenizer=lowerCamelCase , mlm_probability=data_args.mlm_probability )
else:
__lowercase = DataCollatorForLanguageModeling(
tokenizer=lowerCamelCase , mlm=data_args.mlm , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
__lowercase = Trainer(
model=lowerCamelCase , args=lowerCamelCase , data_collator=lowerCamelCase , train_dataset=lowerCamelCase , eval_dataset=lowerCamelCase , prediction_loss_only=lowerCamelCase , )
# Training
if training_args.do_train:
__lowercase = (
model_args.model_name_or_path
if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
else None
)
trainer.train(model_path=lowerCamelCase )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
__lowercase = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__lowercase = trainer.evaluate()
__lowercase = math.exp(eval_output["""eval_loss"""] )
__lowercase = {"""perplexity""": perplexity}
__lowercase = os.path.join(training_args.output_dir , """eval_results_lm.txt""" )
if trainer.is_world_master():
with open(lowerCamelCase , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key in sorted(result.keys() ):
logger.info(""" %s = %s""" , lowerCamelCase , str(result[key] ) )
writer.write("""%s = %s\n""" % (key, str(result[key] )) )
results.update(lowerCamelCase )
return results
def snake_case ( lowerCamelCase ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 53
| 1
|
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
super().tearDown()
gc.collect()
def _a ( self : List[str] ) -> int:
"""simple docstring"""
__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_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512] )
print(F'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def _a ( self : str ) -> List[Any]:
"""simple docstring"""
__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_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297] )
print(F'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 53
|
from __future__ import annotations
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if len(lowerCamelCase ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
if any(i <= 0 for i in nums ):
raise ValueError("""All values must be greater than 0""" )
__lowercase = nums.copy()
copy_nums.sort()
return copy_nums[-1] < sum(copy_nums[:-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 53
| 1
|
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F'''{price_plus_tax(100, 0.2_5) = }''')
print(F'''{price_plus_tax(1_2_5.5_0, 0.0_5) = }''')
| 53
|
from __future__ import annotations
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not nums:
return 0
__lowercase = nums[0]
__lowercase = 0
for num in nums[1:]:
__lowercase , __lowercase = (
max_excluding + num,
max(lowerCamelCase , lowerCamelCase ),
)
return max(lowerCamelCase , lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 53
| 1
|
from __future__ import annotations
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
__lowercase , __lowercase = array[indexa], array[indexa]
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if length > 1:
__lowercase = int(length / 2 )
for i in range(lowerCamelCase , low + middle ):
comp_and_swap(lowerCamelCase , lowerCamelCase , i + middle , lowerCamelCase )
bitonic_merge(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
bitonic_merge(lowerCamelCase , low + middle , lowerCamelCase , lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if length > 1:
__lowercase = int(length / 2 )
bitonic_sort(lowerCamelCase , lowerCamelCase , lowerCamelCase , 1 )
bitonic_sort(lowerCamelCase , low + middle , lowerCamelCase , 0 )
bitonic_merge(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
if __name__ == "__main__":
__UpperCamelCase : Any = input("""Enter numbers separated by a comma:\n""").strip()
__UpperCamelCase : Dict = [int(item.strip()) for item in user_input.split(""",""")]
bitonic_sort(unsorted, 0, len(unsorted), 1)
print("""\nSorted array in ascending order is: """, end="""""")
print(*unsorted, sep=""", """)
bitonic_merge(unsorted, 0, len(unsorted), 0)
print("""Sorted array in descending order is: """, end="""""")
print(*unsorted, sep=""", """)
| 53
|
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase : str = logging.get_logger(__name__)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = RobertaPreLayerNormConfig.from_pretrained(
lowerCamelCase , architectures=["""RobertaPreLayerNormForMaskedLM"""] )
# convert state_dict
__lowercase = torch.load(hf_hub_download(repo_id=lowerCamelCase , filename="""pytorch_model.bin""" ) )
__lowercase = {}
for tensor_key, tensor_value in original_state_dict.items():
# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
if tensor_key.startswith("""roberta.""" ):
__lowercase = """roberta_prelayernorm.""" + tensor_key[len("""roberta.""" ) :]
# The original implementation contains weights which are not used, remove them from the state_dict
if tensor_key.endswith(""".self.LayerNorm.weight""" ) or tensor_key.endswith(""".self.LayerNorm.bias""" ):
continue
__lowercase = tensor_value
__lowercase = RobertaPreLayerNormForMaskedLM.from_pretrained(
pretrained_model_name_or_path=lowerCamelCase , config=lowerCamelCase , state_dict=lowerCamelCase )
model.save_pretrained(lowerCamelCase )
# convert tokenizer
__lowercase = AutoTokenizer.from_pretrained(lowerCamelCase )
tokenizer.save_pretrained(lowerCamelCase )
if __name__ == "__main__":
__UpperCamelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint-repo""",
default=None,
type=str,
required=True,
help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
__UpperCamelCase : Dict = parser.parse_args()
convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)
| 53
| 1
|
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
assert isinstance(lowerCamelCase , lowerCamelCase )
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = tmp_path / """cache"""
__lowercase = {"""text""": """string"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
__lowercase = TextDatasetReader(lowerCamelCase , cache_dir=lowerCamelCase , keep_in_memory=lowerCamelCase ).read()
_check_text_dataset(lowerCamelCase , lowerCamelCase )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""text""": """string"""},
{"""text""": """int32"""},
{"""text""": """float32"""},
] , )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = tmp_path / """cache"""
__lowercase = {"""text""": """string"""}
__lowercase = features.copy() if features else default_expected_features
__lowercase = (
Features({feature: Value(lowerCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
__lowercase = TextDatasetReader(lowerCamelCase , features=lowerCamelCase , cache_dir=lowerCamelCase ).read()
_check_text_dataset(lowerCamelCase , lowerCamelCase )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = tmp_path / """cache"""
__lowercase = {"""text""": """string"""}
__lowercase = TextDatasetReader(lowerCamelCase , cache_dir=lowerCamelCase , split=lowerCamelCase ).read()
_check_text_dataset(lowerCamelCase , lowerCamelCase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type""" , [str, list] )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if issubclass(lowerCamelCase , lowerCamelCase ):
__lowercase = text_path
elif issubclass(lowerCamelCase , lowerCamelCase ):
__lowercase = [text_path]
__lowercase = tmp_path / """cache"""
__lowercase = {"""text""": """string"""}
__lowercase = TextDatasetReader(lowerCamelCase , cache_dir=lowerCamelCase ).read()
_check_text_dataset(lowerCamelCase , lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase=("train",) ):
'''simple docstring'''
assert isinstance(lowerCamelCase , lowerCamelCase )
for split in splits:
__lowercase = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = tmp_path / """cache"""
__lowercase = {"""text""": """string"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
__lowercase = TextDatasetReader({"""train""": text_path} , cache_dir=lowerCamelCase , keep_in_memory=lowerCamelCase ).read()
_check_text_datasetdict(lowerCamelCase , lowerCamelCase )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""text""": """string"""},
{"""text""": """int32"""},
{"""text""": """float32"""},
] , )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = tmp_path / """cache"""
# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
__lowercase = {"""text""": """string"""}
__lowercase = features.copy() if features else default_expected_features
__lowercase = (
Features({feature: Value(lowerCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
__lowercase = TextDatasetReader({"""train""": text_path} , features=lowerCamelCase , cache_dir=lowerCamelCase ).read()
_check_text_datasetdict(lowerCamelCase , lowerCamelCase )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if split:
__lowercase = {split: text_path}
else:
__lowercase = """train"""
__lowercase = {"""train""": text_path, """test""": text_path}
__lowercase = tmp_path / """cache"""
__lowercase = {"""text""": """string"""}
__lowercase = TextDatasetReader(lowerCamelCase , cache_dir=lowerCamelCase ).read()
_check_text_datasetdict(lowerCamelCase , lowerCamelCase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
| 53
|
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if (ksize % 2) == 0:
__lowercase = ksize + 1
__lowercase = np.zeros((ksize, ksize) , dtype=np.floataa )
# each value
for y in range(lowerCamelCase ):
for x in range(lowerCamelCase ):
# distance from center
__lowercase = x - ksize // 2
__lowercase = y - ksize // 2
# degree to radiant
__lowercase = theta / 180 * np.pi
__lowercase = np.cos(_theta )
__lowercase = np.sin(_theta )
# get kernel x
__lowercase = cos_theta * px + sin_theta * py
# get kernel y
__lowercase = -sin_theta * px + cos_theta * py
# fill kernel
__lowercase = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi )
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
__UpperCamelCase : List[Any] = imread("""../image_data/lena.jpg""")
# turn image in gray scale value
__UpperCamelCase : Union[str, Any] = cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
__UpperCamelCase : Union[str, Any] = np.zeros(gray.shape[:2])
for theta in [0, 30, 60, 90, 120, 150]:
__UpperCamelCase : Tuple = gabor_filter_kernel(10, 8, theta, 10, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
__UpperCamelCase : List[str] = out / out.max() * 255
__UpperCamelCase : List[str] = out.astype(np.uinta)
imshow("""Original""", gray)
imshow("""Gabor filter with 20x20 mask and 6 directions""", out)
waitKey(0)
| 53
| 1
|
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = checkpoint
__lowercase = {}
__lowercase = vae_state_dict["""encoder.conv_in.weight"""]
__lowercase = vae_state_dict["""encoder.conv_in.bias"""]
__lowercase = vae_state_dict["""encoder.conv_out.weight"""]
__lowercase = vae_state_dict["""encoder.conv_out.bias"""]
__lowercase = vae_state_dict["""encoder.norm_out.weight"""]
__lowercase = vae_state_dict["""encoder.norm_out.bias"""]
__lowercase = vae_state_dict["""decoder.conv_in.weight"""]
__lowercase = vae_state_dict["""decoder.conv_in.bias"""]
__lowercase = vae_state_dict["""decoder.conv_out.weight"""]
__lowercase = vae_state_dict["""decoder.conv_out.bias"""]
__lowercase = vae_state_dict["""decoder.norm_out.weight"""]
__lowercase = vae_state_dict["""decoder.norm_out.bias"""]
__lowercase = vae_state_dict["""quant_conv.weight"""]
__lowercase = vae_state_dict["""quant_conv.bias"""]
__lowercase = vae_state_dict["""post_quant_conv.weight"""]
__lowercase = vae_state_dict["""post_quant_conv.bias"""]
# Retrieves the keys for the encoder down blocks only
__lowercase = len({""".""".join(layer.split(""".""" )[:3] ) for layer in vae_state_dict if """encoder.down""" in layer} )
__lowercase = {
layer_id: [key for key in vae_state_dict if F'down.{layer_id}' in key] for layer_id in range(lowerCamelCase )
}
# Retrieves the keys for the decoder up blocks only
__lowercase = len({""".""".join(layer.split(""".""" )[:3] ) for layer in vae_state_dict if """decoder.up""" in layer} )
__lowercase = {
layer_id: [key for key in vae_state_dict if F'up.{layer_id}' in key] for layer_id in range(lowerCamelCase )
}
for i in range(lowerCamelCase ):
__lowercase = [key for key in down_blocks[i] if F'down.{i}' in key and F'down.{i}.downsample' not in key]
if F'encoder.down.{i}.downsample.conv.weight' in vae_state_dict:
__lowercase = vae_state_dict.pop(
F'encoder.down.{i}.downsample.conv.weight' )
__lowercase = vae_state_dict.pop(
F'encoder.down.{i}.downsample.conv.bias' )
__lowercase = renew_vae_resnet_paths(lowerCamelCase )
__lowercase = {"""old""": F'down.{i}.block', """new""": F'down_blocks.{i}.resnets'}
assign_to_checkpoint(lowerCamelCase , lowerCamelCase , lowerCamelCase , additional_replacements=[meta_path] , config=lowerCamelCase )
__lowercase = [key for key in vae_state_dict if """encoder.mid.block""" in key]
__lowercase = 2
for i in range(1 , num_mid_res_blocks + 1 ):
__lowercase = [key for key in mid_resnets if F'encoder.mid.block_{i}' in key]
__lowercase = renew_vae_resnet_paths(lowerCamelCase )
__lowercase = {"""old""": F'mid.block_{i}', """new""": F'mid_block.resnets.{i - 1}'}
assign_to_checkpoint(lowerCamelCase , lowerCamelCase , lowerCamelCase , additional_replacements=[meta_path] , config=lowerCamelCase )
__lowercase = [key for key in vae_state_dict if """encoder.mid.attn""" in key]
__lowercase = renew_vae_attention_paths(lowerCamelCase )
__lowercase = {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
assign_to_checkpoint(lowerCamelCase , lowerCamelCase , lowerCamelCase , additional_replacements=[meta_path] , config=lowerCamelCase )
conv_attn_to_linear(lowerCamelCase )
for i in range(lowerCamelCase ):
__lowercase = num_up_blocks - 1 - i
__lowercase = [
key for key in up_blocks[block_id] if F'up.{block_id}' in key and F'up.{block_id}.upsample' not in key
]
if F'decoder.up.{block_id}.upsample.conv.weight' in vae_state_dict:
__lowercase = vae_state_dict[
F'decoder.up.{block_id}.upsample.conv.weight'
]
__lowercase = vae_state_dict[
F'decoder.up.{block_id}.upsample.conv.bias'
]
__lowercase = renew_vae_resnet_paths(lowerCamelCase )
__lowercase = {"""old""": F'up.{block_id}.block', """new""": F'up_blocks.{i}.resnets'}
assign_to_checkpoint(lowerCamelCase , lowerCamelCase , lowerCamelCase , additional_replacements=[meta_path] , config=lowerCamelCase )
__lowercase = [key for key in vae_state_dict if """decoder.mid.block""" in key]
__lowercase = 2
for i in range(1 , num_mid_res_blocks + 1 ):
__lowercase = [key for key in mid_resnets if F'decoder.mid.block_{i}' in key]
__lowercase = renew_vae_resnet_paths(lowerCamelCase )
__lowercase = {"""old""": F'mid.block_{i}', """new""": F'mid_block.resnets.{i - 1}'}
assign_to_checkpoint(lowerCamelCase , lowerCamelCase , lowerCamelCase , additional_replacements=[meta_path] , config=lowerCamelCase )
__lowercase = [key for key in vae_state_dict if """decoder.mid.attn""" in key]
__lowercase = renew_vae_attention_paths(lowerCamelCase )
__lowercase = {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
assign_to_checkpoint(lowerCamelCase , lowerCamelCase , lowerCamelCase , additional_replacements=[meta_path] , config=lowerCamelCase )
conv_attn_to_linear(lowerCamelCase )
return new_checkpoint
def snake_case ( lowerCamelCase , lowerCamelCase , ):
'''simple docstring'''
__lowercase = requests.get(
""" https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml""" )
__lowercase = io.BytesIO(r.content )
__lowercase = OmegaConf.load(lowerCamelCase )
__lowercase = 512
__lowercase = """cuda""" if torch.cuda.is_available() else """cpu"""
if checkpoint_path.endswith("""safetensors""" ):
from safetensors import safe_open
__lowercase = {}
with safe_open(lowerCamelCase , framework="""pt""" , device="""cpu""" ) as f:
for key in f.keys():
__lowercase = f.get_tensor(lowerCamelCase )
else:
__lowercase = torch.load(lowerCamelCase , map_location=lowerCamelCase )["""state_dict"""]
# Convert the VAE model.
__lowercase = create_vae_diffusers_config(lowerCamelCase , image_size=lowerCamelCase )
__lowercase = custom_convert_ldm_vae_checkpoint(lowerCamelCase , lowerCamelCase )
__lowercase = AutoencoderKL(**lowerCamelCase )
vae.load_state_dict(lowerCamelCase )
vae.save_pretrained(lowerCamelCase )
if __name__ == "__main__":
__UpperCamelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("""--vae_pt_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")
__UpperCamelCase : Tuple = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
| 53
|
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = set()
__lowercase = []
def parse_line(lowerCamelCase ):
for line in fp:
if isinstance(lowerCamelCase , lowerCamelCase ):
__lowercase = line.decode("""UTF-8""" )
if "warnings summary (final)" in line:
continue
# This means we are outside the body of a warning
elif not line.startswith(""" """ ):
# process a single warning and move it to `selected_warnings`.
if len(lowerCamelCase ) > 0:
__lowercase = """\n""".join(lowerCamelCase )
# Only keep the warnings specified in `targets`
if any(F': {x}: ' in warning for x in targets ):
selected_warnings.add(lowerCamelCase )
buffer.clear()
continue
else:
__lowercase = line.strip()
buffer.append(lowerCamelCase )
if from_gh:
for filename in os.listdir(lowerCamelCase ):
__lowercase = os.path.join(lowerCamelCase , lowerCamelCase )
if not os.path.isdir(lowerCamelCase ):
# read the file
if filename != "warnings.txt":
continue
with open(lowerCamelCase ) as fp:
parse_line(lowerCamelCase )
else:
try:
with zipfile.ZipFile(lowerCamelCase ) as z:
for filename in z.namelist():
if not os.path.isdir(lowerCamelCase ):
# read the file
if filename != "warnings.txt":
continue
with z.open(lowerCamelCase ) as fp:
parse_line(lowerCamelCase )
except Exception:
logger.warning(
F'{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.' )
return selected_warnings
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = set()
__lowercase = [os.path.join(lowerCamelCase , lowerCamelCase ) for p in os.listdir(lowerCamelCase ) if (p.endswith(""".zip""" ) or from_gh)]
for p in paths:
selected_warnings.update(extract_warnings_from_single_artifact(lowerCamelCase , lowerCamelCase ) )
return selected_warnings
if __name__ == "__main__":
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return values.split(""",""" )
__UpperCamelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
parser.add_argument(
"""--output_dir""",
type=str,
required=True,
help="""Where to store the downloaded artifacts and other result files.""",
)
parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
# optional parameters
parser.add_argument(
"""--targets""",
default="""DeprecationWarning,UserWarning,FutureWarning""",
type=list_str,
help="""Comma-separated list of target warning(s) which we want to extract.""",
)
parser.add_argument(
"""--from_gh""",
action="""store_true""",
help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
)
__UpperCamelCase : List[str] = parser.parse_args()
__UpperCamelCase : Union[str, Any] = args.from_gh
if from_gh:
# The artifacts have to be downloaded using `actions/download-artifact@v3`
pass
else:
os.makedirs(args.output_dir, exist_ok=True)
# get download links
__UpperCamelCase : Any = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
# download artifacts
for idx, (name, url) in enumerate(artifacts.items()):
print(name)
print(url)
print("""=""" * 80)
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
# extract warnings from artifacts
__UpperCamelCase : Union[str, Any] = extract_warnings(args.output_dir, args.targets)
__UpperCamelCase : Any = sorted(selected_warnings)
with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)
| 53
| 1
|
import re
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = re.compile(r"""^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$""" )
if match := re.search(lowerCamelCase , lowerCamelCase ):
return match.string == phone
return False
if __name__ == "__main__":
print(indian_phone_validator("""+918827897895"""))
| 53
|
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCamelCase : Any = {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Dict = [
"""MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MraForMaskedLM""",
"""MraForMultipleChoice""",
"""MraForQuestionAnswering""",
"""MraForSequenceClassification""",
"""MraForTokenClassification""",
"""MraLayer""",
"""MraModel""",
"""MraPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
__UpperCamelCase : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 53
| 1
|
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return "\n".join(
F'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 53
|
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not isinstance(lowerCamelCase , lowerCamelCase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
__lowercase = str(lowerCamelCase )
__lowercase = """""".join(sorted(lowerCamelCase ) )
return sorted_str_n != str_n and sorted_str_n[::-1] != str_n
def snake_case ( lowerCamelCase = 99 ):
'''simple docstring'''
if not 0 < percent < 100:
raise ValueError("""solution() only accepts values from 0 to 100""" )
__lowercase = 0
__lowercase = 1
while True:
if check_bouncy(lowerCamelCase ):
bouncy_num += 1
if (bouncy_num / num) * 100 >= percent:
return num
num += 1
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F'''{solution(99)}''')
| 53
| 1
|
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
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 .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
| 53
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__UpperCamelCase : Tuple = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormerConfig""",
"""SwiftFormerOnnxConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Optional[Any] = [
"""SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SwiftFormerForImageClassification""",
"""SwiftFormerModel""",
"""SwiftFormerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
__UpperCamelCase : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 53
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : List[Any] = {
"""configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
"""configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : str = ["""MaskFormerFeatureExtractor"""]
__UpperCamelCase : Union[str, Any] = ["""MaskFormerImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Any = [
"""MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MaskFormerForInstanceSegmentation""",
"""MaskFormerModel""",
"""MaskFormerPreTrainedModel""",
]
__UpperCamelCase : Any = [
"""MaskFormerSwinBackbone""",
"""MaskFormerSwinModel""",
"""MaskFormerSwinPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
from .configuration_maskformer_swin import MaskFormerSwinConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_maskformer import MaskFormerFeatureExtractor
from .image_processing_maskformer import MaskFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskformer import (
MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskFormerForInstanceSegmentation,
MaskFormerModel,
MaskFormerPreTrainedModel,
)
from .modeling_maskformer_swin import (
MaskFormerSwinBackbone,
MaskFormerSwinModel,
MaskFormerSwinPreTrainedModel,
)
else:
import sys
__UpperCamelCase : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 53
|
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
super().tearDown()
gc.collect()
def _a ( self : List[str] ) -> int:
"""simple docstring"""
__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_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512] )
print(F'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def _a ( self : str ) -> List[Any]:
"""simple docstring"""
__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_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297] )
print(F'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 53
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
__UpperCamelCase : Dict = {
"""google/vivit-b-16x2-kinetics400""": (
"""https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"""
),
# See all Vivit models at https://huggingface.co/models?filter=vivit
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Dict = 'vivit'
def __init__( self : str , _lowerCAmelCase : Any=224 , _lowerCAmelCase : int=32 , _lowerCAmelCase : Dict=[2, 16, 16] , _lowerCAmelCase : Optional[int]=3 , _lowerCAmelCase : str=768 , _lowerCAmelCase : Optional[int]=12 , _lowerCAmelCase : Optional[Any]=12 , _lowerCAmelCase : Tuple=3072 , _lowerCAmelCase : Union[str, Any]="gelu_fast" , _lowerCAmelCase : int=0.0 , _lowerCAmelCase : List[Any]=0.0 , _lowerCAmelCase : Optional[int]=0.02 , _lowerCAmelCase : Union[str, Any]=1e-06 , _lowerCAmelCase : Optional[int]=True , **_lowerCAmelCase : int , ) -> str:
"""simple docstring"""
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = image_size
__lowercase = num_frames
__lowercase = tubelet_size
__lowercase = num_channels
__lowercase = qkv_bias
super().__init__(**_lowerCAmelCase )
| 53
|
import heapq
import sys
import numpy as np
__UpperCamelCase : List[str] = tuple[int, int]
class __UpperCamelCase :
def __init__( self : Optional[int] ) -> Dict:
"""simple docstring"""
__lowercase = []
__lowercase = set()
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
if not self.empty():
return self.elements[0][0]
else:
return float("""inf""" )
def _a ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
return len(self.elements ) == 0
def _a ( self : str , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
if item not in self.set:
heapq.heappush(self.elements , (priority, item) )
self.set.add(_lowerCAmelCase )
else:
# update
# print("update", item)
__lowercase = []
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
while x != item:
temp.append((pri, x) )
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
temp.append((priority, item) )
for pro, xxx in temp:
heapq.heappush(self.elements , (pro, xxx) )
def _a ( self : List[str] , _lowerCAmelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
if item in self.set:
self.set.remove(_lowerCAmelCase )
__lowercase = []
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
while x != item:
temp.append((pro, x) )
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
for prito, yyy in temp:
heapq.heappush(self.elements , (prito, yyy) )
def _a ( self : Any ) -> List[Any]:
"""simple docstring"""
return self.elements[0][1]
def _a ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
self.set.remove(_lowerCAmelCase )
return (priority, item)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = np.array(lowerCamelCase )
__lowercase = np.array(lowerCamelCase )
return np.linalg.norm(a - b )
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return consistent_heuristic(lowerCamelCase , lowerCamelCase ) // t
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return abs(p[0] - goal[0] ) + abs(p[1] - goal[1] )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = g_function[start] + Wa * heuristics[i](lowerCamelCase , lowerCamelCase )
return ans
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = np.chararray((n, n) )
for i in range(lowerCamelCase ):
for j in range(lowerCamelCase ):
__lowercase = """*"""
for i in range(lowerCamelCase ):
for j in range(lowerCamelCase ):
if (j, (n - 1) - i) in blocks:
__lowercase = """#"""
__lowercase = """-"""
__lowercase = back_pointer[goal]
while x != start:
((__lowercase) , (__lowercase)) = x
# print(x)
__lowercase = """-"""
__lowercase = back_pointer[x]
__lowercase = """-"""
for i in range(lowerCamelCase ):
for j in range(lowerCamelCase ):
if (i, j) == (0, n - 1):
print(grid[i][j] , end=""" """ )
print("""<-- End position""" , end=""" """ )
else:
print(grid[i][j] , end=""" """ )
print()
print("""^""" )
print("""Start position""" )
print()
print("""# is an obstacle""" )
print("""- is the path taken by algorithm""" )
print("""PATH TAKEN BY THE ALGORITHM IS:-""" )
__lowercase = back_pointer[goal]
while x != start:
print(lowerCamelCase , end=""" """ )
__lowercase = back_pointer[x]
print(lowerCamelCase )
sys.exit()
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if p[0] < 0 or p[0] > n - 1:
return False
if p[1] < 0 or p[1] > n - 1:
return False
return True
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , ):
'''simple docstring'''
for itera in range(lowerCamelCase ):
open_list[itera].remove_element(lowerCamelCase )
# print("s", s)
# print("j", j)
((__lowercase) , (__lowercase)) = s
__lowercase = (x - 1, y)
__lowercase = (x + 1, y)
__lowercase = (x, y + 1)
__lowercase = (x, y - 1)
for neighbours in [left, right, up, down]:
if neighbours not in blocks:
if valid(lowerCamelCase ) and neighbours not in visited:
# print("neighbour", neighbours)
visited.add(lowerCamelCase )
__lowercase = -1
__lowercase = float("""inf""" )
if valid(lowerCamelCase ) and g_function[neighbours] > g_function[s] + 1:
__lowercase = g_function[s] + 1
__lowercase = s
if neighbours not in close_list_anchor:
open_list[0].put(lowerCamelCase , key(lowerCamelCase , 0 , lowerCamelCase , lowerCamelCase ) )
if neighbours not in close_list_inad:
for var in range(1 , lowerCamelCase ):
if key(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) <= Wa * key(
lowerCamelCase , 0 , lowerCamelCase , lowerCamelCase ):
open_list[j].put(
lowerCamelCase , key(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) )
def snake_case ( ):
'''simple docstring'''
__lowercase = []
for x in range(1 , 5 ):
for y in range(1 , 6 ):
some_list.append((x, y) )
for x in range(15 , 20 ):
some_list.append((x, 17) )
for x in range(10 , 19 ):
for y in range(1 , 15 ):
some_list.append((x, y) )
# L block
for x in range(1 , 4 ):
for y in range(12 , 19 ):
some_list.append((x, y) )
for x in range(3 , 13 ):
for y in range(16 , 19 ):
some_list.append((x, y) )
return some_list
__UpperCamelCase : Optional[int] = {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}
__UpperCamelCase : Optional[Any] = [
(0, 1),
(1, 1),
(2, 1),
(3, 1),
(4, 1),
(5, 1),
(6, 1),
(7, 1),
(8, 1),
(9, 1),
(10, 1),
(11, 1),
(12, 1),
(13, 1),
(14, 1),
(15, 1),
(16, 1),
(17, 1),
(18, 1),
(19, 1),
]
__UpperCamelCase : Optional[Any] = make_common_ground()
__UpperCamelCase : Dict = blocks_blk
# hyper parameters
__UpperCamelCase : Union[str, Any] = 1
__UpperCamelCase : Union[str, Any] = 1
__UpperCamelCase : Optional[int] = 20
__UpperCamelCase : List[str] = 3 # one consistent and two other inconsistent
# start and end destination
__UpperCamelCase : str = (0, 0)
__UpperCamelCase : str = (n - 1, n - 1)
__UpperCamelCase : Optional[Any] = 1
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = {start: 0, goal: float("""inf""" )}
__lowercase = {start: -1, goal: -1}
__lowercase = []
__lowercase = set()
for i in range(lowerCamelCase ):
open_list.append(PriorityQueue() )
open_list[i].put(lowerCamelCase , key(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) )
__lowercase = []
__lowercase = []
while open_list[0].minkey() < float("""inf""" ):
for i in range(1 , lowerCamelCase ):
# print(open_list[0].minkey(), open_list[i].minkey())
if open_list[i].minkey() <= Wa * open_list[0].minkey():
global t
t += 1
if g_function[goal] <= open_list[i].minkey():
if g_function[goal] < float("""inf""" ):
do_something(lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
__lowercase , __lowercase = open_list[i].top_show()
visited.add(lowerCamelCase )
expand_state(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , )
close_list_inad.append(lowerCamelCase )
else:
if g_function[goal] <= open_list[0].minkey():
if g_function[goal] < float("""inf""" ):
do_something(lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
__lowercase = open_list[0].top_show()
visited.add(lowerCamelCase )
expand_state(
lowerCamelCase , 0 , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , )
close_list_anchor.append(lowerCamelCase )
print("""No path found to goal""" )
print()
for i in range(n - 1 , -1 , -1 ):
for j in range(lowerCamelCase ):
if (j, i) in blocks:
print("""#""" , end=""" """ )
elif (j, i) in back_pointer:
if (j, i) == (n - 1, n - 1):
print("""*""" , end=""" """ )
else:
print("""-""" , end=""" """ )
else:
print("""*""" , end=""" """ )
if (j, i) == (n - 1, n - 1):
print("""<-- End position""" , end=""" """ )
print()
print("""^""" )
print("""Start position""" )
print()
print("""# is an obstacle""" )
print("""- is the path taken by algorithm""" )
if __name__ == "__main__":
multi_a_star(start, goal, n_heuristic)
| 53
| 1
|
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
__UpperCamelCase : List[Any] = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :List[Any] = 'git_vision_model'
def __init__( self : Tuple , _lowerCAmelCase : Optional[Any]=768 , _lowerCAmelCase : Union[str, Any]=3072 , _lowerCAmelCase : Optional[int]=12 , _lowerCAmelCase : Optional[Any]=12 , _lowerCAmelCase : Optional[Any]=3 , _lowerCAmelCase : List[str]=224 , _lowerCAmelCase : List[Any]=16 , _lowerCAmelCase : Tuple="quick_gelu" , _lowerCAmelCase : Any=1e-5 , _lowerCAmelCase : int=0.0 , _lowerCAmelCase : Dict=0.02 , **_lowerCAmelCase : List[str] , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(**_lowerCAmelCase )
__lowercase = hidden_size
__lowercase = intermediate_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = num_channels
__lowercase = patch_size
__lowercase = image_size
__lowercase = initializer_range
__lowercase = attention_dropout
__lowercase = layer_norm_eps
__lowercase = hidden_act
@classmethod
def _a ( cls : Optional[int] , _lowerCAmelCase : Union[str, os.PathLike] , **_lowerCAmelCase : int ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(_lowerCAmelCase )
__lowercase , __lowercase = cls.get_config_dict(_lowerCAmelCase , **_lowerCAmelCase )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("""model_type""" ) == "git":
__lowercase = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_lowerCAmelCase , **_lowerCAmelCase )
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Dict = 'git'
def __init__( self : Optional[Any] , _lowerCAmelCase : Dict=None , _lowerCAmelCase : Optional[Any]=3_0522 , _lowerCAmelCase : Any=768 , _lowerCAmelCase : Any=6 , _lowerCAmelCase : Optional[Any]=12 , _lowerCAmelCase : Any=3072 , _lowerCAmelCase : Dict="gelu" , _lowerCAmelCase : List[Any]=0.1 , _lowerCAmelCase : Any=0.1 , _lowerCAmelCase : Any=1024 , _lowerCAmelCase : int=0.02 , _lowerCAmelCase : int=1e-12 , _lowerCAmelCase : Optional[Any]=0 , _lowerCAmelCase : Optional[Any]="absolute" , _lowerCAmelCase : str=True , _lowerCAmelCase : List[Any]=False , _lowerCAmelCase : str=101 , _lowerCAmelCase : Dict=102 , _lowerCAmelCase : str=None , **_lowerCAmelCase : int , ) -> str:
"""simple docstring"""
super().__init__(bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , pad_token_id=_lowerCAmelCase , **_lowerCAmelCase )
if vision_config is None:
__lowercase = {}
logger.info("""vision_config is None. initializing the GitVisionConfig with default values.""" )
__lowercase = GitVisionConfig(**_lowerCAmelCase )
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = hidden_act
__lowercase = intermediate_size
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = position_embedding_type
__lowercase = use_cache
__lowercase = tie_word_embeddings
__lowercase = num_image_with_embedding
__lowercase = bos_token_id
__lowercase = eos_token_id
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = copy.deepcopy(self.__dict__ )
__lowercase = self.vision_config.to_dict()
__lowercase = self.__class__.model_type
return output
| 53
|
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = SwinConfig.from_pretrained(
"""microsoft/swin-tiny-patch4-window7-224""" , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
__lowercase = MaskFormerConfig(backbone_config=lowerCamelCase )
__lowercase = """huggingface/label-files"""
if "ade20k-full" in model_name:
# this should be ok
__lowercase = 847
__lowercase = """maskformer-ade20k-full-id2label.json"""
elif "ade" in model_name:
# this should be ok
__lowercase = 150
__lowercase = """ade20k-id2label.json"""
elif "coco-stuff" in model_name:
# this should be ok
__lowercase = 171
__lowercase = """maskformer-coco-stuff-id2label.json"""
elif "coco" in model_name:
# TODO
__lowercase = 133
__lowercase = """coco-panoptic-id2label.json"""
elif "cityscapes" in model_name:
# this should be ok
__lowercase = 19
__lowercase = """cityscapes-id2label.json"""
elif "vistas" in model_name:
# this should be ok
__lowercase = 65
__lowercase = """mapillary-vistas-id2label.json"""
__lowercase = json.load(open(hf_hub_download(lowerCamelCase , lowerCamelCase , repo_type="""dataset""" ) , """r""" ) )
__lowercase = {int(lowerCamelCase ): v for k, v in idalabel.items()}
return config
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = []
# stem
# fmt: off
rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""") )
rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""") )
rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""") )
rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias') )
if i < 3:
rename_keys.append((F'backbone.layers.{i}.downsample.reduction.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight') )
rename_keys.append((F'backbone.layers.{i}.downsample.norm.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight') )
rename_keys.append((F'backbone.layers.{i}.downsample.norm.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias') )
rename_keys.append((F'backbone.norm{i}.weight', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight') )
rename_keys.append((F'backbone.norm{i}.bias', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias') )
# FPN
rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""") )
rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""") )
rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""") )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight') )
rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight') )
rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias') )
rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""") )
rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""") )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias') )
# cross-attention out projection
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias') )
# MLP 1
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias') )
# MLP 2
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias') )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias') )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias') )
# layernorm 3 (final layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias') )
rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""") )
rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""") )
# heads on top
rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""") )
rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""") )
rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""") )
rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""") )
rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""") )
for i in range(3 ):
rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight') )
rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias') )
# fmt: on
return rename_keys
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = dct.pop(lowerCamelCase )
__lowercase = val
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
__lowercase = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
__lowercase = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight' )
__lowercase = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
__lowercase = in_proj_weight[:dim, :]
__lowercase = in_proj_bias[: dim]
__lowercase = in_proj_weight[
dim : dim * 2, :
]
__lowercase = in_proj_bias[
dim : dim * 2
]
__lowercase = in_proj_weight[
-dim :, :
]
__lowercase = in_proj_bias[-dim :]
# fmt: on
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
__lowercase = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight' )
__lowercase = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
__lowercase = in_proj_weight[: hidden_size, :]
__lowercase = in_proj_bias[:config.hidden_size]
__lowercase = in_proj_weight[hidden_size : hidden_size * 2, :]
__lowercase = in_proj_bias[hidden_size : hidden_size * 2]
__lowercase = in_proj_weight[-hidden_size :, :]
__lowercase = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
__lowercase = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight' )
__lowercase = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
__lowercase = in_proj_weight[: hidden_size, :]
__lowercase = in_proj_bias[:config.hidden_size]
__lowercase = in_proj_weight[hidden_size : hidden_size * 2, :]
__lowercase = in_proj_bias[hidden_size : hidden_size * 2]
__lowercase = in_proj_weight[-hidden_size :, :]
__lowercase = in_proj_bias[-hidden_size :]
# fmt: on
def snake_case ( ):
'''simple docstring'''
__lowercase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__lowercase = Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase ).raw )
return im
@torch.no_grad()
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = False ):
'''simple docstring'''
__lowercase = get_maskformer_config(lowerCamelCase )
# load original state_dict
with open(lowerCamelCase , """rb""" ) as f:
__lowercase = pickle.load(lowerCamelCase )
__lowercase = data["""model"""]
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
__lowercase = create_rename_keys(lowerCamelCase )
for src, dest in rename_keys:
rename_key(lowerCamelCase , lowerCamelCase , lowerCamelCase )
read_in_swin_q_k_v(lowerCamelCase , config.backbone_config )
read_in_decoder_q_k_v(lowerCamelCase , lowerCamelCase )
# update to torch tensors
for key, value in state_dict.items():
__lowercase = torch.from_numpy(lowerCamelCase )
# load 🤗 model
__lowercase = MaskFormerForInstanceSegmentation(lowerCamelCase )
model.eval()
for name, param in model.named_parameters():
print(lowerCamelCase , param.shape )
__lowercase , __lowercase = model.load_state_dict(lowerCamelCase , strict=lowerCamelCase )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(lowerCamelCase ) == 0, F'Unexpected keys: {unexpected_keys}'
# verify results
__lowercase = prepare_img()
if "vistas" in model_name:
__lowercase = 65
elif "cityscapes" in model_name:
__lowercase = 65_535
else:
__lowercase = 255
__lowercase = True if """ade""" in model_name else False
__lowercase = MaskFormerImageProcessor(ignore_index=lowerCamelCase , reduce_labels=lowerCamelCase )
__lowercase = image_processor(lowerCamelCase , return_tensors="""pt""" )
__lowercase = model(**lowerCamelCase )
print("""Logits:""" , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
__lowercase = torch.tensor(
[[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , lowerCamelCase , atol=1e-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F'Saving model and image processor to {pytorch_dump_folder_path}' )
Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
model.save_pretrained(lowerCamelCase )
image_processor.save_pretrained(lowerCamelCase )
if push_to_hub:
print("""Pushing model and image processor to the hub...""" )
model.push_to_hub(F'nielsr/{model_name}' )
image_processor.push_to_hub(F'nielsr/{model_name}' )
if __name__ == "__main__":
__UpperCamelCase : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""maskformer-swin-tiny-ade""",
type=str,
help=("""Name of the MaskFormer model you'd like to convert""",),
)
parser.add_argument(
"""--checkpoint_path""",
default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
type=str,
help="""Path to the original state dict (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
__UpperCamelCase : List[Any] = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 53
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[int] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Dict = [
"""PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""PegasusXForConditionalGeneration""",
"""PegasusXModel""",
"""PegasusXPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus_x import (
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
else:
import sys
__UpperCamelCase : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 53
|
from math import sqrt
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(lowerCamelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def snake_case ( lowerCamelCase = 10_001 ):
'''simple docstring'''
__lowercase = 0
__lowercase = 1
while count != nth and number < 3:
number += 1
if is_prime(lowerCamelCase ):
count += 1
while count != nth:
number += 2
if is_prime(lowerCamelCase ):
count += 1
return number
if __name__ == "__main__":
print(F'''{solution() = }''')
| 53
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
"""processing_git""": ["""GitProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Tuple = [
"""GIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GitForCausalLM""",
"""GitModel""",
"""GitPreTrainedModel""",
"""GitVisionModel""",
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
__UpperCamelCase : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 53
|
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if isinstance(lowerCamelCase , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class __UpperCamelCase :
def _a ( self : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
pass
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
pass
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
pass
def _a ( self : int , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Dict=None , **_lowerCAmelCase : int ) -> str:
"""simple docstring"""
__lowercase = VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) )
def _a ( self : str , _lowerCAmelCase : Tuple , _lowerCAmelCase : str , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : int=None , **_lowerCAmelCase : Dict ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase , text_model=_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def _a ( self : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Dict , _lowerCAmelCase : str , _lowerCAmelCase : Any=None , **_lowerCAmelCase : str ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = {"""vision_model""": vision_model, """text_model""": text_model}
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def _a ( self : Dict , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any]=None , **_lowerCAmelCase : Any ) -> Optional[int]:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase , text_model=_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
__lowercase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
__lowercase = after_output[0].numpy()
__lowercase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCAmelCase , 1e-5 )
def _a ( self : int , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int]=None , **_lowerCAmelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase , text_model=_lowerCAmelCase )
__lowercase = model(
input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase , output_attentions=_lowerCAmelCase )
__lowercase = output.vision_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
__lowercase = to_atuple(vision_model.config.image_size )
__lowercase = to_atuple(vision_model.config.patch_size )
__lowercase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowercase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__lowercase = output.text_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _a ( self : List[Any] , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : float ) -> Optional[int]:
"""simple docstring"""
__lowercase = np.abs((a - b) ).max()
self.assertLessEqual(_lowerCAmelCase , _lowerCAmelCase , F'Difference between torch and flax is {diff} (>= {tol}).' )
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**_lowerCAmelCase )
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> int:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_lowerCAmelCase )
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_save_load(**_lowerCAmelCase )
def _a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_lowerCAmelCase )
@slow
def _a ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.get_pretrained_model_and_inputs()
__lowercase = model_a(**_lowerCAmelCase )
__lowercase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase )
__lowercase = model_a(**_lowerCAmelCase )
__lowercase = after_outputs[0].numpy()
__lowercase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCAmelCase , 1e-5 )
@require_tf
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
def _a ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-random-bert""" )
__lowercase = 13
__lowercase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowercase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowercase = random_attention_mask([batch_size, 4] )
__lowercase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _a ( self : Tuple , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
__lowercase = TFViTModel(_lowerCAmelCase , name="""vision_model""" )
__lowercase = TFBertModel(_lowerCAmelCase , name="""text_model""" )
return vision_model, text_model
def _a ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = TFViTModelTester(self )
__lowercase = TFBertModelTester(self )
__lowercase = vit_model_tester.prepare_config_and_inputs()
__lowercase = bert_model_tester.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = vision_config_and_inputs
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-deit-tf""" , """hf-internal-testing/tiny-random-roberta""" )
__lowercase = 13
__lowercase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowercase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowercase = random_attention_mask([batch_size, 4] )
__lowercase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _a ( self : Optional[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : Tuple , _lowerCAmelCase : int=None , **_lowerCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase , text_model=_lowerCAmelCase )
__lowercase = model(
input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase , output_attentions=_lowerCAmelCase )
__lowercase = output.vision_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
__lowercase = to_atuple(vision_model.config.image_size )
__lowercase = to_atuple(vision_model.config.patch_size )
__lowercase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowercase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__lowercase = output.text_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _a ( self : Tuple , _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict ) -> int:
"""simple docstring"""
__lowercase = TFDeiTModel(_lowerCAmelCase , name="""vision_model""" )
__lowercase = TFRobertaModel(_lowerCAmelCase , name="""text_model""" )
return vision_model, text_model
def _a ( self : Tuple ) -> str:
"""simple docstring"""
__lowercase = TFDeiTModelTester(self )
__lowercase = TFRobertaModelTester(self )
__lowercase = vit_model_tester.prepare_config_and_inputs()
__lowercase = bert_model_tester.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = vision_config_and_inputs
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
def _a ( self : int ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-clip-tf""" , """hf-internal-testing/tiny-random-bert""" )
__lowercase = 13
__lowercase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowercase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowercase = random_attention_mask([batch_size, 4] )
__lowercase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _a ( self : Optional[int] , _lowerCAmelCase : Dict , _lowerCAmelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
__lowercase = TFCLIPVisionModel(_lowerCAmelCase , name="""vision_model""" )
__lowercase = TFBertModel(_lowerCAmelCase , name="""text_model""" )
return vision_model, text_model
def _a ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = TFCLIPVisionModelTester(self )
__lowercase = TFBertModelTester(self )
__lowercase = clip_model_tester.prepare_config_and_inputs()
__lowercase = bert_model_tester.prepare_config_and_inputs()
__lowercase , __lowercase = vision_config_and_inputs
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class __UpperCamelCase ( unittest.TestCase ):
@slow
def _a ( self : int ) -> Tuple:
"""simple docstring"""
__lowercase = TFVisionTextDualEncoderModel.from_pretrained(
"""clip-italian/clip-italian""" , logit_scale_init_value=1.0 , from_pt=_lowerCAmelCase )
__lowercase = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
__lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
__lowercase = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=_lowerCAmelCase , padding=_lowerCAmelCase , return_tensors="""np""" )
__lowercase = model(**_lowerCAmelCase )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
__lowercase = np.array([[1.2_284_727, 0.3_104_122]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , _lowerCAmelCase , atol=1e-3 ) )
| 53
| 1
|
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if (ksize % 2) == 0:
__lowercase = ksize + 1
__lowercase = np.zeros((ksize, ksize) , dtype=np.floataa )
# each value
for y in range(lowerCamelCase ):
for x in range(lowerCamelCase ):
# distance from center
__lowercase = x - ksize // 2
__lowercase = y - ksize // 2
# degree to radiant
__lowercase = theta / 180 * np.pi
__lowercase = np.cos(_theta )
__lowercase = np.sin(_theta )
# get kernel x
__lowercase = cos_theta * px + sin_theta * py
# get kernel y
__lowercase = -sin_theta * px + cos_theta * py
# fill kernel
__lowercase = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi )
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
__UpperCamelCase : List[Any] = imread("""../image_data/lena.jpg""")
# turn image in gray scale value
__UpperCamelCase : Union[str, Any] = cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
__UpperCamelCase : Union[str, Any] = np.zeros(gray.shape[:2])
for theta in [0, 30, 60, 90, 120, 150]:
__UpperCamelCase : Tuple = gabor_filter_kernel(10, 8, theta, 10, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
__UpperCamelCase : List[str] = out / out.max() * 255
__UpperCamelCase : List[str] = out.astype(np.uinta)
imshow("""Original""", gray)
imshow("""Gabor filter with 20x20 mask and 6 directions""", out)
waitKey(0)
| 53
|
from __future__ import annotations
from scipy.special import comb # type: ignore
class __UpperCamelCase :
def __init__( self : int , _lowerCAmelCase : list[tuple[float, float]] ) -> Any:
"""simple docstring"""
__lowercase = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
__lowercase = len(_lowerCAmelCase ) - 1
def _a ( self : Tuple , _lowerCAmelCase : float ) -> list[float]:
"""simple docstring"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__lowercase = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , _lowerCAmelCase ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(_lowerCAmelCase ) , 5 ) == 1
return output_values
def _a ( self : List[str] , _lowerCAmelCase : float ) -> tuple[float, float]:
"""simple docstring"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__lowercase = self.basis_function(_lowerCAmelCase )
__lowercase = 0.0
__lowercase = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def _a ( self : Optional[int] , _lowerCAmelCase : float = 0.01 ) -> Union[str, Any]:
"""simple docstring"""
from matplotlib import pyplot as plt # type: ignore
__lowercase = [] # x coordinates of points to plot
__lowercase = [] # y coordinates of points to plot
__lowercase = 0.0
while t <= 1:
__lowercase = self.bezier_curve_function(_lowerCAmelCase )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
__lowercase = [i[0] for i in self.list_of_points]
__lowercase = [i[1] for i in self.list_of_points]
plt.plot(
_lowerCAmelCase , _lowerCAmelCase , color="""blue""" , label="""Curve of Degree """ + str(self.degree ) , )
plt.scatter(_lowerCAmelCase , _lowerCAmelCase , color="""red""" , label="""Control Points""" )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 53
| 1
|
from sklearn.metrics import recall_score
import datasets
__UpperCamelCase : Optional[Any] = """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""
__UpperCamelCase : Any = """
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
- `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
- `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
- `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
- `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
- `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
- `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
- `0`: If there is a zero division, the return value is `0`.
- `1`: If there is a zero division, the return value is `1`.
Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.
Examples:
Example 1-A simple example with some errors
>>> recall_metric = datasets.load_metric('recall')
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
>>> print(results)
{'recall': 0.6666666666666666}
Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
>>> recall_metric = datasets.load_metric('recall')
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
>>> print(results)
{'recall': 0.5}
Example 3-The same example as Example 1, but with `sample_weight` included.
>>> recall_metric = datasets.load_metric('recall')
>>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
>>> print(results)
{'recall': 0.55}
Example 4-A multiclass example, using different averages.
>>> recall_metric = datasets.load_metric('recall')
>>> predictions = [0, 2, 1, 0, 0, 1]
>>> references = [0, 1, 2, 0, 1, 2]
>>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
>>> print(results)
{'recall': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
>>> print(results)
{'recall': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
>>> print(results)
{'recall': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
>>> print(results)
{'recall': array([1., 0., 0.])}
"""
__UpperCamelCase : 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 __UpperCamelCase ( datasets.Metric ):
def _a ( self : Dict ) -> List[str]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""int32""" ) ),
"""references""": datasets.Sequence(datasets.Value("""int32""" ) ),
}
if self.config_name == """multilabel"""
else {
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"""] , )
def _a ( self : Tuple , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[str]=None , _lowerCAmelCase : str=1 , _lowerCAmelCase : Optional[int]="binary" , _lowerCAmelCase : Optional[int]=None , _lowerCAmelCase : Union[str, Any]="warn" , ) -> Optional[Any]:
"""simple docstring"""
__lowercase = recall_score(
_lowerCAmelCase , _lowerCAmelCase , labels=_lowerCAmelCase , pos_label=_lowerCAmelCase , average=_lowerCAmelCase , sample_weight=_lowerCAmelCase , zero_division=_lowerCAmelCase , )
return {"recall": float(_lowerCAmelCase ) if score.size == 1 else score}
| 53
|
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
)
from transformers.models.efficientformer.modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_vision_available():
from PIL import Image
from transformers import EfficientFormerImageProcessor
class __UpperCamelCase :
def __init__( self : int , _lowerCAmelCase : Tuple , _lowerCAmelCase : int = 13 , _lowerCAmelCase : int = 64 , _lowerCAmelCase : int = 2 , _lowerCAmelCase : int = 3 , _lowerCAmelCase : int = 3 , _lowerCAmelCase : bool = True , _lowerCAmelCase : bool = True , _lowerCAmelCase : int = 128 , _lowerCAmelCase : Optional[int]=[16, 32, 64, 128] , _lowerCAmelCase : int = 7 , _lowerCAmelCase : int = 4 , _lowerCAmelCase : int = 37 , _lowerCAmelCase : str = "gelu" , _lowerCAmelCase : float = 0.1 , _lowerCAmelCase : float = 0.1 , _lowerCAmelCase : int = 10 , _lowerCAmelCase : float = 0.02 , _lowerCAmelCase : int = 2 , _lowerCAmelCase : int = 1 , _lowerCAmelCase : int = 128 , _lowerCAmelCase : List[int] = [2, 2, 2, 2] , _lowerCAmelCase : int = 2 , _lowerCAmelCase : int = 2 , ) -> Tuple:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = is_training
__lowercase = use_labels
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = encoder_stride
__lowercase = num_attention_outputs
__lowercase = embed_dim
__lowercase = embed_dim + 1
__lowercase = resolution
__lowercase = depths
__lowercase = hidden_sizes
__lowercase = dim
__lowercase = mlp_expansion_ratio
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
return EfficientFormerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_lowerCAmelCase , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , resolution=self.resolution , depths=self.depths , hidden_sizes=self.hidden_sizes , dim=self.dim , mlp_expansion_ratio=self.mlp_expansion_ratio , )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = TFEfficientFormerModel(config=_lowerCAmelCase )
__lowercase = model(_lowerCAmelCase , training=_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.type_sequence_label_size
__lowercase = TFEfficientFormerForImageClassification(_lowerCAmelCase )
__lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase , training=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__lowercase = 1
__lowercase = TFEfficientFormerForImageClassification(_lowerCAmelCase )
__lowercase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Any = (
(
TFEfficientFormerModel,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerForImageClassification,
)
if is_tf_available()
else ()
)
__snake_case :Any = (
{
'feature-extraction': TFEfficientFormerModel,
'image-classification': (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
),
}
if is_tf_available()
else {}
)
__snake_case :int = False
__snake_case :Optional[int] = False
__snake_case :int = False
__snake_case :Any = False
__snake_case :Any = False
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
__lowercase = TFEfficientFormerModelTester(self )
__lowercase = ConfigTester(
self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 )
def _a ( self : Optional[int] ) -> int:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""EfficientFormer does not use inputs_embeds""" )
def _a ( self : Optional[int] ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""EfficientFormer does not support input and output embeddings""" )
def _a ( self : int ) -> str:
"""simple docstring"""
pass
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
def check_hidden_states_output(_lowerCAmelCase : int , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] ):
__lowercase = model_class(_lowerCAmelCase )
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) , training=_lowerCAmelCase )
__lowercase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowercase = getattr(
self.model_tester , """expected_num_hidden_layers""" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
if hasattr(self.model_tester , """encoder_seq_length""" ):
__lowercase = self.model_tester.encoder_seq_length
if hasattr(self.model_tester , """chunk_length""" ) and self.model_tester.chunk_length > 1:
__lowercase = seq_length * self.model_tester.chunk_length
else:
__lowercase = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
if config.is_encoder_decoder:
__lowercase = outputs.decoder_hidden_states
self.asseretIsInstance(_lowerCAmelCase , (list, tuple) )
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """seq_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """decoder_seq_length""" , _lowerCAmelCase )
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [decoder_seq_length, self.model_tester.hidden_size] , )
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Optional[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any]=False ) -> Dict:
"""simple docstring"""
__lowercase = super()._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
if return_labels:
if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def _a ( self : int ) -> int:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet""" )
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase )
def _a ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@slow
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = TFEfficientFormerModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = True
__lowercase = getattr(self.model_tester , """seq_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """encoder_seq_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """key_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """chunk_length""" , _lowerCAmelCase )
if chunk_length is not None and hasattr(self.model_tester , """num_hashes""" ):
__lowercase = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
__lowercase = True
__lowercase = False
__lowercase = True
__lowercase = model_class(_lowerCAmelCase )
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) , training=_lowerCAmelCase )
__lowercase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_attention_outputs )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__lowercase = True
__lowercase = model_class(_lowerCAmelCase )
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) , training=_lowerCAmelCase )
__lowercase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_attention_outputs )
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length] , )
else:
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length] , )
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Prepare our model
__lowercase = model_class(_lowerCAmelCase )
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
__lowercase = {
key: tf.keras.Input(shape=val.shape[1:] , dtype=val.dtype , name=_lowerCAmelCase )
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
__lowercase = model(_lowerCAmelCase )
self.assertTrue(outputs_dict is not None )
def snake_case ( ):
'''simple docstring'''
__lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
return (
EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300""" )
if is_vision_available()
else None
)
@slow
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__lowercase = TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300""" )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""tf""" )
# forward pass
__lowercase = model(**_lowerCAmelCase , training=_lowerCAmelCase )
# verify the logits
__lowercase = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
__lowercase = tf.constant([-0.0_555, 0.4_825, -0.0_852] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
@slow
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__lowercase = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
"""snap-research/efficientformer-l1-300""" )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""tf""" )
# forward pass
__lowercase = model(**_lowerCAmelCase , training=_lowerCAmelCase )
# verify the logits
__lowercase = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
__lowercase = tf.constant([-0.1_312, 0.4_353, -1.0_499] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
| 53
| 1
|
from __future__ import annotations
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
print(F'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(lowerCamelCase ):
print(F'{i}\t\t{d}' )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
for j in range(lowerCamelCase ):
__lowercase , __lowercase , __lowercase = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
return True
return False
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = [float("""inf""" )] * vertex_count
__lowercase = 0.0
for _ in range(vertex_count - 1 ):
for j in range(lowerCamelCase ):
__lowercase , __lowercase , __lowercase = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
__lowercase = distance[u] + w
__lowercase = check_negative_cycle(lowerCamelCase , lowerCamelCase , lowerCamelCase )
if negative_cycle_exists:
raise Exception("""Negative cycle found""" )
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCamelCase : Optional[Any] = int(input("""Enter number of vertices: """).strip())
__UpperCamelCase : Tuple = int(input("""Enter number of edges: """).strip())
__UpperCamelCase : list[dict[str, int]] = [{} for _ in range(E)]
for i in range(E):
print("""Edge """, i + 1)
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase : Any = (
int(x)
for x in input("""Enter source, destination, weight: """).strip().split(""" """)
)
__UpperCamelCase : Tuple = {"""src""": src, """dst""": dest, """weight""": weight}
__UpperCamelCase : Any = int(input("""\nEnter shortest path source:""").strip())
__UpperCamelCase : List[Any] = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| 53
|
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
__UpperCamelCase : Tuple = 2
class __UpperCamelCase :
def __init__( self : List[str] , *, # begin keyword-only arguments
_lowerCAmelCase : Optional[int]="<s>" , _lowerCAmelCase : Optional[int]="<pad>" , _lowerCAmelCase : int="</s>" , _lowerCAmelCase : str="<unk>" , _lowerCAmelCase : List[str]=None , ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase , __lowercase , __lowercase = bos, unk, pad, eos
__lowercase = []
__lowercase = []
__lowercase = {}
__lowercase = self.add_symbol(_lowerCAmelCase )
__lowercase = self.add_symbol(_lowerCAmelCase )
__lowercase = self.add_symbol(_lowerCAmelCase )
__lowercase = self.add_symbol(_lowerCAmelCase )
if extra_special_symbols:
for s in extra_special_symbols:
self.add_symbol(_lowerCAmelCase )
__lowercase = len(self.symbols )
def __eq__( self : Dict , _lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
return self.indices == other.indices
def __getitem__( self : Any , _lowerCAmelCase : str ) -> Dict:
"""simple docstring"""
if idx < len(self.symbols ):
return self.symbols[idx]
return self.unk_word
def __len__( self : str ) -> List[str]:
"""simple docstring"""
return len(self.symbols )
def __contains__( self : Union[str, Any] , _lowerCAmelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
return sym in self.indices
@classmethod
def _a ( cls : Dict , _lowerCAmelCase : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = cls()
d.add_from_file(_lowerCAmelCase )
return d
def _a ( self : int , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any]=1 , _lowerCAmelCase : Optional[int]=False ) -> Union[str, Any]:
"""simple docstring"""
if word in self.indices and not overwrite:
__lowercase = self.indices[word]
__lowercase = self.count[idx] + n
return idx
else:
__lowercase = len(self.symbols )
__lowercase = idx
self.symbols.append(_lowerCAmelCase )
self.count.append(_lowerCAmelCase )
return idx
def _a ( self : List[str] , _lowerCAmelCase : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
return 0
def _a ( self : Optional[Any] , _lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
try:
with open(_lowerCAmelCase , """r""" , encoding="""utf-8""" ) as fd:
self.add_from_file(_lowerCAmelCase )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(_lowerCAmelCase ) )
return
__lowercase = f.readlines()
__lowercase = self._load_meta(_lowerCAmelCase )
for line in lines[indices_start_line:]:
try:
__lowercase , __lowercase = line.rstrip().rsplit(""" """ , 1 )
if field == "#fairseq:overwrite":
__lowercase = True
__lowercase , __lowercase = line.rsplit(""" """ , 1 )
else:
__lowercase = False
__lowercase = int(_lowerCAmelCase )
__lowercase = line
if word in self and not overwrite:
raise RuntimeError(
"""Duplicate word found when loading Dictionary: '{}'. """
"""Duplicate words can overwrite earlier ones by adding the """
"""#fairseq:overwrite flag at the end of the corresponding row """
"""in the dictionary file. If using the Camembert model, please """
"""download an updated copy of the model file.""".format(_lowerCAmelCase ) )
self.add_symbol(_lowerCAmelCase , n=_lowerCAmelCase , overwrite=_lowerCAmelCase )
except ValueError:
raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'""" )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = dict((re.sub(r"""@@$""" , """""" , lowerCamelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , lowerCamelCase ), v) for k, v in d.items() )
__lowercase = """<s> <pad> </s> <unk>""".split()
# restore the special tokens
for k in keep_keys:
del da[F'{k}</w>']
__lowercase = d[k] # restore
return da
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if not os.path.exists(lowerCamelCase ):
raise ValueError(F'path {biogpt_checkpoint_path} does not exist!' )
os.makedirs(lowerCamelCase , exist_ok=lowerCamelCase )
print(F'Writing results to {pytorch_dump_folder_path}' )
# handle various types of models
__lowercase = os.path.join(lowerCamelCase , """checkpoint.pt""" )
if not os.path.isfile(lowerCamelCase ):
raise ValueError(F'path to the file {checkpoint_file} does not exist!' )
__lowercase = torch.load(lowerCamelCase , map_location="""cpu""" )
__lowercase = chkpt["""cfg"""]["""model"""]
# dicts
__lowercase = os.path.join(lowerCamelCase , """dict.txt""" )
if not os.path.isfile(lowerCamelCase ):
raise ValueError(F'path to the file {dict_file} does not exist!' )
__lowercase = Dictionary.load(lowerCamelCase )
__lowercase = rewrite_dict_keys(src_dict.indices )
__lowercase = len(lowerCamelCase )
__lowercase = os.path.join(lowerCamelCase , VOCAB_FILES_NAMES["""vocab_file"""] )
print(F'Generating {src_vocab_file} of {src_vocab_size} records' )
with open(lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(lowerCamelCase , ensure_ascii=lowerCamelCase , indent=lowerCamelCase ) )
# merges_file (bpecodes)
__lowercase = os.path.join(lowerCamelCase , """bpecodes""" )
if not os.path.isfile(lowerCamelCase ):
raise ValueError(F'path to the file {bpecodes_file} does not exist!' )
__lowercase = os.path.join(lowerCamelCase , VOCAB_FILES_NAMES["""merges_file"""] )
shutil.copyfile(lowerCamelCase , lowerCamelCase )
# model config
__lowercase = os.path.join(lowerCamelCase , """config.json""" )
__lowercase = {
"""activation_dropout""": args["""activation_dropout"""],
"""architectures""": ["""BioGptForCausalLM"""],
"""attention_probs_dropout_prob""": args["""attention_dropout"""],
"""bos_token_id""": 0,
"""eos_token_id""": 2,
"""hidden_act""": args["""activation_fn"""],
"""hidden_dropout_prob""": args["""dropout"""],
"""hidden_size""": args["""decoder_embed_dim"""],
"""initializer_range""": 0.02,
"""intermediate_size""": args["""decoder_ffn_embed_dim"""],
"""layer_norm_eps""": 1e-12,
"""layerdrop""": args["""decoder_layerdrop"""],
"""max_position_embeddings""": args["""max_target_positions"""],
"""model_type""": """biogpt""",
"""num_attention_heads""": args["""decoder_attention_heads"""],
"""num_hidden_layers""": args["""decoder_layers"""],
"""pad_token_id""": 1,
"""scale_embedding""": not args["""no_scale_embedding"""],
"""tie_word_embeddings""": args["""share_decoder_input_output_embed"""],
"""vocab_size""": src_vocab_size,
}
# good hparam defaults to start with
print(F'Generating {biogpt_model_config_file}' )
with open(lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(lowerCamelCase , ensure_ascii=lowerCamelCase , indent=lowerCamelCase ) )
# tokenizer config
__lowercase = os.path.join(lowerCamelCase , lowerCamelCase )
__lowercase = {
"""bos_token""": """<s>""",
"""eos_token""": """</s>""",
"""model_max_length""": 1_024,
"""pad_token""": """<pad>""",
"""special_tokens_map_file""": None,
"""tokenizer_class""": """BioGptTokenizer""",
"""unk_token""": """<unk>""",
}
print(F'Generating {biogpt_tokenizer_config_file}' )
with open(lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(lowerCamelCase , ensure_ascii=lowerCamelCase , indent=lowerCamelCase ) )
# model
__lowercase = chkpt["""model"""]
# remove unneeded keys
__lowercase = [
"""decoder.version""",
]
for k in ignore_keys:
model_state_dict.pop(lowerCamelCase , lowerCamelCase )
__lowercase = list(model_state_dict.keys() )
for layer_name in layer_names:
if layer_name.endswith("""output_projection.weight""" ):
__lowercase = model_state_dict.pop(lowerCamelCase )
else:
__lowercase = model_state_dict.pop(lowerCamelCase )
__lowercase = BioGptConfig.from_pretrained(lowerCamelCase )
__lowercase = BioGptForCausalLM(lowerCamelCase )
# check that it loads ok
model_new.load_state_dict(lowerCamelCase )
# save
__lowercase = os.path.join(lowerCamelCase , lowerCamelCase )
print(F'Generating {pytorch_weights_dump_path}' )
torch.save(lowerCamelCase , lowerCamelCase )
print("""Conversion is done!""" )
if __name__ == "__main__":
__UpperCamelCase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--biogpt_checkpoint_path""",
default=None,
type=str,
required=True,
help=(
"""Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
""" bpecodes, etc."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
__UpperCamelCase : Optional[Any] = parser.parse_args()
convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
| 53
| 1
|
from __future__ import annotations
from scipy.special import comb # type: ignore
class __UpperCamelCase :
def __init__( self : int , _lowerCAmelCase : list[tuple[float, float]] ) -> Any:
"""simple docstring"""
__lowercase = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
__lowercase = len(_lowerCAmelCase ) - 1
def _a ( self : Tuple , _lowerCAmelCase : float ) -> list[float]:
"""simple docstring"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__lowercase = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , _lowerCAmelCase ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(_lowerCAmelCase ) , 5 ) == 1
return output_values
def _a ( self : List[str] , _lowerCAmelCase : float ) -> tuple[float, float]:
"""simple docstring"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__lowercase = self.basis_function(_lowerCAmelCase )
__lowercase = 0.0
__lowercase = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def _a ( self : Optional[int] , _lowerCAmelCase : float = 0.01 ) -> Union[str, Any]:
"""simple docstring"""
from matplotlib import pyplot as plt # type: ignore
__lowercase = [] # x coordinates of points to plot
__lowercase = [] # y coordinates of points to plot
__lowercase = 0.0
while t <= 1:
__lowercase = self.bezier_curve_function(_lowerCAmelCase )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
__lowercase = [i[0] for i in self.list_of_points]
__lowercase = [i[1] for i in self.list_of_points]
plt.plot(
_lowerCAmelCase , _lowerCAmelCase , color="""blue""" , label="""Curve of Degree """ + str(self.degree ) , )
plt.scatter(_lowerCAmelCase , _lowerCAmelCase , color="""red""" , label="""Control Points""" )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 53
|
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
__snake_case :Union[str, Any] = 'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
def _a ( self : Any , _lowerCAmelCase : str=0 ) -> str:
"""simple docstring"""
__lowercase = np.random.RandomState(_lowerCAmelCase )
__lowercase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.65_072, 0.58_492, 0.48_219, 0.55_521, 0.53_180, 0.55_939, 0.50_697, 0.39_800, 0.46_455] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.65_863, 0.59_425, 0.49_326, 0.56_313, 0.53_875, 0.56_627, 0.51_065, 0.39_777, 0.46_330] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Tuple ) -> int:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_817, 0.60_812, 0.47_384, 0.49_530, 0.51_894, 0.49_814, 0.47_984, 0.38_958, 0.44_271] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_895, 0.60_808, 0.47_933, 0.49_608, 0.51_886, 0.49_950, 0.48_053, 0.38_957, 0.44_200] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * [inputs["""prompt"""]]
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * [inputs.pop("""prompt""" )]
__lowercase = pipe.tokenizer(
_lowerCAmelCase , padding="""max_length""" , max_length=pipe.tokenizer.model_max_length , truncation=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = text_inputs["""input_ids"""]
__lowercase = pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]
__lowercase = prompt_embeds
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
def _a ( self : int ) -> str:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * ["""this is a negative prompt"""]
__lowercase = negative_prompt
__lowercase = 3 * [inputs["""prompt"""]]
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * [inputs.pop("""prompt""" )]
__lowercase = []
for p in [prompt, negative_prompt]:
__lowercase = pipe.tokenizer(
_lowerCAmelCase , padding="""max_length""" , max_length=pipe.tokenizer.model_max_length , truncation=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = text_inputs["""input_ids"""]
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )
__lowercase , __lowercase = embeds
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
@nightly
@require_onnxruntime
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
@property
def _a ( self : Dict ) -> str:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _a ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = ort.SessionOptions()
__lowercase = False
return options
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""onnx""" , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """A painting of a squirrel eating a burger"""
np.random.seed(0 )
__lowercase = sd_pipe([prompt] , guidance_scale=6.0 , num_inference_steps=10 , output_type="""np""" )
__lowercase = output.images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.0_452, 0.0_390, 0.0_087, 0.0_350, 0.0_617, 0.0_364, 0.0_544, 0.0_523, 0.0_720] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
__lowercase = DDIMScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """open neural network exchange"""
__lowercase = np.random.RandomState(0 )
__lowercase = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=_lowerCAmelCase , output_type="""np""" )
__lowercase = output.images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.2_867, 0.1_974, 0.1_481, 0.7_294, 0.7_251, 0.6_667, 0.4_194, 0.5_642, 0.6_486] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
__lowercase = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """open neural network exchange"""
__lowercase = np.random.RandomState(0 )
__lowercase = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=_lowerCAmelCase , output_type="""np""" )
__lowercase = output.images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.2_306, 0.1_959, 0.1_593, 0.6_549, 0.6_394, 0.5_408, 0.5_065, 0.6_010, 0.6_161] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : str ) -> List[str]:
"""simple docstring"""
__lowercase = 0
def test_callback_fn(_lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : np.ndarray ) -> None:
__lowercase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 64, 64)
__lowercase = latents[0, -3:, -3:, -1]
__lowercase = np.array(
[-0.6_772, -0.3_835, -1.2_456, 0.1_905, -1.0_974, 0.6_967, -1.9_353, 0.0_178, 1.0_167] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
elif step == 5:
assert latents.shape == (1, 4, 64, 64)
__lowercase = latents[0, -3:, -3:, -1]
__lowercase = np.array(
[-0.3_351, 0.2_241, -0.1_837, -0.2_325, -0.6_577, 0.3_393, -0.0_241, 0.5_899, 1.3_875] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
__lowercase = False
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """Andromeda galaxy in a bottle"""
__lowercase = np.random.RandomState(0 )
pipe(
prompt=_lowerCAmelCase , num_inference_steps=5 , guidance_scale=7.5 , generator=_lowerCAmelCase , callback=_lowerCAmelCase , callback_steps=1 , )
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
assert pipe.safety_checker is None
__lowercase = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_lowerCAmelCase )
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(_lowerCAmelCase )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
__lowercase = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
| 53
| 1
|
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = set()
__lowercase = []
def parse_line(lowerCamelCase ):
for line in fp:
if isinstance(lowerCamelCase , lowerCamelCase ):
__lowercase = line.decode("""UTF-8""" )
if "warnings summary (final)" in line:
continue
# This means we are outside the body of a warning
elif not line.startswith(""" """ ):
# process a single warning and move it to `selected_warnings`.
if len(lowerCamelCase ) > 0:
__lowercase = """\n""".join(lowerCamelCase )
# Only keep the warnings specified in `targets`
if any(F': {x}: ' in warning for x in targets ):
selected_warnings.add(lowerCamelCase )
buffer.clear()
continue
else:
__lowercase = line.strip()
buffer.append(lowerCamelCase )
if from_gh:
for filename in os.listdir(lowerCamelCase ):
__lowercase = os.path.join(lowerCamelCase , lowerCamelCase )
if not os.path.isdir(lowerCamelCase ):
# read the file
if filename != "warnings.txt":
continue
with open(lowerCamelCase ) as fp:
parse_line(lowerCamelCase )
else:
try:
with zipfile.ZipFile(lowerCamelCase ) as z:
for filename in z.namelist():
if not os.path.isdir(lowerCamelCase ):
# read the file
if filename != "warnings.txt":
continue
with z.open(lowerCamelCase ) as fp:
parse_line(lowerCamelCase )
except Exception:
logger.warning(
F'{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.' )
return selected_warnings
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = set()
__lowercase = [os.path.join(lowerCamelCase , lowerCamelCase ) for p in os.listdir(lowerCamelCase ) if (p.endswith(""".zip""" ) or from_gh)]
for p in paths:
selected_warnings.update(extract_warnings_from_single_artifact(lowerCamelCase , lowerCamelCase ) )
return selected_warnings
if __name__ == "__main__":
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return values.split(""",""" )
__UpperCamelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
parser.add_argument(
"""--output_dir""",
type=str,
required=True,
help="""Where to store the downloaded artifacts and other result files.""",
)
parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
# optional parameters
parser.add_argument(
"""--targets""",
default="""DeprecationWarning,UserWarning,FutureWarning""",
type=list_str,
help="""Comma-separated list of target warning(s) which we want to extract.""",
)
parser.add_argument(
"""--from_gh""",
action="""store_true""",
help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
)
__UpperCamelCase : List[str] = parser.parse_args()
__UpperCamelCase : Union[str, Any] = args.from_gh
if from_gh:
# The artifacts have to be downloaded using `actions/download-artifact@v3`
pass
else:
os.makedirs(args.output_dir, exist_ok=True)
# get download links
__UpperCamelCase : Any = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
# download artifacts
for idx, (name, url) in enumerate(artifacts.items()):
print(name)
print(url)
print("""=""" * 80)
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
# extract warnings from artifacts
__UpperCamelCase : Union[str, Any] = extract_warnings(args.output_dir, args.targets)
__UpperCamelCase : Any = sorted(selected_warnings)
with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)
| 53
|
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = """"""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
__lowercase = remove_duplicates(key.upper() )
__lowercase = len(lowerCamelCase )
# First fill cipher with key characters
__lowercase = {alphabet[i]: char for i, char in enumerate(lowerCamelCase )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(lowerCamelCase ) , 26 ):
__lowercase = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
__lowercase = alphabet[i - offset]
__lowercase = char
return cipher_alphabet
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return "".join(cipher_map.get(lowerCamelCase , lowerCamelCase ) for ch in message.upper() )
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(lowerCamelCase , lowerCamelCase ) for ch in message.upper() )
def snake_case ( ):
'''simple docstring'''
__lowercase = input("""Enter message to encode or decode: """ ).strip()
__lowercase = input("""Enter keyword: """ ).strip()
__lowercase = input("""Encipher or decipher? E/D:""" ).strip()[0].lower()
try:
__lowercase = {"""e""": encipher, """d""": decipher}[option]
except KeyError:
raise KeyError("""invalid input option""" )
__lowercase = create_cipher_map(lowerCamelCase )
print(func(lowerCamelCase , lowerCamelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 53
| 1
|
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCamelCase : Tuple = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
__snake_case :Any = XLNetTokenizer
__snake_case :int = XLNetTokenizerFast
__snake_case :Union[str, Any] = True
__snake_case :str = True
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowercase = XLNetTokenizer(_lowerCAmelCase , keep_accents=_lowerCAmelCase )
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname )
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
__lowercase = """<s>"""
__lowercase = 1
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 : Tuple ) -> Optional[int]:
"""simple docstring"""
__lowercase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<unk>""" )
self.assertEqual(vocab_keys[1] , """<s>""" )
self.assertEqual(vocab_keys[-1] , """<eod>""" )
self.assertEqual(len(_lowerCAmelCase ) , 1006 )
def _a ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def _a ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = XLNetTokenizer(_lowerCAmelCase , keep_accents=_lowerCAmelCase )
__lowercase = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(_lowerCAmelCase , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase ) , [285, 46, 10, 170, 382] )
__lowercase = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
_lowerCAmelCase , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
__lowercase = tokenizer.convert_tokens_to_ids(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] )
__lowercase = tokenizer.convert_ids_to_tokens(_lowerCAmelCase )
self.assertListEqual(
_lowerCAmelCase , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__lowercase = XLNetTokenizer(_lowerCAmelCase , do_lower_case=_lowerCAmelCase )
__lowercase = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
_lowerCAmelCase , [
SPIECE_UNDERLINE + """""",
"""i""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""se""",
""".""",
] , )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""▁he""", """ll""", """o"""] )
def _a ( self : List[Any] ) -> Tuple:
"""simple docstring"""
__lowercase = XLNetTokenizer(_lowerCAmelCase , do_lower_case=_lowerCAmelCase )
__lowercase = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
_lowerCAmelCase , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""se""",
""".""",
] , )
@slow
def _a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
__lowercase = XLNetTokenizer.from_pretrained("""xlnet-base-cased""" )
__lowercase = tokenizer.encode("""sequence builders""" , add_special_tokens=_lowerCAmelCase )
__lowercase = tokenizer.encode("""multi-sequence build""" , add_special_tokens=_lowerCAmelCase )
__lowercase = tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase )
__lowercase = tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase , _lowerCAmelCase )
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = {"""input_ids""": [[17, 2_1442, 270, 17, 10, 1_4645, 318, 34, 17, 4546, 3145, 787, 13, 7752, 2_2018, 23, 21, 17, 4546, 3145, 787, 13, 3352, 1_4431, 13, 5500, 11, 1176, 580, 13, 1_6819, 4797, 23, 17, 10, 1_7135, 658, 19, 457, 7932, 13, 184, 19, 3154, 1_7135, 6468, 19, 1404, 1_2269, 19, 4229, 5356, 1_6264, 46, 19, 17, 2_0545, 1_0395, 9, 9, 9, 11, 28, 6421, 9531, 2_0729, 17, 10, 353, 1_7022, 11, 21, 6421, 9531, 1_6949, 17, 10, 1_1509, 753, 11, 33, 95, 2421, 7385, 956, 1_4431, 2626, 25, 842, 7385, 4836, 21, 1429, 2272, 9855, 3120, 161, 2_4738, 19, 1_3203, 658, 218, 787, 21, 430, 1_8482, 847, 2637, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 322, 2_2178, 27, 1064, 22, 956, 13, 1_1101, 1429, 5854, 2_4313, 1_8953, 40, 422, 2_4366, 68, 1758, 37, 1_0483, 1_4257, 31, 207, 263, 21, 203, 3773, 25, 71, 9735, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 2049, 3442, 17, 1_3894, 3380, 23, 95, 18, 1_7634, 2288, 9, 4, 3]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_lowerCAmelCase , model_name="""xlnet-base-cased""" , revision="""c841166438c31ec7ca9a106dee7bb312b73ae511""" , )
| 53
|
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Any = IFInpaintingPipeline
__snake_case :str = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
__snake_case :Optional[int] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__snake_case :str = PipelineTesterMixin.required_optional_params - {'latents'}
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
return self._get_dummy_components()
def _a ( self : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict=0 ) -> Any:
"""simple docstring"""
if str(_lowerCAmelCase ).startswith("""mps""" ):
__lowercase = torch.manual_seed(_lowerCAmelCase )
else:
__lowercase = torch.Generator(device=_lowerCAmelCase ).manual_seed(_lowerCAmelCase )
__lowercase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_lowerCAmelCase ) ).to(_lowerCAmelCase )
__lowercase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_lowerCAmelCase ) ).to(_lowerCAmelCase )
__lowercase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""mask_image""": mask_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def _a ( self : Tuple ) -> Dict:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def _a ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""" )
def _a ( self : List[str] ) -> Tuple:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1e-1 )
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def _a ( self : str ) -> Optional[int]:
"""simple docstring"""
self._test_save_load_local()
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 53
| 1
|
import os
import time
import numpy as np
import onnxruntime as ort
__UpperCamelCase : List[str] = """1"""
__UpperCamelCase : Any = """0"""
__UpperCamelCase : Dict = """1"""
__UpperCamelCase : int = ort.SessionOptions()
__UpperCamelCase : Optional[int] = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("""Create inference session...""")
__UpperCamelCase : Dict = ["""TensorrtExecutionProvider""", """CUDAExecutionProvider"""]
__UpperCamelCase : str = ort.InferenceSession("""model.onnx""", sess_options=sess_opt, providers=execution_provider)
__UpperCamelCase : str = ort.RunOptions()
__UpperCamelCase : List[str] = 128
__UpperCamelCase : Any = 1
__UpperCamelCase : int = np.ones((batch, sequence), dtype=np.intaa)
__UpperCamelCase : Any = np.ones((batch, sequence), dtype=np.intaa)
__UpperCamelCase : Tuple = np.ones((batch, sequence), dtype=np.intaa)
print("""Warm up phase...""")
sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Start inference...""")
__UpperCamelCase : Dict = time.time()
__UpperCamelCase : Optional[Any] = 2000
__UpperCamelCase : List[Any] = {}
for iter in range(max_iters):
__UpperCamelCase : Any = sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Average Inference Time = {:.3f} ms""".format((time.time() - start_time) * 1000 / max_iters))
| 53
|
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :str = (UnCLIPScheduler,)
def _a ( self : Optional[int] , **_lowerCAmelCase : Any ) -> Tuple:
"""simple docstring"""
__lowercase = {
"""num_train_timesteps""": 1000,
"""variance_type""": """fixed_small_log""",
"""clip_sample""": True,
"""clip_sample_range""": 1.0,
"""prediction_type""": """epsilon""",
}
config.update(**_lowerCAmelCase )
return config
def _a ( self : Dict ) -> List[Any]:
"""simple docstring"""
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=_lowerCAmelCase )
def _a ( self : List[str] ) -> Tuple:
"""simple docstring"""
for variance in ["fixed_small_log", "learned_range"]:
self.check_over_configs(variance_type=_lowerCAmelCase )
def _a ( self : Any ) -> Any:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_lowerCAmelCase )
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
for clip_sample_range in [1, 5, 10, 20]:
self.check_over_configs(clip_sample_range=_lowerCAmelCase )
def _a ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(prediction_type=_lowerCAmelCase )
def _a ( self : str ) -> int:
"""simple docstring"""
for time_step in [0, 500, 999]:
for prev_timestep in [None, 5, 100, 250, 500, 750]:
if prev_timestep is not None and prev_timestep >= time_step:
continue
self.check_over_forward(time_step=_lowerCAmelCase , prev_timestep=_lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config(variance_type="""fixed_small_log""" )
__lowercase = scheduler_class(**_lowerCAmelCase )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.0000e-10 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_549_625 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.9_994_987 ) ) < 1e-5
def _a ( self : str ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config(variance_type="""learned_range""" )
__lowercase = scheduler_class(**_lowerCAmelCase )
__lowercase = 0.5
assert scheduler._get_variance(1 , predicted_variance=_lowerCAmelCase ) - -10.1_712_790 < 1e-5
assert scheduler._get_variance(487 , predicted_variance=_lowerCAmelCase ) - -5.7_998_052 < 1e-5
assert scheduler._get_variance(999 , predicted_variance=_lowerCAmelCase ) - -0.0_010_011 < 1e-5
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config()
__lowercase = scheduler_class(**_lowerCAmelCase )
__lowercase = scheduler.timesteps
__lowercase = self.dummy_model()
__lowercase = self.dummy_sample_deter
__lowercase = torch.manual_seed(0 )
for i, t in enumerate(_lowerCAmelCase ):
# 1. predict noise residual
__lowercase = model(_lowerCAmelCase , _lowerCAmelCase )
# 2. predict previous mean of sample x_t-1
__lowercase = scheduler.step(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , generator=_lowerCAmelCase ).prev_sample
__lowercase = pred_prev_sample
__lowercase = torch.sum(torch.abs(_lowerCAmelCase ) )
__lowercase = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 252.2_682_495 ) < 1e-2
assert abs(result_mean.item() - 0.3_284_743 ) < 1e-3
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config()
__lowercase = scheduler_class(**_lowerCAmelCase )
scheduler.set_timesteps(25 )
__lowercase = scheduler.timesteps
__lowercase = self.dummy_model()
__lowercase = self.dummy_sample_deter
__lowercase = torch.manual_seed(0 )
for i, t in enumerate(_lowerCAmelCase ):
# 1. predict noise residual
__lowercase = model(_lowerCAmelCase , _lowerCAmelCase )
if i + 1 == timesteps.shape[0]:
__lowercase = None
else:
__lowercase = timesteps[i + 1]
# 2. predict previous mean of sample x_t-1
__lowercase = scheduler.step(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , prev_timestep=_lowerCAmelCase , generator=_lowerCAmelCase ).prev_sample
__lowercase = pred_prev_sample
__lowercase = torch.sum(torch.abs(_lowerCAmelCase ) )
__lowercase = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 258.2_044_983 ) < 1e-2
assert abs(result_mean.item() - 0.3_362_038 ) < 1e-3
def _a ( self : str ) -> Union[str, Any]:
"""simple docstring"""
pass
def _a ( self : int ) -> List[str]:
"""simple docstring"""
pass
| 53
| 1
|
class __UpperCamelCase :
def __init__( self : str , _lowerCAmelCase : int ) -> None:
"""simple docstring"""
__lowercase = size
__lowercase = [0] * size
__lowercase = [0] * size
@staticmethod
def _a ( _lowerCAmelCase : int ) -> int:
"""simple docstring"""
return index | (index + 1)
@staticmethod
def _a ( _lowerCAmelCase : int ) -> int:
"""simple docstring"""
return (index & (index + 1)) - 1
def _a ( self : Any , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> None:
"""simple docstring"""
__lowercase = value
while index < self.size:
__lowercase = self.get_prev(_lowerCAmelCase ) + 1
if current_left_border == index:
__lowercase = value
else:
__lowercase = max(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
__lowercase = self.get_next(_lowerCAmelCase )
def _a ( self : List[str] , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> int:
"""simple docstring"""
right -= 1 # Because of right is exclusive
__lowercase = 0
while left <= right:
__lowercase = self.get_prev(_lowerCAmelCase )
if left <= current_left:
__lowercase = max(_lowerCAmelCase , self.tree[right] )
__lowercase = current_left
else:
__lowercase = max(_lowerCAmelCase , self.arr[right] )
right -= 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 53
|
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
__UpperCamelCase : Any = logging.get_logger(__name__)
@dataclass
class __UpperCamelCase :
__snake_case :str = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys() )} )
__snake_case :str = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
__snake_case :int = field(
default=1_2_8 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def _a ( self : Dict ) -> List[Any]:
"""simple docstring"""
__lowercase = self.task_name.lower()
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Optional[int] = 'train'
__snake_case :int = 'dev'
__snake_case :Any = 'test'
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :GlueDataTrainingArguments
__snake_case :str
__snake_case :List[InputFeatures]
def __init__( self : Dict , _lowerCAmelCase : GlueDataTrainingArguments , _lowerCAmelCase : PreTrainedTokenizerBase , _lowerCAmelCase : Optional[int] = None , _lowerCAmelCase : Union[str, Split] = Split.train , _lowerCAmelCase : Optional[str] = None , ) -> List[Any]:
"""simple docstring"""
warnings.warn(
"""This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
"""library. You can have a look at this example script for pointers: """
"""https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py""" , _lowerCAmelCase , )
__lowercase = args
__lowercase = glue_processors[args.task_name]()
__lowercase = glue_output_modes[args.task_name]
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
try:
__lowercase = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
# Load data features from cache or dataset file
__lowercase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}' , )
__lowercase = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
__lowercase , __lowercase = label_list[2], label_list[1]
__lowercase = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowercase = cached_features_file + """.lock"""
with FileLock(_lowerCAmelCase ):
if os.path.exists(_lowerCAmelCase ) and not args.overwrite_cache:
__lowercase = time.time()
__lowercase = torch.load(_lowerCAmelCase )
logger.info(
F'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start )
else:
logger.info(F'Creating features from dataset file at {args.data_dir}' )
if mode == Split.dev:
__lowercase = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
__lowercase = self.processor.get_test_examples(args.data_dir )
else:
__lowercase = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
__lowercase = examples[:limit_length]
__lowercase = glue_convert_examples_to_features(
_lowerCAmelCase , _lowerCAmelCase , max_length=args.max_seq_length , label_list=_lowerCAmelCase , output_mode=self.output_mode , )
__lowercase = time.time()
torch.save(self.features , _lowerCAmelCase )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self : Dict ) -> Optional[int]:
"""simple docstring"""
return len(self.features )
def __getitem__( self : Tuple , _lowerCAmelCase : Optional[int] ) -> InputFeatures:
"""simple docstring"""
return self.features[i]
def _a ( self : str ) -> int:
"""simple docstring"""
return self.label_list
| 53
| 1
|
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__UpperCamelCase : Tuple = (
"""4S 3H 2C 7S 5H""",
"""9D 8H 2C 6S 7H""",
"""2D 6D 9D TH 7D""",
"""TC 8C 2S JH 6C""",
"""JH 8S TH AH QH""",
"""TS KS 5S 9S AC""",
"""KD 6S 9D TH AD""",
"""KS 8D 4D 9S 4S""", # pair
"""8C 4S KH JS 4D""", # pair
"""QH 8H KD JH 8S""", # pair
"""KC 4H KS 2H 8D""", # pair
"""KD 4S KC 3H 8S""", # pair
"""AH 8S AS KC JH""", # pair
"""3H 4C 4H 3S 2H""", # 2 pairs
"""5S 5D 2C KH KH""", # 2 pairs
"""3C KH 5D 5S KH""", # 2 pairs
"""AS 3C KH AD KH""", # 2 pairs
"""7C 7S 3S 7H 5S""", # 3 of a kind
"""7C 7S KH 2H 7H""", # 3 of a kind
"""AC KH QH AH AS""", # 3 of a kind
"""2H 4D 3C AS 5S""", # straight (low ace)
"""3C 5C 4C 2C 6H""", # straight
"""6S 8S 7S 5H 9H""", # straight
"""JS QS 9H TS KH""", # straight
"""QC KH TS JS AH""", # straight (high ace)
"""8C 9C 5C 3C TC""", # flush
"""3S 8S 9S 5S KS""", # flush
"""4C 5C 9C 8C KC""", # flush
"""JH 8H AH KH QH""", # flush
"""3D 2H 3H 2C 2D""", # full house
"""2H 2C 3S 3H 3D""", # full house
"""KH KC 3S 3H 3D""", # full house
"""JC 6H JS JD JH""", # 4 of a kind
"""JC 7H JS JD JH""", # 4 of a kind
"""JC KH JS JD JH""", # 4 of a kind
"""2S AS 4S 5S 3S""", # straight flush (low ace)
"""2D 6D 3D 4D 5D""", # straight flush
"""5C 6C 3C 7C 4C""", # straight flush
"""JH 9H TH KH QH""", # straight flush
"""JH AH TH KH QH""", # royal flush (high ace straight flush)
)
__UpperCamelCase : Dict = (
("""2H 3H 4H 5H 6H""", """KS AS TS QS JS""", """Loss"""),
("""2H 3H 4H 5H 6H""", """AS AD AC AH JD""", """Win"""),
("""AS AH 2H AD AC""", """JS JD JC JH 3D""", """Win"""),
("""2S AH 2H AS AC""", """JS JD JC JH AD""", """Loss"""),
("""2S AH 2H AS AC""", """2H 3H 5H 6H 7H""", """Win"""),
("""AS 3S 4S 8S 2S""", """2H 3H 5H 6H 7H""", """Win"""),
("""2H 3H 5H 6H 7H""", """2S 3H 4H 5S 6C""", """Win"""),
("""2S 3H 4H 5S 6C""", """3D 4C 5H 6H 2S""", """Tie"""),
("""2S 3H 4H 5S 6C""", """AH AC 5H 6H AS""", """Win"""),
("""2S 2H 4H 5S 4C""", """AH AC 5H 6H AS""", """Loss"""),
("""2S 2H 4H 5S 4C""", """AH AC 5H 6H 7S""", """Win"""),
("""6S AD 7H 4S AS""", """AH AC 5H 6H 7S""", """Loss"""),
("""2S AH 4H 5S KC""", """AH AC 5H 6H 7S""", """Loss"""),
("""2S 3H 6H 7S 9C""", """7H 3C TH 6H 9S""", """Loss"""),
("""4S 5H 6H TS AC""", """3S 5H 6H TS AC""", """Win"""),
("""2S AH 4H 5S 6C""", """AD 4C 5H 6H 2C""", """Tie"""),
("""AS AH 3H AD AC""", """AS AH 2H AD AC""", """Win"""),
("""AH AC 5H 5C QS""", """AH AC 5H 5C KS""", """Loss"""),
("""AH AC 5H 5C QS""", """KH KC 5H 5C QS""", """Win"""),
("""7C 7S KH 2H 7H""", """3C 3S AH 2H 3H""", """Win"""),
("""3C 3S AH 2H 3H""", """7C 7S KH 2H 7H""", """Loss"""),
("""6H 5H 4H 3H 2H""", """5H 4H 3H 2H AH""", """Win"""),
("""5H 4H 3H 2H AH""", """5H 4H 3H 2H AH""", """Tie"""),
("""5H 4H 3H 2H AH""", """6H 5H 4H 3H 2H""", """Loss"""),
("""AH AD KS KC AC""", """AH KD KH AC KC""", """Win"""),
("""2H 4D 3C AS 5S""", """2H 4D 3C 6S 5S""", """Loss"""),
("""2H 3S 3C 3H 2S""", """3S 3C 2S 2H 2D""", """Win"""),
("""4D 6D 5D 2D JH""", """3S 8S 3H TC KH""", """Loss"""),
("""4S 6C 8S 3S 7S""", """AD KS 2D 7D 7C""", """Loss"""),
("""6S 4C 7H 8C 3H""", """5H JC AH 9D 9C""", """Loss"""),
("""9D 9H JH TC QH""", """3C 2S JS 5C 7H""", """Win"""),
("""2H TC 8S AD 9S""", """4H TS 7H 2C 5C""", """Win"""),
("""9D 3S 2C 7S 7C""", """JC TD 3C TC 9H""", """Loss"""),
)
__UpperCamelCase : Optional[int] = (
("""2H 3H 4H 5H 6H""", True),
("""AS AH 2H AD AC""", False),
("""2H 3H 5H 6H 7H""", True),
("""KS AS TS QS JS""", True),
("""8H 9H QS JS TH""", False),
("""AS 3S 4S 8S 2S""", True),
)
__UpperCamelCase : int = (
("""2H 3H 4H 5H 6H""", True),
("""AS AH 2H AD AC""", False),
("""2H 3H 5H 6H 7H""", False),
("""KS AS TS QS JS""", True),
("""8H 9H QS JS TH""", True),
)
__UpperCamelCase : str = (
("""2H 4D 3C AS 5S""", True, [5, 4, 3, 2, 14]),
("""2H 5D 3C AS 5S""", False, [14, 5, 5, 3, 2]),
("""JH QD KC AS TS""", False, [14, 13, 12, 11, 10]),
("""9D 3S 2C 7S 7C""", False, [9, 7, 7, 3, 2]),
)
__UpperCamelCase : Any = (
("""JH AH TH KH QH""", 0),
("""JH 9H TH KH QH""", 0),
("""JC KH JS JD JH""", 7),
("""KH KC 3S 3H 3D""", 6),
("""8C 9C 5C 3C TC""", 0),
("""JS QS 9H TS KH""", 0),
("""7C 7S KH 2H 7H""", 3),
("""3C KH 5D 5S KH""", 2),
("""QH 8H KD JH 8S""", 1),
("""2D 6D 9D TH 7D""", 0),
)
__UpperCamelCase : Tuple = (
("""JH AH TH KH QH""", 23),
("""JH 9H TH KH QH""", 22),
("""JC KH JS JD JH""", 21),
("""KH KC 3S 3H 3D""", 20),
("""8C 9C 5C 3C TC""", 19),
("""JS QS 9H TS KH""", 18),
("""7C 7S KH 2H 7H""", 17),
("""3C KH 5D 5S KH""", 16),
("""QH 8H KD JH 8S""", 15),
("""2D 6D 9D TH 7D""", 14),
)
def snake_case ( ):
'''simple docstring'''
__lowercase , __lowercase = randrange(len(lowerCamelCase ) ), randrange(len(lowerCamelCase ) )
__lowercase = ["""Loss""", """Tie""", """Win"""][(play >= oppo) + (play > oppo)]
__lowercase , __lowercase = SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def snake_case ( lowerCamelCase = 100 ):
'''simple docstring'''
return (generate_random_hand() for _ in range(lowerCamelCase ))
@pytest.mark.parametrize("""hand, expected""" , lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
assert PokerHand(lowerCamelCase )._is_flush() == expected
@pytest.mark.parametrize("""hand, expected""" , lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
assert PokerHand(lowerCamelCase )._is_straight() == expected
@pytest.mark.parametrize("""hand, expected, card_values""" , lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = PokerHand(lowerCamelCase )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize("""hand, expected""" , lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
assert PokerHand(lowerCamelCase )._is_same_kind() == expected
@pytest.mark.parametrize("""hand, expected""" , lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
assert PokerHand(lowerCamelCase )._hand_type == expected
@pytest.mark.parametrize("""hand, other, expected""" , lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
assert PokerHand(lowerCamelCase ).compare_with(PokerHand(lowerCamelCase ) ) == expected
@pytest.mark.parametrize("""hand, other, expected""" , generate_random_hands() )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
assert PokerHand(lowerCamelCase ).compare_with(PokerHand(lowerCamelCase ) ) == expected
def snake_case ( ):
'''simple docstring'''
__lowercase = [PokerHand(lowerCamelCase ) for hand in SORTED_HANDS]
__lowercase = poker_hands.copy()
shuffle(lowerCamelCase )
__lowercase = chain(sorted(lowerCamelCase ) )
for index, hand in enumerate(lowerCamelCase ):
assert hand == poker_hands[index]
def snake_case ( ):
'''simple docstring'''
__lowercase = [PokerHand("""2D AC 3H 4H 5S""" ), PokerHand("""2S 3H 4H 5S 6C""" )]
pokerhands.sort(reverse=lowerCamelCase )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def snake_case ( ):
'''simple docstring'''
__lowercase = PokerHand("""2C 4S AS 3D 5C""" )
__lowercase = True
__lowercase = [5, 4, 3, 2, 14]
for _ in range(10 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def snake_case ( ):
'''simple docstring'''
__lowercase = 0
__lowercase = os.path.abspath(os.path.dirname(lowerCamelCase ) )
__lowercase = os.path.join(lowerCamelCase , """poker_hands.txt""" )
with open(lowerCamelCase ) as file_hand:
for line in file_hand:
__lowercase = line[:14].strip()
__lowercase = line[15:].strip()
__lowercase , __lowercase = PokerHand(lowerCamelCase ), PokerHand(lowerCamelCase )
__lowercase = player.compare_with(lowerCamelCase )
if output == "Win":
answer += 1
assert answer == 376
| 53
|
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoTokenizer,
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForWholeWordMask,
HfArgumentParser,
LineByLineTextDataset,
LineByLineWithRefDataset,
PreTrainedTokenizer,
TextDataset,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
__UpperCamelCase : List[Any] = logging.getLogger(__name__)
__UpperCamelCase : Optional[Any] = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
__UpperCamelCase : List[str] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class __UpperCamelCase :
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={
'help': (
'The model checkpoint for weights initialization. Leave None if you want to train a model from'
' scratch.'
)
} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_lowerCAmelCase )} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class __UpperCamelCase :
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'The input training data file (a text file).'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={
'help': (
'The input training data files (multiple files in glob format). '
'Very often splitting large files to smaller files can prevent tokenizer going out of memory'
)
} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'An optional input train ref data file for whole word mask in Chinese.'} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'An optional input eval ref data file for whole word mask in Chinese.'} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Whether distinct lines of text in the dataset are to be handled as distinct sequences.'} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Train with masked-language modeling loss instead of language modeling.'} )
__snake_case :bool = field(default=_lowerCAmelCase , metadata={'help': 'Whether ot not to use whole word mask.'} )
__snake_case :float = field(
default=0.15 , metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'} )
__snake_case :float = field(
default=1 / 6 , metadata={
'help': (
'Ratio of length of a span of masked tokens to surrounding context length for permutation language'
' modeling.'
)
} , )
__snake_case :int = field(
default=5 , metadata={'help': 'Maximum length of a span of masked tokens for permutation language modeling.'} )
__snake_case :int = field(
default=-1 , metadata={
'help': (
'Optional input sequence length after tokenization.'
'The training dataset will be truncated in block of this size for training.'
'Default to the model max input length for single sentence inputs (take into account special tokens).'
)
} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase = False , lowerCamelCase = None , ):
'''simple docstring'''
def _dataset(lowerCamelCase , lowerCamelCase=None ):
if args.line_by_line:
if ref_path is not None:
if not args.whole_word_mask or not args.mlm:
raise ValueError("""You need to set world whole masking and mlm to True for Chinese Whole Word Mask""" )
return LineByLineWithRefDataset(
tokenizer=lowerCamelCase , file_path=lowerCamelCase , block_size=args.block_size , ref_path=lowerCamelCase , )
return LineByLineTextDataset(tokenizer=lowerCamelCase , file_path=lowerCamelCase , block_size=args.block_size )
else:
return TextDataset(
tokenizer=lowerCamelCase , file_path=lowerCamelCase , block_size=args.block_size , overwrite_cache=args.overwrite_cache , cache_dir=lowerCamelCase , )
if evaluate:
return _dataset(args.eval_data_file , args.eval_ref_file )
elif args.train_data_files:
return ConcatDataset([_dataset(lowerCamelCase ) for f in glob(args.train_data_files )] )
else:
return _dataset(args.train_data_file , args.train_ref_file )
def snake_case ( ):
'''simple docstring'''
__lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
__lowercase , __lowercase , __lowercase = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_args.do_eval:
raise ValueError(
"""Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file """
"""or remove the --do_eval argument.""" )
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
""" --overwrite_output_dir to overcome.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , lowerCamelCase )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if model_args.config_name:
__lowercase = AutoConfig.from_pretrained(model_args.config_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
__lowercase = AutoConfig.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
__lowercase = CONFIG_MAPPING[model_args.model_type]()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.tokenizer_name:
__lowercase = AutoTokenizer.from_pretrained(model_args.tokenizer_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
__lowercase = AutoTokenizer.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
raise ValueError(
"""You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"""
""" script, save it,and load it from here, using --tokenizer_name""" )
if model_args.model_name_or_path:
__lowercase = AutoModelWithLMHead.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 , )
else:
logger.info("""Training new model from scratch""" )
__lowercase = AutoModelWithLMHead.from_config(lowerCamelCase )
model.resize_token_embeddings(len(lowerCamelCase ) )
if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
raise ValueError(
"""BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"""
"""--mlm flag (masked language modeling).""" )
if data_args.block_size <= 0:
__lowercase = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
__lowercase = min(data_args.block_size , tokenizer.max_len )
# Get datasets
__lowercase = (
get_dataset(lowerCamelCase , tokenizer=lowerCamelCase , cache_dir=model_args.cache_dir ) if training_args.do_train else None
)
__lowercase = (
get_dataset(lowerCamelCase , tokenizer=lowerCamelCase , evaluate=lowerCamelCase , cache_dir=model_args.cache_dir )
if training_args.do_eval
else None
)
if config.model_type == "xlnet":
__lowercase = DataCollatorForPermutationLanguageModeling(
tokenizer=lowerCamelCase , plm_probability=data_args.plm_probability , max_span_length=data_args.max_span_length , )
else:
if data_args.mlm and data_args.whole_word_mask:
__lowercase = DataCollatorForWholeWordMask(
tokenizer=lowerCamelCase , mlm_probability=data_args.mlm_probability )
else:
__lowercase = DataCollatorForLanguageModeling(
tokenizer=lowerCamelCase , mlm=data_args.mlm , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
__lowercase = Trainer(
model=lowerCamelCase , args=lowerCamelCase , data_collator=lowerCamelCase , train_dataset=lowerCamelCase , eval_dataset=lowerCamelCase , prediction_loss_only=lowerCamelCase , )
# Training
if training_args.do_train:
__lowercase = (
model_args.model_name_or_path
if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
else None
)
trainer.train(model_path=lowerCamelCase )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
__lowercase = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__lowercase = trainer.evaluate()
__lowercase = math.exp(eval_output["""eval_loss"""] )
__lowercase = {"""perplexity""": perplexity}
__lowercase = os.path.join(training_args.output_dir , """eval_results_lm.txt""" )
if trainer.is_world_master():
with open(lowerCamelCase , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key in sorted(result.keys() ):
logger.info(""" %s = %s""" , lowerCamelCase , str(result[key] ) )
writer.write("""%s = %s\n""" % (key, str(result[key] )) )
results.update(lowerCamelCase )
return results
def snake_case ( lowerCamelCase ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 53
| 1
|
import math
from datetime import datetime, timedelta
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = year % 19
__lowercase = year % 4
__lowercase = year % 7
__lowercase = math.floor(year / 100 )
__lowercase = math.floor((13 + 8 * leap_day_inhibits) / 25 )
__lowercase = leap_day_inhibits / 4
__lowercase = (
15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
) % 30
__lowercase = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7
# days to be added to March 21
__lowercase = (19 * metonic_cycle + secular_moon_shift) % 30
# PHM -> Paschal Full Moon
__lowercase = (
2 * julian_leap_year
+ 4 * non_leap_year
+ 6 * days_to_add
+ century_starting_point
) % 7
if days_to_add == 29 and days_from_phm_to_sunday == 6:
return datetime(lowerCamelCase , 4 , 19 )
elif days_to_add == 28 and days_from_phm_to_sunday == 6:
return datetime(lowerCamelCase , 4 , 18 )
else:
return datetime(lowerCamelCase , 3 , 22 ) + timedelta(
days=int(days_to_add + days_from_phm_to_sunday ) )
if __name__ == "__main__":
for year in (1994, 2000, 2010, 2021, 2023):
__UpperCamelCase : int = """will be""" if year > datetime.now().year else """was"""
print(F'''Easter in {year} {tense} {gauss_easter(year)}''')
| 53
|
from __future__ import annotations
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if len(lowerCamelCase ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
if any(i <= 0 for i in nums ):
raise ValueError("""All values must be greater than 0""" )
__lowercase = nums.copy()
copy_nums.sort()
return copy_nums[-1] < sum(copy_nums[:-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 53
| 1
|
import math
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = len(lowerCamelCase )
__lowercase = int(math.floor(math.sqrt(lowerCamelCase ) ) )
__lowercase = 0
while arr[min(lowerCamelCase , lowerCamelCase ) - 1] < x:
__lowercase = step
step += int(math.floor(math.sqrt(lowerCamelCase ) ) )
if prev >= n:
return -1
while arr[prev] < x:
__lowercase = prev + 1
if prev == min(lowerCamelCase , lowerCamelCase ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
__UpperCamelCase : List[str] = input("""Enter numbers separated by a comma:\n""").strip()
__UpperCamelCase : List[str] = [int(item) for item in user_input.split(""",""")]
__UpperCamelCase : Dict = int(input("""Enter the number to be searched:\n"""))
__UpperCamelCase : List[Any] = jump_search(arr, x)
if res == -1:
print("""Number not found!""")
else:
print(F'''Number {x} is at index {res}''')
| 53
|
from __future__ import annotations
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not nums:
return 0
__lowercase = nums[0]
__lowercase = 0
for num in nums[1:]:
__lowercase , __lowercase = (
max_excluding + num,
max(lowerCamelCase , lowerCamelCase ),
)
return max(lowerCamelCase , lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 53
| 1
|
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__UpperCamelCase : Any = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
"""Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :str = 'instructblip_vision_model'
def __init__( self : str , _lowerCAmelCase : Optional[int]=1408 , _lowerCAmelCase : Any=6144 , _lowerCAmelCase : Dict=39 , _lowerCAmelCase : Optional[int]=16 , _lowerCAmelCase : Optional[Any]=224 , _lowerCAmelCase : List[Any]=14 , _lowerCAmelCase : Optional[Any]="gelu" , _lowerCAmelCase : List[str]=1e-6 , _lowerCAmelCase : int=0.0 , _lowerCAmelCase : Tuple=1e-10 , _lowerCAmelCase : List[Any]=True , **_lowerCAmelCase : Tuple , ) -> str:
"""simple docstring"""
super().__init__(**_lowerCAmelCase )
__lowercase = hidden_size
__lowercase = intermediate_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = patch_size
__lowercase = image_size
__lowercase = initializer_range
__lowercase = attention_dropout
__lowercase = layer_norm_eps
__lowercase = hidden_act
__lowercase = qkv_bias
@classmethod
def _a ( cls : Tuple , _lowerCAmelCase : Union[str, os.PathLike] , **_lowerCAmelCase : Optional[int] ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(_lowerCAmelCase )
__lowercase , __lowercase = cls.get_config_dict(_lowerCAmelCase , **_lowerCAmelCase )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
__lowercase = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_lowerCAmelCase , **_lowerCAmelCase )
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :int = 'instructblip_qformer'
def __init__( self : Dict , _lowerCAmelCase : Optional[Any]=3_0522 , _lowerCAmelCase : Union[str, Any]=768 , _lowerCAmelCase : Dict=12 , _lowerCAmelCase : int=12 , _lowerCAmelCase : Optional[Any]=3072 , _lowerCAmelCase : str="gelu" , _lowerCAmelCase : Union[str, Any]=0.1 , _lowerCAmelCase : str=0.1 , _lowerCAmelCase : Any=512 , _lowerCAmelCase : List[str]=0.02 , _lowerCAmelCase : int=1e-12 , _lowerCAmelCase : Any=0 , _lowerCAmelCase : Tuple="absolute" , _lowerCAmelCase : Optional[int]=2 , _lowerCAmelCase : List[Any]=1408 , **_lowerCAmelCase : str , ) -> Optional[int]:
"""simple docstring"""
super().__init__(pad_token_id=_lowerCAmelCase , **_lowerCAmelCase )
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = hidden_act
__lowercase = intermediate_size
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = position_embedding_type
__lowercase = cross_attention_frequency
__lowercase = encoder_hidden_size
@classmethod
def _a ( cls : Optional[Any] , _lowerCAmelCase : Union[str, os.PathLike] , **_lowerCAmelCase : List[Any] ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(_lowerCAmelCase )
__lowercase , __lowercase = cls.get_config_dict(_lowerCAmelCase , **_lowerCAmelCase )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
__lowercase = config_dict["""qformer_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_lowerCAmelCase , **_lowerCAmelCase )
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :str = 'instructblip'
__snake_case :Optional[Any] = True
def __init__( self : Optional[Any] , _lowerCAmelCase : str=None , _lowerCAmelCase : Union[str, Any]=None , _lowerCAmelCase : Optional[int]=None , _lowerCAmelCase : Dict=32 , **_lowerCAmelCase : List[str] ) -> str:
"""simple docstring"""
super().__init__(**_lowerCAmelCase )
if vision_config is None:
__lowercase = {}
logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values.""" )
if qformer_config is None:
__lowercase = {}
logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.""" )
if text_config is None:
__lowercase = {}
logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`).""" )
__lowercase = InstructBlipVisionConfig(**_lowerCAmelCase )
__lowercase = InstructBlipQFormerConfig(**_lowerCAmelCase )
__lowercase = text_config["""model_type"""] if """model_type""" in text_config else """opt"""
__lowercase = CONFIG_MAPPING[text_model_type](**_lowerCAmelCase )
__lowercase = self.text_config.tie_word_embeddings
__lowercase = self.text_config.is_encoder_decoder
__lowercase = num_query_tokens
__lowercase = self.vision_config.hidden_size
__lowercase = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
__lowercase = 1.0
__lowercase = 0.02
@classmethod
def _a ( cls : Optional[Any] , _lowerCAmelCase : InstructBlipVisionConfig , _lowerCAmelCase : InstructBlipQFormerConfig , _lowerCAmelCase : PretrainedConfig , **_lowerCAmelCase : Any , ) -> List[str]:
"""simple docstring"""
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **_lowerCAmelCase , )
def _a ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = copy.deepcopy(self.__dict__ )
__lowercase = self.vision_config.to_dict()
__lowercase = self.qformer_config.to_dict()
__lowercase = self.text_config.to_dict()
__lowercase = self.__class__.model_type
return output
| 53
|
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase : str = logging.get_logger(__name__)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = RobertaPreLayerNormConfig.from_pretrained(
lowerCamelCase , architectures=["""RobertaPreLayerNormForMaskedLM"""] )
# convert state_dict
__lowercase = torch.load(hf_hub_download(repo_id=lowerCamelCase , filename="""pytorch_model.bin""" ) )
__lowercase = {}
for tensor_key, tensor_value in original_state_dict.items():
# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
if tensor_key.startswith("""roberta.""" ):
__lowercase = """roberta_prelayernorm.""" + tensor_key[len("""roberta.""" ) :]
# The original implementation contains weights which are not used, remove them from the state_dict
if tensor_key.endswith(""".self.LayerNorm.weight""" ) or tensor_key.endswith(""".self.LayerNorm.bias""" ):
continue
__lowercase = tensor_value
__lowercase = RobertaPreLayerNormForMaskedLM.from_pretrained(
pretrained_model_name_or_path=lowerCamelCase , config=lowerCamelCase , state_dict=lowerCamelCase )
model.save_pretrained(lowerCamelCase )
# convert tokenizer
__lowercase = AutoTokenizer.from_pretrained(lowerCamelCase )
tokenizer.save_pretrained(lowerCamelCase )
if __name__ == "__main__":
__UpperCamelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint-repo""",
default=None,
type=str,
required=True,
help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
__UpperCamelCase : Dict = parser.parse_args()
convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)
| 53
| 1
|
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class __UpperCamelCase ( _lowerCAmelCase ):
def _a ( self : Dict ) -> Any:
"""simple docstring"""
__lowercase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(_lowerCAmelCase , """hidden_sizes""" ) )
self.parent.assertTrue(hasattr(_lowerCAmelCase , """neck_hidden_sizes""" ) )
self.parent.assertTrue(hasattr(_lowerCAmelCase , """num_attention_heads""" ) )
class __UpperCamelCase :
def __init__( self : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : List[str]=13 , _lowerCAmelCase : int=32 , _lowerCAmelCase : str=2 , _lowerCAmelCase : List[Any]=3 , _lowerCAmelCase : List[str]=640 , _lowerCAmelCase : List[str]=4 , _lowerCAmelCase : int="silu" , _lowerCAmelCase : Tuple=3 , _lowerCAmelCase : Union[str, Any]=32 , _lowerCAmelCase : Tuple=0.1 , _lowerCAmelCase : Any=0.1 , _lowerCAmelCase : Optional[int]=0.1 , _lowerCAmelCase : Union[str, Any]=0.02 , _lowerCAmelCase : Union[str, Any]=True , _lowerCAmelCase : Optional[int]=True , _lowerCAmelCase : str=10 , _lowerCAmelCase : Optional[Any]=None , ) -> Tuple:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = last_hidden_size
__lowercase = num_attention_heads
__lowercase = hidden_act
__lowercase = conv_kernel_size
__lowercase = output_stride
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = classifier_dropout_prob
__lowercase = use_labels
__lowercase = is_training
__lowercase = num_labels
__lowercase = initializer_range
__lowercase = scope
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.num_labels )
__lowercase = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
__lowercase = self.get_config()
return config, pixel_values, labels, pixel_labels
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return MobileViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def _a ( self : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = MobileViTModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def _a ( self : Optional[int] , _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple , _lowerCAmelCase : int , _lowerCAmelCase : Dict ) -> List[str]:
"""simple docstring"""
__lowercase = self.num_labels
__lowercase = MobileViTForImageClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : List[str] , _lowerCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.num_labels
__lowercase = MobileViTForSemanticSegmentation(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
__lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def _a ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :List[Any] = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
__snake_case :List[Any] = (
{
'feature-extraction': MobileViTModel,
'image-classification': MobileViTForImageClassification,
'image-segmentation': MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__snake_case :List[str] = False
__snake_case :Tuple = False
__snake_case :Union[str, Any] = False
__snake_case :int = False
def _a ( self : int ) -> Optional[Any]:
"""simple docstring"""
__lowercase = MobileViTModelTester(self )
__lowercase = MobileViTConfigTester(self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""MobileViT does not use inputs_embeds""" )
def _a ( self : str ) -> Dict:
"""simple docstring"""
pass
@unittest.skip(reason="""MobileViT does not support input and output embeddings""" )
def _a ( self : List[Any] ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""MobileViT does not output attentions""" )
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
pass
def _a ( self : int ) -> str:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
pass
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _a ( self : str ) -> str:
"""simple docstring"""
def check_hidden_states_output(_lowerCAmelCase : Dict , _lowerCAmelCase : Tuple , _lowerCAmelCase : Any ):
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = outputs.hidden_states
__lowercase = 5
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
__lowercase = 2
for i in range(len(_lowerCAmelCase ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
def _a ( self : str ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCAmelCase )
@slow
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = MobileViTModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def snake_case ( ):
'''simple docstring'''
__lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
return MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small""" ) if is_vision_available() else None
@slow
def _a ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = MobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small""" ).to(_lowerCAmelCase )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
# verify the logits
__lowercase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
__lowercase = torch.tensor([-1.9_364, -1.2_327, -0.4_653] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
@slow
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__lowercase = MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )
__lowercase = model.to(_lowerCAmelCase )
__lowercase = MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )
__lowercase = prepare_img()
__lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
__lowercase = outputs.logits
# verify the logits
__lowercase = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , _lowerCAmelCase )
__lowercase = torch.tensor(
[
[[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]],
[[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]],
[[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]],
] , device=_lowerCAmelCase , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , _lowerCAmelCase , atol=1e-4 ) )
@slow
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__lowercase = MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )
__lowercase = model.to(_lowerCAmelCase )
__lowercase = MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )
__lowercase = prepare_img()
__lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
__lowercase = outputs.logits.detach().cpu()
__lowercase = image_processor.post_process_semantic_segmentation(outputs=_lowerCAmelCase , target_sizes=[(50, 60)] )
__lowercase = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , _lowerCAmelCase )
__lowercase = image_processor.post_process_semantic_segmentation(outputs=_lowerCAmelCase )
__lowercase = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , _lowerCAmelCase )
| 53
|
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if (ksize % 2) == 0:
__lowercase = ksize + 1
__lowercase = np.zeros((ksize, ksize) , dtype=np.floataa )
# each value
for y in range(lowerCamelCase ):
for x in range(lowerCamelCase ):
# distance from center
__lowercase = x - ksize // 2
__lowercase = y - ksize // 2
# degree to radiant
__lowercase = theta / 180 * np.pi
__lowercase = np.cos(_theta )
__lowercase = np.sin(_theta )
# get kernel x
__lowercase = cos_theta * px + sin_theta * py
# get kernel y
__lowercase = -sin_theta * px + cos_theta * py
# fill kernel
__lowercase = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi )
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
__UpperCamelCase : List[Any] = imread("""../image_data/lena.jpg""")
# turn image in gray scale value
__UpperCamelCase : Union[str, Any] = cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
__UpperCamelCase : Union[str, Any] = np.zeros(gray.shape[:2])
for theta in [0, 30, 60, 90, 120, 150]:
__UpperCamelCase : Tuple = gabor_filter_kernel(10, 8, theta, 10, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
__UpperCamelCase : List[str] = out / out.max() * 255
__UpperCamelCase : List[str] = out.astype(np.uinta)
imshow("""Original""", gray)
imshow("""Gabor filter with 20x20 mask and 6 directions""", out)
waitKey(0)
| 53
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|
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCamelCase : Dict = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
__snake_case :Dict = XLMProphetNetTokenizer
__snake_case :Optional[Any] = False
__snake_case :str = True
def _a ( self : Dict ) -> List[str]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowercase = XLMProphetNetTokenizer(_lowerCAmelCase , keep_accents=_lowerCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def _a ( self : str ) -> List[str]:
"""simple docstring"""
__lowercase = """[PAD]"""
__lowercase = 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 : int ) -> Optional[Any]:
"""simple docstring"""
__lowercase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """[PAD]""" )
self.assertEqual(vocab_keys[1] , """[CLS]""" )
self.assertEqual(vocab_keys[-1] , """j""" )
self.assertEqual(len(_lowerCAmelCase ) , 1012 )
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1012 )
def _a ( self : Tuple ) -> List[str]:
"""simple docstring"""
__lowercase = XLMProphetNetTokenizer(_lowerCAmelCase , keep_accents=_lowerCAmelCase )
__lowercase = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(_lowerCAmelCase , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_lowerCAmelCase ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__lowercase = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
_lowerCAmelCase , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
__lowercase = tokenizer.convert_tokens_to_ids(_lowerCAmelCase )
self.assertListEqual(
_lowerCAmelCase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
] , )
__lowercase = tokenizer.convert_ids_to_tokens(_lowerCAmelCase )
self.assertListEqual(
_lowerCAmelCase , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""[UNK]""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""[UNK]""",
""".""",
] , )
@cached_property
def _a ( self : Optional[int] ) -> Any:
"""simple docstring"""
return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased""" )
@slow
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = """Hello World!"""
__lowercase = [3_5389, 6672, 49, 2]
self.assertListEqual(_lowerCAmelCase , self.big_tokenizer.encode(_lowerCAmelCase ) )
@slow
def _a ( self : Dict ) -> str:
"""simple docstring"""
__lowercase = {"""input_ids""": [[1_1073, 8_2783, 18, 26, 8_2783, 549, 5_1540, 248, 1_7209, 1301, 217, 20, 21_5186, 1325, 147, 1_7209, 1301, 217, 20, 5_6370, 53, 12_2020, 20, 1_6477, 27, 8_7355, 4548, 20, 4728, 7_8392, 17, 15_9969, 18, 26, 2_4491, 629, 15, 538, 2_2704, 5439, 15, 2788, 2_4491, 9885, 15, 4_3534, 605, 15, 814, 1_8403, 3_3200, 29, 15, 4_3534, 2_4458, 1_2410, 111, 2_4966, 8_3669, 9637, 14_4068, 26, 850, 2_2346, 27, 147, 2_4966, 8_3669, 8_3490, 26, 3_9113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 12_2020, 11_5785, 34, 816, 1339, 4_6887, 18, 147, 5_3905, 1951, 4_2238, 4_1170, 1_7732, 834, 436, 15, 2_7523, 9_8733, 217, 147, 5542, 4981, 930, 1_7347, 16, 2], [2_0091, 629, 94, 8_2786, 58, 490, 20, 1528, 84, 5_3905, 344, 8_0592, 11_0128, 1_8822, 5267, 1306, 62, 15_2537, 308, 7997, 401, 12_4427, 549, 3_5442, 225, 109, 1_5055, 2_5748, 147, 7119, 4_3712, 34, 767, 13_5366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 6_3784, 11_9466, 17, 14_7808, 8_8214, 18, 656, 81, 32, 3296, 1_0280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_lowerCAmelCase , model_name="""microsoft/xprophetnet-large-wiki100-cased""" , revision="""1acad1643ddd54a44df6a1b797ada8373685d90e""" , )
| 53
|
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = set()
__lowercase = []
def parse_line(lowerCamelCase ):
for line in fp:
if isinstance(lowerCamelCase , lowerCamelCase ):
__lowercase = line.decode("""UTF-8""" )
if "warnings summary (final)" in line:
continue
# This means we are outside the body of a warning
elif not line.startswith(""" """ ):
# process a single warning and move it to `selected_warnings`.
if len(lowerCamelCase ) > 0:
__lowercase = """\n""".join(lowerCamelCase )
# Only keep the warnings specified in `targets`
if any(F': {x}: ' in warning for x in targets ):
selected_warnings.add(lowerCamelCase )
buffer.clear()
continue
else:
__lowercase = line.strip()
buffer.append(lowerCamelCase )
if from_gh:
for filename in os.listdir(lowerCamelCase ):
__lowercase = os.path.join(lowerCamelCase , lowerCamelCase )
if not os.path.isdir(lowerCamelCase ):
# read the file
if filename != "warnings.txt":
continue
with open(lowerCamelCase ) as fp:
parse_line(lowerCamelCase )
else:
try:
with zipfile.ZipFile(lowerCamelCase ) as z:
for filename in z.namelist():
if not os.path.isdir(lowerCamelCase ):
# read the file
if filename != "warnings.txt":
continue
with z.open(lowerCamelCase ) as fp:
parse_line(lowerCamelCase )
except Exception:
logger.warning(
F'{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.' )
return selected_warnings
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = set()
__lowercase = [os.path.join(lowerCamelCase , lowerCamelCase ) for p in os.listdir(lowerCamelCase ) if (p.endswith(""".zip""" ) or from_gh)]
for p in paths:
selected_warnings.update(extract_warnings_from_single_artifact(lowerCamelCase , lowerCamelCase ) )
return selected_warnings
if __name__ == "__main__":
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return values.split(""",""" )
__UpperCamelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
parser.add_argument(
"""--output_dir""",
type=str,
required=True,
help="""Where to store the downloaded artifacts and other result files.""",
)
parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
# optional parameters
parser.add_argument(
"""--targets""",
default="""DeprecationWarning,UserWarning,FutureWarning""",
type=list_str,
help="""Comma-separated list of target warning(s) which we want to extract.""",
)
parser.add_argument(
"""--from_gh""",
action="""store_true""",
help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
)
__UpperCamelCase : List[str] = parser.parse_args()
__UpperCamelCase : Union[str, Any] = args.from_gh
if from_gh:
# The artifacts have to be downloaded using `actions/download-artifact@v3`
pass
else:
os.makedirs(args.output_dir, exist_ok=True)
# get download links
__UpperCamelCase : Any = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
# download artifacts
for idx, (name, url) in enumerate(artifacts.items()):
print(name)
print(url)
print("""=""" * 80)
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
# extract warnings from artifacts
__UpperCamelCase : Union[str, Any] = extract_warnings(args.output_dir, args.targets)
__UpperCamelCase : Any = sorted(selected_warnings)
with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)
| 53
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|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Any = StableUnCLIPImgaImgPipeline
__snake_case :List[str] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
__snake_case :Tuple = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
__snake_case :List[Any] = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
__snake_case :Dict = frozenset([] )
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
__lowercase = 32
__lowercase = embedder_hidden_size
# image encoding components
__lowercase = CLIPImageProcessor(crop_size=32 , size=32 )
torch.manual_seed(0 )
__lowercase = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=_lowerCAmelCase , projection_dim=_lowerCAmelCase , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ) )
# regular denoising components
torch.manual_seed(0 )
__lowercase = StableUnCLIPImageNormalizer(embedding_dim=_lowerCAmelCase )
__lowercase = DDPMScheduler(beta_schedule="""squaredcos_cap_v2""" )
torch.manual_seed(0 )
__lowercase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
torch.manual_seed(0 )
__lowercase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_lowerCAmelCase , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""") , up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="""projection""" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=_lowerCAmelCase , layers_per_block=1 , upcast_attention=_lowerCAmelCase , use_linear_projection=_lowerCAmelCase , )
torch.manual_seed(0 )
__lowercase = DDIMScheduler(
beta_schedule="""scaled_linear""" , beta_start=0.00_085 , beta_end=0.012 , prediction_type="""v_prediction""" , set_alpha_to_one=_lowerCAmelCase , steps_offset=1 , )
torch.manual_seed(0 )
__lowercase = AutoencoderKL()
__lowercase = {
# image encoding components
"""feature_extractor""": feature_extractor,
"""image_encoder""": image_encoder.eval(),
# image noising components
"""image_normalizer""": image_normalizer.eval(),
"""image_noising_scheduler""": image_noising_scheduler,
# regular denoising components
"""tokenizer""": tokenizer,
"""text_encoder""": text_encoder.eval(),
"""unet""": unet.eval(),
"""scheduler""": scheduler,
"""vae""": vae.eval(),
}
return components
def _a ( self : List[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Union[str, Any]=0 , _lowerCAmelCase : Optional[int]=True ) -> Tuple:
"""simple docstring"""
if str(_lowerCAmelCase ).startswith("""mps""" ):
__lowercase = torch.manual_seed(_lowerCAmelCase )
else:
__lowercase = torch.Generator(device=_lowerCAmelCase ).manual_seed(_lowerCAmelCase )
__lowercase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_lowerCAmelCase ) ).to(_lowerCAmelCase )
if pil_image:
__lowercase = input_image * 0.5 + 0.5
__lowercase = input_image.clamp(0 , 1 )
__lowercase = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__lowercase = DiffusionPipeline.numpy_to_pil(_lowerCAmelCase )[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def _a ( self : str ) -> Any:
"""simple docstring"""
__lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowercase = self.get_dummy_components()
__lowercase = StableUnCLIPImgaImgPipeline(**_lowerCAmelCase )
__lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs(_lowerCAmelCase )
inputs.update({"""image_embeds""": None} )
__lowercase = sd_pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowercase = np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = torch_device in ["""cpu""", """mps"""]
self._test_attention_slicing_forward_pass(test_max_difference=_lowerCAmelCase )
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
__lowercase = torch_device in ["""cpu""", """mps"""]
self._test_inference_batch_single_identical(test_max_difference=_lowerCAmelCase )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def _a ( self : int ) -> Tuple:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=_lowerCAmelCase )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : List[str] ) -> int:
"""simple docstring"""
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png""" )
__lowercase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy""" )
__lowercase = StableUnCLIPImgaImgPipeline.from_pretrained(
"""fusing/stable-unclip-2-1-l-img2img""" , torch_dtype=torch.floataa )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowercase = torch.Generator(device="""cpu""" ).manual_seed(0 )
__lowercase = pipe(_lowerCAmelCase , """anime turle""" , generator=_lowerCAmelCase , output_type="""np""" )
__lowercase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self : str ) -> Tuple:
"""simple docstring"""
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png""" )
__lowercase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy""" )
__lowercase = StableUnCLIPImgaImgPipeline.from_pretrained(
"""fusing/stable-unclip-2-1-h-img2img""" , torch_dtype=torch.floataa )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowercase = torch.Generator(device="""cpu""" ).manual_seed(0 )
__lowercase = pipe(_lowerCAmelCase , """anime turle""" , generator=_lowerCAmelCase , output_type="""np""" )
__lowercase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Dict ) -> List[str]:
"""simple docstring"""
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png""" )
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__lowercase = StableUnCLIPImgaImgPipeline.from_pretrained(
"""fusing/stable-unclip-2-1-h-img2img""" , torch_dtype=torch.floataa )
__lowercase = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowercase = pipe(
_lowerCAmelCase , """anime turtle""" , num_inference_steps=2 , output_type="""np""" , )
__lowercase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 53
|
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCamelCase : Any = {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Dict = [
"""MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MraForMaskedLM""",
"""MraForMultipleChoice""",
"""MraForQuestionAnswering""",
"""MraForSequenceClassification""",
"""MraForTokenClassification""",
"""MraLayer""",
"""MraModel""",
"""MraPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
__UpperCamelCase : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 53
| 1
|
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = RemBertConfig.from_json_file(lowerCamelCase )
print("""Building PyTorch model from configuration: {}""".format(str(lowerCamelCase ) ) )
__lowercase = RemBertModel(lowerCamelCase )
# Load weights from tf checkpoint
load_tf_weights_in_rembert(lowerCamelCase , lowerCamelCase , lowerCamelCase )
# Save pytorch-model
print("""Save PyTorch model to {}""".format(lowerCamelCase ) )
torch.save(model.state_dict() , lowerCamelCase )
if __name__ == "__main__":
__UpperCamelCase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--rembert_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained RemBERT 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."""
)
__UpperCamelCase : int = parser.parse_args()
convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)
| 53
|
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not isinstance(lowerCamelCase , lowerCamelCase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
__lowercase = str(lowerCamelCase )
__lowercase = """""".join(sorted(lowerCamelCase ) )
return sorted_str_n != str_n and sorted_str_n[::-1] != str_n
def snake_case ( lowerCamelCase = 99 ):
'''simple docstring'''
if not 0 < percent < 100:
raise ValueError("""solution() only accepts values from 0 to 100""" )
__lowercase = 0
__lowercase = 1
while True:
if check_bouncy(lowerCamelCase ):
bouncy_num += 1
if (bouncy_num / num) * 100 >= percent:
return num
num += 1
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F'''{solution(99)}''')
| 53
| 1
|
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if digit_amount > 0:
return round(number - int(lowerCamelCase ) , lowerCamelCase )
return number - int(lowerCamelCase )
if __name__ == "__main__":
print(decimal_isolate(1.5_3, 0))
print(decimal_isolate(3_5.3_4_5, 1))
print(decimal_isolate(3_5.3_4_5, 2))
print(decimal_isolate(3_5.3_4_5, 3))
print(decimal_isolate(-1_4.7_8_9, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-1_4.1_2_3, 1))
print(decimal_isolate(-1_4.1_2_3, 2))
print(decimal_isolate(-1_4.1_2_3, 3))
| 53
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__UpperCamelCase : Tuple = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormerConfig""",
"""SwiftFormerOnnxConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Optional[Any] = [
"""SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SwiftFormerForImageClassification""",
"""SwiftFormerModel""",
"""SwiftFormerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
__UpperCamelCase : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 53
| 1
|
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__UpperCamelCase : int = 16
__UpperCamelCase : int = 32
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = 16 ):
'''simple docstring'''
__lowercase = AutoTokenizer.from_pretrained("""bert-base-cased""" )
__lowercase = DatasetDict(
{
"""train""": dataset["""train"""].select(lowerCamelCase ),
"""validation""": dataset["""train"""].select(lowerCamelCase ),
"""test""": dataset["""validation"""],
} )
def tokenize_function(lowerCamelCase ):
# max_length=None => use the model max length (it's actually the default)
__lowercase = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCamelCase , max_length=lowerCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__lowercase = datasets.map(
lowerCamelCase , batched=lowerCamelCase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowercase = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowerCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__lowercase = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__lowercase = 16
elif accelerator.mixed_precision != "no":
__lowercase = 8
else:
__lowercase = None
return tokenizer.pad(
lowerCamelCase , padding="""longest""" , max_length=lowerCamelCase , pad_to_multiple_of=lowerCamelCase , return_tensors="""pt""" , )
# Instantiate dataloaders.
__lowercase = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCamelCase , collate_fn=lowerCamelCase , batch_size=lowerCamelCase )
__lowercase = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCamelCase , collate_fn=lowerCamelCase , batch_size=lowerCamelCase )
__lowercase = DataLoader(
tokenized_datasets["""test"""] , shuffle=lowerCamelCase , collate_fn=lowerCamelCase , batch_size=lowerCamelCase )
return train_dataloader, eval_dataloader, test_dataloader
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = []
# Download the dataset
__lowercase = load_dataset("""glue""" , """mrpc""" )
# Create our splits
__lowercase = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
__lowercase = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowercase = config["""lr"""]
__lowercase = int(config["""num_epochs"""] )
__lowercase = int(config["""seed"""] )
__lowercase = int(config["""batch_size"""] )
__lowercase = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
__lowercase = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
__lowercase = batch_size // MAX_GPU_BATCH_SIZE
__lowercase = MAX_GPU_BATCH_SIZE
set_seed(lowerCamelCase )
# New Code #
# Create our folds:
__lowercase = kfold.split(np.zeros(datasets["""train"""].num_rows ) , datasets["""train"""]["""label"""] )
__lowercase = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(lowerCamelCase ):
__lowercase , __lowercase , __lowercase = get_fold_dataloaders(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowercase = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowerCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__lowercase = model.to(accelerator.device )
# Instantiate optimizer
__lowercase = AdamW(params=model.parameters() , lr=lowerCamelCase )
# Instantiate scheduler
__lowercase = get_linear_schedule_with_warmup(
optimizer=lowerCamelCase , num_warmup_steps=100 , num_training_steps=(len(lowerCamelCase ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase = accelerator.prepare(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
# Now we train the model
for epoch in range(lowerCamelCase ):
model.train()
for step, batch in enumerate(lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__lowercase = model(**lowerCamelCase )
__lowercase = outputs.loss
__lowercase = loss / gradient_accumulation_steps
accelerator.backward(lowerCamelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__lowercase = model(**lowerCamelCase )
__lowercase = outputs.logits.argmax(dim=-1 )
__lowercase , __lowercase = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=lowerCamelCase , references=lowerCamelCase , )
__lowercase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:' , lowerCamelCase )
# New Code #
# We also run predictions on the test set at the very end
__lowercase = []
for step, batch in enumerate(lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__lowercase = model(**lowerCamelCase )
__lowercase = outputs.logits
__lowercase , __lowercase = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(lowerCamelCase , dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
__lowercase = torch.cat(lowerCamelCase , dim=0 )
__lowercase = torch.stack(lowerCamelCase , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
__lowercase = metric.compute(predictions=lowerCamelCase , references=lowerCamelCase )
accelerator.print("""Average test metrics from all folds:""" , lowerCamelCase )
def snake_case ( ):
'''simple docstring'''
__lowercase = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowerCamelCase , default=lowerCamelCase , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
# New Code #
parser.add_argument("""--num_folds""" , type=lowerCamelCase , default=3 , help="""The number of splits to perform across the dataset""" )
__lowercase = parser.parse_args()
__lowercase = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(lowerCamelCase , lowerCamelCase )
if __name__ == "__main__":
main()
| 53
|
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
super().tearDown()
gc.collect()
def _a ( self : List[str] ) -> int:
"""simple docstring"""
__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_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512] )
print(F'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def _a ( self : str ) -> List[Any]:
"""simple docstring"""
__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_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297] )
print(F'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 53
| 1
|
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 1 / sqrt(2 ) ):
'''simple docstring'''
__lowercase = tau * frequency / samplerate
__lowercase = sin(lowerCamelCase )
__lowercase = cos(lowerCamelCase )
__lowercase = _sin / (2 * q_factor)
__lowercase = (1 - _cos) / 2
__lowercase = 1 - _cos
__lowercase = 1 + alpha
__lowercase = -2 * _cos
__lowercase = 1 - alpha
__lowercase = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 1 / sqrt(2 ) ):
'''simple docstring'''
__lowercase = tau * frequency / samplerate
__lowercase = sin(lowerCamelCase )
__lowercase = cos(lowerCamelCase )
__lowercase = _sin / (2 * q_factor)
__lowercase = (1 + _cos) / 2
__lowercase = -1 - _cos
__lowercase = 1 + alpha
__lowercase = -2 * _cos
__lowercase = 1 - alpha
__lowercase = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 1 / sqrt(2 ) ):
'''simple docstring'''
__lowercase = tau * frequency / samplerate
__lowercase = sin(lowerCamelCase )
__lowercase = cos(lowerCamelCase )
__lowercase = _sin / (2 * q_factor)
__lowercase = _sin / 2
__lowercase = 0
__lowercase = -ba
__lowercase = 1 + alpha
__lowercase = -2 * _cos
__lowercase = 1 - alpha
__lowercase = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 1 / sqrt(2 ) ):
'''simple docstring'''
__lowercase = tau * frequency / samplerate
__lowercase = sin(lowerCamelCase )
__lowercase = cos(lowerCamelCase )
__lowercase = _sin / (2 * q_factor)
__lowercase = 1 - alpha
__lowercase = -2 * _cos
__lowercase = 1 + alpha
__lowercase = IIRFilter(2 )
filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] )
return filt
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = 1 / sqrt(2 ) , ):
'''simple docstring'''
__lowercase = tau * frequency / samplerate
__lowercase = sin(lowerCamelCase )
__lowercase = cos(lowerCamelCase )
__lowercase = _sin / (2 * q_factor)
__lowercase = 10 ** (gain_db / 40)
__lowercase = 1 + alpha * big_a
__lowercase = -2 * _cos
__lowercase = 1 - alpha * big_a
__lowercase = 1 + alpha / big_a
__lowercase = -2 * _cos
__lowercase = 1 - alpha / big_a
__lowercase = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = 1 / sqrt(2 ) , ):
'''simple docstring'''
__lowercase = tau * frequency / samplerate
__lowercase = sin(lowerCamelCase )
__lowercase = cos(lowerCamelCase )
__lowercase = _sin / (2 * q_factor)
__lowercase = 10 ** (gain_db / 40)
__lowercase = (big_a + 1) - (big_a - 1) * _cos
__lowercase = (big_a + 1) + (big_a - 1) * _cos
__lowercase = (big_a - 1) - (big_a + 1) * _cos
__lowercase = (big_a - 1) + (big_a + 1) * _cos
__lowercase = 2 * sqrt(lowerCamelCase ) * alpha
__lowercase = big_a * (pmc + aaa)
__lowercase = 2 * big_a * mpc
__lowercase = big_a * (pmc - aaa)
__lowercase = ppmc + aaa
__lowercase = -2 * pmpc
__lowercase = ppmc - aaa
__lowercase = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = 1 / sqrt(2 ) , ):
'''simple docstring'''
__lowercase = tau * frequency / samplerate
__lowercase = sin(lowerCamelCase )
__lowercase = cos(lowerCamelCase )
__lowercase = _sin / (2 * q_factor)
__lowercase = 10 ** (gain_db / 40)
__lowercase = (big_a + 1) - (big_a - 1) * _cos
__lowercase = (big_a + 1) + (big_a - 1) * _cos
__lowercase = (big_a - 1) - (big_a + 1) * _cos
__lowercase = (big_a - 1) + (big_a + 1) * _cos
__lowercase = 2 * sqrt(lowerCamelCase ) * alpha
__lowercase = big_a * (ppmc + aaa)
__lowercase = -2 * big_a * pmpc
__lowercase = big_a * (ppmc - aaa)
__lowercase = pmc + aaa
__lowercase = 2 * mpc
__lowercase = pmc - aaa
__lowercase = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
| 53
|
import heapq
import sys
import numpy as np
__UpperCamelCase : List[str] = tuple[int, int]
class __UpperCamelCase :
def __init__( self : Optional[int] ) -> Dict:
"""simple docstring"""
__lowercase = []
__lowercase = set()
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
if not self.empty():
return self.elements[0][0]
else:
return float("""inf""" )
def _a ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
return len(self.elements ) == 0
def _a ( self : str , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
if item not in self.set:
heapq.heappush(self.elements , (priority, item) )
self.set.add(_lowerCAmelCase )
else:
# update
# print("update", item)
__lowercase = []
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
while x != item:
temp.append((pri, x) )
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
temp.append((priority, item) )
for pro, xxx in temp:
heapq.heappush(self.elements , (pro, xxx) )
def _a ( self : List[str] , _lowerCAmelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
if item in self.set:
self.set.remove(_lowerCAmelCase )
__lowercase = []
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
while x != item:
temp.append((pro, x) )
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
for prito, yyy in temp:
heapq.heappush(self.elements , (prito, yyy) )
def _a ( self : Any ) -> List[Any]:
"""simple docstring"""
return self.elements[0][1]
def _a ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
self.set.remove(_lowerCAmelCase )
return (priority, item)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = np.array(lowerCamelCase )
__lowercase = np.array(lowerCamelCase )
return np.linalg.norm(a - b )
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return consistent_heuristic(lowerCamelCase , lowerCamelCase ) // t
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return abs(p[0] - goal[0] ) + abs(p[1] - goal[1] )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = g_function[start] + Wa * heuristics[i](lowerCamelCase , lowerCamelCase )
return ans
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = np.chararray((n, n) )
for i in range(lowerCamelCase ):
for j in range(lowerCamelCase ):
__lowercase = """*"""
for i in range(lowerCamelCase ):
for j in range(lowerCamelCase ):
if (j, (n - 1) - i) in blocks:
__lowercase = """#"""
__lowercase = """-"""
__lowercase = back_pointer[goal]
while x != start:
((__lowercase) , (__lowercase)) = x
# print(x)
__lowercase = """-"""
__lowercase = back_pointer[x]
__lowercase = """-"""
for i in range(lowerCamelCase ):
for j in range(lowerCamelCase ):
if (i, j) == (0, n - 1):
print(grid[i][j] , end=""" """ )
print("""<-- End position""" , end=""" """ )
else:
print(grid[i][j] , end=""" """ )
print()
print("""^""" )
print("""Start position""" )
print()
print("""# is an obstacle""" )
print("""- is the path taken by algorithm""" )
print("""PATH TAKEN BY THE ALGORITHM IS:-""" )
__lowercase = back_pointer[goal]
while x != start:
print(lowerCamelCase , end=""" """ )
__lowercase = back_pointer[x]
print(lowerCamelCase )
sys.exit()
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if p[0] < 0 or p[0] > n - 1:
return False
if p[1] < 0 or p[1] > n - 1:
return False
return True
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , ):
'''simple docstring'''
for itera in range(lowerCamelCase ):
open_list[itera].remove_element(lowerCamelCase )
# print("s", s)
# print("j", j)
((__lowercase) , (__lowercase)) = s
__lowercase = (x - 1, y)
__lowercase = (x + 1, y)
__lowercase = (x, y + 1)
__lowercase = (x, y - 1)
for neighbours in [left, right, up, down]:
if neighbours not in blocks:
if valid(lowerCamelCase ) and neighbours not in visited:
# print("neighbour", neighbours)
visited.add(lowerCamelCase )
__lowercase = -1
__lowercase = float("""inf""" )
if valid(lowerCamelCase ) and g_function[neighbours] > g_function[s] + 1:
__lowercase = g_function[s] + 1
__lowercase = s
if neighbours not in close_list_anchor:
open_list[0].put(lowerCamelCase , key(lowerCamelCase , 0 , lowerCamelCase , lowerCamelCase ) )
if neighbours not in close_list_inad:
for var in range(1 , lowerCamelCase ):
if key(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) <= Wa * key(
lowerCamelCase , 0 , lowerCamelCase , lowerCamelCase ):
open_list[j].put(
lowerCamelCase , key(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) )
def snake_case ( ):
'''simple docstring'''
__lowercase = []
for x in range(1 , 5 ):
for y in range(1 , 6 ):
some_list.append((x, y) )
for x in range(15 , 20 ):
some_list.append((x, 17) )
for x in range(10 , 19 ):
for y in range(1 , 15 ):
some_list.append((x, y) )
# L block
for x in range(1 , 4 ):
for y in range(12 , 19 ):
some_list.append((x, y) )
for x in range(3 , 13 ):
for y in range(16 , 19 ):
some_list.append((x, y) )
return some_list
__UpperCamelCase : Optional[int] = {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}
__UpperCamelCase : Optional[Any] = [
(0, 1),
(1, 1),
(2, 1),
(3, 1),
(4, 1),
(5, 1),
(6, 1),
(7, 1),
(8, 1),
(9, 1),
(10, 1),
(11, 1),
(12, 1),
(13, 1),
(14, 1),
(15, 1),
(16, 1),
(17, 1),
(18, 1),
(19, 1),
]
__UpperCamelCase : Optional[Any] = make_common_ground()
__UpperCamelCase : Dict = blocks_blk
# hyper parameters
__UpperCamelCase : Union[str, Any] = 1
__UpperCamelCase : Union[str, Any] = 1
__UpperCamelCase : Optional[int] = 20
__UpperCamelCase : List[str] = 3 # one consistent and two other inconsistent
# start and end destination
__UpperCamelCase : str = (0, 0)
__UpperCamelCase : str = (n - 1, n - 1)
__UpperCamelCase : Optional[Any] = 1
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = {start: 0, goal: float("""inf""" )}
__lowercase = {start: -1, goal: -1}
__lowercase = []
__lowercase = set()
for i in range(lowerCamelCase ):
open_list.append(PriorityQueue() )
open_list[i].put(lowerCamelCase , key(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) )
__lowercase = []
__lowercase = []
while open_list[0].minkey() < float("""inf""" ):
for i in range(1 , lowerCamelCase ):
# print(open_list[0].minkey(), open_list[i].minkey())
if open_list[i].minkey() <= Wa * open_list[0].minkey():
global t
t += 1
if g_function[goal] <= open_list[i].minkey():
if g_function[goal] < float("""inf""" ):
do_something(lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
__lowercase , __lowercase = open_list[i].top_show()
visited.add(lowerCamelCase )
expand_state(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , )
close_list_inad.append(lowerCamelCase )
else:
if g_function[goal] <= open_list[0].minkey():
if g_function[goal] < float("""inf""" ):
do_something(lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
__lowercase = open_list[0].top_show()
visited.add(lowerCamelCase )
expand_state(
lowerCamelCase , 0 , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , )
close_list_anchor.append(lowerCamelCase )
print("""No path found to goal""" )
print()
for i in range(n - 1 , -1 , -1 ):
for j in range(lowerCamelCase ):
if (j, i) in blocks:
print("""#""" , end=""" """ )
elif (j, i) in back_pointer:
if (j, i) == (n - 1, n - 1):
print("""*""" , end=""" """ )
else:
print("""-""" , end=""" """ )
else:
print("""*""" , end=""" """ )
if (j, i) == (n - 1, n - 1):
print("""<-- End position""" , end=""" """ )
print()
print("""^""" )
print("""Start position""" )
print()
print("""# is an obstacle""" )
print("""- is the path taken by algorithm""" )
if __name__ == "__main__":
multi_a_star(start, goal, n_heuristic)
| 53
| 1
|
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.md""", """dataset_infos.json"""],
["""dataset_infos.json"""],
["""full:README.md"""],
] , )
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = tmp_path_factory.mktemp("""dset_infos_dir""" )
if "full:README.md" in files:
with open(dataset_infos_dir / """README.md""" , """w""" ) as f:
f.write("""---\ndataset_info:\n dataset_size: 42\n---""" )
if "empty:README.md" in files:
with open(dataset_infos_dir / """README.md""" , """w""" ) as f:
f.write("""""" )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / """dataset_infos.json""" , """w""" ) as f:
f.write("""{\"default\": {\"dataset_size\": 42}}""" )
__lowercase = DatasetInfosDict.from_directory(lowerCamelCase )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
"""dataset_info""" , [
DatasetInfo(),
DatasetInfo(
description="""foo""" , features=Features({"""a""": Value("""int32""" )} ) , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train"""}] , download_size=42 , ),
] , )
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = str(lowerCamelCase )
dataset_info.write_to_directory(lowerCamelCase )
__lowercase = DatasetInfo.from_directory(lowerCamelCase )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(lowerCamelCase , """dataset_info.json""" ) )
def snake_case ( ):
'''simple docstring'''
__lowercase = DatasetInfo(
description="""foo""" , citation="""bar""" , homepage="""https://foo.bar""" , license="""CC0""" , features=Features({"""a""": Value("""int32""" )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train""", """num_examples""": 42}] , download_checksums={} , download_size=1_337 , post_processing_size=442 , dataset_size=1_234 , size_in_bytes=1_337 + 442 + 1_234 , )
__lowercase = dataset_info._to_yaml_dict()
assert sorted(lowerCamelCase ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
__lowercase = yaml.safe_dump(lowerCamelCase )
__lowercase = yaml.safe_load(lowerCamelCase )
assert dataset_info_yaml_dict == reloaded
def snake_case ( ):
'''simple docstring'''
__lowercase = DatasetInfo()
__lowercase = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
"""dataset_infos_dict""" , [
DatasetInfosDict(),
DatasetInfosDict({"""default""": DatasetInfo()} ),
DatasetInfosDict({"""my_config_name""": DatasetInfo()} ),
DatasetInfosDict(
{
"""default""": DatasetInfo(
description="""foo""" , features=Features({"""a""": Value("""int32""" )} ) , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train"""}] , download_size=42 , )
} ),
DatasetInfosDict(
{
"""v1""": DatasetInfo(dataset_size=42 ),
"""v2""": DatasetInfo(dataset_size=1_337 ),
} ),
] , )
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = str(lowerCamelCase )
dataset_infos_dict.write_to_directory(lowerCamelCase )
__lowercase = DatasetInfosDict.from_directory(lowerCamelCase )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
__lowercase = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
__lowercase = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(lowerCamelCase , """README.md""" ) )
| 53
|
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = SwinConfig.from_pretrained(
"""microsoft/swin-tiny-patch4-window7-224""" , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
__lowercase = MaskFormerConfig(backbone_config=lowerCamelCase )
__lowercase = """huggingface/label-files"""
if "ade20k-full" in model_name:
# this should be ok
__lowercase = 847
__lowercase = """maskformer-ade20k-full-id2label.json"""
elif "ade" in model_name:
# this should be ok
__lowercase = 150
__lowercase = """ade20k-id2label.json"""
elif "coco-stuff" in model_name:
# this should be ok
__lowercase = 171
__lowercase = """maskformer-coco-stuff-id2label.json"""
elif "coco" in model_name:
# TODO
__lowercase = 133
__lowercase = """coco-panoptic-id2label.json"""
elif "cityscapes" in model_name:
# this should be ok
__lowercase = 19
__lowercase = """cityscapes-id2label.json"""
elif "vistas" in model_name:
# this should be ok
__lowercase = 65
__lowercase = """mapillary-vistas-id2label.json"""
__lowercase = json.load(open(hf_hub_download(lowerCamelCase , lowerCamelCase , repo_type="""dataset""" ) , """r""" ) )
__lowercase = {int(lowerCamelCase ): v for k, v in idalabel.items()}
return config
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = []
# stem
# fmt: off
rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""") )
rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""") )
rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""") )
rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias') )
if i < 3:
rename_keys.append((F'backbone.layers.{i}.downsample.reduction.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight') )
rename_keys.append((F'backbone.layers.{i}.downsample.norm.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight') )
rename_keys.append((F'backbone.layers.{i}.downsample.norm.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias') )
rename_keys.append((F'backbone.norm{i}.weight', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight') )
rename_keys.append((F'backbone.norm{i}.bias', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias') )
# FPN
rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""") )
rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""") )
rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""") )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight') )
rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight') )
rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias') )
rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""") )
rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""") )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias') )
# cross-attention out projection
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias') )
# MLP 1
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias') )
# MLP 2
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias') )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias') )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias') )
# layernorm 3 (final layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias') )
rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""") )
rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""") )
# heads on top
rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""") )
rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""") )
rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""") )
rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""") )
rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""") )
for i in range(3 ):
rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight') )
rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias') )
# fmt: on
return rename_keys
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = dct.pop(lowerCamelCase )
__lowercase = val
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
__lowercase = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
__lowercase = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight' )
__lowercase = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
__lowercase = in_proj_weight[:dim, :]
__lowercase = in_proj_bias[: dim]
__lowercase = in_proj_weight[
dim : dim * 2, :
]
__lowercase = in_proj_bias[
dim : dim * 2
]
__lowercase = in_proj_weight[
-dim :, :
]
__lowercase = in_proj_bias[-dim :]
# fmt: on
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
__lowercase = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight' )
__lowercase = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
__lowercase = in_proj_weight[: hidden_size, :]
__lowercase = in_proj_bias[:config.hidden_size]
__lowercase = in_proj_weight[hidden_size : hidden_size * 2, :]
__lowercase = in_proj_bias[hidden_size : hidden_size * 2]
__lowercase = in_proj_weight[-hidden_size :, :]
__lowercase = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
__lowercase = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight' )
__lowercase = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
__lowercase = in_proj_weight[: hidden_size, :]
__lowercase = in_proj_bias[:config.hidden_size]
__lowercase = in_proj_weight[hidden_size : hidden_size * 2, :]
__lowercase = in_proj_bias[hidden_size : hidden_size * 2]
__lowercase = in_proj_weight[-hidden_size :, :]
__lowercase = in_proj_bias[-hidden_size :]
# fmt: on
def snake_case ( ):
'''simple docstring'''
__lowercase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__lowercase = Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase ).raw )
return im
@torch.no_grad()
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = False ):
'''simple docstring'''
__lowercase = get_maskformer_config(lowerCamelCase )
# load original state_dict
with open(lowerCamelCase , """rb""" ) as f:
__lowercase = pickle.load(lowerCamelCase )
__lowercase = data["""model"""]
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
__lowercase = create_rename_keys(lowerCamelCase )
for src, dest in rename_keys:
rename_key(lowerCamelCase , lowerCamelCase , lowerCamelCase )
read_in_swin_q_k_v(lowerCamelCase , config.backbone_config )
read_in_decoder_q_k_v(lowerCamelCase , lowerCamelCase )
# update to torch tensors
for key, value in state_dict.items():
__lowercase = torch.from_numpy(lowerCamelCase )
# load 🤗 model
__lowercase = MaskFormerForInstanceSegmentation(lowerCamelCase )
model.eval()
for name, param in model.named_parameters():
print(lowerCamelCase , param.shape )
__lowercase , __lowercase = model.load_state_dict(lowerCamelCase , strict=lowerCamelCase )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(lowerCamelCase ) == 0, F'Unexpected keys: {unexpected_keys}'
# verify results
__lowercase = prepare_img()
if "vistas" in model_name:
__lowercase = 65
elif "cityscapes" in model_name:
__lowercase = 65_535
else:
__lowercase = 255
__lowercase = True if """ade""" in model_name else False
__lowercase = MaskFormerImageProcessor(ignore_index=lowerCamelCase , reduce_labels=lowerCamelCase )
__lowercase = image_processor(lowerCamelCase , return_tensors="""pt""" )
__lowercase = model(**lowerCamelCase )
print("""Logits:""" , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
__lowercase = torch.tensor(
[[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , lowerCamelCase , atol=1e-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F'Saving model and image processor to {pytorch_dump_folder_path}' )
Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
model.save_pretrained(lowerCamelCase )
image_processor.save_pretrained(lowerCamelCase )
if push_to_hub:
print("""Pushing model and image processor to the hub...""" )
model.push_to_hub(F'nielsr/{model_name}' )
image_processor.push_to_hub(F'nielsr/{model_name}' )
if __name__ == "__main__":
__UpperCamelCase : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""maskformer-swin-tiny-ade""",
type=str,
help=("""Name of the MaskFormer model you'd like to convert""",),
)
parser.add_argument(
"""--checkpoint_path""",
default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
type=str,
help="""Path to the original state dict (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
__UpperCamelCase : List[Any] = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 53
| 1
|
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class __UpperCamelCase ( unittest.TestCase ):
def __init__( self : List[str] , _lowerCAmelCase : int , _lowerCAmelCase : List[str]=13 , _lowerCAmelCase : List[str]=7 , _lowerCAmelCase : Optional[Any]=True , _lowerCAmelCase : Union[str, Any]=True , _lowerCAmelCase : Dict=True , _lowerCAmelCase : Any=True , _lowerCAmelCase : Tuple=99 , _lowerCAmelCase : Dict=32 , _lowerCAmelCase : Tuple=5 , _lowerCAmelCase : Dict=4 , _lowerCAmelCase : Tuple=37 , _lowerCAmelCase : Optional[int]="gelu" , _lowerCAmelCase : Dict=0.1 , _lowerCAmelCase : Union[str, Any]=0.1 , _lowerCAmelCase : Union[str, Any]=512 , _lowerCAmelCase : Optional[Any]=16 , _lowerCAmelCase : str=2 , _lowerCAmelCase : int=0.02 , _lowerCAmelCase : Optional[Any]=4 , ) -> List[Any]:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = seq_length
__lowercase = is_training
__lowercase = use_attention_mask
__lowercase = use_token_type_ids
__lowercase = use_labels
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = num_choices
def _a ( self : List[Any] ) -> Dict:
"""simple docstring"""
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowercase = None
if self.use_attention_mask:
__lowercase = random_attention_mask([self.batch_size, self.seq_length] )
__lowercase = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , tie_weights_=_lowerCAmelCase , )
return config, input_ids, attention_mask
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
__snake_case :int = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _a ( self : Union[str, Any] ) -> int:
"""simple docstring"""
__lowercase = FlaxDistilBertModelTester(self )
@slow
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
__lowercase = model_class_name.from_pretrained("""distilbert-base-uncased""" )
__lowercase = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowerCAmelCase )
@require_flax
class __UpperCamelCase ( unittest.TestCase ):
@slow
def _a ( self : List[Any] ) -> Tuple:
"""simple docstring"""
__lowercase = FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased""" )
__lowercase = np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
__lowercase = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
__lowercase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase )[0]
__lowercase = (1, 11, 768)
self.assertEqual(output.shape , _lowerCAmelCase )
__lowercase = np.array([[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , _lowerCAmelCase , atol=1e-4 ) )
| 53
|
from math import sqrt
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(lowerCamelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def snake_case ( lowerCamelCase = 10_001 ):
'''simple docstring'''
__lowercase = 0
__lowercase = 1
while count != nth and number < 3:
number += 1
if is_prime(lowerCamelCase ):
count += 1
while count != nth:
number += 2
if is_prime(lowerCamelCase ):
count += 1
return number
if __name__ == "__main__":
print(F'''{solution() = }''')
| 53
| 1
|
import math
class __UpperCamelCase :
def _a ( self : int , _lowerCAmelCase : list[list[float]] , _lowerCAmelCase : list[int] ) -> int:
"""simple docstring"""
__lowercase = 0.0
__lowercase = 0.0
for i in range(len(_lowerCAmelCase ) ):
da += math.pow((sample[i] - weights[0][i]) , 2 )
da += math.pow((sample[i] - weights[1][i]) , 2 )
return 0 if da > da else 1
return 0
def _a ( self : str , _lowerCAmelCase : list[list[int | float]] , _lowerCAmelCase : list[int] , _lowerCAmelCase : int , _lowerCAmelCase : float ) -> list[list[int | float]]:
"""simple docstring"""
for i in range(len(_lowerCAmelCase ) ):
weights[j][i] += alpha * (sample[i] - weights[j][i])
return weights
def snake_case ( ):
'''simple docstring'''
__lowercase = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
# weight initialization ( n, C )
__lowercase = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
# training
__lowercase = SelfOrganizingMap()
__lowercase = 3
__lowercase = 0.5
for _ in range(lowerCamelCase ):
for j in range(len(lowerCamelCase ) ):
# training sample
__lowercase = training_samples[j]
# Compute the winning vector
__lowercase = self_organizing_map.get_winner(lowerCamelCase , lowerCamelCase )
# Update the winning vector
__lowercase = self_organizing_map.update(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
# classify test sample
__lowercase = [0, 0, 0, 1]
__lowercase = self_organizing_map.get_winner(lowerCamelCase , lowerCamelCase )
# results
print(F'Clusters that the test sample belongs to : {winner}' )
print(F'Weights that have been trained : {weights}' )
# running the main() function
if __name__ == "__main__":
main()
| 53
|
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if isinstance(lowerCamelCase , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class __UpperCamelCase :
def _a ( self : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
pass
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
pass
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
pass
def _a ( self : int , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Dict=None , **_lowerCAmelCase : int ) -> str:
"""simple docstring"""
__lowercase = VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) )
def _a ( self : str , _lowerCAmelCase : Tuple , _lowerCAmelCase : str , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : int=None , **_lowerCAmelCase : Dict ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase , text_model=_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def _a ( self : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Dict , _lowerCAmelCase : str , _lowerCAmelCase : Any=None , **_lowerCAmelCase : str ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = {"""vision_model""": vision_model, """text_model""": text_model}
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def _a ( self : Dict , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any]=None , **_lowerCAmelCase : Any ) -> Optional[int]:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase , text_model=_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
__lowercase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
__lowercase = after_output[0].numpy()
__lowercase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCAmelCase , 1e-5 )
def _a ( self : int , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int]=None , **_lowerCAmelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase , text_model=_lowerCAmelCase )
__lowercase = model(
input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase , output_attentions=_lowerCAmelCase )
__lowercase = output.vision_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
__lowercase = to_atuple(vision_model.config.image_size )
__lowercase = to_atuple(vision_model.config.patch_size )
__lowercase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowercase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__lowercase = output.text_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _a ( self : List[Any] , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : float ) -> Optional[int]:
"""simple docstring"""
__lowercase = np.abs((a - b) ).max()
self.assertLessEqual(_lowerCAmelCase , _lowerCAmelCase , F'Difference between torch and flax is {diff} (>= {tol}).' )
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**_lowerCAmelCase )
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> int:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_lowerCAmelCase )
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_save_load(**_lowerCAmelCase )
def _a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_lowerCAmelCase )
@slow
def _a ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.get_pretrained_model_and_inputs()
__lowercase = model_a(**_lowerCAmelCase )
__lowercase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase )
__lowercase = model_a(**_lowerCAmelCase )
__lowercase = after_outputs[0].numpy()
__lowercase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCAmelCase , 1e-5 )
@require_tf
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
def _a ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-random-bert""" )
__lowercase = 13
__lowercase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowercase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowercase = random_attention_mask([batch_size, 4] )
__lowercase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _a ( self : Tuple , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
__lowercase = TFViTModel(_lowerCAmelCase , name="""vision_model""" )
__lowercase = TFBertModel(_lowerCAmelCase , name="""text_model""" )
return vision_model, text_model
def _a ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = TFViTModelTester(self )
__lowercase = TFBertModelTester(self )
__lowercase = vit_model_tester.prepare_config_and_inputs()
__lowercase = bert_model_tester.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = vision_config_and_inputs
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-deit-tf""" , """hf-internal-testing/tiny-random-roberta""" )
__lowercase = 13
__lowercase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowercase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowercase = random_attention_mask([batch_size, 4] )
__lowercase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _a ( self : Optional[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : Tuple , _lowerCAmelCase : int=None , **_lowerCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase , text_model=_lowerCAmelCase )
__lowercase = model(
input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase , output_attentions=_lowerCAmelCase )
__lowercase = output.vision_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
__lowercase = to_atuple(vision_model.config.image_size )
__lowercase = to_atuple(vision_model.config.patch_size )
__lowercase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowercase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__lowercase = output.text_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _a ( self : Tuple , _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict ) -> int:
"""simple docstring"""
__lowercase = TFDeiTModel(_lowerCAmelCase , name="""vision_model""" )
__lowercase = TFRobertaModel(_lowerCAmelCase , name="""text_model""" )
return vision_model, text_model
def _a ( self : Tuple ) -> str:
"""simple docstring"""
__lowercase = TFDeiTModelTester(self )
__lowercase = TFRobertaModelTester(self )
__lowercase = vit_model_tester.prepare_config_and_inputs()
__lowercase = bert_model_tester.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = vision_config_and_inputs
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
def _a ( self : int ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-clip-tf""" , """hf-internal-testing/tiny-random-bert""" )
__lowercase = 13
__lowercase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowercase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowercase = random_attention_mask([batch_size, 4] )
__lowercase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _a ( self : Optional[int] , _lowerCAmelCase : Dict , _lowerCAmelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
__lowercase = TFCLIPVisionModel(_lowerCAmelCase , name="""vision_model""" )
__lowercase = TFBertModel(_lowerCAmelCase , name="""text_model""" )
return vision_model, text_model
def _a ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = TFCLIPVisionModelTester(self )
__lowercase = TFBertModelTester(self )
__lowercase = clip_model_tester.prepare_config_and_inputs()
__lowercase = bert_model_tester.prepare_config_and_inputs()
__lowercase , __lowercase = vision_config_and_inputs
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class __UpperCamelCase ( unittest.TestCase ):
@slow
def _a ( self : int ) -> Tuple:
"""simple docstring"""
__lowercase = TFVisionTextDualEncoderModel.from_pretrained(
"""clip-italian/clip-italian""" , logit_scale_init_value=1.0 , from_pt=_lowerCAmelCase )
__lowercase = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
__lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
__lowercase = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=_lowerCAmelCase , padding=_lowerCAmelCase , return_tensors="""np""" )
__lowercase = model(**_lowerCAmelCase )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
__lowercase = np.array([[1.2_284_727, 0.3_104_122]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , _lowerCAmelCase , atol=1e-3 ) )
| 53
| 1
|
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return credit_card_number.startswith(("""34""", """35""", """37""", """4""", """5""", """6""") )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = credit_card_number
__lowercase = 0
__lowercase = len(lowerCamelCase ) - 2
for i in range(lowerCamelCase , -1 , -2 ):
# double the value of every second digit
__lowercase = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
__lowercase = cc_number[:i] + str(lowerCamelCase ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(lowerCamelCase ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = F'{credit_card_number} is an invalid credit card number because'
if not credit_card_number.isdigit():
print(F'{error_message} it has nonnumerical characters.' )
return False
if not 13 <= len(lowerCamelCase ) <= 16:
print(F'{error_message} of its length.' )
return False
if not validate_initial_digits(lowerCamelCase ):
print(F'{error_message} of its first two digits.' )
return False
if not luhn_validation(lowerCamelCase ):
print(F'{error_message} it fails the Luhn check.' )
return False
print(F'{credit_card_number} is a valid credit card number.' )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("""4111111111111111""")
validate_credit_card_number("""32323""")
| 53
|
from __future__ import annotations
from scipy.special import comb # type: ignore
class __UpperCamelCase :
def __init__( self : int , _lowerCAmelCase : list[tuple[float, float]] ) -> Any:
"""simple docstring"""
__lowercase = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
__lowercase = len(_lowerCAmelCase ) - 1
def _a ( self : Tuple , _lowerCAmelCase : float ) -> list[float]:
"""simple docstring"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__lowercase = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , _lowerCAmelCase ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(_lowerCAmelCase ) , 5 ) == 1
return output_values
def _a ( self : List[str] , _lowerCAmelCase : float ) -> tuple[float, float]:
"""simple docstring"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__lowercase = self.basis_function(_lowerCAmelCase )
__lowercase = 0.0
__lowercase = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def _a ( self : Optional[int] , _lowerCAmelCase : float = 0.01 ) -> Union[str, Any]:
"""simple docstring"""
from matplotlib import pyplot as plt # type: ignore
__lowercase = [] # x coordinates of points to plot
__lowercase = [] # y coordinates of points to plot
__lowercase = 0.0
while t <= 1:
__lowercase = self.bezier_curve_function(_lowerCAmelCase )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
__lowercase = [i[0] for i in self.list_of_points]
__lowercase = [i[1] for i in self.list_of_points]
plt.plot(
_lowerCAmelCase , _lowerCAmelCase , color="""blue""" , label="""Curve of Degree """ + str(self.degree ) , )
plt.scatter(_lowerCAmelCase , _lowerCAmelCase , color="""red""" , label="""Control Points""" )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 53
| 1
|
from manim import *
class __UpperCamelCase ( _lowerCAmelCase ):
def _a ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = Rectangle(height=0.5 , width=0.5 )
__lowercase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
__lowercase = [mem.copy() for i in range(6 )]
__lowercase = [mem.copy() for i in range(6 )]
__lowercase = VGroup(*_lowerCAmelCase ).arrange(_lowerCAmelCase , buff=0 )
__lowercase = VGroup(*_lowerCAmelCase ).arrange(_lowerCAmelCase , buff=0 )
__lowercase = VGroup(_lowerCAmelCase , _lowerCAmelCase ).arrange(_lowerCAmelCase , buff=0 )
__lowercase = Text("""CPU""" , font_size=24 )
__lowercase = Group(_lowerCAmelCase , _lowerCAmelCase ).arrange(_lowerCAmelCase , buff=0.5 , aligned_edge=_lowerCAmelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_lowerCAmelCase )
__lowercase = [mem.copy() for i in range(1 )]
__lowercase = VGroup(*_lowerCAmelCase ).arrange(_lowerCAmelCase , buff=0 )
__lowercase = Text("""GPU""" , font_size=24 )
__lowercase = Group(_lowerCAmelCase , _lowerCAmelCase ).arrange(_lowerCAmelCase , buff=0.5 , aligned_edge=_lowerCAmelCase )
gpu.align_to(_lowerCAmelCase , _lowerCAmelCase )
gpu.set_x(gpu.get_x() - 1 )
self.add(_lowerCAmelCase )
__lowercase = [mem.copy() for i in range(6 )]
__lowercase = VGroup(*_lowerCAmelCase ).arrange(_lowerCAmelCase , buff=0 )
__lowercase = Text("""Model""" , font_size=24 )
__lowercase = Group(_lowerCAmelCase , _lowerCAmelCase ).arrange(_lowerCAmelCase , buff=0.5 , aligned_edge=_lowerCAmelCase )
model.move_to([3, -1.0, 0] )
self.play(
Create(_lowerCAmelCase , run_time=1 ) , Create(_lowerCAmelCase , run_time=1 ) , Create(_lowerCAmelCase , run_time=1 ) , )
__lowercase = MarkupText(
F'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.' , font_size=24 , )
__lowercase = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
__lowercase = MarkupText(
F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(_lowerCAmelCase , run_time=2.5 ) , Write(_lowerCAmelCase ) , Write(_lowerCAmelCase ) )
self.add(_lowerCAmelCase )
__lowercase = []
__lowercase = []
__lowercase = []
for i, rect in enumerate(_lowerCAmelCase ):
__lowercase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(_lowerCAmelCase , opacity=0.7 )
cpu_target.move_to(_lowerCAmelCase )
cpu_target.generate_target()
__lowercase = 0.46 / 4
__lowercase = 0.46 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=_lowerCAmelCase )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=_lowerCAmelCase , buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=_lowerCAmelCase , buff=0.0 )
cpu_targs.append(_lowerCAmelCase )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(_lowerCAmelCase ) )
second_animations.append(MoveToTarget(_lowerCAmelCase , run_time=1.5 ) )
self.play(*_lowerCAmelCase )
self.play(*_lowerCAmelCase )
self.wait()
| 53
|
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
)
from transformers.models.efficientformer.modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_vision_available():
from PIL import Image
from transformers import EfficientFormerImageProcessor
class __UpperCamelCase :
def __init__( self : int , _lowerCAmelCase : Tuple , _lowerCAmelCase : int = 13 , _lowerCAmelCase : int = 64 , _lowerCAmelCase : int = 2 , _lowerCAmelCase : int = 3 , _lowerCAmelCase : int = 3 , _lowerCAmelCase : bool = True , _lowerCAmelCase : bool = True , _lowerCAmelCase : int = 128 , _lowerCAmelCase : Optional[int]=[16, 32, 64, 128] , _lowerCAmelCase : int = 7 , _lowerCAmelCase : int = 4 , _lowerCAmelCase : int = 37 , _lowerCAmelCase : str = "gelu" , _lowerCAmelCase : float = 0.1 , _lowerCAmelCase : float = 0.1 , _lowerCAmelCase : int = 10 , _lowerCAmelCase : float = 0.02 , _lowerCAmelCase : int = 2 , _lowerCAmelCase : int = 1 , _lowerCAmelCase : int = 128 , _lowerCAmelCase : List[int] = [2, 2, 2, 2] , _lowerCAmelCase : int = 2 , _lowerCAmelCase : int = 2 , ) -> Tuple:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = is_training
__lowercase = use_labels
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = encoder_stride
__lowercase = num_attention_outputs
__lowercase = embed_dim
__lowercase = embed_dim + 1
__lowercase = resolution
__lowercase = depths
__lowercase = hidden_sizes
__lowercase = dim
__lowercase = mlp_expansion_ratio
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
return EfficientFormerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_lowerCAmelCase , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , resolution=self.resolution , depths=self.depths , hidden_sizes=self.hidden_sizes , dim=self.dim , mlp_expansion_ratio=self.mlp_expansion_ratio , )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = TFEfficientFormerModel(config=_lowerCAmelCase )
__lowercase = model(_lowerCAmelCase , training=_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.type_sequence_label_size
__lowercase = TFEfficientFormerForImageClassification(_lowerCAmelCase )
__lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase , training=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__lowercase = 1
__lowercase = TFEfficientFormerForImageClassification(_lowerCAmelCase )
__lowercase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Any = (
(
TFEfficientFormerModel,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerForImageClassification,
)
if is_tf_available()
else ()
)
__snake_case :Any = (
{
'feature-extraction': TFEfficientFormerModel,
'image-classification': (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
),
}
if is_tf_available()
else {}
)
__snake_case :int = False
__snake_case :Optional[int] = False
__snake_case :int = False
__snake_case :Any = False
__snake_case :Any = False
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
__lowercase = TFEfficientFormerModelTester(self )
__lowercase = ConfigTester(
self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 )
def _a ( self : Optional[int] ) -> int:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""EfficientFormer does not use inputs_embeds""" )
def _a ( self : Optional[int] ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""EfficientFormer does not support input and output embeddings""" )
def _a ( self : int ) -> str:
"""simple docstring"""
pass
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
def check_hidden_states_output(_lowerCAmelCase : int , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] ):
__lowercase = model_class(_lowerCAmelCase )
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) , training=_lowerCAmelCase )
__lowercase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowercase = getattr(
self.model_tester , """expected_num_hidden_layers""" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
if hasattr(self.model_tester , """encoder_seq_length""" ):
__lowercase = self.model_tester.encoder_seq_length
if hasattr(self.model_tester , """chunk_length""" ) and self.model_tester.chunk_length > 1:
__lowercase = seq_length * self.model_tester.chunk_length
else:
__lowercase = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
if config.is_encoder_decoder:
__lowercase = outputs.decoder_hidden_states
self.asseretIsInstance(_lowerCAmelCase , (list, tuple) )
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """seq_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """decoder_seq_length""" , _lowerCAmelCase )
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [decoder_seq_length, self.model_tester.hidden_size] , )
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Optional[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any]=False ) -> Dict:
"""simple docstring"""
__lowercase = super()._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
if return_labels:
if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def _a ( self : int ) -> int:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet""" )
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase )
def _a ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@slow
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = TFEfficientFormerModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = True
__lowercase = getattr(self.model_tester , """seq_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """encoder_seq_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """key_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """chunk_length""" , _lowerCAmelCase )
if chunk_length is not None and hasattr(self.model_tester , """num_hashes""" ):
__lowercase = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
__lowercase = True
__lowercase = False
__lowercase = True
__lowercase = model_class(_lowerCAmelCase )
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) , training=_lowerCAmelCase )
__lowercase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_attention_outputs )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__lowercase = True
__lowercase = model_class(_lowerCAmelCase )
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) , training=_lowerCAmelCase )
__lowercase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_attention_outputs )
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length] , )
else:
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length] , )
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Prepare our model
__lowercase = model_class(_lowerCAmelCase )
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
__lowercase = {
key: tf.keras.Input(shape=val.shape[1:] , dtype=val.dtype , name=_lowerCAmelCase )
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
__lowercase = model(_lowerCAmelCase )
self.assertTrue(outputs_dict is not None )
def snake_case ( ):
'''simple docstring'''
__lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
return (
EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300""" )
if is_vision_available()
else None
)
@slow
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__lowercase = TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300""" )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""tf""" )
# forward pass
__lowercase = model(**_lowerCAmelCase , training=_lowerCAmelCase )
# verify the logits
__lowercase = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
__lowercase = tf.constant([-0.0_555, 0.4_825, -0.0_852] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
@slow
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__lowercase = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
"""snap-research/efficientformer-l1-300""" )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""tf""" )
# forward pass
__lowercase = model(**_lowerCAmelCase , training=_lowerCAmelCase )
# verify the logits
__lowercase = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
__lowercase = tf.constant([-0.1_312, 0.4_353, -1.0_499] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
| 53
| 1
|
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
__UpperCamelCase : List[Any] = True
from torch.cuda.amp import autocast
__UpperCamelCase : Tuple = logging.getLogger(__name__)
def snake_case ( lowerCamelCase=None , lowerCamelCase=None ):
'''simple docstring'''
return field(default_factory=lambda: default , metadata=lowerCamelCase )
@dataclass
class __UpperCamelCase :
__snake_case :str = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
__snake_case :Optional[bool] = field(
default=_lowerCAmelCase , metadata={'help': 'Whether to freeze the feature extractor layers of the model.'} )
__snake_case :Optional[float] = field(
default=0.1 , metadata={'help': 'The dropout ratio for the attention probabilities.'} )
__snake_case :Optional[float] = field(
default=0.1 , metadata={'help': 'The dropout ratio for activations inside the fully connected layer.'} )
__snake_case :Optional[float] = field(
default=0.1 , metadata={
'help': 'The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.'
} , )
__snake_case :Optional[float] = field(
default=0.1 , metadata={'help': 'The dropout probabilitiy for all 1D convolutional layers in feature extractor.'} , )
__snake_case :Optional[float] = field(
default=0.05 , metadata={
'help': (
'Propability of each feature vector along the time axis to be chosen as the start of the vector'
'span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature'
'vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``.'
)
} , )
__snake_case :Optional[float] = field(default=0.0 , metadata={'help': 'The LayerDrop probability.'} )
@dataclass
class __UpperCamelCase :
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
__snake_case :Optional[str] = field(
default='train+validation' , metadata={
'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
__snake_case :Optional[int] = field(
default=_lowerCAmelCase , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
__snake_case :Optional[int] = field(
default=_lowerCAmelCase , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
__snake_case :Optional[int] = field(
default=_lowerCAmelCase , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of validation examples to this '
'value if set.'
)
} , )
__snake_case :List[str] = list_field(
default=[',', '?', '.', '!', '-', ';', ':', '""', '%', '\'', '"', '�'] , metadata={'help': 'A list of characters to remove from the transcripts.'} , )
@dataclass
class __UpperCamelCase :
__snake_case :WavaVecaProcessor
__snake_case :Union[bool, str] = True
__snake_case :Optional[int] = None
__snake_case :Optional[int] = None
__snake_case :Optional[int] = None
__snake_case :Optional[int] = None
def __call__( self : Tuple , _lowerCAmelCase : List[Dict[str, Union[List[int], torch.Tensor]]] ) -> Dict[str, torch.Tensor]:
"""simple docstring"""
__lowercase = [{"""input_values""": feature["""input_values"""]} for feature in features]
__lowercase = [{"""input_ids""": feature["""labels"""]} for feature in features]
__lowercase = self.processor.pad(
_lowerCAmelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="""pt""" , )
__lowercase = self.processor.pad(
labels=_lowerCAmelCase , padding=self.padding , max_length=self.max_length_labels , pad_to_multiple_of=self.pad_to_multiple_of_labels , return_tensors="""pt""" , )
# replace padding with -100 to ignore loss correctly
__lowercase = labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1 ) , -100 )
__lowercase = labels
return batch
class __UpperCamelCase ( _lowerCAmelCase ):
def _a ( self : List[Any] , _lowerCAmelCase : nn.Module , _lowerCAmelCase : Dict[str, Union[torch.Tensor, Any]] ) -> torch.Tensor:
"""simple docstring"""
model.train()
__lowercase = self._prepare_inputs(_lowerCAmelCase )
if self.use_amp:
with autocast():
__lowercase = self.compute_loss(_lowerCAmelCase , _lowerCAmelCase )
else:
__lowercase = self.compute_loss(_lowerCAmelCase , _lowerCAmelCase )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
__lowercase = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
__lowercase = loss.sum() / (inputs["""labels"""] >= 0).sum()
else:
raise ValueError(F'{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']' )
if self.args.gradient_accumulation_steps > 1:
__lowercase = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(_lowerCAmelCase ).backward()
elif self.use_apex:
with amp.scale_loss(_lowerCAmelCase , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(_lowerCAmelCase )
else:
loss.backward()
return loss.detach()
def snake_case ( ):
'''simple docstring'''
__lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__lowercase , __lowercase , __lowercase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowercase , __lowercase , __lowercase = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
__lowercase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowercase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. '
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ F'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("""Training/evaluation parameters %s""" , lowerCamelCase )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
__lowercase = datasets.load_dataset(
"""common_voice""" , data_args.dataset_config_name , split=data_args.train_split_name )
__lowercase = datasets.load_dataset("""common_voice""" , data_args.dataset_config_name , split="""test""" )
# Create and save tokenizer
__lowercase = F'[{"".join(data_args.chars_to_ignore )}]'
def remove_special_characters(lowerCamelCase ):
__lowercase = re.sub(lowerCamelCase , """""" , batch["""sentence"""] ).lower() + """ """
return batch
__lowercase = train_dataset.map(lowerCamelCase , remove_columns=["""sentence"""] )
__lowercase = eval_dataset.map(lowerCamelCase , remove_columns=["""sentence"""] )
def extract_all_chars(lowerCamelCase ):
__lowercase = """ """.join(batch["""text"""] )
__lowercase = list(set(lowerCamelCase ) )
return {"vocab": [vocab], "all_text": [all_text]}
__lowercase = train_dataset.map(
lowerCamelCase , batched=lowerCamelCase , batch_size=-1 , keep_in_memory=lowerCamelCase , remove_columns=train_dataset.column_names , )
__lowercase = train_dataset.map(
lowerCamelCase , batched=lowerCamelCase , batch_size=-1 , keep_in_memory=lowerCamelCase , remove_columns=eval_dataset.column_names , )
__lowercase = list(set(vocab_train["""vocab"""][0] ) | set(vocab_test["""vocab"""][0] ) )
__lowercase = {v: k for k, v in enumerate(lowerCamelCase )}
__lowercase = vocab_dict[""" """]
del vocab_dict[" "]
__lowercase = len(lowerCamelCase )
__lowercase = len(lowerCamelCase )
with open("""vocab.json""" , """w""" ) as vocab_file:
json.dump(lowerCamelCase , lowerCamelCase )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowercase = WavaVecaCTCTokenizer(
"""vocab.json""" , unk_token="""[UNK]""" , pad_token="""[PAD]""" , word_delimiter_token="""|""" , )
__lowercase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0.0 , do_normalize=lowerCamelCase , return_attention_mask=lowerCamelCase )
__lowercase = WavaVecaProcessor(feature_extractor=lowerCamelCase , tokenizer=lowerCamelCase )
__lowercase = WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , activation_dropout=model_args.activation_dropout , attention_dropout=model_args.attention_dropout , hidden_dropout=model_args.hidden_dropout , feat_proj_dropout=model_args.feat_proj_dropout , mask_time_prob=model_args.mask_time_prob , gradient_checkpointing=training_args.gradient_checkpointing , layerdrop=model_args.layerdrop , ctc_loss_reduction="""mean""" , pad_token_id=processor.tokenizer.pad_token_id , vocab_size=len(processor.tokenizer ) , )
if data_args.max_train_samples is not None:
__lowercase = min(len(lowerCamelCase ) , data_args.max_train_samples )
__lowercase = train_dataset.select(range(lowerCamelCase ) )
if data_args.max_val_samples is not None:
__lowercase = eval_dataset.select(range(data_args.max_val_samples ) )
__lowercase = torchaudio.transforms.Resample(48_000 , 16_000 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(lowerCamelCase ):
__lowercase , __lowercase = torchaudio.load(batch["""path"""] )
__lowercase = resampler(lowerCamelCase ).squeeze().numpy()
__lowercase = 16_000
__lowercase = batch["""text"""]
return batch
__lowercase = train_dataset.map(
lowerCamelCase , remove_columns=train_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
__lowercase = eval_dataset.map(
lowerCamelCase , remove_columns=eval_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
def prepare_dataset(lowerCamelCase ):
# check that all files have the correct sampling rate
assert (
len(set(batch["""sampling_rate"""] ) ) == 1
), F'Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'
__lowercase = processor(
audio=batch["""speech"""] , text=batch["""target_text"""] , sampling_rate=batch["""sampling_rate"""][0] )
batch.update(lowerCamelCase )
return batch
__lowercase = train_dataset.map(
lowerCamelCase , remove_columns=train_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=lowerCamelCase , num_proc=data_args.preprocessing_num_workers , )
__lowercase = eval_dataset.map(
lowerCamelCase , remove_columns=eval_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=lowerCamelCase , num_proc=data_args.preprocessing_num_workers , )
# Metric
__lowercase = datasets.load_metric("""wer""" )
def compute_metrics(lowerCamelCase ):
__lowercase = pred.predictions
__lowercase = np.argmax(lowerCamelCase , axis=-1 )
__lowercase = processor.tokenizer.pad_token_id
__lowercase = processor.batch_decode(lowerCamelCase )
# we do not want to group tokens when computing the metrics
__lowercase = processor.batch_decode(pred.label_ids , group_tokens=lowerCamelCase )
__lowercase = wer_metric.compute(predictions=lowerCamelCase , references=lowerCamelCase )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
__lowercase = DataCollatorCTCWithPadding(processor=lowerCamelCase , padding=lowerCamelCase )
# Initialize our Trainer
__lowercase = CTCTrainer(
model=lowerCamelCase , data_collator=lowerCamelCase , args=lowerCamelCase , compute_metrics=lowerCamelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=processor.feature_extractor , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
__lowercase = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
__lowercase = model_args.model_name_or_path
else:
__lowercase = None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
__lowercase = trainer.train(resume_from_checkpoint=lowerCamelCase )
trainer.save_model()
__lowercase = train_result.metrics
__lowercase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCamelCase )
)
__lowercase = min(lowerCamelCase , len(lowerCamelCase ) )
trainer.log_metrics("""train""" , lowerCamelCase )
trainer.save_metrics("""train""" , lowerCamelCase )
trainer.save_state()
# Evaluation
__lowercase = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__lowercase = trainer.evaluate()
__lowercase = data_args.max_val_samples if data_args.max_val_samples is not None else len(lowerCamelCase )
__lowercase = min(lowerCamelCase , len(lowerCamelCase ) )
trainer.log_metrics("""eval""" , lowerCamelCase )
trainer.save_metrics("""eval""" , lowerCamelCase )
return results
if __name__ == "__main__":
main()
| 53
|
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
__UpperCamelCase : Tuple = 2
class __UpperCamelCase :
def __init__( self : List[str] , *, # begin keyword-only arguments
_lowerCAmelCase : Optional[int]="<s>" , _lowerCAmelCase : Optional[int]="<pad>" , _lowerCAmelCase : int="</s>" , _lowerCAmelCase : str="<unk>" , _lowerCAmelCase : List[str]=None , ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase , __lowercase , __lowercase = bos, unk, pad, eos
__lowercase = []
__lowercase = []
__lowercase = {}
__lowercase = self.add_symbol(_lowerCAmelCase )
__lowercase = self.add_symbol(_lowerCAmelCase )
__lowercase = self.add_symbol(_lowerCAmelCase )
__lowercase = self.add_symbol(_lowerCAmelCase )
if extra_special_symbols:
for s in extra_special_symbols:
self.add_symbol(_lowerCAmelCase )
__lowercase = len(self.symbols )
def __eq__( self : Dict , _lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
return self.indices == other.indices
def __getitem__( self : Any , _lowerCAmelCase : str ) -> Dict:
"""simple docstring"""
if idx < len(self.symbols ):
return self.symbols[idx]
return self.unk_word
def __len__( self : str ) -> List[str]:
"""simple docstring"""
return len(self.symbols )
def __contains__( self : Union[str, Any] , _lowerCAmelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
return sym in self.indices
@classmethod
def _a ( cls : Dict , _lowerCAmelCase : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = cls()
d.add_from_file(_lowerCAmelCase )
return d
def _a ( self : int , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any]=1 , _lowerCAmelCase : Optional[int]=False ) -> Union[str, Any]:
"""simple docstring"""
if word in self.indices and not overwrite:
__lowercase = self.indices[word]
__lowercase = self.count[idx] + n
return idx
else:
__lowercase = len(self.symbols )
__lowercase = idx
self.symbols.append(_lowerCAmelCase )
self.count.append(_lowerCAmelCase )
return idx
def _a ( self : List[str] , _lowerCAmelCase : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
return 0
def _a ( self : Optional[Any] , _lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
try:
with open(_lowerCAmelCase , """r""" , encoding="""utf-8""" ) as fd:
self.add_from_file(_lowerCAmelCase )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(_lowerCAmelCase ) )
return
__lowercase = f.readlines()
__lowercase = self._load_meta(_lowerCAmelCase )
for line in lines[indices_start_line:]:
try:
__lowercase , __lowercase = line.rstrip().rsplit(""" """ , 1 )
if field == "#fairseq:overwrite":
__lowercase = True
__lowercase , __lowercase = line.rsplit(""" """ , 1 )
else:
__lowercase = False
__lowercase = int(_lowerCAmelCase )
__lowercase = line
if word in self and not overwrite:
raise RuntimeError(
"""Duplicate word found when loading Dictionary: '{}'. """
"""Duplicate words can overwrite earlier ones by adding the """
"""#fairseq:overwrite flag at the end of the corresponding row """
"""in the dictionary file. If using the Camembert model, please """
"""download an updated copy of the model file.""".format(_lowerCAmelCase ) )
self.add_symbol(_lowerCAmelCase , n=_lowerCAmelCase , overwrite=_lowerCAmelCase )
except ValueError:
raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'""" )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = dict((re.sub(r"""@@$""" , """""" , lowerCamelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , lowerCamelCase ), v) for k, v in d.items() )
__lowercase = """<s> <pad> </s> <unk>""".split()
# restore the special tokens
for k in keep_keys:
del da[F'{k}</w>']
__lowercase = d[k] # restore
return da
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if not os.path.exists(lowerCamelCase ):
raise ValueError(F'path {biogpt_checkpoint_path} does not exist!' )
os.makedirs(lowerCamelCase , exist_ok=lowerCamelCase )
print(F'Writing results to {pytorch_dump_folder_path}' )
# handle various types of models
__lowercase = os.path.join(lowerCamelCase , """checkpoint.pt""" )
if not os.path.isfile(lowerCamelCase ):
raise ValueError(F'path to the file {checkpoint_file} does not exist!' )
__lowercase = torch.load(lowerCamelCase , map_location="""cpu""" )
__lowercase = chkpt["""cfg"""]["""model"""]
# dicts
__lowercase = os.path.join(lowerCamelCase , """dict.txt""" )
if not os.path.isfile(lowerCamelCase ):
raise ValueError(F'path to the file {dict_file} does not exist!' )
__lowercase = Dictionary.load(lowerCamelCase )
__lowercase = rewrite_dict_keys(src_dict.indices )
__lowercase = len(lowerCamelCase )
__lowercase = os.path.join(lowerCamelCase , VOCAB_FILES_NAMES["""vocab_file"""] )
print(F'Generating {src_vocab_file} of {src_vocab_size} records' )
with open(lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(lowerCamelCase , ensure_ascii=lowerCamelCase , indent=lowerCamelCase ) )
# merges_file (bpecodes)
__lowercase = os.path.join(lowerCamelCase , """bpecodes""" )
if not os.path.isfile(lowerCamelCase ):
raise ValueError(F'path to the file {bpecodes_file} does not exist!' )
__lowercase = os.path.join(lowerCamelCase , VOCAB_FILES_NAMES["""merges_file"""] )
shutil.copyfile(lowerCamelCase , lowerCamelCase )
# model config
__lowercase = os.path.join(lowerCamelCase , """config.json""" )
__lowercase = {
"""activation_dropout""": args["""activation_dropout"""],
"""architectures""": ["""BioGptForCausalLM"""],
"""attention_probs_dropout_prob""": args["""attention_dropout"""],
"""bos_token_id""": 0,
"""eos_token_id""": 2,
"""hidden_act""": args["""activation_fn"""],
"""hidden_dropout_prob""": args["""dropout"""],
"""hidden_size""": args["""decoder_embed_dim"""],
"""initializer_range""": 0.02,
"""intermediate_size""": args["""decoder_ffn_embed_dim"""],
"""layer_norm_eps""": 1e-12,
"""layerdrop""": args["""decoder_layerdrop"""],
"""max_position_embeddings""": args["""max_target_positions"""],
"""model_type""": """biogpt""",
"""num_attention_heads""": args["""decoder_attention_heads"""],
"""num_hidden_layers""": args["""decoder_layers"""],
"""pad_token_id""": 1,
"""scale_embedding""": not args["""no_scale_embedding"""],
"""tie_word_embeddings""": args["""share_decoder_input_output_embed"""],
"""vocab_size""": src_vocab_size,
}
# good hparam defaults to start with
print(F'Generating {biogpt_model_config_file}' )
with open(lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(lowerCamelCase , ensure_ascii=lowerCamelCase , indent=lowerCamelCase ) )
# tokenizer config
__lowercase = os.path.join(lowerCamelCase , lowerCamelCase )
__lowercase = {
"""bos_token""": """<s>""",
"""eos_token""": """</s>""",
"""model_max_length""": 1_024,
"""pad_token""": """<pad>""",
"""special_tokens_map_file""": None,
"""tokenizer_class""": """BioGptTokenizer""",
"""unk_token""": """<unk>""",
}
print(F'Generating {biogpt_tokenizer_config_file}' )
with open(lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(lowerCamelCase , ensure_ascii=lowerCamelCase , indent=lowerCamelCase ) )
# model
__lowercase = chkpt["""model"""]
# remove unneeded keys
__lowercase = [
"""decoder.version""",
]
for k in ignore_keys:
model_state_dict.pop(lowerCamelCase , lowerCamelCase )
__lowercase = list(model_state_dict.keys() )
for layer_name in layer_names:
if layer_name.endswith("""output_projection.weight""" ):
__lowercase = model_state_dict.pop(lowerCamelCase )
else:
__lowercase = model_state_dict.pop(lowerCamelCase )
__lowercase = BioGptConfig.from_pretrained(lowerCamelCase )
__lowercase = BioGptForCausalLM(lowerCamelCase )
# check that it loads ok
model_new.load_state_dict(lowerCamelCase )
# save
__lowercase = os.path.join(lowerCamelCase , lowerCamelCase )
print(F'Generating {pytorch_weights_dump_path}' )
torch.save(lowerCamelCase , lowerCamelCase )
print("""Conversion is done!""" )
if __name__ == "__main__":
__UpperCamelCase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--biogpt_checkpoint_path""",
default=None,
type=str,
required=True,
help=(
"""Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
""" bpecodes, etc."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
__UpperCamelCase : Optional[Any] = parser.parse_args()
convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
| 53
| 1
|
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class __UpperCamelCase :
def __init__( self : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str]=2 , _lowerCAmelCase : List[Any]=True , _lowerCAmelCase : Union[str, Any]=False , _lowerCAmelCase : int=10 , _lowerCAmelCase : Tuple=3 , _lowerCAmelCase : Any=32 * 4 , _lowerCAmelCase : str=32 * 6 , _lowerCAmelCase : str=4 , _lowerCAmelCase : Union[str, Any]=32 , ) -> Any:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = is_training
__lowercase = use_auxiliary_loss
__lowercase = num_queries
__lowercase = num_channels
__lowercase = min_size
__lowercase = max_size
__lowercase = num_labels
__lowercase = mask_feature_size
def _a ( self : int ) -> Tuple:
"""simple docstring"""
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_lowerCAmelCase )
__lowercase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_lowerCAmelCase )
__lowercase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_lowerCAmelCase ) > 0.5
).float()
__lowercase = (torch.rand((self.batch_size, self.num_labels) , device=_lowerCAmelCase ) > 0.5).long()
__lowercase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=128 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def _a ( self : Tuple ) -> int:
"""simple docstring"""
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase = self.prepare_config_and_inputs()
__lowercase = {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def _a ( self : List[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Dict ) -> int:
"""simple docstring"""
__lowercase = output.encoder_hidden_states
__lowercase = output.pixel_decoder_hidden_states
__lowercase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCAmelCase ) , config.decoder_config.decoder_layers )
def _a ( self : Optional[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any]=False ) -> Tuple:
"""simple docstring"""
with torch.no_grad():
__lowercase = MaskFormerModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(pixel_values=_lowerCAmelCase , pixel_mask=_lowerCAmelCase )
__lowercase = model(_lowerCAmelCase , output_hidden_states=_lowerCAmelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : Any , _lowerCAmelCase : int ) -> int:
"""simple docstring"""
__lowercase = MaskFormerForInstanceSegmentation(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
def comm_check_on_output(_lowerCAmelCase : List[Any] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__lowercase = model(pixel_values=_lowerCAmelCase , pixel_mask=_lowerCAmelCase )
__lowercase = model(_lowerCAmelCase )
comm_check_on_output(_lowerCAmelCase )
__lowercase = model(
pixel_values=_lowerCAmelCase , pixel_mask=_lowerCAmelCase , mask_labels=_lowerCAmelCase , class_labels=_lowerCAmelCase )
comm_check_on_output(_lowerCAmelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :str = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
__snake_case :Tuple = (
{'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
__snake_case :Tuple = False
__snake_case :List[str] = False
__snake_case :List[str] = False
__snake_case :List[str] = False
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
__lowercase = MaskFormerModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase )
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCAmelCase , **_lowerCAmelCase , output_hidden_states=_lowerCAmelCase )
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowerCAmelCase )
@unittest.skip(reason="""MaskFormer does not use inputs_embeds""" )
def _a ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method""" )
def _a ( self : List[str] ) -> Dict:
"""simple docstring"""
pass
@unittest.skip(reason="""MaskFormer is not a generative model""" )
def _a ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
pass
@unittest.skip(reason="""MaskFormer does not use token embeddings""" )
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def _a ( self : Any ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
pass
def _a ( self : int ) -> str:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
@slow
def _a ( self : Any ) -> Any:
"""simple docstring"""
for model_name in ["facebook/maskformer-swin-small-coco"]:
__lowercase = MaskFormerModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def _a ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = (self.model_tester.min_size,) * 2
__lowercase = {
"""pixel_values""": torch.randn((2, 3, *size) , device=_lowerCAmelCase ),
"""mask_labels""": torch.randn((2, 10, *size) , device=_lowerCAmelCase ),
"""class_labels""": torch.zeros(2 , 10 , device=_lowerCAmelCase ).long(),
}
__lowercase = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(_lowerCAmelCase )
__lowercase = model(**_lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCAmelCase , **_lowerCAmelCase , output_hidden_states=_lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase ).to(_lowerCAmelCase )
__lowercase = model(**_lowerCAmelCase , output_attentions=_lowerCAmelCase )
self.assertTrue(outputs.attentions is not None )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
__lowercase = self.all_model_classes[1]
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase = self.model_tester.prepare_config_and_inputs()
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.train()
__lowercase = model(_lowerCAmelCase , mask_labels=_lowerCAmelCase , class_labels=_lowerCAmelCase ).loss
loss.backward()
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.all_model_classes[1]
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase = self.model_tester.prepare_config_and_inputs()
__lowercase = True
__lowercase = True
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.train()
__lowercase = model(_lowerCAmelCase , mask_labels=_lowerCAmelCase , class_labels=_lowerCAmelCase )
__lowercase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__lowercase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
__lowercase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__lowercase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_lowerCAmelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__UpperCamelCase : Optional[Any] = 1e-4
def snake_case ( ):
'''simple docstring'''
__lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def _a ( self : Dict ) -> int:
"""simple docstring"""
return (
MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco""" )
if is_vision_available()
else None
)
def _a ( self : str ) -> str:
"""simple docstring"""
__lowercase = MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco""" ).to(_lowerCAmelCase )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
__lowercase = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(_lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
__lowercase = torch.tensor(
[[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]] ).to(_lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
__lowercase = torch.tensor(
[[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]] ).to(_lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
__lowercase = torch.tensor(
[[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]] ).to(_lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
def _a ( self : int ) -> List[str]:
"""simple docstring"""
__lowercase = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" )
.to(_lowerCAmelCase )
.eval()
)
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
__lowercase = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(_lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
# masks_queries_logits
__lowercase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__lowercase = [
[-1.3_737_124, -1.7_724_937, -1.9_364_233],
[-1.5_977_281, -1.9_867_939, -2.1_523_695],
[-1.5_795_398, -1.9_269_832, -2.093_942],
]
__lowercase = torch.tensor(_lowerCAmelCase ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
# class_queries_logits
__lowercase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__lowercase = torch.tensor(
[
[1.6512e00, -5.2572e00, -3.3519e00],
[3.6169e-02, -5.9025e00, -2.9313e00],
[1.0766e-04, -7.7630e00, -5.1263e00],
] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
def _a ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff""" )
.to(_lowerCAmelCase )
.eval()
)
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
__lowercase = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(_lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
# masks_queries_logits
__lowercase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__lowercase = [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
__lowercase = torch.tensor(_lowerCAmelCase ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
# class_queries_logits
__lowercase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__lowercase = torch.tensor(
[[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
def _a ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" )
.to(_lowerCAmelCase )
.eval()
)
__lowercase = self.default_image_processor
__lowercase = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="""pt""" , )
__lowercase = inputs["""pixel_values"""].to(_lowerCAmelCase )
__lowercase = [el.to(_lowerCAmelCase ) for el in inputs["""mask_labels"""]]
__lowercase = [el.to(_lowerCAmelCase ) for el in inputs["""class_labels"""]]
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
| 53
|
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
__snake_case :Union[str, Any] = 'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
def _a ( self : Any , _lowerCAmelCase : str=0 ) -> str:
"""simple docstring"""
__lowercase = np.random.RandomState(_lowerCAmelCase )
__lowercase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.65_072, 0.58_492, 0.48_219, 0.55_521, 0.53_180, 0.55_939, 0.50_697, 0.39_800, 0.46_455] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.65_863, 0.59_425, 0.49_326, 0.56_313, 0.53_875, 0.56_627, 0.51_065, 0.39_777, 0.46_330] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Tuple ) -> int:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_817, 0.60_812, 0.47_384, 0.49_530, 0.51_894, 0.49_814, 0.47_984, 0.38_958, 0.44_271] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_895, 0.60_808, 0.47_933, 0.49_608, 0.51_886, 0.49_950, 0.48_053, 0.38_957, 0.44_200] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * [inputs["""prompt"""]]
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * [inputs.pop("""prompt""" )]
__lowercase = pipe.tokenizer(
_lowerCAmelCase , padding="""max_length""" , max_length=pipe.tokenizer.model_max_length , truncation=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = text_inputs["""input_ids"""]
__lowercase = pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]
__lowercase = prompt_embeds
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
def _a ( self : int ) -> str:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * ["""this is a negative prompt"""]
__lowercase = negative_prompt
__lowercase = 3 * [inputs["""prompt"""]]
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * [inputs.pop("""prompt""" )]
__lowercase = []
for p in [prompt, negative_prompt]:
__lowercase = pipe.tokenizer(
_lowerCAmelCase , padding="""max_length""" , max_length=pipe.tokenizer.model_max_length , truncation=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = text_inputs["""input_ids"""]
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )
__lowercase , __lowercase = embeds
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
@nightly
@require_onnxruntime
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
@property
def _a ( self : Dict ) -> str:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _a ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = ort.SessionOptions()
__lowercase = False
return options
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""onnx""" , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """A painting of a squirrel eating a burger"""
np.random.seed(0 )
__lowercase = sd_pipe([prompt] , guidance_scale=6.0 , num_inference_steps=10 , output_type="""np""" )
__lowercase = output.images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.0_452, 0.0_390, 0.0_087, 0.0_350, 0.0_617, 0.0_364, 0.0_544, 0.0_523, 0.0_720] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
__lowercase = DDIMScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """open neural network exchange"""
__lowercase = np.random.RandomState(0 )
__lowercase = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=_lowerCAmelCase , output_type="""np""" )
__lowercase = output.images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.2_867, 0.1_974, 0.1_481, 0.7_294, 0.7_251, 0.6_667, 0.4_194, 0.5_642, 0.6_486] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
__lowercase = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """open neural network exchange"""
__lowercase = np.random.RandomState(0 )
__lowercase = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=_lowerCAmelCase , output_type="""np""" )
__lowercase = output.images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.2_306, 0.1_959, 0.1_593, 0.6_549, 0.6_394, 0.5_408, 0.5_065, 0.6_010, 0.6_161] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : str ) -> List[str]:
"""simple docstring"""
__lowercase = 0
def test_callback_fn(_lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : np.ndarray ) -> None:
__lowercase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 64, 64)
__lowercase = latents[0, -3:, -3:, -1]
__lowercase = np.array(
[-0.6_772, -0.3_835, -1.2_456, 0.1_905, -1.0_974, 0.6_967, -1.9_353, 0.0_178, 1.0_167] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
elif step == 5:
assert latents.shape == (1, 4, 64, 64)
__lowercase = latents[0, -3:, -3:, -1]
__lowercase = np.array(
[-0.3_351, 0.2_241, -0.1_837, -0.2_325, -0.6_577, 0.3_393, -0.0_241, 0.5_899, 1.3_875] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
__lowercase = False
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """Andromeda galaxy in a bottle"""
__lowercase = np.random.RandomState(0 )
pipe(
prompt=_lowerCAmelCase , num_inference_steps=5 , guidance_scale=7.5 , generator=_lowerCAmelCase , callback=_lowerCAmelCase , callback_steps=1 , )
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
assert pipe.safety_checker is None
__lowercase = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_lowerCAmelCase )
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(_lowerCAmelCase )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
__lowercase = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
| 53
| 1
|
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Dict = 'linear'
__snake_case :str = 'cosine'
__snake_case :int = 'cosine_with_restarts'
__snake_case :List[str] = 'polynomial'
__snake_case :List[Any] = 'constant'
__snake_case :Optional[Any] = 'constant_with_warmup'
__snake_case :Optional[int] = 'piecewise_constant'
def snake_case ( lowerCamelCase , lowerCamelCase = -1 ):
'''simple docstring'''
return LambdaLR(lowerCamelCase , lambda lowerCamelCase : 1 , last_epoch=lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase = -1 ):
'''simple docstring'''
def lr_lambda(lowerCamelCase ):
if current_step < num_warmup_steps:
return float(lowerCamelCase ) / float(max(1.0 , lowerCamelCase ) )
return 1.0
return LambdaLR(lowerCamelCase , lowerCamelCase , last_epoch=lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase = -1 ):
'''simple docstring'''
__lowercase = {}
__lowercase = step_rules.split(""",""" )
for rule_str in rule_list[:-1]:
__lowercase , __lowercase = rule_str.split(""":""" )
__lowercase = int(lowerCamelCase )
__lowercase = float(lowerCamelCase )
__lowercase = value
__lowercase = float(rule_list[-1] )
def create_rules_function(lowerCamelCase , lowerCamelCase ):
def rule_func(lowerCamelCase ) -> float:
__lowercase = sorted(rules_dict.keys() )
for i, sorted_step in enumerate(lowerCamelCase ):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
__lowercase = create_rules_function(lowerCamelCase , lowerCamelCase )
return LambdaLR(lowerCamelCase , lowerCamelCase , last_epoch=lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=-1 ):
'''simple docstring'''
def lr_lambda(lowerCamelCase ):
if current_step < num_warmup_steps:
return float(lowerCamelCase ) / float(max(1 , lowerCamelCase ) )
return max(
0.0 , float(num_training_steps - current_step ) / float(max(1 , num_training_steps - num_warmup_steps ) ) )
return LambdaLR(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = 0.5 , lowerCamelCase = -1 ):
'''simple docstring'''
def lr_lambda(lowerCamelCase ):
if current_step < num_warmup_steps:
return float(lowerCamelCase ) / float(max(1 , lowerCamelCase ) )
__lowercase = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * float(lowerCamelCase ) * 2.0 * progress )) )
return LambdaLR(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = 1 , lowerCamelCase = -1 ):
'''simple docstring'''
def lr_lambda(lowerCamelCase ):
if current_step < num_warmup_steps:
return float(lowerCamelCase ) / float(max(1 , lowerCamelCase ) )
__lowercase = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
if progress >= 1.0:
return 0.0
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * ((float(lowerCamelCase ) * progress) % 1.0) )) )
return LambdaLR(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=1e-7 , lowerCamelCase=1.0 , lowerCamelCase=-1 ):
'''simple docstring'''
__lowercase = optimizer.defaults["""lr"""]
if not (lr_init > lr_end):
raise ValueError(F'lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})' )
def lr_lambda(lowerCamelCase ):
if current_step < num_warmup_steps:
return float(lowerCamelCase ) / float(max(1 , lowerCamelCase ) )
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
__lowercase = lr_init - lr_end
__lowercase = num_training_steps - num_warmup_steps
__lowercase = 1 - (current_step - num_warmup_steps) / decay_steps
__lowercase = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(lowerCamelCase , lowerCamelCase , lowerCamelCase )
__UpperCamelCase : Optional[Any] = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = 1 , lowerCamelCase = 1.0 , lowerCamelCase = -1 , ):
'''simple docstring'''
__lowercase = SchedulerType(lowerCamelCase )
__lowercase = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(lowerCamelCase , last_epoch=lowerCamelCase )
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(lowerCamelCase , step_rules=lowerCamelCase , last_epoch=lowerCamelCase )
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(F'{name} requires `num_warmup_steps`, please provide that argument.' )
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(lowerCamelCase , num_warmup_steps=lowerCamelCase , last_epoch=lowerCamelCase )
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(F'{name} requires `num_training_steps`, please provide that argument.' )
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
lowerCamelCase , num_warmup_steps=lowerCamelCase , num_training_steps=lowerCamelCase , num_cycles=lowerCamelCase , last_epoch=lowerCamelCase , )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
lowerCamelCase , num_warmup_steps=lowerCamelCase , num_training_steps=lowerCamelCase , power=lowerCamelCase , last_epoch=lowerCamelCase , )
return schedule_func(
lowerCamelCase , num_warmup_steps=lowerCamelCase , num_training_steps=lowerCamelCase , last_epoch=lowerCamelCase )
| 53
|
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = """"""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
__lowercase = remove_duplicates(key.upper() )
__lowercase = len(lowerCamelCase )
# First fill cipher with key characters
__lowercase = {alphabet[i]: char for i, char in enumerate(lowerCamelCase )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(lowerCamelCase ) , 26 ):
__lowercase = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
__lowercase = alphabet[i - offset]
__lowercase = char
return cipher_alphabet
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return "".join(cipher_map.get(lowerCamelCase , lowerCamelCase ) for ch in message.upper() )
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(lowerCamelCase , lowerCamelCase ) for ch in message.upper() )
def snake_case ( ):
'''simple docstring'''
__lowercase = input("""Enter message to encode or decode: """ ).strip()
__lowercase = input("""Enter keyword: """ ).strip()
__lowercase = input("""Encipher or decipher? E/D:""" ).strip()[0].lower()
try:
__lowercase = {"""e""": encipher, """d""": decipher}[option]
except KeyError:
raise KeyError("""invalid input option""" )
__lowercase = create_cipher_map(lowerCamelCase )
print(func(lowerCamelCase , lowerCamelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 53
| 1
|
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:
__UpperCamelCase : Optional[Any] = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class __UpperCamelCase ( unittest.TestCase ):
def __init__( self : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int=7 , _lowerCAmelCase : Optional[int]=3 , _lowerCAmelCase : str=18 , _lowerCAmelCase : List[Any]=30 , _lowerCAmelCase : Dict=400 , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : Optional[int]=True , _lowerCAmelCase : Dict=True , _lowerCAmelCase : int=None , ) -> Optional[Any]:
"""simple docstring"""
__lowercase = size if size is not None else {"""height""": 20, """width""": 20}
__lowercase = parent
__lowercase = batch_size
__lowercase = num_channels
__lowercase = image_size
__lowercase = min_resolution
__lowercase = max_resolution
__lowercase = size
__lowercase = do_normalize
__lowercase = do_convert_rgb
__lowercase = [512, 1024, 2048, 4096]
__lowercase = patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
def _a ( self : str ) -> Tuple:
"""simple docstring"""
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
__lowercase = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).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 __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
__snake_case :List[str] = PixaStructImageProcessor if is_vision_available() else None
def _a ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = PixaStructImageProcessingTester(self )
@property
def _a ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCAmelCase , """do_normalize""" ) )
self.assertTrue(hasattr(_lowerCAmelCase , """do_convert_rgb""" ) )
def _a ( self : Tuple ) -> str:
"""simple docstring"""
__lowercase = self.image_processor_tester.prepare_dummy_image()
__lowercase = self.image_processing_class(**self.image_processor_dict )
__lowercase = 2048
__lowercase = image_processor(_lowerCAmelCase , return_tensors="""pt""" , max_patches=_lowerCAmelCase )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0_606 ) , atol=1e-3 , rtol=1e-3 ) )
def _a ( self : Any ) -> Any:
"""simple docstring"""
__lowercase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowercase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCAmelCase , Image.Image )
# Test not batched input
__lowercase = (
(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
__lowercase = image_processor(
image_inputs[0] , return_tensors="""pt""" , max_patches=_lowerCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__lowercase = image_processor(
_lowerCAmelCase , return_tensors="""pt""" , max_patches=_lowerCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowercase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCAmelCase , Image.Image )
# Test not batched input
__lowercase = (
(self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
* self.image_processor_tester.num_channels
) + 2
__lowercase = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(_lowerCAmelCase ):
__lowercase = image_processor(
image_inputs[0] , return_tensors="""pt""" , max_patches=_lowerCAmelCase ).flattened_patches
__lowercase = """Hello"""
__lowercase = image_processor(
image_inputs[0] , return_tensors="""pt""" , max_patches=_lowerCAmelCase , header_text=_lowerCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__lowercase = image_processor(
_lowerCAmelCase , return_tensors="""pt""" , max_patches=_lowerCAmelCase , header_text=_lowerCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
__lowercase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__lowercase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCAmelCase , numpify=_lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCAmelCase , np.ndarray )
__lowercase = (
(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
__lowercase = image_processor(
image_inputs[0] , return_tensors="""pt""" , max_patches=_lowerCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__lowercase = image_processor(
_lowerCAmelCase , return_tensors="""pt""" , max_patches=_lowerCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__lowercase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCAmelCase , torchify=_lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCAmelCase , torch.Tensor )
# Test not batched input
__lowercase = (
(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
__lowercase = image_processor(
image_inputs[0] , return_tensors="""pt""" , max_patches=_lowerCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__lowercase = image_processor(
_lowerCAmelCase , return_tensors="""pt""" , max_patches=_lowerCAmelCase ).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 __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
__snake_case :int = PixaStructImageProcessor if is_vision_available() else None
def _a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = PixaStructImageProcessingTester(self , num_channels=4 )
__lowercase = 3
@property
def _a ( self : int ) -> int:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
__lowercase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCAmelCase , """do_normalize""" ) )
self.assertTrue(hasattr(_lowerCAmelCase , """do_convert_rgb""" ) )
def _a ( self : Dict ) -> int:
"""simple docstring"""
__lowercase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowercase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCAmelCase , Image.Image )
# Test not batched input
__lowercase = (
(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
__lowercase = image_processor(
image_inputs[0] , return_tensors="""pt""" , max_patches=_lowerCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__lowercase = image_processor(
_lowerCAmelCase , return_tensors="""pt""" , max_patches=_lowerCAmelCase ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 53
|
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Any = IFInpaintingPipeline
__snake_case :str = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
__snake_case :Optional[int] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__snake_case :str = PipelineTesterMixin.required_optional_params - {'latents'}
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
return self._get_dummy_components()
def _a ( self : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict=0 ) -> Any:
"""simple docstring"""
if str(_lowerCAmelCase ).startswith("""mps""" ):
__lowercase = torch.manual_seed(_lowerCAmelCase )
else:
__lowercase = torch.Generator(device=_lowerCAmelCase ).manual_seed(_lowerCAmelCase )
__lowercase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_lowerCAmelCase ) ).to(_lowerCAmelCase )
__lowercase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_lowerCAmelCase ) ).to(_lowerCAmelCase )
__lowercase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""mask_image""": mask_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def _a ( self : Tuple ) -> Dict:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def _a ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""" )
def _a ( self : List[str] ) -> Tuple:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1e-1 )
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def _a ( self : str ) -> Optional[int]:
"""simple docstring"""
self._test_save_load_local()
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 53
| 1
|
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase : str = logging.get_logger(__name__)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = RobertaPreLayerNormConfig.from_pretrained(
lowerCamelCase , architectures=["""RobertaPreLayerNormForMaskedLM"""] )
# convert state_dict
__lowercase = torch.load(hf_hub_download(repo_id=lowerCamelCase , filename="""pytorch_model.bin""" ) )
__lowercase = {}
for tensor_key, tensor_value in original_state_dict.items():
# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
if tensor_key.startswith("""roberta.""" ):
__lowercase = """roberta_prelayernorm.""" + tensor_key[len("""roberta.""" ) :]
# The original implementation contains weights which are not used, remove them from the state_dict
if tensor_key.endswith(""".self.LayerNorm.weight""" ) or tensor_key.endswith(""".self.LayerNorm.bias""" ):
continue
__lowercase = tensor_value
__lowercase = RobertaPreLayerNormForMaskedLM.from_pretrained(
pretrained_model_name_or_path=lowerCamelCase , config=lowerCamelCase , state_dict=lowerCamelCase )
model.save_pretrained(lowerCamelCase )
# convert tokenizer
__lowercase = AutoTokenizer.from_pretrained(lowerCamelCase )
tokenizer.save_pretrained(lowerCamelCase )
if __name__ == "__main__":
__UpperCamelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint-repo""",
default=None,
type=str,
required=True,
help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
__UpperCamelCase : Dict = parser.parse_args()
convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)
| 53
|
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :str = (UnCLIPScheduler,)
def _a ( self : Optional[int] , **_lowerCAmelCase : Any ) -> Tuple:
"""simple docstring"""
__lowercase = {
"""num_train_timesteps""": 1000,
"""variance_type""": """fixed_small_log""",
"""clip_sample""": True,
"""clip_sample_range""": 1.0,
"""prediction_type""": """epsilon""",
}
config.update(**_lowerCAmelCase )
return config
def _a ( self : Dict ) -> List[Any]:
"""simple docstring"""
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=_lowerCAmelCase )
def _a ( self : List[str] ) -> Tuple:
"""simple docstring"""
for variance in ["fixed_small_log", "learned_range"]:
self.check_over_configs(variance_type=_lowerCAmelCase )
def _a ( self : Any ) -> Any:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_lowerCAmelCase )
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
for clip_sample_range in [1, 5, 10, 20]:
self.check_over_configs(clip_sample_range=_lowerCAmelCase )
def _a ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(prediction_type=_lowerCAmelCase )
def _a ( self : str ) -> int:
"""simple docstring"""
for time_step in [0, 500, 999]:
for prev_timestep in [None, 5, 100, 250, 500, 750]:
if prev_timestep is not None and prev_timestep >= time_step:
continue
self.check_over_forward(time_step=_lowerCAmelCase , prev_timestep=_lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config(variance_type="""fixed_small_log""" )
__lowercase = scheduler_class(**_lowerCAmelCase )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.0000e-10 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_549_625 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.9_994_987 ) ) < 1e-5
def _a ( self : str ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config(variance_type="""learned_range""" )
__lowercase = scheduler_class(**_lowerCAmelCase )
__lowercase = 0.5
assert scheduler._get_variance(1 , predicted_variance=_lowerCAmelCase ) - -10.1_712_790 < 1e-5
assert scheduler._get_variance(487 , predicted_variance=_lowerCAmelCase ) - -5.7_998_052 < 1e-5
assert scheduler._get_variance(999 , predicted_variance=_lowerCAmelCase ) - -0.0_010_011 < 1e-5
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config()
__lowercase = scheduler_class(**_lowerCAmelCase )
__lowercase = scheduler.timesteps
__lowercase = self.dummy_model()
__lowercase = self.dummy_sample_deter
__lowercase = torch.manual_seed(0 )
for i, t in enumerate(_lowerCAmelCase ):
# 1. predict noise residual
__lowercase = model(_lowerCAmelCase , _lowerCAmelCase )
# 2. predict previous mean of sample x_t-1
__lowercase = scheduler.step(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , generator=_lowerCAmelCase ).prev_sample
__lowercase = pred_prev_sample
__lowercase = torch.sum(torch.abs(_lowerCAmelCase ) )
__lowercase = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 252.2_682_495 ) < 1e-2
assert abs(result_mean.item() - 0.3_284_743 ) < 1e-3
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config()
__lowercase = scheduler_class(**_lowerCAmelCase )
scheduler.set_timesteps(25 )
__lowercase = scheduler.timesteps
__lowercase = self.dummy_model()
__lowercase = self.dummy_sample_deter
__lowercase = torch.manual_seed(0 )
for i, t in enumerate(_lowerCAmelCase ):
# 1. predict noise residual
__lowercase = model(_lowerCAmelCase , _lowerCAmelCase )
if i + 1 == timesteps.shape[0]:
__lowercase = None
else:
__lowercase = timesteps[i + 1]
# 2. predict previous mean of sample x_t-1
__lowercase = scheduler.step(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , prev_timestep=_lowerCAmelCase , generator=_lowerCAmelCase ).prev_sample
__lowercase = pred_prev_sample
__lowercase = torch.sum(torch.abs(_lowerCAmelCase ) )
__lowercase = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 258.2_044_983 ) < 1e-2
assert abs(result_mean.item() - 0.3_362_038 ) < 1e-3
def _a ( self : str ) -> Union[str, Any]:
"""simple docstring"""
pass
def _a ( self : int ) -> List[str]:
"""simple docstring"""
pass
| 53
| 1
|
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = r"""\w+[.]\d+"""
__lowercase = re.findall(lowerCamelCase , lowerCamelCase )
for pat in pats:
__lowercase = key.replace(lowerCamelCase , """_""".join(pat.split(""".""" ) ) )
return key
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = pt_tuple_key[:-1] + ("""scale""",)
if (
any("""norm""" in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
__lowercase = pt_tuple_key[:-1] + ("""scale""",)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
__lowercase = pt_tuple_key[:-1] + ("""scale""",)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
__lowercase = pt_tuple_key[:-1] + ("""embedding""",)
return renamed_pt_tuple_key, pt_tensor
# conv layer
__lowercase = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
__lowercase = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
__lowercase = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight":
__lowercase = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
__lowercase = pt_tuple_key[:-1] + ("""weight""",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
__lowercase = pt_tuple_key[:-1] + ("""bias""",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase=42 ):
'''simple docstring'''
__lowercase = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
__lowercase = flax_model.init_weights(PRNGKey(lowerCamelCase ) )
__lowercase = flatten_dict(lowerCamelCase )
__lowercase = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
__lowercase = rename_key(lowerCamelCase )
__lowercase = tuple(renamed_pt_key.split(""".""" ) )
# Correctly rename weight parameters
__lowercase , __lowercase = rename_key_and_reshape_tensor(lowerCamelCase , lowerCamelCase , lowerCamelCase )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
# also add unexpected weight so that warning is thrown
__lowercase = jnp.asarray(lowerCamelCase )
return unflatten_dict(lowerCamelCase )
| 53
|
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
__UpperCamelCase : Any = logging.get_logger(__name__)
@dataclass
class __UpperCamelCase :
__snake_case :str = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys() )} )
__snake_case :str = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
__snake_case :int = field(
default=1_2_8 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def _a ( self : Dict ) -> List[Any]:
"""simple docstring"""
__lowercase = self.task_name.lower()
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Optional[int] = 'train'
__snake_case :int = 'dev'
__snake_case :Any = 'test'
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :GlueDataTrainingArguments
__snake_case :str
__snake_case :List[InputFeatures]
def __init__( self : Dict , _lowerCAmelCase : GlueDataTrainingArguments , _lowerCAmelCase : PreTrainedTokenizerBase , _lowerCAmelCase : Optional[int] = None , _lowerCAmelCase : Union[str, Split] = Split.train , _lowerCAmelCase : Optional[str] = None , ) -> List[Any]:
"""simple docstring"""
warnings.warn(
"""This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
"""library. You can have a look at this example script for pointers: """
"""https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py""" , _lowerCAmelCase , )
__lowercase = args
__lowercase = glue_processors[args.task_name]()
__lowercase = glue_output_modes[args.task_name]
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
try:
__lowercase = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
# Load data features from cache or dataset file
__lowercase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}' , )
__lowercase = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
__lowercase , __lowercase = label_list[2], label_list[1]
__lowercase = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowercase = cached_features_file + """.lock"""
with FileLock(_lowerCAmelCase ):
if os.path.exists(_lowerCAmelCase ) and not args.overwrite_cache:
__lowercase = time.time()
__lowercase = torch.load(_lowerCAmelCase )
logger.info(
F'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start )
else:
logger.info(F'Creating features from dataset file at {args.data_dir}' )
if mode == Split.dev:
__lowercase = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
__lowercase = self.processor.get_test_examples(args.data_dir )
else:
__lowercase = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
__lowercase = examples[:limit_length]
__lowercase = glue_convert_examples_to_features(
_lowerCAmelCase , _lowerCAmelCase , max_length=args.max_seq_length , label_list=_lowerCAmelCase , output_mode=self.output_mode , )
__lowercase = time.time()
torch.save(self.features , _lowerCAmelCase )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self : Dict ) -> Optional[int]:
"""simple docstring"""
return len(self.features )
def __getitem__( self : Tuple , _lowerCAmelCase : Optional[int] ) -> InputFeatures:
"""simple docstring"""
return self.features[i]
def _a ( self : str ) -> int:
"""simple docstring"""
return self.label_list
| 53
| 1
|
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : Union[str, Any] = TypeVar("""DatasetType""", Dataset, IterableDataset)
def snake_case ( lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = "first_exhausted" , ):
'''simple docstring'''
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError("""Unable to interleave an empty list of datasets.""" )
for i, dataset in enumerate(lowerCamelCase ):
if not isinstance(lowerCamelCase , (Dataset, IterableDataset) ):
if isinstance(lowerCamelCase , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
"""is an empty dataset dictionary.""" )
raise ValueError(
F'Dataset at position {i} has at least one split: {list(lowerCamelCase )}\n'
F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(lowerCamelCase ) )}\']' )
raise ValueError(
F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowerCamelCase ).__name__}.' )
if i == 0:
__lowercase , __lowercase = (
(Dataset, IterableDataset) if isinstance(lowerCamelCase , lowerCamelCase ) else (IterableDataset, Dataset)
)
elif not isinstance(lowerCamelCase , lowerCamelCase ):
raise ValueError(
F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
lowerCamelCase , lowerCamelCase , lowerCamelCase , info=lowerCamelCase , split=lowerCamelCase , stopping_strategy=lowerCamelCase )
else:
return _interleave_iterable_datasets(
lowerCamelCase , lowerCamelCase , lowerCamelCase , info=lowerCamelCase , split=lowerCamelCase , stopping_strategy=lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = 0 , ):
'''simple docstring'''
if not dsets:
raise ValueError("""Unable to concatenate an empty list of datasets.""" )
for i, dataset in enumerate(lowerCamelCase ):
if not isinstance(lowerCamelCase , (Dataset, IterableDataset) ):
if isinstance(lowerCamelCase , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
"""is an empty dataset dictionary.""" )
raise ValueError(
F'Dataset at position {i} has at least one split: {list(lowerCamelCase )}\n'
F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(lowerCamelCase ) )}\']' )
raise ValueError(
F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowerCamelCase ).__name__}.' )
if i == 0:
__lowercase , __lowercase = (
(Dataset, IterableDataset) if isinstance(lowerCamelCase , lowerCamelCase ) else (IterableDataset, Dataset)
)
elif not isinstance(lowerCamelCase , lowerCamelCase ):
raise ValueError(
F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(lowerCamelCase , info=lowerCamelCase , split=lowerCamelCase , axis=lowerCamelCase )
else:
return _concatenate_iterable_datasets(lowerCamelCase , info=lowerCamelCase , split=lowerCamelCase , axis=lowerCamelCase )
| 53
|
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoTokenizer,
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForWholeWordMask,
HfArgumentParser,
LineByLineTextDataset,
LineByLineWithRefDataset,
PreTrainedTokenizer,
TextDataset,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
__UpperCamelCase : List[Any] = logging.getLogger(__name__)
__UpperCamelCase : Optional[Any] = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
__UpperCamelCase : List[str] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class __UpperCamelCase :
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={
'help': (
'The model checkpoint for weights initialization. Leave None if you want to train a model from'
' scratch.'
)
} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_lowerCAmelCase )} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class __UpperCamelCase :
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'The input training data file (a text file).'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={
'help': (
'The input training data files (multiple files in glob format). '
'Very often splitting large files to smaller files can prevent tokenizer going out of memory'
)
} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'An optional input train ref data file for whole word mask in Chinese.'} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'An optional input eval ref data file for whole word mask in Chinese.'} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Whether distinct lines of text in the dataset are to be handled as distinct sequences.'} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Train with masked-language modeling loss instead of language modeling.'} )
__snake_case :bool = field(default=_lowerCAmelCase , metadata={'help': 'Whether ot not to use whole word mask.'} )
__snake_case :float = field(
default=0.15 , metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'} )
__snake_case :float = field(
default=1 / 6 , metadata={
'help': (
'Ratio of length of a span of masked tokens to surrounding context length for permutation language'
' modeling.'
)
} , )
__snake_case :int = field(
default=5 , metadata={'help': 'Maximum length of a span of masked tokens for permutation language modeling.'} )
__snake_case :int = field(
default=-1 , metadata={
'help': (
'Optional input sequence length after tokenization.'
'The training dataset will be truncated in block of this size for training.'
'Default to the model max input length for single sentence inputs (take into account special tokens).'
)
} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase = False , lowerCamelCase = None , ):
'''simple docstring'''
def _dataset(lowerCamelCase , lowerCamelCase=None ):
if args.line_by_line:
if ref_path is not None:
if not args.whole_word_mask or not args.mlm:
raise ValueError("""You need to set world whole masking and mlm to True for Chinese Whole Word Mask""" )
return LineByLineWithRefDataset(
tokenizer=lowerCamelCase , file_path=lowerCamelCase , block_size=args.block_size , ref_path=lowerCamelCase , )
return LineByLineTextDataset(tokenizer=lowerCamelCase , file_path=lowerCamelCase , block_size=args.block_size )
else:
return TextDataset(
tokenizer=lowerCamelCase , file_path=lowerCamelCase , block_size=args.block_size , overwrite_cache=args.overwrite_cache , cache_dir=lowerCamelCase , )
if evaluate:
return _dataset(args.eval_data_file , args.eval_ref_file )
elif args.train_data_files:
return ConcatDataset([_dataset(lowerCamelCase ) for f in glob(args.train_data_files )] )
else:
return _dataset(args.train_data_file , args.train_ref_file )
def snake_case ( ):
'''simple docstring'''
__lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
__lowercase , __lowercase , __lowercase = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_args.do_eval:
raise ValueError(
"""Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file """
"""or remove the --do_eval argument.""" )
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
""" --overwrite_output_dir to overcome.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , lowerCamelCase )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if model_args.config_name:
__lowercase = AutoConfig.from_pretrained(model_args.config_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
__lowercase = AutoConfig.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
__lowercase = CONFIG_MAPPING[model_args.model_type]()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.tokenizer_name:
__lowercase = AutoTokenizer.from_pretrained(model_args.tokenizer_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
__lowercase = AutoTokenizer.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
raise ValueError(
"""You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"""
""" script, save it,and load it from here, using --tokenizer_name""" )
if model_args.model_name_or_path:
__lowercase = AutoModelWithLMHead.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 , )
else:
logger.info("""Training new model from scratch""" )
__lowercase = AutoModelWithLMHead.from_config(lowerCamelCase )
model.resize_token_embeddings(len(lowerCamelCase ) )
if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
raise ValueError(
"""BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"""
"""--mlm flag (masked language modeling).""" )
if data_args.block_size <= 0:
__lowercase = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
__lowercase = min(data_args.block_size , tokenizer.max_len )
# Get datasets
__lowercase = (
get_dataset(lowerCamelCase , tokenizer=lowerCamelCase , cache_dir=model_args.cache_dir ) if training_args.do_train else None
)
__lowercase = (
get_dataset(lowerCamelCase , tokenizer=lowerCamelCase , evaluate=lowerCamelCase , cache_dir=model_args.cache_dir )
if training_args.do_eval
else None
)
if config.model_type == "xlnet":
__lowercase = DataCollatorForPermutationLanguageModeling(
tokenizer=lowerCamelCase , plm_probability=data_args.plm_probability , max_span_length=data_args.max_span_length , )
else:
if data_args.mlm and data_args.whole_word_mask:
__lowercase = DataCollatorForWholeWordMask(
tokenizer=lowerCamelCase , mlm_probability=data_args.mlm_probability )
else:
__lowercase = DataCollatorForLanguageModeling(
tokenizer=lowerCamelCase , mlm=data_args.mlm , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
__lowercase = Trainer(
model=lowerCamelCase , args=lowerCamelCase , data_collator=lowerCamelCase , train_dataset=lowerCamelCase , eval_dataset=lowerCamelCase , prediction_loss_only=lowerCamelCase , )
# Training
if training_args.do_train:
__lowercase = (
model_args.model_name_or_path
if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
else None
)
trainer.train(model_path=lowerCamelCase )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
__lowercase = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__lowercase = trainer.evaluate()
__lowercase = math.exp(eval_output["""eval_loss"""] )
__lowercase = {"""perplexity""": perplexity}
__lowercase = os.path.join(training_args.output_dir , """eval_results_lm.txt""" )
if trainer.is_world_master():
with open(lowerCamelCase , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key in sorted(result.keys() ):
logger.info(""" %s = %s""" , lowerCamelCase , str(result[key] ) )
writer.write("""%s = %s\n""" % (key, str(result[key] )) )
results.update(lowerCamelCase )
return results
def snake_case ( lowerCamelCase ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 53
| 1
|
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__UpperCamelCase : Optional[Any] = 16
__UpperCamelCase : int = 32
def snake_case ( lowerCamelCase , lowerCamelCase = 16 ):
'''simple docstring'''
__lowercase = AutoTokenizer.from_pretrained("""bert-base-cased""" )
__lowercase = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(lowerCamelCase ):
# max_length=None => use the model max length (it's actually the default)
__lowercase = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCamelCase , max_length=lowerCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__lowercase = datasets.map(
lowerCamelCase , batched=lowerCamelCase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowercase = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowerCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__lowercase = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__lowercase = 16
elif accelerator.mixed_precision != "no":
__lowercase = 8
else:
__lowercase = None
return tokenizer.pad(
lowerCamelCase , padding="""longest""" , max_length=lowerCamelCase , pad_to_multiple_of=lowerCamelCase , return_tensors="""pt""" , )
# Instantiate dataloaders.
__lowercase = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCamelCase , collate_fn=lowerCamelCase , batch_size=lowerCamelCase )
__lowercase = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCamelCase , collate_fn=lowerCamelCase , batch_size=lowerCamelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__UpperCamelCase : List[str] = mocked_dataloaders # noqa: F811
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , lowerCamelCase ) == "1":
__lowercase = 2
# Initialize accelerator
__lowercase = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowercase = config["""lr"""]
__lowercase = int(config["""num_epochs"""] )
__lowercase = int(config["""seed"""] )
__lowercase = int(config["""batch_size"""] )
__lowercase = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
__lowercase = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
__lowercase = batch_size // MAX_GPU_BATCH_SIZE
__lowercase = MAX_GPU_BATCH_SIZE
set_seed(lowerCamelCase )
__lowercase , __lowercase = get_dataloaders(lowerCamelCase , lowerCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowercase = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowerCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__lowercase = model.to(accelerator.device )
# Instantiate optimizer
__lowercase = AdamW(params=model.parameters() , lr=lowerCamelCase )
# Instantiate scheduler
__lowercase = get_linear_schedule_with_warmup(
optimizer=lowerCamelCase , num_warmup_steps=100 , num_training_steps=(len(lowerCamelCase ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase = accelerator.prepare(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
# Now we train the model
for epoch in range(lowerCamelCase ):
model.train()
for step, batch in enumerate(lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__lowercase = model(**lowerCamelCase )
__lowercase = outputs.loss
__lowercase = loss / gradient_accumulation_steps
accelerator.backward(lowerCamelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
__lowercase = 0
for step, batch in enumerate(lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__lowercase = model(**lowerCamelCase )
__lowercase = outputs.logits.argmax(dim=-1 )
__lowercase , __lowercase = accelerator.gather((predictions, batch["""labels"""]) )
# New Code #
# First we check if it's a distributed system
if accelerator.use_distributed:
# Then see if we're on the last batch of our eval dataloader
if step == len(lowerCamelCase ) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
__lowercase = predictions[: len(eval_dataloader.dataset ) - samples_seen]
__lowercase = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
# Otherwise we add the number of samples seen
samples_seen += references.shape[0]
# All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
# accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=lowerCamelCase , references=lowerCamelCase , )
__lowercase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:' , lowerCamelCase )
def snake_case ( ):
'''simple docstring'''
__lowercase = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowerCamelCase , default=lowerCamelCase , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
__lowercase = parser.parse_args()
__lowercase = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(lowerCamelCase , lowerCamelCase )
if __name__ == "__main__":
main()
| 53
|
from __future__ import annotations
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if len(lowerCamelCase ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
if any(i <= 0 for i in nums ):
raise ValueError("""All values must be greater than 0""" )
__lowercase = nums.copy()
copy_nums.sort()
return copy_nums[-1] < sum(copy_nums[:-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 53
| 1
|
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCamelCase : Any = {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Dict = [
"""MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MraForMaskedLM""",
"""MraForMultipleChoice""",
"""MraForQuestionAnswering""",
"""MraForSequenceClassification""",
"""MraForTokenClassification""",
"""MraLayer""",
"""MraModel""",
"""MraPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
__UpperCamelCase : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 53
|
from __future__ import annotations
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not nums:
return 0
__lowercase = nums[0]
__lowercase = 0
for num in nums[1:]:
__lowercase , __lowercase = (
max_excluding + num,
max(lowerCamelCase , lowerCamelCase ),
)
return max(lowerCamelCase , lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 53
| 1
|
import math
import qiskit
def snake_case ( lowerCamelCase = 1 , lowerCamelCase = 1 , lowerCamelCase = 1 ):
'''simple docstring'''
if (
isinstance(lowerCamelCase , lowerCamelCase )
or isinstance(lowerCamelCase , lowerCamelCase )
or isinstance(lowerCamelCase , lowerCamelCase )
):
raise TypeError("""inputs must be integers.""" )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError("""inputs must be positive.""" )
if (
(math.floor(lowerCamelCase ) != input_a)
or (math.floor(lowerCamelCase ) != input_a)
or (math.floor(lowerCamelCase ) != carry_in)
):
raise ValueError("""inputs must be exact integers.""" )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError("""inputs must be less or equal to 2.""" )
# build registers
__lowercase = qiskit.QuantumRegister(4 , """qr""" )
__lowercase = qiskit.ClassicalRegister(2 , """cr""" )
# list the entries
__lowercase = [input_a, input_a, carry_in]
__lowercase = qiskit.QuantumCircuit(lowerCamelCase , lowerCamelCase )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(lowerCamelCase ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(lowerCamelCase ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(lowerCamelCase ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , lowerCamelCase ) # measure the last two qbits
__lowercase = qiskit.Aer.get_backend("""aer_simulator""" )
__lowercase = qiskit.execute(lowerCamelCase , lowerCamelCase , shots=1_000 )
return job.result().get_counts(lowerCamelCase )
if __name__ == "__main__":
print(F'''Total sum count for state is: {quantum_full_adder(1, 1, 1)}''')
| 53
|
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase : str = logging.get_logger(__name__)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = RobertaPreLayerNormConfig.from_pretrained(
lowerCamelCase , architectures=["""RobertaPreLayerNormForMaskedLM"""] )
# convert state_dict
__lowercase = torch.load(hf_hub_download(repo_id=lowerCamelCase , filename="""pytorch_model.bin""" ) )
__lowercase = {}
for tensor_key, tensor_value in original_state_dict.items():
# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
if tensor_key.startswith("""roberta.""" ):
__lowercase = """roberta_prelayernorm.""" + tensor_key[len("""roberta.""" ) :]
# The original implementation contains weights which are not used, remove them from the state_dict
if tensor_key.endswith(""".self.LayerNorm.weight""" ) or tensor_key.endswith(""".self.LayerNorm.bias""" ):
continue
__lowercase = tensor_value
__lowercase = RobertaPreLayerNormForMaskedLM.from_pretrained(
pretrained_model_name_or_path=lowerCamelCase , config=lowerCamelCase , state_dict=lowerCamelCase )
model.save_pretrained(lowerCamelCase )
# convert tokenizer
__lowercase = AutoTokenizer.from_pretrained(lowerCamelCase )
tokenizer.save_pretrained(lowerCamelCase )
if __name__ == "__main__":
__UpperCamelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint-repo""",
default=None,
type=str,
required=True,
help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
__UpperCamelCase : Dict = parser.parse_args()
convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)
| 53
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|
def snake_case ( lowerCamelCase = 10 ):
'''simple docstring'''
if not isinstance(lowerCamelCase , lowerCamelCase ) or n < 0:
raise ValueError("""Invalid input""" )
__lowercase = 10**n
__lowercase = 28_433 * (pow(2 , 7_830_457 , lowerCamelCase )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F'''{solution(10) = }''')
| 53
|
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if (ksize % 2) == 0:
__lowercase = ksize + 1
__lowercase = np.zeros((ksize, ksize) , dtype=np.floataa )
# each value
for y in range(lowerCamelCase ):
for x in range(lowerCamelCase ):
# distance from center
__lowercase = x - ksize // 2
__lowercase = y - ksize // 2
# degree to radiant
__lowercase = theta / 180 * np.pi
__lowercase = np.cos(_theta )
__lowercase = np.sin(_theta )
# get kernel x
__lowercase = cos_theta * px + sin_theta * py
# get kernel y
__lowercase = -sin_theta * px + cos_theta * py
# fill kernel
__lowercase = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi )
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
__UpperCamelCase : List[Any] = imread("""../image_data/lena.jpg""")
# turn image in gray scale value
__UpperCamelCase : Union[str, Any] = cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
__UpperCamelCase : Union[str, Any] = np.zeros(gray.shape[:2])
for theta in [0, 30, 60, 90, 120, 150]:
__UpperCamelCase : Tuple = gabor_filter_kernel(10, 8, theta, 10, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
__UpperCamelCase : List[str] = out / out.max() * 255
__UpperCamelCase : List[str] = out.astype(np.uinta)
imshow("""Original""", gray)
imshow("""Gabor filter with 20x20 mask and 6 directions""", out)
waitKey(0)
| 53
| 1
|
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = [0 for i in range(len(lowerCamelCase ) )]
# initialize interval's left pointer and right pointer
__lowercase , __lowercase = 0, 0
for i in range(1 , len(lowerCamelCase ) ):
# case when current index is inside the interval
if i <= right_pointer:
__lowercase = min(right_pointer - i + 1 , z_result[i - left_pointer] )
__lowercase = min_edge
while go_next(lowerCamelCase , lowerCamelCase , lowerCamelCase ):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
__lowercase , __lowercase = i, i + z_result[i] - 1
return z_result
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return i + z_result[i] < len(lowerCamelCase ) and s[z_result[i]] == s[i + z_result[i]]
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = 0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
__lowercase = z_function(pattern + input_str )
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(lowerCamelCase ):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| 53
|
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = set()
__lowercase = []
def parse_line(lowerCamelCase ):
for line in fp:
if isinstance(lowerCamelCase , lowerCamelCase ):
__lowercase = line.decode("""UTF-8""" )
if "warnings summary (final)" in line:
continue
# This means we are outside the body of a warning
elif not line.startswith(""" """ ):
# process a single warning and move it to `selected_warnings`.
if len(lowerCamelCase ) > 0:
__lowercase = """\n""".join(lowerCamelCase )
# Only keep the warnings specified in `targets`
if any(F': {x}: ' in warning for x in targets ):
selected_warnings.add(lowerCamelCase )
buffer.clear()
continue
else:
__lowercase = line.strip()
buffer.append(lowerCamelCase )
if from_gh:
for filename in os.listdir(lowerCamelCase ):
__lowercase = os.path.join(lowerCamelCase , lowerCamelCase )
if not os.path.isdir(lowerCamelCase ):
# read the file
if filename != "warnings.txt":
continue
with open(lowerCamelCase ) as fp:
parse_line(lowerCamelCase )
else:
try:
with zipfile.ZipFile(lowerCamelCase ) as z:
for filename in z.namelist():
if not os.path.isdir(lowerCamelCase ):
# read the file
if filename != "warnings.txt":
continue
with z.open(lowerCamelCase ) as fp:
parse_line(lowerCamelCase )
except Exception:
logger.warning(
F'{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.' )
return selected_warnings
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = set()
__lowercase = [os.path.join(lowerCamelCase , lowerCamelCase ) for p in os.listdir(lowerCamelCase ) if (p.endswith(""".zip""" ) or from_gh)]
for p in paths:
selected_warnings.update(extract_warnings_from_single_artifact(lowerCamelCase , lowerCamelCase ) )
return selected_warnings
if __name__ == "__main__":
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return values.split(""",""" )
__UpperCamelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
parser.add_argument(
"""--output_dir""",
type=str,
required=True,
help="""Where to store the downloaded artifacts and other result files.""",
)
parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
# optional parameters
parser.add_argument(
"""--targets""",
default="""DeprecationWarning,UserWarning,FutureWarning""",
type=list_str,
help="""Comma-separated list of target warning(s) which we want to extract.""",
)
parser.add_argument(
"""--from_gh""",
action="""store_true""",
help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
)
__UpperCamelCase : List[str] = parser.parse_args()
__UpperCamelCase : Union[str, Any] = args.from_gh
if from_gh:
# The artifacts have to be downloaded using `actions/download-artifact@v3`
pass
else:
os.makedirs(args.output_dir, exist_ok=True)
# get download links
__UpperCamelCase : Any = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
# download artifacts
for idx, (name, url) in enumerate(artifacts.items()):
print(name)
print(url)
print("""=""" * 80)
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
# extract warnings from artifacts
__UpperCamelCase : Union[str, Any] = extract_warnings(args.output_dir, args.targets)
__UpperCamelCase : Any = sorted(selected_warnings)
with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)
| 53
| 1
|
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = Mock()
__lowercase = conn, Mock()
__lowercase = iter([1, None] )
__lowercase = lambda lowerCamelCase : next(lowerCamelCase )
# ===== invoke =====
send_file(filename="""mytext.txt""" , testing=lowerCamelCase )
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| 53
|
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCamelCase : Any = {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Dict = [
"""MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MraForMaskedLM""",
"""MraForMultipleChoice""",
"""MraForQuestionAnswering""",
"""MraForSequenceClassification""",
"""MraForTokenClassification""",
"""MraLayer""",
"""MraModel""",
"""MraPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
__UpperCamelCase : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 53
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : Optional[Any] = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Dict = ["""YolosFeatureExtractor"""]
__UpperCamelCase : int = ["""YolosImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : int = [
"""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
__UpperCamelCase : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 53
|
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not isinstance(lowerCamelCase , lowerCamelCase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
__lowercase = str(lowerCamelCase )
__lowercase = """""".join(sorted(lowerCamelCase ) )
return sorted_str_n != str_n and sorted_str_n[::-1] != str_n
def snake_case ( lowerCamelCase = 99 ):
'''simple docstring'''
if not 0 < percent < 100:
raise ValueError("""solution() only accepts values from 0 to 100""" )
__lowercase = 0
__lowercase = 1
while True:
if check_bouncy(lowerCamelCase ):
bouncy_num += 1
if (bouncy_num / num) * 100 >= percent:
return num
num += 1
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F'''{solution(99)}''')
| 53
| 1
|
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = """"""
for i in table:
res += inp[i - 1]
return res
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return data[1:] + data[0]
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = """"""
for i in range(len(lowerCamelCase ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = int("""0b""" + data[0] + data[-1] , 2 )
__lowercase = int("""0b""" + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = message[:4]
__lowercase = message[4:]
__lowercase = apply_table(lowerCamelCase , lowerCamelCase )
__lowercase = xor(lowerCamelCase , lowerCamelCase )
__lowercase = apply_sbox(lowerCamelCase , temp[:4] ) # noqa: E741
__lowercase = apply_sbox(lowerCamelCase , temp[4:] )
__lowercase = """0""" * (2 - len(lowerCamelCase )) + l # noqa: E741
__lowercase = """0""" * (2 - len(lowerCamelCase )) + r
__lowercase = apply_table(l + r , lowerCamelCase )
__lowercase = xor(lowerCamelCase , lowerCamelCase )
return temp + right
if __name__ == "__main__":
__UpperCamelCase : Union[str, Any] = input("""Enter 10 bit key: """)
__UpperCamelCase : str = input("""Enter 8 bit message: """)
__UpperCamelCase : List[str] = [6, 3, 7, 4, 8, 5, 10, 9]
__UpperCamelCase : Tuple = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
__UpperCamelCase : Optional[Any] = [2, 4, 3, 1]
__UpperCamelCase : int = [2, 6, 3, 1, 4, 8, 5, 7]
__UpperCamelCase : int = [4, 1, 3, 5, 7, 2, 8, 6]
__UpperCamelCase : Dict = [4, 1, 2, 3, 2, 3, 4, 1]
__UpperCamelCase : Tuple = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
__UpperCamelCase : str = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
__UpperCamelCase : Dict = apply_table(key, paa_table)
__UpperCamelCase : str = temp[:5]
__UpperCamelCase : str = temp[5:]
__UpperCamelCase : Tuple = left_shift(left)
__UpperCamelCase : Union[str, Any] = left_shift(right)
__UpperCamelCase : Union[str, Any] = apply_table(left + right, pa_table)
__UpperCamelCase : Union[str, Any] = left_shift(left)
__UpperCamelCase : int = left_shift(right)
__UpperCamelCase : Any = left_shift(left)
__UpperCamelCase : List[str] = left_shift(right)
__UpperCamelCase : Optional[Any] = apply_table(left + right, pa_table)
# encryption
__UpperCamelCase : int = apply_table(message, IP)
__UpperCamelCase : Dict = function(expansion, sa, sa, keya, temp)
__UpperCamelCase : Dict = temp[4:] + temp[:4]
__UpperCamelCase : Dict = function(expansion, sa, sa, keya, temp)
__UpperCamelCase : Union[str, Any] = apply_table(temp, IP_inv)
print("""Cipher text is:""", CT)
# decryption
__UpperCamelCase : Optional[Any] = apply_table(CT, IP)
__UpperCamelCase : Union[str, Any] = function(expansion, sa, sa, keya, temp)
__UpperCamelCase : str = temp[4:] + temp[:4]
__UpperCamelCase : Tuple = function(expansion, sa, sa, keya, temp)
__UpperCamelCase : Tuple = apply_table(temp, IP_inv)
print("""Plain text after decypting is:""", PT)
| 53
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__UpperCamelCase : Tuple = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormerConfig""",
"""SwiftFormerOnnxConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Optional[Any] = [
"""SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SwiftFormerForImageClassification""",
"""SwiftFormerModel""",
"""SwiftFormerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
__UpperCamelCase : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 53
| 1
|
import itertools
import math
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowerCamelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def snake_case ( ):
'''simple docstring'''
__lowercase = 2
while True:
if is_prime(lowerCamelCase ):
yield num
num += 1
def snake_case ( lowerCamelCase = 10_001 ):
'''simple docstring'''
return next(itertools.islice(prime_generator() , nth - 1 , lowerCamelCase ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 53
|
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
super().tearDown()
gc.collect()
def _a ( self : List[str] ) -> int:
"""simple docstring"""
__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_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512] )
print(F'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def _a ( self : str ) -> List[Any]:
"""simple docstring"""
__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_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297] )
print(F'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 53
| 1
|
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
__UpperCamelCase : Optional[int] = datasets.logging.get_logger(__name__)
__UpperCamelCase : int = """\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for Text Generation},
author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
booktitle={ACL},
year={2020},
url={https://arxiv.org/abs/2004.04696}
}
"""
__UpperCamelCase : List[str] = """\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).
See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""
__UpperCamelCase : Optional[Any] = """
BLEURT score.
Args:
`predictions` (list of str): prediction/candidate sentences
`references` (list of str): reference sentences
`checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.
Returns:
'scores': List of scores.
Examples:
>>> predictions = [\"hello there\", \"general kenobi\"]
>>> references = [\"hello there\", \"general kenobi\"]
>>> bleurt = datasets.load_metric(\"bleurt\")
>>> results = bleurt.compute(predictions=predictions, references=references)
>>> print([round(v, 2) for v in results[\"scores\"]])
[1.03, 1.04]
"""
__UpperCamelCase : str = {
"""bleurt-tiny-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip""",
"""bleurt-tiny-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip""",
"""bleurt-base-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip""",
"""bleurt-base-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip""",
"""bleurt-large-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip""",
"""bleurt-large-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip""",
"""BLEURT-20-D3""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip""",
"""BLEURT-20-D6""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip""",
"""BLEURT-20-D12""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip""",
"""BLEURT-20""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip""",
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCamelCase ( datasets.Metric ):
def _a ( self : List[str] ) -> Any:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""https://github.com/google-research/bleurt""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/google-research/bleurt"""] , reference_urls=["""https://github.com/google-research/bleurt""", """https://arxiv.org/abs/2004.04696"""] , )
def _a ( self : Tuple , _lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
if self.config_name == "default":
logger.warning(
"""Using default BLEURT-Base checkpoint for sequence maximum length 128. """
"""You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').""" )
__lowercase = """bleurt-base-128"""
if self.config_name.lower() in CHECKPOINT_URLS:
__lowercase = self.config_name.lower()
elif self.config_name.upper() in CHECKPOINT_URLS:
__lowercase = self.config_name.upper()
else:
raise KeyError(
F'{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}' )
# download the model checkpoint specified by self.config_name and set up the scorer
__lowercase = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
__lowercase = score.BleurtScorer(os.path.join(_lowerCAmelCase , _lowerCAmelCase ) )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[str] ) -> str:
"""simple docstring"""
__lowercase = self.scorer.score(references=_lowerCAmelCase , candidates=_lowerCAmelCase )
return {"scores": scores}
| 53
|
import heapq
import sys
import numpy as np
__UpperCamelCase : List[str] = tuple[int, int]
class __UpperCamelCase :
def __init__( self : Optional[int] ) -> Dict:
"""simple docstring"""
__lowercase = []
__lowercase = set()
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
if not self.empty():
return self.elements[0][0]
else:
return float("""inf""" )
def _a ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
return len(self.elements ) == 0
def _a ( self : str , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
if item not in self.set:
heapq.heappush(self.elements , (priority, item) )
self.set.add(_lowerCAmelCase )
else:
# update
# print("update", item)
__lowercase = []
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
while x != item:
temp.append((pri, x) )
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
temp.append((priority, item) )
for pro, xxx in temp:
heapq.heappush(self.elements , (pro, xxx) )
def _a ( self : List[str] , _lowerCAmelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
if item in self.set:
self.set.remove(_lowerCAmelCase )
__lowercase = []
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
while x != item:
temp.append((pro, x) )
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
for prito, yyy in temp:
heapq.heappush(self.elements , (prito, yyy) )
def _a ( self : Any ) -> List[Any]:
"""simple docstring"""
return self.elements[0][1]
def _a ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
((__lowercase) , (__lowercase)) = heapq.heappop(self.elements )
self.set.remove(_lowerCAmelCase )
return (priority, item)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = np.array(lowerCamelCase )
__lowercase = np.array(lowerCamelCase )
return np.linalg.norm(a - b )
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return consistent_heuristic(lowerCamelCase , lowerCamelCase ) // t
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return abs(p[0] - goal[0] ) + abs(p[1] - goal[1] )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = g_function[start] + Wa * heuristics[i](lowerCamelCase , lowerCamelCase )
return ans
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = np.chararray((n, n) )
for i in range(lowerCamelCase ):
for j in range(lowerCamelCase ):
__lowercase = """*"""
for i in range(lowerCamelCase ):
for j in range(lowerCamelCase ):
if (j, (n - 1) - i) in blocks:
__lowercase = """#"""
__lowercase = """-"""
__lowercase = back_pointer[goal]
while x != start:
((__lowercase) , (__lowercase)) = x
# print(x)
__lowercase = """-"""
__lowercase = back_pointer[x]
__lowercase = """-"""
for i in range(lowerCamelCase ):
for j in range(lowerCamelCase ):
if (i, j) == (0, n - 1):
print(grid[i][j] , end=""" """ )
print("""<-- End position""" , end=""" """ )
else:
print(grid[i][j] , end=""" """ )
print()
print("""^""" )
print("""Start position""" )
print()
print("""# is an obstacle""" )
print("""- is the path taken by algorithm""" )
print("""PATH TAKEN BY THE ALGORITHM IS:-""" )
__lowercase = back_pointer[goal]
while x != start:
print(lowerCamelCase , end=""" """ )
__lowercase = back_pointer[x]
print(lowerCamelCase )
sys.exit()
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if p[0] < 0 or p[0] > n - 1:
return False
if p[1] < 0 or p[1] > n - 1:
return False
return True
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , ):
'''simple docstring'''
for itera in range(lowerCamelCase ):
open_list[itera].remove_element(lowerCamelCase )
# print("s", s)
# print("j", j)
((__lowercase) , (__lowercase)) = s
__lowercase = (x - 1, y)
__lowercase = (x + 1, y)
__lowercase = (x, y + 1)
__lowercase = (x, y - 1)
for neighbours in [left, right, up, down]:
if neighbours not in blocks:
if valid(lowerCamelCase ) and neighbours not in visited:
# print("neighbour", neighbours)
visited.add(lowerCamelCase )
__lowercase = -1
__lowercase = float("""inf""" )
if valid(lowerCamelCase ) and g_function[neighbours] > g_function[s] + 1:
__lowercase = g_function[s] + 1
__lowercase = s
if neighbours not in close_list_anchor:
open_list[0].put(lowerCamelCase , key(lowerCamelCase , 0 , lowerCamelCase , lowerCamelCase ) )
if neighbours not in close_list_inad:
for var in range(1 , lowerCamelCase ):
if key(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) <= Wa * key(
lowerCamelCase , 0 , lowerCamelCase , lowerCamelCase ):
open_list[j].put(
lowerCamelCase , key(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) )
def snake_case ( ):
'''simple docstring'''
__lowercase = []
for x in range(1 , 5 ):
for y in range(1 , 6 ):
some_list.append((x, y) )
for x in range(15 , 20 ):
some_list.append((x, 17) )
for x in range(10 , 19 ):
for y in range(1 , 15 ):
some_list.append((x, y) )
# L block
for x in range(1 , 4 ):
for y in range(12 , 19 ):
some_list.append((x, y) )
for x in range(3 , 13 ):
for y in range(16 , 19 ):
some_list.append((x, y) )
return some_list
__UpperCamelCase : Optional[int] = {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}
__UpperCamelCase : Optional[Any] = [
(0, 1),
(1, 1),
(2, 1),
(3, 1),
(4, 1),
(5, 1),
(6, 1),
(7, 1),
(8, 1),
(9, 1),
(10, 1),
(11, 1),
(12, 1),
(13, 1),
(14, 1),
(15, 1),
(16, 1),
(17, 1),
(18, 1),
(19, 1),
]
__UpperCamelCase : Optional[Any] = make_common_ground()
__UpperCamelCase : Dict = blocks_blk
# hyper parameters
__UpperCamelCase : Union[str, Any] = 1
__UpperCamelCase : Union[str, Any] = 1
__UpperCamelCase : Optional[int] = 20
__UpperCamelCase : List[str] = 3 # one consistent and two other inconsistent
# start and end destination
__UpperCamelCase : str = (0, 0)
__UpperCamelCase : str = (n - 1, n - 1)
__UpperCamelCase : Optional[Any] = 1
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = {start: 0, goal: float("""inf""" )}
__lowercase = {start: -1, goal: -1}
__lowercase = []
__lowercase = set()
for i in range(lowerCamelCase ):
open_list.append(PriorityQueue() )
open_list[i].put(lowerCamelCase , key(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) )
__lowercase = []
__lowercase = []
while open_list[0].minkey() < float("""inf""" ):
for i in range(1 , lowerCamelCase ):
# print(open_list[0].minkey(), open_list[i].minkey())
if open_list[i].minkey() <= Wa * open_list[0].minkey():
global t
t += 1
if g_function[goal] <= open_list[i].minkey():
if g_function[goal] < float("""inf""" ):
do_something(lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
__lowercase , __lowercase = open_list[i].top_show()
visited.add(lowerCamelCase )
expand_state(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , )
close_list_inad.append(lowerCamelCase )
else:
if g_function[goal] <= open_list[0].minkey():
if g_function[goal] < float("""inf""" ):
do_something(lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
__lowercase = open_list[0].top_show()
visited.add(lowerCamelCase )
expand_state(
lowerCamelCase , 0 , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , )
close_list_anchor.append(lowerCamelCase )
print("""No path found to goal""" )
print()
for i in range(n - 1 , -1 , -1 ):
for j in range(lowerCamelCase ):
if (j, i) in blocks:
print("""#""" , end=""" """ )
elif (j, i) in back_pointer:
if (j, i) == (n - 1, n - 1):
print("""*""" , end=""" """ )
else:
print("""-""" , end=""" """ )
else:
print("""*""" , end=""" """ )
if (j, i) == (n - 1, n - 1):
print("""<-- End position""" , end=""" """ )
print()
print("""^""" )
print("""Start position""" )
print()
print("""# is an obstacle""" )
print("""- is the path taken by algorithm""" )
if __name__ == "__main__":
multi_a_star(start, goal, n_heuristic)
| 53
| 1
|
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs
__UpperCamelCase : List[Any] = imread(r"""digital_image_processing/image_data/lena_small.jpg""")
__UpperCamelCase : Any = cvtColor(img, COLOR_BGR2GRAY)
def snake_case ( ):
'''simple docstring'''
__lowercase = cn.convert_to_negative(lowerCamelCase )
# assert negative_img array for at least one True
assert negative_img.any()
def snake_case ( ):
'''simple docstring'''
with Image.open("""digital_image_processing/image_data/lena_small.jpg""" ) as img:
# Work around assertion for response
assert str(cc.change_contrast(lowerCamelCase , 110 ) ).startswith(
"""<PIL.Image.Image image mode=RGB size=100x100 at""" )
def snake_case ( ):
'''simple docstring'''
__lowercase = canny.gen_gaussian_kernel(9 , sigma=1.4 )
# Assert ambiguous array
assert resp.all()
def snake_case ( ):
'''simple docstring'''
__lowercase = imread("""digital_image_processing/image_data/lena_small.jpg""" , 0 )
# assert ambiguous array for all == True
assert canny_img.all()
__lowercase = canny.canny(lowerCamelCase )
# assert canny array for at least one True
assert canny_array.any()
def snake_case ( ):
'''simple docstring'''
assert gg.gaussian_filter(lowerCamelCase , 5 , sigma=0.9 ).all()
def snake_case ( ):
'''simple docstring'''
__lowercase = array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
__lowercase = conv.img_convolve(lowerCamelCase , lowerCamelCase ).astype(lowerCamelCase )
assert res.any()
def snake_case ( ):
'''simple docstring'''
assert med.median_filter(lowerCamelCase , 3 ).any()
def snake_case ( ):
'''simple docstring'''
__lowercase , __lowercase = sob.sobel_filter(lowerCamelCase )
assert grad.any() and theta.any()
def snake_case ( ):
'''simple docstring'''
__lowercase = sp.make_sepia(lowerCamelCase , 20 )
assert sepia.all()
def snake_case ( lowerCamelCase = "digital_image_processing/image_data/lena_small.jpg" ):
'''simple docstring'''
__lowercase = bs.Burkes(imread(lowerCamelCase , 1 ) , 120 )
burkes.process()
assert burkes.output_img.any()
def snake_case ( lowerCamelCase = "digital_image_processing/image_data/lena_small.jpg" , ):
'''simple docstring'''
__lowercase = rs.NearestNeighbour(imread(lowerCamelCase , 1 ) , 400 , 200 )
nn.process()
assert nn.output.any()
def snake_case ( ):
'''simple docstring'''
__lowercase = """digital_image_processing/image_data/lena.jpg"""
# Reading the image and converting it to grayscale.
__lowercase = imread(lowerCamelCase , 0 )
# Test for get_neighbors_pixel function() return not None
__lowercase = 0
__lowercase = 0
__lowercase = image[x_coordinate][y_coordinate]
__lowercase = lbp.get_neighbors_pixel(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
assert neighbors_pixels is not None
# Test for local_binary_pattern function()
# Create a numpy array as the same height and width of read image
__lowercase = np.zeros((image.shape[0], image.shape[1]) )
# Iterating through the image and calculating the local binary pattern value
# for each pixel.
for i in range(0 , image.shape[0] ):
for j in range(0 , image.shape[1] ):
__lowercase = lbp.local_binary_value(lowerCamelCase , lowerCamelCase , lowerCamelCase )
assert lbp_image.any()
| 53
|
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = SwinConfig.from_pretrained(
"""microsoft/swin-tiny-patch4-window7-224""" , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
__lowercase = MaskFormerConfig(backbone_config=lowerCamelCase )
__lowercase = """huggingface/label-files"""
if "ade20k-full" in model_name:
# this should be ok
__lowercase = 847
__lowercase = """maskformer-ade20k-full-id2label.json"""
elif "ade" in model_name:
# this should be ok
__lowercase = 150
__lowercase = """ade20k-id2label.json"""
elif "coco-stuff" in model_name:
# this should be ok
__lowercase = 171
__lowercase = """maskformer-coco-stuff-id2label.json"""
elif "coco" in model_name:
# TODO
__lowercase = 133
__lowercase = """coco-panoptic-id2label.json"""
elif "cityscapes" in model_name:
# this should be ok
__lowercase = 19
__lowercase = """cityscapes-id2label.json"""
elif "vistas" in model_name:
# this should be ok
__lowercase = 65
__lowercase = """mapillary-vistas-id2label.json"""
__lowercase = json.load(open(hf_hub_download(lowerCamelCase , lowerCamelCase , repo_type="""dataset""" ) , """r""" ) )
__lowercase = {int(lowerCamelCase ): v for k, v in idalabel.items()}
return config
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = []
# stem
# fmt: off
rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""") )
rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""") )
rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""") )
rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias') )
if i < 3:
rename_keys.append((F'backbone.layers.{i}.downsample.reduction.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight') )
rename_keys.append((F'backbone.layers.{i}.downsample.norm.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight') )
rename_keys.append((F'backbone.layers.{i}.downsample.norm.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias') )
rename_keys.append((F'backbone.norm{i}.weight', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight') )
rename_keys.append((F'backbone.norm{i}.bias', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias') )
# FPN
rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""") )
rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""") )
rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""") )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight') )
rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight') )
rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias') )
rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""") )
rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""") )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias') )
# cross-attention out projection
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias') )
# MLP 1
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias') )
# MLP 2
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias') )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias') )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias') )
# layernorm 3 (final layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias') )
rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""") )
rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""") )
# heads on top
rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""") )
rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""") )
rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""") )
rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""") )
rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""") )
for i in range(3 ):
rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight') )
rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias') )
# fmt: on
return rename_keys
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = dct.pop(lowerCamelCase )
__lowercase = val
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
__lowercase = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
__lowercase = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight' )
__lowercase = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
__lowercase = in_proj_weight[:dim, :]
__lowercase = in_proj_bias[: dim]
__lowercase = in_proj_weight[
dim : dim * 2, :
]
__lowercase = in_proj_bias[
dim : dim * 2
]
__lowercase = in_proj_weight[
-dim :, :
]
__lowercase = in_proj_bias[-dim :]
# fmt: on
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
__lowercase = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight' )
__lowercase = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
__lowercase = in_proj_weight[: hidden_size, :]
__lowercase = in_proj_bias[:config.hidden_size]
__lowercase = in_proj_weight[hidden_size : hidden_size * 2, :]
__lowercase = in_proj_bias[hidden_size : hidden_size * 2]
__lowercase = in_proj_weight[-hidden_size :, :]
__lowercase = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
__lowercase = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight' )
__lowercase = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
__lowercase = in_proj_weight[: hidden_size, :]
__lowercase = in_proj_bias[:config.hidden_size]
__lowercase = in_proj_weight[hidden_size : hidden_size * 2, :]
__lowercase = in_proj_bias[hidden_size : hidden_size * 2]
__lowercase = in_proj_weight[-hidden_size :, :]
__lowercase = in_proj_bias[-hidden_size :]
# fmt: on
def snake_case ( ):
'''simple docstring'''
__lowercase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__lowercase = Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase ).raw )
return im
@torch.no_grad()
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = False ):
'''simple docstring'''
__lowercase = get_maskformer_config(lowerCamelCase )
# load original state_dict
with open(lowerCamelCase , """rb""" ) as f:
__lowercase = pickle.load(lowerCamelCase )
__lowercase = data["""model"""]
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
__lowercase = create_rename_keys(lowerCamelCase )
for src, dest in rename_keys:
rename_key(lowerCamelCase , lowerCamelCase , lowerCamelCase )
read_in_swin_q_k_v(lowerCamelCase , config.backbone_config )
read_in_decoder_q_k_v(lowerCamelCase , lowerCamelCase )
# update to torch tensors
for key, value in state_dict.items():
__lowercase = torch.from_numpy(lowerCamelCase )
# load 🤗 model
__lowercase = MaskFormerForInstanceSegmentation(lowerCamelCase )
model.eval()
for name, param in model.named_parameters():
print(lowerCamelCase , param.shape )
__lowercase , __lowercase = model.load_state_dict(lowerCamelCase , strict=lowerCamelCase )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(lowerCamelCase ) == 0, F'Unexpected keys: {unexpected_keys}'
# verify results
__lowercase = prepare_img()
if "vistas" in model_name:
__lowercase = 65
elif "cityscapes" in model_name:
__lowercase = 65_535
else:
__lowercase = 255
__lowercase = True if """ade""" in model_name else False
__lowercase = MaskFormerImageProcessor(ignore_index=lowerCamelCase , reduce_labels=lowerCamelCase )
__lowercase = image_processor(lowerCamelCase , return_tensors="""pt""" )
__lowercase = model(**lowerCamelCase )
print("""Logits:""" , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
__lowercase = torch.tensor(
[[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , lowerCamelCase , atol=1e-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F'Saving model and image processor to {pytorch_dump_folder_path}' )
Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
model.save_pretrained(lowerCamelCase )
image_processor.save_pretrained(lowerCamelCase )
if push_to_hub:
print("""Pushing model and image processor to the hub...""" )
model.push_to_hub(F'nielsr/{model_name}' )
image_processor.push_to_hub(F'nielsr/{model_name}' )
if __name__ == "__main__":
__UpperCamelCase : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""maskformer-swin-tiny-ade""",
type=str,
help=("""Name of the MaskFormer model you'd like to convert""",),
)
parser.add_argument(
"""--checkpoint_path""",
default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
type=str,
help="""Path to the original state dict (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
__UpperCamelCase : List[Any] = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 53
| 1
|
import torch
from diffusers import StableDiffusionPipeline
__UpperCamelCase : Union[str, Any] = """path-to-your-trained-model"""
__UpperCamelCase : List[Any] = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
__UpperCamelCase : List[Any] = """A photo of sks dog in a bucket"""
__UpperCamelCase : str = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("""dog-bucket.png""")
| 53
|
from math import sqrt
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(lowerCamelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def snake_case ( lowerCamelCase = 10_001 ):
'''simple docstring'''
__lowercase = 0
__lowercase = 1
while count != nth and number < 3:
number += 1
if is_prime(lowerCamelCase ):
count += 1
while count != nth:
number += 2
if is_prime(lowerCamelCase ):
count += 1
return number
if __name__ == "__main__":
print(F'''{solution() = }''')
| 53
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__UpperCamelCase : Optional[Any] = {
"""configuration_mega""": ["""MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegaConfig""", """MegaOnnxConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Optional[Any] = [
"""MEGA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MegaForCausalLM""",
"""MegaForMaskedLM""",
"""MegaForMultipleChoice""",
"""MegaForQuestionAnswering""",
"""MegaForSequenceClassification""",
"""MegaForTokenClassification""",
"""MegaModel""",
"""MegaPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
__UpperCamelCase : Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 53
|
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if isinstance(lowerCamelCase , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class __UpperCamelCase :
def _a ( self : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
pass
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
pass
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
pass
def _a ( self : int , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Dict=None , **_lowerCAmelCase : int ) -> str:
"""simple docstring"""
__lowercase = VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) )
def _a ( self : str , _lowerCAmelCase : Tuple , _lowerCAmelCase : str , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : int=None , **_lowerCAmelCase : Dict ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase , text_model=_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def _a ( self : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Dict , _lowerCAmelCase : str , _lowerCAmelCase : Any=None , **_lowerCAmelCase : str ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = {"""vision_model""": vision_model, """text_model""": text_model}
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def _a ( self : Dict , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any]=None , **_lowerCAmelCase : Any ) -> Optional[int]:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase , text_model=_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
__lowercase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase )
__lowercase = model(input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase )
__lowercase = after_output[0].numpy()
__lowercase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCAmelCase , 1e-5 )
def _a ( self : int , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int]=None , **_lowerCAmelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase , text_model=_lowerCAmelCase )
__lowercase = model(
input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase , output_attentions=_lowerCAmelCase )
__lowercase = output.vision_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
__lowercase = to_atuple(vision_model.config.image_size )
__lowercase = to_atuple(vision_model.config.patch_size )
__lowercase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowercase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__lowercase = output.text_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _a ( self : List[Any] , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : float ) -> Optional[int]:
"""simple docstring"""
__lowercase = np.abs((a - b) ).max()
self.assertLessEqual(_lowerCAmelCase , _lowerCAmelCase , F'Difference between torch and flax is {diff} (>= {tol}).' )
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**_lowerCAmelCase )
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> int:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_lowerCAmelCase )
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_save_load(**_lowerCAmelCase )
def _a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_lowerCAmelCase )
@slow
def _a ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.get_pretrained_model_and_inputs()
__lowercase = model_a(**_lowerCAmelCase )
__lowercase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase )
__lowercase = model_a(**_lowerCAmelCase )
__lowercase = after_outputs[0].numpy()
__lowercase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCAmelCase , 1e-5 )
@require_tf
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
def _a ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-random-bert""" )
__lowercase = 13
__lowercase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowercase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowercase = random_attention_mask([batch_size, 4] )
__lowercase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _a ( self : Tuple , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
__lowercase = TFViTModel(_lowerCAmelCase , name="""vision_model""" )
__lowercase = TFBertModel(_lowerCAmelCase , name="""text_model""" )
return vision_model, text_model
def _a ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = TFViTModelTester(self )
__lowercase = TFBertModelTester(self )
__lowercase = vit_model_tester.prepare_config_and_inputs()
__lowercase = bert_model_tester.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = vision_config_and_inputs
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-deit-tf""" , """hf-internal-testing/tiny-random-roberta""" )
__lowercase = 13
__lowercase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowercase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowercase = random_attention_mask([batch_size, 4] )
__lowercase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _a ( self : Optional[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : Tuple , _lowerCAmelCase : int=None , **_lowerCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
__lowercase , __lowercase = self.get_vision_text_model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase , text_model=_lowerCAmelCase )
__lowercase = model(
input_ids=_lowerCAmelCase , pixel_values=_lowerCAmelCase , attention_mask=_lowerCAmelCase , output_attentions=_lowerCAmelCase )
__lowercase = output.vision_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
__lowercase = to_atuple(vision_model.config.image_size )
__lowercase = to_atuple(vision_model.config.patch_size )
__lowercase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowercase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__lowercase = output.text_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _a ( self : Tuple , _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict ) -> int:
"""simple docstring"""
__lowercase = TFDeiTModel(_lowerCAmelCase , name="""vision_model""" )
__lowercase = TFRobertaModel(_lowerCAmelCase , name="""text_model""" )
return vision_model, text_model
def _a ( self : Tuple ) -> str:
"""simple docstring"""
__lowercase = TFDeiTModelTester(self )
__lowercase = TFRobertaModelTester(self )
__lowercase = vit_model_tester.prepare_config_and_inputs()
__lowercase = bert_model_tester.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = vision_config_and_inputs
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
def _a ( self : int ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-clip-tf""" , """hf-internal-testing/tiny-random-bert""" )
__lowercase = 13
__lowercase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowercase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__lowercase = random_attention_mask([batch_size, 4] )
__lowercase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _a ( self : Optional[int] , _lowerCAmelCase : Dict , _lowerCAmelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
__lowercase = TFCLIPVisionModel(_lowerCAmelCase , name="""vision_model""" )
__lowercase = TFBertModel(_lowerCAmelCase , name="""text_model""" )
return vision_model, text_model
def _a ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = TFCLIPVisionModelTester(self )
__lowercase = TFBertModelTester(self )
__lowercase = clip_model_tester.prepare_config_and_inputs()
__lowercase = bert_model_tester.prepare_config_and_inputs()
__lowercase , __lowercase = vision_config_and_inputs
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class __UpperCamelCase ( unittest.TestCase ):
@slow
def _a ( self : int ) -> Tuple:
"""simple docstring"""
__lowercase = TFVisionTextDualEncoderModel.from_pretrained(
"""clip-italian/clip-italian""" , logit_scale_init_value=1.0 , from_pt=_lowerCAmelCase )
__lowercase = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
__lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
__lowercase = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=_lowerCAmelCase , padding=_lowerCAmelCase , return_tensors="""np""" )
__lowercase = model(**_lowerCAmelCase )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
__lowercase = np.array([[1.2_284_727, 0.3_104_122]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , _lowerCAmelCase , atol=1e-3 ) )
| 53
| 1
|
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
__UpperCamelCase : Any = logging.get_logger(__name__)
@dataclass
class __UpperCamelCase :
__snake_case :str = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys() )} )
__snake_case :str = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
__snake_case :int = field(
default=1_2_8 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def _a ( self : Dict ) -> List[Any]:
"""simple docstring"""
__lowercase = self.task_name.lower()
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Optional[int] = 'train'
__snake_case :int = 'dev'
__snake_case :Any = 'test'
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :GlueDataTrainingArguments
__snake_case :str
__snake_case :List[InputFeatures]
def __init__( self : Dict , _lowerCAmelCase : GlueDataTrainingArguments , _lowerCAmelCase : PreTrainedTokenizerBase , _lowerCAmelCase : Optional[int] = None , _lowerCAmelCase : Union[str, Split] = Split.train , _lowerCAmelCase : Optional[str] = None , ) -> List[Any]:
"""simple docstring"""
warnings.warn(
"""This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
"""library. You can have a look at this example script for pointers: """
"""https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py""" , _lowerCAmelCase , )
__lowercase = args
__lowercase = glue_processors[args.task_name]()
__lowercase = glue_output_modes[args.task_name]
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
try:
__lowercase = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
# Load data features from cache or dataset file
__lowercase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}' , )
__lowercase = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
__lowercase , __lowercase = label_list[2], label_list[1]
__lowercase = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowercase = cached_features_file + """.lock"""
with FileLock(_lowerCAmelCase ):
if os.path.exists(_lowerCAmelCase ) and not args.overwrite_cache:
__lowercase = time.time()
__lowercase = torch.load(_lowerCAmelCase )
logger.info(
F'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start )
else:
logger.info(F'Creating features from dataset file at {args.data_dir}' )
if mode == Split.dev:
__lowercase = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
__lowercase = self.processor.get_test_examples(args.data_dir )
else:
__lowercase = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
__lowercase = examples[:limit_length]
__lowercase = glue_convert_examples_to_features(
_lowerCAmelCase , _lowerCAmelCase , max_length=args.max_seq_length , label_list=_lowerCAmelCase , output_mode=self.output_mode , )
__lowercase = time.time()
torch.save(self.features , _lowerCAmelCase )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self : Dict ) -> Optional[int]:
"""simple docstring"""
return len(self.features )
def __getitem__( self : Tuple , _lowerCAmelCase : Optional[int] ) -> InputFeatures:
"""simple docstring"""
return self.features[i]
def _a ( self : str ) -> int:
"""simple docstring"""
return self.label_list
| 53
|
from __future__ import annotations
from scipy.special import comb # type: ignore
class __UpperCamelCase :
def __init__( self : int , _lowerCAmelCase : list[tuple[float, float]] ) -> Any:
"""simple docstring"""
__lowercase = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
__lowercase = len(_lowerCAmelCase ) - 1
def _a ( self : Tuple , _lowerCAmelCase : float ) -> list[float]:
"""simple docstring"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__lowercase = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , _lowerCAmelCase ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(_lowerCAmelCase ) , 5 ) == 1
return output_values
def _a ( self : List[str] , _lowerCAmelCase : float ) -> tuple[float, float]:
"""simple docstring"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__lowercase = self.basis_function(_lowerCAmelCase )
__lowercase = 0.0
__lowercase = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def _a ( self : Optional[int] , _lowerCAmelCase : float = 0.01 ) -> Union[str, Any]:
"""simple docstring"""
from matplotlib import pyplot as plt # type: ignore
__lowercase = [] # x coordinates of points to plot
__lowercase = [] # y coordinates of points to plot
__lowercase = 0.0
while t <= 1:
__lowercase = self.bezier_curve_function(_lowerCAmelCase )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
__lowercase = [i[0] for i in self.list_of_points]
__lowercase = [i[1] for i in self.list_of_points]
plt.plot(
_lowerCAmelCase , _lowerCAmelCase , color="""blue""" , label="""Curve of Degree """ + str(self.degree ) , )
plt.scatter(_lowerCAmelCase , _lowerCAmelCase , color="""red""" , label="""Control Points""" )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 53
| 1
|
from __future__ import annotations
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not nums:
return 0
__lowercase = nums[0]
__lowercase = 0
for num in nums[1:]:
__lowercase , __lowercase = (
max_excluding + num,
max(lowerCamelCase , lowerCamelCase ),
)
return max(lowerCamelCase , lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 53
|
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
)
from transformers.models.efficientformer.modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_vision_available():
from PIL import Image
from transformers import EfficientFormerImageProcessor
class __UpperCamelCase :
def __init__( self : int , _lowerCAmelCase : Tuple , _lowerCAmelCase : int = 13 , _lowerCAmelCase : int = 64 , _lowerCAmelCase : int = 2 , _lowerCAmelCase : int = 3 , _lowerCAmelCase : int = 3 , _lowerCAmelCase : bool = True , _lowerCAmelCase : bool = True , _lowerCAmelCase : int = 128 , _lowerCAmelCase : Optional[int]=[16, 32, 64, 128] , _lowerCAmelCase : int = 7 , _lowerCAmelCase : int = 4 , _lowerCAmelCase : int = 37 , _lowerCAmelCase : str = "gelu" , _lowerCAmelCase : float = 0.1 , _lowerCAmelCase : float = 0.1 , _lowerCAmelCase : int = 10 , _lowerCAmelCase : float = 0.02 , _lowerCAmelCase : int = 2 , _lowerCAmelCase : int = 1 , _lowerCAmelCase : int = 128 , _lowerCAmelCase : List[int] = [2, 2, 2, 2] , _lowerCAmelCase : int = 2 , _lowerCAmelCase : int = 2 , ) -> Tuple:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = is_training
__lowercase = use_labels
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = encoder_stride
__lowercase = num_attention_outputs
__lowercase = embed_dim
__lowercase = embed_dim + 1
__lowercase = resolution
__lowercase = depths
__lowercase = hidden_sizes
__lowercase = dim
__lowercase = mlp_expansion_ratio
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
return EfficientFormerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_lowerCAmelCase , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , resolution=self.resolution , depths=self.depths , hidden_sizes=self.hidden_sizes , dim=self.dim , mlp_expansion_ratio=self.mlp_expansion_ratio , )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = TFEfficientFormerModel(config=_lowerCAmelCase )
__lowercase = model(_lowerCAmelCase , training=_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.type_sequence_label_size
__lowercase = TFEfficientFormerForImageClassification(_lowerCAmelCase )
__lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase , training=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__lowercase = 1
__lowercase = TFEfficientFormerForImageClassification(_lowerCAmelCase )
__lowercase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Any = (
(
TFEfficientFormerModel,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerForImageClassification,
)
if is_tf_available()
else ()
)
__snake_case :Any = (
{
'feature-extraction': TFEfficientFormerModel,
'image-classification': (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
),
}
if is_tf_available()
else {}
)
__snake_case :int = False
__snake_case :Optional[int] = False
__snake_case :int = False
__snake_case :Any = False
__snake_case :Any = False
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
__lowercase = TFEfficientFormerModelTester(self )
__lowercase = ConfigTester(
self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 )
def _a ( self : Optional[int] ) -> int:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""EfficientFormer does not use inputs_embeds""" )
def _a ( self : Optional[int] ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""EfficientFormer does not support input and output embeddings""" )
def _a ( self : int ) -> str:
"""simple docstring"""
pass
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
def check_hidden_states_output(_lowerCAmelCase : int , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] ):
__lowercase = model_class(_lowerCAmelCase )
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) , training=_lowerCAmelCase )
__lowercase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowercase = getattr(
self.model_tester , """expected_num_hidden_layers""" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
if hasattr(self.model_tester , """encoder_seq_length""" ):
__lowercase = self.model_tester.encoder_seq_length
if hasattr(self.model_tester , """chunk_length""" ) and self.model_tester.chunk_length > 1:
__lowercase = seq_length * self.model_tester.chunk_length
else:
__lowercase = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
if config.is_encoder_decoder:
__lowercase = outputs.decoder_hidden_states
self.asseretIsInstance(_lowerCAmelCase , (list, tuple) )
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """seq_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """decoder_seq_length""" , _lowerCAmelCase )
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [decoder_seq_length, self.model_tester.hidden_size] , )
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Optional[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any]=False ) -> Dict:
"""simple docstring"""
__lowercase = super()._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
if return_labels:
if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def _a ( self : int ) -> int:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet""" )
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase )
def _a ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@slow
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = TFEfficientFormerModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = True
__lowercase = getattr(self.model_tester , """seq_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """encoder_seq_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """key_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """chunk_length""" , _lowerCAmelCase )
if chunk_length is not None and hasattr(self.model_tester , """num_hashes""" ):
__lowercase = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
__lowercase = True
__lowercase = False
__lowercase = True
__lowercase = model_class(_lowerCAmelCase )
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) , training=_lowerCAmelCase )
__lowercase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_attention_outputs )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__lowercase = True
__lowercase = model_class(_lowerCAmelCase )
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) , training=_lowerCAmelCase )
__lowercase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_attention_outputs )
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length] , )
else:
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length] , )
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Prepare our model
__lowercase = model_class(_lowerCAmelCase )
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
__lowercase = {
key: tf.keras.Input(shape=val.shape[1:] , dtype=val.dtype , name=_lowerCAmelCase )
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
__lowercase = model(_lowerCAmelCase )
self.assertTrue(outputs_dict is not None )
def snake_case ( ):
'''simple docstring'''
__lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
return (
EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300""" )
if is_vision_available()
else None
)
@slow
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__lowercase = TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300""" )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""tf""" )
# forward pass
__lowercase = model(**_lowerCAmelCase , training=_lowerCAmelCase )
# verify the logits
__lowercase = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
__lowercase = tf.constant([-0.0_555, 0.4_825, -0.0_852] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
@slow
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__lowercase = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
"""snap-research/efficientformer-l1-300""" )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""tf""" )
# forward pass
__lowercase = model(**_lowerCAmelCase , training=_lowerCAmelCase )
# verify the logits
__lowercase = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
__lowercase = tf.constant([-0.1_312, 0.4_353, -1.0_499] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
| 53
| 1
|
# Function to print upper half of diamond (pyramid)
def snake_case ( lowerCamelCase ):
'''simple docstring'''
for i in range(0 , lowerCamelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
for _ in range(0 , i + 1 ): # printing stars
print("""* """ , end="""""" )
print()
def snake_case ( lowerCamelCase ):
'''simple docstring'''
for i in range(lowerCamelCase , 0 , -1 ):
for _ in range(lowerCamelCase , 0 , -1 ): # printing stars
print("""* """ , end="""""" )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(""" """ , end="""""" )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if n <= 0:
print(""" ... .... nothing printing :(""" )
return
floyd(lowerCamelCase ) # upper half
reverse_floyd(lowerCamelCase ) # lower half
if __name__ == "__main__":
print(r"""| /\ | |- | |- |--| |\ /| |-""")
print(r"""|/ \| |- |_ |_ |__| | \/ | |_""")
__UpperCamelCase : str = 1
while K:
__UpperCamelCase : Optional[int] = int(input("""enter the number and , and see the magic : """))
print()
pretty_print(user_number)
__UpperCamelCase : str = int(input("""press 0 to exit... and 1 to continue..."""))
print("""Good Bye...""")
| 53
|
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
__UpperCamelCase : Tuple = 2
class __UpperCamelCase :
def __init__( self : List[str] , *, # begin keyword-only arguments
_lowerCAmelCase : Optional[int]="<s>" , _lowerCAmelCase : Optional[int]="<pad>" , _lowerCAmelCase : int="</s>" , _lowerCAmelCase : str="<unk>" , _lowerCAmelCase : List[str]=None , ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase , __lowercase , __lowercase = bos, unk, pad, eos
__lowercase = []
__lowercase = []
__lowercase = {}
__lowercase = self.add_symbol(_lowerCAmelCase )
__lowercase = self.add_symbol(_lowerCAmelCase )
__lowercase = self.add_symbol(_lowerCAmelCase )
__lowercase = self.add_symbol(_lowerCAmelCase )
if extra_special_symbols:
for s in extra_special_symbols:
self.add_symbol(_lowerCAmelCase )
__lowercase = len(self.symbols )
def __eq__( self : Dict , _lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
return self.indices == other.indices
def __getitem__( self : Any , _lowerCAmelCase : str ) -> Dict:
"""simple docstring"""
if idx < len(self.symbols ):
return self.symbols[idx]
return self.unk_word
def __len__( self : str ) -> List[str]:
"""simple docstring"""
return len(self.symbols )
def __contains__( self : Union[str, Any] , _lowerCAmelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
return sym in self.indices
@classmethod
def _a ( cls : Dict , _lowerCAmelCase : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = cls()
d.add_from_file(_lowerCAmelCase )
return d
def _a ( self : int , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any]=1 , _lowerCAmelCase : Optional[int]=False ) -> Union[str, Any]:
"""simple docstring"""
if word in self.indices and not overwrite:
__lowercase = self.indices[word]
__lowercase = self.count[idx] + n
return idx
else:
__lowercase = len(self.symbols )
__lowercase = idx
self.symbols.append(_lowerCAmelCase )
self.count.append(_lowerCAmelCase )
return idx
def _a ( self : List[str] , _lowerCAmelCase : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
return 0
def _a ( self : Optional[Any] , _lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
try:
with open(_lowerCAmelCase , """r""" , encoding="""utf-8""" ) as fd:
self.add_from_file(_lowerCAmelCase )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(_lowerCAmelCase ) )
return
__lowercase = f.readlines()
__lowercase = self._load_meta(_lowerCAmelCase )
for line in lines[indices_start_line:]:
try:
__lowercase , __lowercase = line.rstrip().rsplit(""" """ , 1 )
if field == "#fairseq:overwrite":
__lowercase = True
__lowercase , __lowercase = line.rsplit(""" """ , 1 )
else:
__lowercase = False
__lowercase = int(_lowerCAmelCase )
__lowercase = line
if word in self and not overwrite:
raise RuntimeError(
"""Duplicate word found when loading Dictionary: '{}'. """
"""Duplicate words can overwrite earlier ones by adding the """
"""#fairseq:overwrite flag at the end of the corresponding row """
"""in the dictionary file. If using the Camembert model, please """
"""download an updated copy of the model file.""".format(_lowerCAmelCase ) )
self.add_symbol(_lowerCAmelCase , n=_lowerCAmelCase , overwrite=_lowerCAmelCase )
except ValueError:
raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'""" )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = dict((re.sub(r"""@@$""" , """""" , lowerCamelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , lowerCamelCase ), v) for k, v in d.items() )
__lowercase = """<s> <pad> </s> <unk>""".split()
# restore the special tokens
for k in keep_keys:
del da[F'{k}</w>']
__lowercase = d[k] # restore
return da
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if not os.path.exists(lowerCamelCase ):
raise ValueError(F'path {biogpt_checkpoint_path} does not exist!' )
os.makedirs(lowerCamelCase , exist_ok=lowerCamelCase )
print(F'Writing results to {pytorch_dump_folder_path}' )
# handle various types of models
__lowercase = os.path.join(lowerCamelCase , """checkpoint.pt""" )
if not os.path.isfile(lowerCamelCase ):
raise ValueError(F'path to the file {checkpoint_file} does not exist!' )
__lowercase = torch.load(lowerCamelCase , map_location="""cpu""" )
__lowercase = chkpt["""cfg"""]["""model"""]
# dicts
__lowercase = os.path.join(lowerCamelCase , """dict.txt""" )
if not os.path.isfile(lowerCamelCase ):
raise ValueError(F'path to the file {dict_file} does not exist!' )
__lowercase = Dictionary.load(lowerCamelCase )
__lowercase = rewrite_dict_keys(src_dict.indices )
__lowercase = len(lowerCamelCase )
__lowercase = os.path.join(lowerCamelCase , VOCAB_FILES_NAMES["""vocab_file"""] )
print(F'Generating {src_vocab_file} of {src_vocab_size} records' )
with open(lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(lowerCamelCase , ensure_ascii=lowerCamelCase , indent=lowerCamelCase ) )
# merges_file (bpecodes)
__lowercase = os.path.join(lowerCamelCase , """bpecodes""" )
if not os.path.isfile(lowerCamelCase ):
raise ValueError(F'path to the file {bpecodes_file} does not exist!' )
__lowercase = os.path.join(lowerCamelCase , VOCAB_FILES_NAMES["""merges_file"""] )
shutil.copyfile(lowerCamelCase , lowerCamelCase )
# model config
__lowercase = os.path.join(lowerCamelCase , """config.json""" )
__lowercase = {
"""activation_dropout""": args["""activation_dropout"""],
"""architectures""": ["""BioGptForCausalLM"""],
"""attention_probs_dropout_prob""": args["""attention_dropout"""],
"""bos_token_id""": 0,
"""eos_token_id""": 2,
"""hidden_act""": args["""activation_fn"""],
"""hidden_dropout_prob""": args["""dropout"""],
"""hidden_size""": args["""decoder_embed_dim"""],
"""initializer_range""": 0.02,
"""intermediate_size""": args["""decoder_ffn_embed_dim"""],
"""layer_norm_eps""": 1e-12,
"""layerdrop""": args["""decoder_layerdrop"""],
"""max_position_embeddings""": args["""max_target_positions"""],
"""model_type""": """biogpt""",
"""num_attention_heads""": args["""decoder_attention_heads"""],
"""num_hidden_layers""": args["""decoder_layers"""],
"""pad_token_id""": 1,
"""scale_embedding""": not args["""no_scale_embedding"""],
"""tie_word_embeddings""": args["""share_decoder_input_output_embed"""],
"""vocab_size""": src_vocab_size,
}
# good hparam defaults to start with
print(F'Generating {biogpt_model_config_file}' )
with open(lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(lowerCamelCase , ensure_ascii=lowerCamelCase , indent=lowerCamelCase ) )
# tokenizer config
__lowercase = os.path.join(lowerCamelCase , lowerCamelCase )
__lowercase = {
"""bos_token""": """<s>""",
"""eos_token""": """</s>""",
"""model_max_length""": 1_024,
"""pad_token""": """<pad>""",
"""special_tokens_map_file""": None,
"""tokenizer_class""": """BioGptTokenizer""",
"""unk_token""": """<unk>""",
}
print(F'Generating {biogpt_tokenizer_config_file}' )
with open(lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(lowerCamelCase , ensure_ascii=lowerCamelCase , indent=lowerCamelCase ) )
# model
__lowercase = chkpt["""model"""]
# remove unneeded keys
__lowercase = [
"""decoder.version""",
]
for k in ignore_keys:
model_state_dict.pop(lowerCamelCase , lowerCamelCase )
__lowercase = list(model_state_dict.keys() )
for layer_name in layer_names:
if layer_name.endswith("""output_projection.weight""" ):
__lowercase = model_state_dict.pop(lowerCamelCase )
else:
__lowercase = model_state_dict.pop(lowerCamelCase )
__lowercase = BioGptConfig.from_pretrained(lowerCamelCase )
__lowercase = BioGptForCausalLM(lowerCamelCase )
# check that it loads ok
model_new.load_state_dict(lowerCamelCase )
# save
__lowercase = os.path.join(lowerCamelCase , lowerCamelCase )
print(F'Generating {pytorch_weights_dump_path}' )
torch.save(lowerCamelCase , lowerCamelCase )
print("""Conversion is done!""" )
if __name__ == "__main__":
__UpperCamelCase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--biogpt_checkpoint_path""",
default=None,
type=str,
required=True,
help=(
"""Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
""" bpecodes, etc."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
__UpperCamelCase : Optional[Any] = parser.parse_args()
convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
| 53
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCamelCase : Optional[int] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Union[str, Any] = ["""MLukeTokenizer"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
__UpperCamelCase : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 53
|
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
__snake_case :Union[str, Any] = 'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
def _a ( self : Any , _lowerCAmelCase : str=0 ) -> str:
"""simple docstring"""
__lowercase = np.random.RandomState(_lowerCAmelCase )
__lowercase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.65_072, 0.58_492, 0.48_219, 0.55_521, 0.53_180, 0.55_939, 0.50_697, 0.39_800, 0.46_455] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.65_863, 0.59_425, 0.49_326, 0.56_313, 0.53_875, 0.56_627, 0.51_065, 0.39_777, 0.46_330] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Tuple ) -> int:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_817, 0.60_812, 0.47_384, 0.49_530, 0.51_894, 0.49_814, 0.47_984, 0.38_958, 0.44_271] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_895, 0.60_808, 0.47_933, 0.49_608, 0.51_886, 0.49_950, 0.48_053, 0.38_957, 0.44_200] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * [inputs["""prompt"""]]
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * [inputs.pop("""prompt""" )]
__lowercase = pipe.tokenizer(
_lowerCAmelCase , padding="""max_length""" , max_length=pipe.tokenizer.model_max_length , truncation=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = text_inputs["""input_ids"""]
__lowercase = pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]
__lowercase = prompt_embeds
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
def _a ( self : int ) -> str:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * ["""this is a negative prompt"""]
__lowercase = negative_prompt
__lowercase = 3 * [inputs["""prompt"""]]
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * [inputs.pop("""prompt""" )]
__lowercase = []
for p in [prompt, negative_prompt]:
__lowercase = pipe.tokenizer(
_lowerCAmelCase , padding="""max_length""" , max_length=pipe.tokenizer.model_max_length , truncation=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = text_inputs["""input_ids"""]
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )
__lowercase , __lowercase = embeds
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
@nightly
@require_onnxruntime
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
@property
def _a ( self : Dict ) -> str:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _a ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = ort.SessionOptions()
__lowercase = False
return options
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""onnx""" , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """A painting of a squirrel eating a burger"""
np.random.seed(0 )
__lowercase = sd_pipe([prompt] , guidance_scale=6.0 , num_inference_steps=10 , output_type="""np""" )
__lowercase = output.images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.0_452, 0.0_390, 0.0_087, 0.0_350, 0.0_617, 0.0_364, 0.0_544, 0.0_523, 0.0_720] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
__lowercase = DDIMScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """open neural network exchange"""
__lowercase = np.random.RandomState(0 )
__lowercase = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=_lowerCAmelCase , output_type="""np""" )
__lowercase = output.images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.2_867, 0.1_974, 0.1_481, 0.7_294, 0.7_251, 0.6_667, 0.4_194, 0.5_642, 0.6_486] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
__lowercase = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """open neural network exchange"""
__lowercase = np.random.RandomState(0 )
__lowercase = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=_lowerCAmelCase , output_type="""np""" )
__lowercase = output.images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.2_306, 0.1_959, 0.1_593, 0.6_549, 0.6_394, 0.5_408, 0.5_065, 0.6_010, 0.6_161] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : str ) -> List[str]:
"""simple docstring"""
__lowercase = 0
def test_callback_fn(_lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : np.ndarray ) -> None:
__lowercase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 64, 64)
__lowercase = latents[0, -3:, -3:, -1]
__lowercase = np.array(
[-0.6_772, -0.3_835, -1.2_456, 0.1_905, -1.0_974, 0.6_967, -1.9_353, 0.0_178, 1.0_167] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
elif step == 5:
assert latents.shape == (1, 4, 64, 64)
__lowercase = latents[0, -3:, -3:, -1]
__lowercase = np.array(
[-0.3_351, 0.2_241, -0.1_837, -0.2_325, -0.6_577, 0.3_393, -0.0_241, 0.5_899, 1.3_875] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
__lowercase = False
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """Andromeda galaxy in a bottle"""
__lowercase = np.random.RandomState(0 )
pipe(
prompt=_lowerCAmelCase , num_inference_steps=5 , guidance_scale=7.5 , generator=_lowerCAmelCase , callback=_lowerCAmelCase , callback_steps=1 , )
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
assert pipe.safety_checker is None
__lowercase = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_lowerCAmelCase )
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(_lowerCAmelCase )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
__lowercase = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
| 53
| 1
|
def snake_case ( lowerCamelCase = 1_000_000 ):
'''simple docstring'''
__lowercase = limit + 1
__lowercase = [0] * limit
for first_term in range(1 , lowerCamelCase ):
for n in range(lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__lowercase = 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
__lowercase = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(F'''{solution() = }''')
| 53
|
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = """"""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
__lowercase = remove_duplicates(key.upper() )
__lowercase = len(lowerCamelCase )
# First fill cipher with key characters
__lowercase = {alphabet[i]: char for i, char in enumerate(lowerCamelCase )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(lowerCamelCase ) , 26 ):
__lowercase = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
__lowercase = alphabet[i - offset]
__lowercase = char
return cipher_alphabet
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return "".join(cipher_map.get(lowerCamelCase , lowerCamelCase ) for ch in message.upper() )
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(lowerCamelCase , lowerCamelCase ) for ch in message.upper() )
def snake_case ( ):
'''simple docstring'''
__lowercase = input("""Enter message to encode or decode: """ ).strip()
__lowercase = input("""Enter keyword: """ ).strip()
__lowercase = input("""Encipher or decipher? E/D:""" ).strip()[0].lower()
try:
__lowercase = {"""e""": encipher, """d""": decipher}[option]
except KeyError:
raise KeyError("""invalid input option""" )
__lowercase = create_cipher_map(lowerCamelCase )
print(func(lowerCamelCase , lowerCamelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 53
| 1
|
__UpperCamelCase : Union[str, Any] = {
"""a""": """AAAAA""",
"""b""": """AAAAB""",
"""c""": """AAABA""",
"""d""": """AAABB""",
"""e""": """AABAA""",
"""f""": """AABAB""",
"""g""": """AABBA""",
"""h""": """AABBB""",
"""i""": """ABAAA""",
"""j""": """BBBAA""",
"""k""": """ABAAB""",
"""l""": """ABABA""",
"""m""": """ABABB""",
"""n""": """ABBAA""",
"""o""": """ABBAB""",
"""p""": """ABBBA""",
"""q""": """ABBBB""",
"""r""": """BAAAA""",
"""s""": """BAAAB""",
"""t""": """BAABA""",
"""u""": """BAABB""",
"""v""": """BBBAB""",
"""w""": """BABAA""",
"""x""": """BABAB""",
"""y""": """BABBA""",
"""z""": """BABBB""",
""" """: """ """,
}
__UpperCamelCase : List[str] = {value: key for key, value in encode_dict.items()}
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = """"""
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception("""encode() accepts only letters of the alphabet and spaces""" )
return encoded
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if set(lowerCamelCase ) - {"A", "B", " "} != set():
raise Exception("""decode() accepts only 'A', 'B' and spaces""" )
__lowercase = """"""
for word in coded.split():
while len(lowerCamelCase ) != 0:
decoded += decode_dict[word[:5]]
__lowercase = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 53
|
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Any = IFInpaintingPipeline
__snake_case :str = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
__snake_case :Optional[int] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__snake_case :str = PipelineTesterMixin.required_optional_params - {'latents'}
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
return self._get_dummy_components()
def _a ( self : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict=0 ) -> Any:
"""simple docstring"""
if str(_lowerCAmelCase ).startswith("""mps""" ):
__lowercase = torch.manual_seed(_lowerCAmelCase )
else:
__lowercase = torch.Generator(device=_lowerCAmelCase ).manual_seed(_lowerCAmelCase )
__lowercase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_lowerCAmelCase ) ).to(_lowerCAmelCase )
__lowercase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_lowerCAmelCase ) ).to(_lowerCAmelCase )
__lowercase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""mask_image""": mask_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def _a ( self : Tuple ) -> Dict:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def _a ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""" )
def _a ( self : List[str] ) -> Tuple:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1e-1 )
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def _a ( self : str ) -> Optional[int]:
"""simple docstring"""
self._test_save_load_local()
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 53
| 1
|
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
class __UpperCamelCase ( _lowerCAmelCase ):
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
__lowercase = tempfile.mkdtemp()
__lowercase = 5
# Realm tok
__lowercase = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""test""",
"""question""",
"""this""",
"""is""",
"""the""",
"""first""",
"""second""",
"""third""",
"""fourth""",
"""fifth""",
"""record""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
__lowercase = os.path.join(self.tmpdirname , """realm_tokenizer""" )
os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase )
__lowercase = os.path.join(_lowerCAmelCase , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
__lowercase = os.path.join(self.tmpdirname , """realm_block_records""" )
os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase )
def _a ( self : Dict ) -> RealmTokenizer:
"""simple docstring"""
return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname , """realm_tokenizer""" ) )
def _a ( self : int ) -> List[str]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self : Tuple ) -> List[str]:
"""simple docstring"""
__lowercase = RealmConfig(num_block_records=self.num_block_records )
return config
def _a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = Dataset.from_dict(
{
"""id""": ["""0""", """1"""],
"""question""": ["""foo""", """bar"""],
"""answers""": [["""Foo""", """Bar"""], ["""Bar"""]],
} )
return dataset
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
__lowercase = np.array(
[
B"""This is the first record""",
B"""This is the second record""",
B"""This is the third record""",
B"""This is the fourth record""",
B"""This is the fifth record""",
B"""This is a longer longer longer record""",
] , dtype=_lowerCAmelCase , )
return block_records
def _a ( self : Tuple ) -> str:
"""simple docstring"""
__lowercase = RealmRetriever(
block_records=self.get_dummy_block_records() , tokenizer=self.get_tokenizer() , )
return retriever
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.get_config()
__lowercase = self.get_dummy_retriever()
__lowercase = retriever.tokenizer
__lowercase = np.array([0, 3] , dtype="""long""" )
__lowercase = tokenizer(["""Test question"""] ).input_ids
__lowercase = tokenizer(
["""the fourth"""] , add_special_tokens=_lowerCAmelCase , return_token_type_ids=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , ).input_ids
__lowercase = config.reader_seq_len
__lowercase , __lowercase , __lowercase , __lowercase = retriever(
_lowerCAmelCase , _lowerCAmelCase , answer_ids=_lowerCAmelCase , max_length=_lowerCAmelCase , return_tensors="""np""" )
self.assertEqual(len(_lowerCAmelCase ) , 2 )
self.assertEqual(len(_lowerCAmelCase ) , 2 )
self.assertEqual(len(_lowerCAmelCase ) , 2 )
self.assertEqual(concat_inputs.input_ids.shape , (2, 10) )
self.assertEqual(concat_inputs.attention_mask.shape , (2, 10) )
self.assertEqual(concat_inputs.token_type_ids.shape , (2, 10) )
self.assertEqual(concat_inputs.special_tokens_mask.shape , (2, 10) )
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] ) , ["""[CLS]""", """test""", """question""", """[SEP]""", """this""", """is""", """the""", """first""", """record""", """[SEP]"""] , )
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] ) , ["""[CLS]""", """test""", """question""", """[SEP]""", """this""", """is""", """the""", """fourth""", """record""", """[SEP]"""] , )
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
__lowercase = self.get_config()
__lowercase = self.get_dummy_retriever()
__lowercase = retriever.tokenizer
__lowercase = np.array([0, 3, 5] , dtype="""long""" )
__lowercase = tokenizer(["""Test question"""] ).input_ids
__lowercase = tokenizer(
["""the fourth""", """longer longer"""] , add_special_tokens=_lowerCAmelCase , return_token_type_ids=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , ).input_ids
__lowercase = config.reader_seq_len
__lowercase , __lowercase , __lowercase , __lowercase = retriever(
_lowerCAmelCase , _lowerCAmelCase , answer_ids=_lowerCAmelCase , max_length=_lowerCAmelCase , return_tensors="""np""" )
self.assertEqual([False, True, True] , _lowerCAmelCase )
self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]] , _lowerCAmelCase )
self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]] , _lowerCAmelCase )
def _a ( self : int ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.get_dummy_retriever()
retriever.save_pretrained(os.path.join(self.tmpdirname , """realm_block_records""" ) )
# Test local path
__lowercase = retriever.from_pretrained(os.path.join(self.tmpdirname , """realm_block_records""" ) )
self.assertEqual(retriever.block_records[0] , B"""This is the first record""" )
# Test mocked remote path
with patch("""transformers.models.realm.retrieval_realm.hf_hub_download""" ) as mock_hf_hub_download:
__lowercase = os.path.join(
os.path.join(self.tmpdirname , """realm_block_records""" ) , _REALM_BLOCK_RECORDS_FILENAME )
__lowercase = RealmRetriever.from_pretrained("""google/realm-cc-news-pretrained-openqa""" )
self.assertEqual(retriever.block_records[0] , B"""This is the first record""" )
| 53
|
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :str = (UnCLIPScheduler,)
def _a ( self : Optional[int] , **_lowerCAmelCase : Any ) -> Tuple:
"""simple docstring"""
__lowercase = {
"""num_train_timesteps""": 1000,
"""variance_type""": """fixed_small_log""",
"""clip_sample""": True,
"""clip_sample_range""": 1.0,
"""prediction_type""": """epsilon""",
}
config.update(**_lowerCAmelCase )
return config
def _a ( self : Dict ) -> List[Any]:
"""simple docstring"""
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=_lowerCAmelCase )
def _a ( self : List[str] ) -> Tuple:
"""simple docstring"""
for variance in ["fixed_small_log", "learned_range"]:
self.check_over_configs(variance_type=_lowerCAmelCase )
def _a ( self : Any ) -> Any:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_lowerCAmelCase )
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
for clip_sample_range in [1, 5, 10, 20]:
self.check_over_configs(clip_sample_range=_lowerCAmelCase )
def _a ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(prediction_type=_lowerCAmelCase )
def _a ( self : str ) -> int:
"""simple docstring"""
for time_step in [0, 500, 999]:
for prev_timestep in [None, 5, 100, 250, 500, 750]:
if prev_timestep is not None and prev_timestep >= time_step:
continue
self.check_over_forward(time_step=_lowerCAmelCase , prev_timestep=_lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config(variance_type="""fixed_small_log""" )
__lowercase = scheduler_class(**_lowerCAmelCase )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.0000e-10 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_549_625 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.9_994_987 ) ) < 1e-5
def _a ( self : str ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config(variance_type="""learned_range""" )
__lowercase = scheduler_class(**_lowerCAmelCase )
__lowercase = 0.5
assert scheduler._get_variance(1 , predicted_variance=_lowerCAmelCase ) - -10.1_712_790 < 1e-5
assert scheduler._get_variance(487 , predicted_variance=_lowerCAmelCase ) - -5.7_998_052 < 1e-5
assert scheduler._get_variance(999 , predicted_variance=_lowerCAmelCase ) - -0.0_010_011 < 1e-5
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config()
__lowercase = scheduler_class(**_lowerCAmelCase )
__lowercase = scheduler.timesteps
__lowercase = self.dummy_model()
__lowercase = self.dummy_sample_deter
__lowercase = torch.manual_seed(0 )
for i, t in enumerate(_lowerCAmelCase ):
# 1. predict noise residual
__lowercase = model(_lowerCAmelCase , _lowerCAmelCase )
# 2. predict previous mean of sample x_t-1
__lowercase = scheduler.step(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , generator=_lowerCAmelCase ).prev_sample
__lowercase = pred_prev_sample
__lowercase = torch.sum(torch.abs(_lowerCAmelCase ) )
__lowercase = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 252.2_682_495 ) < 1e-2
assert abs(result_mean.item() - 0.3_284_743 ) < 1e-3
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__lowercase = self.scheduler_classes[0]
__lowercase = self.get_scheduler_config()
__lowercase = scheduler_class(**_lowerCAmelCase )
scheduler.set_timesteps(25 )
__lowercase = scheduler.timesteps
__lowercase = self.dummy_model()
__lowercase = self.dummy_sample_deter
__lowercase = torch.manual_seed(0 )
for i, t in enumerate(_lowerCAmelCase ):
# 1. predict noise residual
__lowercase = model(_lowerCAmelCase , _lowerCAmelCase )
if i + 1 == timesteps.shape[0]:
__lowercase = None
else:
__lowercase = timesteps[i + 1]
# 2. predict previous mean of sample x_t-1
__lowercase = scheduler.step(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , prev_timestep=_lowerCAmelCase , generator=_lowerCAmelCase ).prev_sample
__lowercase = pred_prev_sample
__lowercase = torch.sum(torch.abs(_lowerCAmelCase ) )
__lowercase = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 258.2_044_983 ) < 1e-2
assert abs(result_mean.item() - 0.3_362_038 ) < 1e-3
def _a ( self : str ) -> Union[str, Any]:
"""simple docstring"""
pass
def _a ( self : int ) -> List[str]:
"""simple docstring"""
pass
| 53
| 1
|
__UpperCamelCase : dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.6_0_9_3_4_4,
"knot": 1.8_5_2,
}
__UpperCamelCase : dict[str, float] = {
"km/h": 1.0,
"m/s": 0.2_7_7_7_7_7_7_7_8,
"mph": 0.6_2_1_3_7_1_1_9_2,
"knot": 0.5_3_9_9_5_6_8_0_3,
}
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
__lowercase = (
F'Incorrect \'from_type\' or \'to_type\' value: {unit_from!r}, {unit_to!r}\n'
F'Valid values are: {", ".join(lowerCamelCase )}'
)
raise ValueError(lowerCamelCase )
return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to] , 3 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 53
|
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
__UpperCamelCase : Any = logging.get_logger(__name__)
@dataclass
class __UpperCamelCase :
__snake_case :str = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys() )} )
__snake_case :str = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
__snake_case :int = field(
default=1_2_8 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def _a ( self : Dict ) -> List[Any]:
"""simple docstring"""
__lowercase = self.task_name.lower()
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Optional[int] = 'train'
__snake_case :int = 'dev'
__snake_case :Any = 'test'
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :GlueDataTrainingArguments
__snake_case :str
__snake_case :List[InputFeatures]
def __init__( self : Dict , _lowerCAmelCase : GlueDataTrainingArguments , _lowerCAmelCase : PreTrainedTokenizerBase , _lowerCAmelCase : Optional[int] = None , _lowerCAmelCase : Union[str, Split] = Split.train , _lowerCAmelCase : Optional[str] = None , ) -> List[Any]:
"""simple docstring"""
warnings.warn(
"""This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
"""library. You can have a look at this example script for pointers: """
"""https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py""" , _lowerCAmelCase , )
__lowercase = args
__lowercase = glue_processors[args.task_name]()
__lowercase = glue_output_modes[args.task_name]
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
try:
__lowercase = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
# Load data features from cache or dataset file
__lowercase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}' , )
__lowercase = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
__lowercase , __lowercase = label_list[2], label_list[1]
__lowercase = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowercase = cached_features_file + """.lock"""
with FileLock(_lowerCAmelCase ):
if os.path.exists(_lowerCAmelCase ) and not args.overwrite_cache:
__lowercase = time.time()
__lowercase = torch.load(_lowerCAmelCase )
logger.info(
F'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start )
else:
logger.info(F'Creating features from dataset file at {args.data_dir}' )
if mode == Split.dev:
__lowercase = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
__lowercase = self.processor.get_test_examples(args.data_dir )
else:
__lowercase = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
__lowercase = examples[:limit_length]
__lowercase = glue_convert_examples_to_features(
_lowerCAmelCase , _lowerCAmelCase , max_length=args.max_seq_length , label_list=_lowerCAmelCase , output_mode=self.output_mode , )
__lowercase = time.time()
torch.save(self.features , _lowerCAmelCase )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self : Dict ) -> Optional[int]:
"""simple docstring"""
return len(self.features )
def __getitem__( self : Tuple , _lowerCAmelCase : Optional[int] ) -> InputFeatures:
"""simple docstring"""
return self.features[i]
def _a ( self : str ) -> int:
"""simple docstring"""
return self.label_list
| 53
| 1
|
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
)
from transformers.models.efficientformer.modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_vision_available():
from PIL import Image
from transformers import EfficientFormerImageProcessor
class __UpperCamelCase :
def __init__( self : int , _lowerCAmelCase : Tuple , _lowerCAmelCase : int = 13 , _lowerCAmelCase : int = 64 , _lowerCAmelCase : int = 2 , _lowerCAmelCase : int = 3 , _lowerCAmelCase : int = 3 , _lowerCAmelCase : bool = True , _lowerCAmelCase : bool = True , _lowerCAmelCase : int = 128 , _lowerCAmelCase : Optional[int]=[16, 32, 64, 128] , _lowerCAmelCase : int = 7 , _lowerCAmelCase : int = 4 , _lowerCAmelCase : int = 37 , _lowerCAmelCase : str = "gelu" , _lowerCAmelCase : float = 0.1 , _lowerCAmelCase : float = 0.1 , _lowerCAmelCase : int = 10 , _lowerCAmelCase : float = 0.02 , _lowerCAmelCase : int = 2 , _lowerCAmelCase : int = 1 , _lowerCAmelCase : int = 128 , _lowerCAmelCase : List[int] = [2, 2, 2, 2] , _lowerCAmelCase : int = 2 , _lowerCAmelCase : int = 2 , ) -> Tuple:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = is_training
__lowercase = use_labels
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = encoder_stride
__lowercase = num_attention_outputs
__lowercase = embed_dim
__lowercase = embed_dim + 1
__lowercase = resolution
__lowercase = depths
__lowercase = hidden_sizes
__lowercase = dim
__lowercase = mlp_expansion_ratio
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
return EfficientFormerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_lowerCAmelCase , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , resolution=self.resolution , depths=self.depths , hidden_sizes=self.hidden_sizes , dim=self.dim , mlp_expansion_ratio=self.mlp_expansion_ratio , )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = TFEfficientFormerModel(config=_lowerCAmelCase )
__lowercase = model(_lowerCAmelCase , training=_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.type_sequence_label_size
__lowercase = TFEfficientFormerForImageClassification(_lowerCAmelCase )
__lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase , training=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__lowercase = 1
__lowercase = TFEfficientFormerForImageClassification(_lowerCAmelCase )
__lowercase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Any = (
(
TFEfficientFormerModel,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerForImageClassification,
)
if is_tf_available()
else ()
)
__snake_case :Any = (
{
'feature-extraction': TFEfficientFormerModel,
'image-classification': (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
),
}
if is_tf_available()
else {}
)
__snake_case :int = False
__snake_case :Optional[int] = False
__snake_case :int = False
__snake_case :Any = False
__snake_case :Any = False
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
__lowercase = TFEfficientFormerModelTester(self )
__lowercase = ConfigTester(
self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 )
def _a ( self : Optional[int] ) -> int:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""EfficientFormer does not use inputs_embeds""" )
def _a ( self : Optional[int] ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""EfficientFormer does not support input and output embeddings""" )
def _a ( self : int ) -> str:
"""simple docstring"""
pass
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
def check_hidden_states_output(_lowerCAmelCase : int , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] ):
__lowercase = model_class(_lowerCAmelCase )
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) , training=_lowerCAmelCase )
__lowercase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowercase = getattr(
self.model_tester , """expected_num_hidden_layers""" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
if hasattr(self.model_tester , """encoder_seq_length""" ):
__lowercase = self.model_tester.encoder_seq_length
if hasattr(self.model_tester , """chunk_length""" ) and self.model_tester.chunk_length > 1:
__lowercase = seq_length * self.model_tester.chunk_length
else:
__lowercase = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
if config.is_encoder_decoder:
__lowercase = outputs.decoder_hidden_states
self.asseretIsInstance(_lowerCAmelCase , (list, tuple) )
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """seq_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """decoder_seq_length""" , _lowerCAmelCase )
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [decoder_seq_length, self.model_tester.hidden_size] , )
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Optional[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any]=False ) -> Dict:
"""simple docstring"""
__lowercase = super()._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
if return_labels:
if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def _a ( self : int ) -> int:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet""" )
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase )
def _a ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@slow
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = TFEfficientFormerModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = True
__lowercase = getattr(self.model_tester , """seq_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """encoder_seq_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """key_length""" , _lowerCAmelCase )
__lowercase = getattr(self.model_tester , """chunk_length""" , _lowerCAmelCase )
if chunk_length is not None and hasattr(self.model_tester , """num_hashes""" ):
__lowercase = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
__lowercase = True
__lowercase = False
__lowercase = True
__lowercase = model_class(_lowerCAmelCase )
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) , training=_lowerCAmelCase )
__lowercase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_attention_outputs )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__lowercase = True
__lowercase = model_class(_lowerCAmelCase )
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) , training=_lowerCAmelCase )
__lowercase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(_lowerCAmelCase ) , self.model_tester.num_attention_outputs )
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length] , )
else:
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length] , )
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Prepare our model
__lowercase = model_class(_lowerCAmelCase )
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
__lowercase = {
key: tf.keras.Input(shape=val.shape[1:] , dtype=val.dtype , name=_lowerCAmelCase )
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
__lowercase = model(_lowerCAmelCase )
self.assertTrue(outputs_dict is not None )
def snake_case ( ):
'''simple docstring'''
__lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
return (
EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300""" )
if is_vision_available()
else None
)
@slow
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__lowercase = TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300""" )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""tf""" )
# forward pass
__lowercase = model(**_lowerCAmelCase , training=_lowerCAmelCase )
# verify the logits
__lowercase = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
__lowercase = tf.constant([-0.0_555, 0.4_825, -0.0_852] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
@slow
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__lowercase = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
"""snap-research/efficientformer-l1-300""" )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""tf""" )
# forward pass
__lowercase = model(**_lowerCAmelCase , training=_lowerCAmelCase )
# verify the logits
__lowercase = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
__lowercase = tf.constant([-0.1_312, 0.4_353, -1.0_499] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
| 53
|
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoTokenizer,
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForWholeWordMask,
HfArgumentParser,
LineByLineTextDataset,
LineByLineWithRefDataset,
PreTrainedTokenizer,
TextDataset,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
__UpperCamelCase : List[Any] = logging.getLogger(__name__)
__UpperCamelCase : Optional[Any] = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
__UpperCamelCase : List[str] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class __UpperCamelCase :
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={
'help': (
'The model checkpoint for weights initialization. Leave None if you want to train a model from'
' scratch.'
)
} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_lowerCAmelCase )} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class __UpperCamelCase :
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'The input training data file (a text file).'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={
'help': (
'The input training data files (multiple files in glob format). '
'Very often splitting large files to smaller files can prevent tokenizer going out of memory'
)
} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'An optional input train ref data file for whole word mask in Chinese.'} , )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'An optional input eval ref data file for whole word mask in Chinese.'} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Whether distinct lines of text in the dataset are to be handled as distinct sequences.'} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Train with masked-language modeling loss instead of language modeling.'} )
__snake_case :bool = field(default=_lowerCAmelCase , metadata={'help': 'Whether ot not to use whole word mask.'} )
__snake_case :float = field(
default=0.15 , metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'} )
__snake_case :float = field(
default=1 / 6 , metadata={
'help': (
'Ratio of length of a span of masked tokens to surrounding context length for permutation language'
' modeling.'
)
} , )
__snake_case :int = field(
default=5 , metadata={'help': 'Maximum length of a span of masked tokens for permutation language modeling.'} )
__snake_case :int = field(
default=-1 , metadata={
'help': (
'Optional input sequence length after tokenization.'
'The training dataset will be truncated in block of this size for training.'
'Default to the model max input length for single sentence inputs (take into account special tokens).'
)
} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase = False , lowerCamelCase = None , ):
'''simple docstring'''
def _dataset(lowerCamelCase , lowerCamelCase=None ):
if args.line_by_line:
if ref_path is not None:
if not args.whole_word_mask or not args.mlm:
raise ValueError("""You need to set world whole masking and mlm to True for Chinese Whole Word Mask""" )
return LineByLineWithRefDataset(
tokenizer=lowerCamelCase , file_path=lowerCamelCase , block_size=args.block_size , ref_path=lowerCamelCase , )
return LineByLineTextDataset(tokenizer=lowerCamelCase , file_path=lowerCamelCase , block_size=args.block_size )
else:
return TextDataset(
tokenizer=lowerCamelCase , file_path=lowerCamelCase , block_size=args.block_size , overwrite_cache=args.overwrite_cache , cache_dir=lowerCamelCase , )
if evaluate:
return _dataset(args.eval_data_file , args.eval_ref_file )
elif args.train_data_files:
return ConcatDataset([_dataset(lowerCamelCase ) for f in glob(args.train_data_files )] )
else:
return _dataset(args.train_data_file , args.train_ref_file )
def snake_case ( ):
'''simple docstring'''
__lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
__lowercase , __lowercase , __lowercase = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_args.do_eval:
raise ValueError(
"""Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file """
"""or remove the --do_eval argument.""" )
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
""" --overwrite_output_dir to overcome.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , lowerCamelCase )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if model_args.config_name:
__lowercase = AutoConfig.from_pretrained(model_args.config_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
__lowercase = AutoConfig.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
__lowercase = CONFIG_MAPPING[model_args.model_type]()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.tokenizer_name:
__lowercase = AutoTokenizer.from_pretrained(model_args.tokenizer_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
__lowercase = AutoTokenizer.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
raise ValueError(
"""You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"""
""" script, save it,and load it from here, using --tokenizer_name""" )
if model_args.model_name_or_path:
__lowercase = AutoModelWithLMHead.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 , )
else:
logger.info("""Training new model from scratch""" )
__lowercase = AutoModelWithLMHead.from_config(lowerCamelCase )
model.resize_token_embeddings(len(lowerCamelCase ) )
if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
raise ValueError(
"""BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"""
"""--mlm flag (masked language modeling).""" )
if data_args.block_size <= 0:
__lowercase = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
__lowercase = min(data_args.block_size , tokenizer.max_len )
# Get datasets
__lowercase = (
get_dataset(lowerCamelCase , tokenizer=lowerCamelCase , cache_dir=model_args.cache_dir ) if training_args.do_train else None
)
__lowercase = (
get_dataset(lowerCamelCase , tokenizer=lowerCamelCase , evaluate=lowerCamelCase , cache_dir=model_args.cache_dir )
if training_args.do_eval
else None
)
if config.model_type == "xlnet":
__lowercase = DataCollatorForPermutationLanguageModeling(
tokenizer=lowerCamelCase , plm_probability=data_args.plm_probability , max_span_length=data_args.max_span_length , )
else:
if data_args.mlm and data_args.whole_word_mask:
__lowercase = DataCollatorForWholeWordMask(
tokenizer=lowerCamelCase , mlm_probability=data_args.mlm_probability )
else:
__lowercase = DataCollatorForLanguageModeling(
tokenizer=lowerCamelCase , mlm=data_args.mlm , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
__lowercase = Trainer(
model=lowerCamelCase , args=lowerCamelCase , data_collator=lowerCamelCase , train_dataset=lowerCamelCase , eval_dataset=lowerCamelCase , prediction_loss_only=lowerCamelCase , )
# Training
if training_args.do_train:
__lowercase = (
model_args.model_name_or_path
if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
else None
)
trainer.train(model_path=lowerCamelCase )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
__lowercase = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__lowercase = trainer.evaluate()
__lowercase = math.exp(eval_output["""eval_loss"""] )
__lowercase = {"""perplexity""": perplexity}
__lowercase = os.path.join(training_args.output_dir , """eval_results_lm.txt""" )
if trainer.is_world_master():
with open(lowerCamelCase , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key in sorted(result.keys() ):
logger.info(""" %s = %s""" , lowerCamelCase , str(result[key] ) )
writer.write("""%s = %s\n""" % (key, str(result[key] )) )
results.update(lowerCamelCase )
return results
def snake_case ( lowerCamelCase ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 53
| 1
|
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not isinstance(lowerCamelCase , lowerCamelCase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
__lowercase = str(lowerCamelCase )
__lowercase = """""".join(sorted(lowerCamelCase ) )
return sorted_str_n != str_n and sorted_str_n[::-1] != str_n
def snake_case ( lowerCamelCase = 99 ):
'''simple docstring'''
if not 0 < percent < 100:
raise ValueError("""solution() only accepts values from 0 to 100""" )
__lowercase = 0
__lowercase = 1
while True:
if check_bouncy(lowerCamelCase ):
bouncy_num += 1
if (bouncy_num / num) * 100 >= percent:
return num
num += 1
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F'''{solution(99)}''')
| 53
|
from __future__ import annotations
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if len(lowerCamelCase ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
if any(i <= 0 for i in nums ):
raise ValueError("""All values must be greater than 0""" )
__lowercase = nums.copy()
copy_nums.sort()
return copy_nums[-1] < sum(copy_nums[:-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 53
| 1
|
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = len(lowerCamelCase )
__lowercase = sum(lowerCamelCase )
__lowercase = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
__lowercase = True
for i in range(1 , s + 1 ):
__lowercase = False
for i in range(1 , n + 1 ):
for j in range(1 , s + 1 ):
__lowercase = dp[i][j - 1]
if arr[i - 1] <= j:
__lowercase = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2 ) , -1 , -1 ):
if dp[n][j] is True:
__lowercase = s - 2 * j
break
return diff
| 53
|
from __future__ import annotations
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not nums:
return 0
__lowercase = nums[0]
__lowercase = 0
for num in nums[1:]:
__lowercase , __lowercase = (
max_excluding + num,
max(lowerCamelCase , lowerCamelCase ),
)
return max(lowerCamelCase , lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 53
| 1
|
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