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"""simple docstring"""
from __future__ import annotations
import numpy as np
def _A ( lowercase ):
"""simple docstring"""
a , a =np.shape(lowercase )
if rows != columns:
a =(
'''\'table\' has to be of square shaped array but got a '''
f'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(lowercase )
a =np.zeros((rows, columns) )
a =np.zeros((rows, columns) )
for i in range(lowercase ):
for j in range(lowercase ):
a =sum(lower[i][k] * upper[k][j] for k in range(lowercase ) )
if upper[j][j] == 0:
raise ArithmeticError('''No LU decomposition exists''' )
a =(table[i][j] - total) / upper[j][j]
a =1
for j in range(lowercase , lowercase ):
a =sum(lower[i][k] * upper[k][j] for k in range(lowercase ) )
a =table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 81
|
import argparse
import os
import re
__a = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__a = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
__a = re.compile(r'\s*\(\s*"(\S[^"]+)"')
def a ( snake_case__: str , snake_case__: bool = False ):
'''simple docstring'''
with open(snake_case__ , '''r''' , encoding='''utf-8''' ) as f:
lowercase_ = f.read()
lowercase_ = content.split('''\n''' )
lowercase_ = []
lowercase_ = 0
while line_idx < len(snake_case__ ):
if _re_intro_mapping.search(lines[line_idx] ) is not None:
lowercase_ = len(re.search(r'''^(\s*)\S''' , lines[line_idx] ).groups()[0] ) + 8
# Start of a new mapping!
while not lines[line_idx].startswith(''' ''' * indent + '''(''' ):
new_lines.append(lines[line_idx] )
line_idx += 1
lowercase_ = []
while lines[line_idx].strip() != "]":
# Blocks either fit in one line or not
if lines[line_idx].strip() == "(":
lowercase_ = line_idx
while not lines[line_idx].startswith(''' ''' * indent + ''')''' ):
line_idx += 1
blocks.append('''\n'''.join(lines[start_idx : line_idx + 1] ) )
else:
blocks.append(lines[line_idx] )
line_idx += 1
# Sort blocks by their identifiers
lowercase_ = sorted(snake_case__ , key=lambda snake_case__ : _re_identifier.search(snake_case__ ).groups()[0] )
new_lines += blocks
else:
new_lines.append(lines[line_idx] )
line_idx += 1
if overwrite:
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as f:
f.write('''\n'''.join(snake_case__ ) )
elif "\n".join(snake_case__ ) != content:
return True
def a ( snake_case__: bool = False ):
'''simple docstring'''
lowercase_ = [os.path.join(snake_case__ , snake_case__ ) for f in os.listdir(snake_case__ ) if f.endswith('''.py''' )]
lowercase_ = [sort_auto_mapping(snake_case__ , overwrite=snake_case__ ) for fname in fnames]
if not overwrite and any(snake_case__ ):
lowercase_ = [f for f, d in zip(snake_case__ , snake_case__ ) if d]
raise ValueError(
F'''The following files have auto mappings that need sorting: {', '.join(snake_case__ )}. Run `make style` to fix'''
''' this.''' )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
__a = parser.parse_args()
sort_all_auto_mappings(not args.check_only)
| 30
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|
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = [
"""FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FocalNetForImageClassification""",
"""FocalNetForMaskedImageModeling""",
"""FocalNetBackbone""",
"""FocalNetModel""",
"""FocalNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
A__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 82
|
def a ( snake_case__: list , snake_case__: list , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if index == number_of_items:
return 0
lowercase_ = 0
lowercase_ = 0
lowercase_ = knapsack(snake_case__ , snake_case__ , snake_case__ , snake_case__ , index + 1 )
if weights[index] <= max_weight:
lowercase_ = values[index] + knapsack(
snake_case__ , snake_case__ , snake_case__ , max_weight - weights[index] , index + 1 )
return max(snake_case__ , snake_case__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 30
| 0
|
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
snake_case_ : List[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
snake_case_ : List[str] = [0, 25, 50]
snake_case_ : Optional[int] = [25, 50, 75]
snake_case_ : List[Any] = fuzz.membership.trimf(X, abca)
snake_case_ : Optional[int] = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
snake_case_ : List[Any] = np.ones(75)
snake_case_ : Optional[Any] = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
snake_case_ : Dict = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
snake_case_ : str = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
snake_case_ : Any = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
snake_case_ : List[str] = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
snake_case_ : Union[str, Any] = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
snake_case_ : Dict = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
snake_case_ : Optional[int] = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
snake_case_ : Optional[int] = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title('Young')
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title('Middle aged')
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title('union')
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title('intersection')
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title('complement_a')
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title('difference a/b')
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title('alg_sum')
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title('alg_product')
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title('bdd_sum')
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title('bdd_difference')
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 83
|
import argparse
from collections import defaultdict
import yaml
__a = 'docs/source/en/_toctree.yml'
def a ( snake_case__: Dict ):
'''simple docstring'''
lowercase_ = defaultdict(snake_case__ )
for doc in model_doc:
counts[doc["local"]] += 1
lowercase_ = [key for key, value in counts.items() if value > 1]
lowercase_ = []
for duplicate_key in duplicates:
lowercase_ = list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key} )
if len(snake_case__ ) > 1:
raise ValueError(
F'''{duplicate_key} is present several times in the documentation table of content at '''
'''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
'''others.''' )
# Only add this once
new_doc.append({'''local''': duplicate_key, '''title''': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1] )
# Sort
return sorted(snake_case__ , key=lambda snake_case__ : s["title"].lower() )
def a ( snake_case__: List[Any]=False ):
'''simple docstring'''
with open(snake_case__ , encoding='''utf-8''' ) as f:
lowercase_ = yaml.safe_load(f.read() )
# Get to the API doc
lowercase_ = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase_ = content[api_idx]['''sections''']
# Then to the model doc
lowercase_ = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
lowercase_ = api_doc[model_idx]['''sections''']
lowercase_ = [(idx, section) for idx, section in enumerate(snake_case__ ) if '''sections''' in section]
lowercase_ = False
for idx, modality_doc in modalities_docs:
lowercase_ = modality_doc['''sections''']
lowercase_ = clean_model_doc_toc(snake_case__ )
if old_modality_doc != new_modality_doc:
lowercase_ = True
if overwrite:
lowercase_ = new_modality_doc
if diff:
if overwrite:
lowercase_ = model_doc
lowercase_ = api_doc
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(snake_case__ , allow_unicode=snake_case__ ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
__a = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 30
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"""simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case ( lowercase__ : Dict , lowercase__ : Dict , lowercase__ : str , lowercase__ : Tuple="attention" ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Tuple = params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
lowerCAmelCase_ :Union[str, Any] = params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
lowerCAmelCase_ :Any = params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
lowerCAmelCase_ :Optional[int] = params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
return k, o, q, v
def _snake_case ( lowercase__ : Optional[Any] , lowercase__ : Any , lowercase__ : int , lowercase__ : Any=False ) -> int:
'''simple docstring'''
if split_mlp_wi:
lowerCAmelCase_ :Tuple = params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
lowerCAmelCase_ :List[str] = params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
lowerCAmelCase_ :Tuple = (wi_a, wi_a)
else:
lowerCAmelCase_ :List[Any] = params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]
lowerCAmelCase_ :Dict = params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
return wi, wo
def _snake_case ( lowercase__ : Any , lowercase__ : Dict , lowercase__ : Union[str, Any] , lowercase__ : Optional[int] ) -> Tuple:
'''simple docstring'''
return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]
def _snake_case ( lowercase__ : dict , *, lowercase__ : int , lowercase__ : bool ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = traverse_util.flatten_dict(variables["""target"""] )
lowerCAmelCase_ :Tuple = {"""/""".join(lowercase__ ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
lowerCAmelCase_ :Any = """encoder/layers_0/mlp/wi_0/kernel""" in old
print("""Split MLP:""" , lowercase__ )
lowerCAmelCase_ :List[Any] = collections.OrderedDict()
# Shared embeddings.
lowerCAmelCase_ :Optional[int] = old["""token_embedder/embedding"""]
# Encoder.
for i in range(lowercase__ ):
# Block i, layer 0 (Self Attention).
lowerCAmelCase_ :int = tax_layer_norm_lookup(lowercase__ , lowercase__ , """encoder""" , """pre_attention_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :str = tax_attention_lookup(lowercase__ , lowercase__ , """encoder""" , """attention""" )
lowerCAmelCase_ :Optional[Any] = layer_norm
lowerCAmelCase_ :Any = k.T
lowerCAmelCase_ :Tuple = o.T
lowerCAmelCase_ :Tuple = q.T
lowerCAmelCase_ :str = v.T
# Block i, layer 1 (MLP).
lowerCAmelCase_ :Dict = tax_layer_norm_lookup(lowercase__ , lowercase__ , """encoder""" , """pre_mlp_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ :Any = tax_mlp_lookup(lowercase__ , lowercase__ , """encoder""" , lowercase__ )
lowerCAmelCase_ :Union[str, Any] = layer_norm
if split_mlp_wi:
lowerCAmelCase_ :List[Any] = wi[0].T
lowerCAmelCase_ :Dict = wi[1].T
else:
lowerCAmelCase_ :int = wi.T
lowerCAmelCase_ :List[str] = wo.T
lowerCAmelCase_ :Tuple = old[
"""encoder/relpos_bias/rel_embedding"""
].T
lowerCAmelCase_ :List[str] = old["""encoder/encoder_norm/scale"""]
if not is_encoder_only:
# Decoder.
for i in range(lowercase__ ):
# Block i, layer 0 (Self Attention).
lowerCAmelCase_ :Optional[Any] = tax_layer_norm_lookup(lowercase__ , lowercase__ , """decoder""" , """pre_self_attention_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = tax_attention_lookup(lowercase__ , lowercase__ , """decoder""" , """self_attention""" )
lowerCAmelCase_ :List[Any] = layer_norm
lowerCAmelCase_ :List[str] = k.T
lowerCAmelCase_ :Any = o.T
lowerCAmelCase_ :Any = q.T
lowerCAmelCase_ :Dict = v.T
# Block i, layer 1 (Cross Attention).
lowerCAmelCase_ :int = tax_layer_norm_lookup(lowercase__ , lowercase__ , """decoder""" , """pre_cross_attention_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :Tuple = tax_attention_lookup(lowercase__ , lowercase__ , """decoder""" , """encoder_decoder_attention""" )
lowerCAmelCase_ :Optional[int] = layer_norm
lowerCAmelCase_ :str = k.T
lowerCAmelCase_ :Tuple = o.T
lowerCAmelCase_ :Any = q.T
lowerCAmelCase_ :int = v.T
# Block i, layer 2 (MLP).
lowerCAmelCase_ :Any = tax_layer_norm_lookup(lowercase__ , lowercase__ , """decoder""" , """pre_mlp_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ :Dict = tax_mlp_lookup(lowercase__ , lowercase__ , """decoder""" , lowercase__ )
lowerCAmelCase_ :List[Any] = layer_norm
if split_mlp_wi:
lowerCAmelCase_ :Any = wi[0].T
lowerCAmelCase_ :Any = wi[1].T
else:
lowerCAmelCase_ :Tuple = wi.T
lowerCAmelCase_ :List[str] = wo.T
lowerCAmelCase_ :Optional[Any] = old["""decoder/decoder_norm/scale"""]
lowerCAmelCase_ :Optional[Any] = old[
"""decoder/relpos_bias/rel_embedding"""
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
lowerCAmelCase_ :Tuple = old["""decoder/logits_dense/kernel"""].T
return new
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : bool ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
lowerCAmelCase_ :Optional[int] = state_dict["""shared.weight"""]
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
lowerCAmelCase_ :Tuple = state_dict["""shared.weight"""]
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print("""Using shared word embeddings as lm_head.""" )
lowerCAmelCase_ :Any = state_dict["""shared.weight"""]
return state_dict
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : str , lowercase__ : List[Any] , lowercase__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = checkpoints.load_tax_checkpoint(lowercase__ )
lowerCAmelCase_ :Optional[int] = convert_tax_to_pytorch(lowercase__ , num_layers=config.num_layers , is_encoder_only=lowercase__ )
lowerCAmelCase_ :Union[str, Any] = make_state_dict(lowercase__ , lowercase__ )
model.load_state_dict(lowercase__ , strict=lowercase__ )
def _snake_case ( lowercase__ : List[Any] , lowercase__ : Optional[Any] , lowercase__ : str , lowercase__ : bool = False ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Any = TaConfig.from_json_file(lowercase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
lowerCAmelCase_ :List[Any] = TaEncoderModel(lowercase__ )
else:
lowerCAmelCase_ :List[str] = TaForConditionalGeneration(lowercase__ )
# Load weights from tf checkpoint
load_tax_weights_in_ta(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(lowercase__ )
# Verify that we can load the checkpoint.
model.from_pretrained(lowercase__ )
print("""Done""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
# Required parameters
parser.add_argument(
'--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
)
__UpperCAmelCase = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 84
|
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Union[str, Any] = 'upernet'
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : Dict=None , SCREAMING_SNAKE_CASE_ : str=5_1_2 , SCREAMING_SNAKE_CASE_ : Tuple=0.02 , SCREAMING_SNAKE_CASE_ : Optional[Any]=[1, 2, 3, 6] , SCREAMING_SNAKE_CASE_ : Optional[int]=True , SCREAMING_SNAKE_CASE_ : Tuple=0.4 , SCREAMING_SNAKE_CASE_ : Optional[int]=3_8_4 , SCREAMING_SNAKE_CASE_ : str=2_5_6 , SCREAMING_SNAKE_CASE_ : Dict=1 , SCREAMING_SNAKE_CASE_ : Tuple=False , SCREAMING_SNAKE_CASE_ : str=2_5_5 , **SCREAMING_SNAKE_CASE_ : str , ) -> int:
super().__init__(**SCREAMING_SNAKE_CASE_ )
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowercase_ = CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase_ = backbone_config.get('''model_type''' )
lowercase_ = CONFIG_MAPPING[backbone_model_type]
lowercase_ = config_class.from_dict(SCREAMING_SNAKE_CASE_ )
lowercase_ = backbone_config
lowercase_ = hidden_size
lowercase_ = initializer_range
lowercase_ = pool_scales
lowercase_ = use_auxiliary_head
lowercase_ = auxiliary_loss_weight
lowercase_ = auxiliary_in_channels
lowercase_ = auxiliary_channels
lowercase_ = auxiliary_num_convs
lowercase_ = auxiliary_concat_input
lowercase_ = loss_ignore_index
def _lowercase ( self : List[str] ) -> List[str]:
lowercase_ = copy.deepcopy(self.__dict__ )
lowercase_ = self.backbone_config.to_dict()
lowercase_ = self.__class__.model_type
return output
| 30
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|
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def UpperCamelCase_( snake_case : Optional[int] ):
'''simple docstring'''
snake_case_ = SwinConfig()
snake_case_ = swin_name.split("_" )
snake_case_ = name_split[1]
snake_case_ = int(name_split[4] )
snake_case_ = int(name_split[3][-1] )
if model_size == "tiny":
snake_case_ = 9_6
snake_case_ = (2, 2, 6, 2)
snake_case_ = (3, 6, 1_2, 2_4)
elif model_size == "small":
snake_case_ = 9_6
snake_case_ = (2, 2, 1_8, 2)
snake_case_ = (3, 6, 1_2, 2_4)
elif model_size == "base":
snake_case_ = 1_2_8
snake_case_ = (2, 2, 1_8, 2)
snake_case_ = (4, 8, 1_6, 3_2)
else:
snake_case_ = 1_9_2
snake_case_ = (2, 2, 1_8, 2)
snake_case_ = (6, 1_2, 2_4, 4_8)
if "in22k" in swin_name:
snake_case_ = 2_1_8_4_1
else:
snake_case_ = 1_0_0_0
snake_case_ = "huggingface/label-files"
snake_case_ = "imagenet-1k-id2label.json"
snake_case_ = json.load(open(hf_hub_download(snake_case , snake_case , repo_type="dataset" ) , "r" ) )
snake_case_ = {int(snake_case ): v for k, v in idalabel.items()}
snake_case_ = idalabel
snake_case_ = {v: k for k, v in idalabel.items()}
snake_case_ = img_size
snake_case_ = num_classes
snake_case_ = embed_dim
snake_case_ = depths
snake_case_ = num_heads
snake_case_ = window_size
return config
def UpperCamelCase_( snake_case : Optional[int] ):
'''simple docstring'''
if "patch_embed.proj" in name:
snake_case_ = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
snake_case_ = name.replace("patch_embed.norm" , "embeddings.norm" )
if "layers" in name:
snake_case_ = "encoder." + name
if "attn.proj" in name:
snake_case_ = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
snake_case_ = name.replace("attn" , "attention.self" )
if "norm1" in name:
snake_case_ = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
snake_case_ = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
snake_case_ = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
snake_case_ = name.replace("mlp.fc2" , "output.dense" )
if name == "norm.weight":
snake_case_ = "layernorm.weight"
if name == "norm.bias":
snake_case_ = "layernorm.bias"
if "head" in name:
snake_case_ = name.replace("head" , "classifier" )
else:
snake_case_ = "swin." + name
return name
def UpperCamelCase_( snake_case : Optional[Any] , snake_case : Any ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
snake_case_ = orig_state_dict.pop(snake_case )
if "mask" in key:
continue
elif "qkv" in key:
snake_case_ = key.split("." )
snake_case_ = int(key_split[1] )
snake_case_ = int(key_split[3] )
snake_case_ = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
snake_case_ = val[:dim, :]
snake_case_ = val[
dim : dim * 2, :
]
snake_case_ = val[-dim:, :]
else:
snake_case_ = val[
:dim
]
snake_case_ = val[
dim : dim * 2
]
snake_case_ = val[
-dim:
]
else:
snake_case_ = val
return orig_state_dict
def UpperCamelCase_( snake_case : str , snake_case : str ):
'''simple docstring'''
snake_case_ = timm.create_model(snake_case , pretrained=snake_case )
timm_model.eval()
snake_case_ = get_swin_config(snake_case )
snake_case_ = SwinForImageClassification(snake_case )
model.eval()
snake_case_ = convert_state_dict(timm_model.state_dict() , snake_case )
model.load_state_dict(snake_case )
snake_case_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
snake_case_ = AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_" , "-" ) ) )
snake_case_ = Image.open(requests.get(snake_case , stream=snake_case ).raw )
snake_case_ = image_processor(images=snake_case , return_tensors="pt" )
snake_case_ = timm_model(inputs["pixel_values"] )
snake_case_ = model(**snake_case ).logits
assert torch.allclose(snake_case , snake_case , atol=1e-3 )
print(f'Saving model {swin_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(snake_case )
print(f'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(snake_case )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--swin_name",
default="swin_tiny_patch4_window7_224",
type=str,
help="Name of the Swin 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."
)
_SCREAMING_SNAKE_CASE : Dict = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 85
|
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
__a = 'sshleifer/bart-tiny-random'
__a = 'patrickvonplaten/t5-tiny-random'
@require_torch
class lowercase__( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _lowercase ( self : Any ) -> Tuple:
return AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : int ) -> List[Any]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.num_hidden_layers , 1 )
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : List[str] ) -> List[Any]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=SCREAMING_SNAKE_CASE_ )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , self.teacher_config.encoder_layers )
def _lowercase ( self : List[Any] ) -> List[Any]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , 1 )
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=SCREAMING_SNAKE_CASE_ , d=SCREAMING_SNAKE_CASE_ )
| 30
| 0
|
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = Dict[str, Any]
lowerCamelCase__ = List[Prediction]
@add_end_docstrings(_lowerCamelCase)
class A__ ( _lowerCamelCase):
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
super().__init__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if self.framework == "tf":
raise ValueError(f"The {self.__class__} is only available in PyTorch." )
requires_backends(self , 'vision' )
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )
def __lowerCamelCase ( self , **_SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : List[Any] = {}
if "threshold" in kwargs:
__lowerCAmelCase : int = kwargs['threshold']
return {}, {}, postprocess_kwargs
def __call__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
return super().__call__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : List[str] = load_image(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : int = torch.IntTensor([[image.height, image.width]] )
__lowerCAmelCase : int = self.image_processor(images=[image] , return_tensors='pt' )
if self.tokenizer is not None:
__lowerCAmelCase : Tuple = self.tokenizer(text=inputs['words'] , boxes=inputs['boxes'] , return_tensors='pt' )
__lowerCAmelCase : str = target_size
return inputs
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Union[str, Any] = model_inputs.pop('target_size' )
__lowerCAmelCase : int = self.model(**_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[Any] = outputs.__class__({'target_size': target_size, **outputs} )
if self.tokenizer is not None:
__lowerCAmelCase : Dict = model_inputs['bbox']
return model_outputs
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=0.9 ):
__lowerCAmelCase : Union[str, Any] = model_outputs['target_size']
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
__lowerCAmelCase , __lowerCAmelCase : int = target_size[0].tolist()
def unnormalize(_SCREAMING_SNAKE_CASE ):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 10_00),
(height * bbox[1] / 10_00),
(width * bbox[2] / 10_00),
(height * bbox[3] / 10_00),
] ) )
__lowerCAmelCase , __lowerCAmelCase : List[Any] = model_outputs['logits'].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
__lowerCAmelCase : Optional[Any] = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
__lowerCAmelCase : Any = [unnormalize(_SCREAMING_SNAKE_CASE ) for bbox in model_outputs['bbox'].squeeze(0 )]
__lowerCAmelCase : List[str] = ['score', 'label', 'box']
__lowerCAmelCase : Tuple = [dict(zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ) for vals in zip(scores.tolist() , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
__lowerCAmelCase : Tuple = self.image_processor.post_process_object_detection(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = raw_annotations[0]
__lowerCAmelCase : Dict = raw_annotation['scores']
__lowerCAmelCase : Dict = raw_annotation['labels']
__lowerCAmelCase : int = raw_annotation['boxes']
__lowerCAmelCase : Any = scores.tolist()
__lowerCAmelCase : Any = [self.model.config.idalabel[label.item()] for label in labels]
__lowerCAmelCase : Optional[int] = [self._get_bounding_box(_SCREAMING_SNAKE_CASE ) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
__lowerCAmelCase : List[Any] = ['score', 'label', 'box']
__lowerCAmelCase : str = [
dict(zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
for vals in zip(raw_annotation['scores'] , raw_annotation['labels'] , raw_annotation['boxes'] )
]
return annotation
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
if self.framework != "pt":
raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.' )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Optional[int] = box.int().tolist()
__lowerCAmelCase : str = {
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax,
}
return bbox
| 86
|
def a ( snake_case__: int = 100 ):
'''simple docstring'''
lowercase_ = (n * (n + 1) // 2) ** 2
lowercase_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"{solution() = }")
| 30
| 0
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
UpperCamelCase = '''3'''
print('''Python version:''', sys.version)
print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
print('''Torch version:''', None)
try:
import transformers
print('''transformers version:''', transformers.__version__)
except ImportError:
print('''transformers version:''', None)
| 87
|
import logging
from transformers.configuration_utils import PretrainedConfig
__a = logging.getLogger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Optional[int] = 'masked_bert'
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : List[Any]=3_0_5_2_2 , SCREAMING_SNAKE_CASE_ : List[str]=7_6_8 , SCREAMING_SNAKE_CASE_ : Optional[int]=1_2 , SCREAMING_SNAKE_CASE_ : Any=1_2 , SCREAMING_SNAKE_CASE_ : str=3_0_7_2 , SCREAMING_SNAKE_CASE_ : Union[str, Any]="gelu" , SCREAMING_SNAKE_CASE_ : Optional[int]=0.1 , SCREAMING_SNAKE_CASE_ : List[str]=0.1 , SCREAMING_SNAKE_CASE_ : Tuple=5_1_2 , SCREAMING_SNAKE_CASE_ : str=2 , SCREAMING_SNAKE_CASE_ : Dict=0.02 , SCREAMING_SNAKE_CASE_ : Any=1e-12 , SCREAMING_SNAKE_CASE_ : Any=0 , SCREAMING_SNAKE_CASE_ : Optional[int]="topK" , SCREAMING_SNAKE_CASE_ : Dict="constant" , SCREAMING_SNAKE_CASE_ : Optional[Any]=0.0 , **SCREAMING_SNAKE_CASE_ : Optional[int] , ) -> Optional[Any]:
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
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_ = type_vocab_size
lowercase_ = initializer_range
lowercase_ = layer_norm_eps
lowercase_ = pruning_method
lowercase_ = mask_init
lowercase_ = mask_scale
| 30
| 0
|
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowerCAmelCase : str = {'tokenization_bertweet': ['BertweetTokenizer']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
__lowerCAmelCase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 88
|
import os
def a ( ):
'''simple docstring'''
lowercase_ = os.path.join(os.path.dirname(snake_case__ ) , '''num.txt''' )
with open(snake_case__ ) as file_hand:
return str(sum(int(snake_case__ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 30
| 0
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
def __lowerCamelCase ( lowerCAmelCase_ ) -> Union[str, Any]:
_a : str = [tensor.shape for tensor in tensor_list]
return all(shape == shapes[0] for shape in shapes[1:] )
class __magic_name__ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
lowerCAmelCase : int = StableDiffusionLatentUpscalePipeline
lowerCAmelCase : Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
'height',
'width',
'cross_attention_kwargs',
'negative_prompt_embeds',
'prompt_embeds',
}
lowerCAmelCase : Union[str, Any] = PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
lowerCAmelCase : Union[str, Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowerCAmelCase : Dict = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowerCAmelCase : List[Any] = frozenset([] )
lowerCAmelCase : List[str] = True
@property
def __lowercase ( self : List[str] ):
_a : int = 1
_a : str = 4
_a : str = (16, 16)
_a : Dict = floats_tensor((batch_size, num_channels) + sizes ,rng=random.Random(0 ) ).to(_UpperCAmelCase )
return image
def __lowercase ( self : List[str] ):
torch.manual_seed(0 )
_a : Tuple = UNetaDConditionModel(
act_fn='gelu' ,attention_head_dim=8 ,norm_num_groups=_UpperCAmelCase ,block_out_channels=[32, 32, 64, 64] ,time_cond_proj_dim=160 ,conv_in_kernel=1 ,conv_out_kernel=1 ,cross_attention_dim=32 ,down_block_types=(
'KDownBlock2D',
'KCrossAttnDownBlock2D',
'KCrossAttnDownBlock2D',
'KCrossAttnDownBlock2D',
) ,in_channels=8 ,mid_block_type=_UpperCAmelCase ,only_cross_attention=_UpperCAmelCase ,out_channels=5 ,resnet_time_scale_shift='scale_shift' ,time_embedding_type='fourier' ,timestep_post_act='gelu' ,up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D') ,)
_a : Dict = AutoencoderKL(
block_out_channels=[32, 32, 64, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=[
'DownEncoderBlock2D',
'DownEncoderBlock2D',
'DownEncoderBlock2D',
'DownEncoderBlock2D',
] ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D'] ,latent_channels=4 ,)
_a : Optional[int] = EulerDiscreteScheduler(prediction_type='sample' )
_a : List[str] = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1E-05 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=1000 ,hidden_act='quick_gelu' ,projection_dim=512 ,)
_a : str = CLIPTextModel(_UpperCAmelCase )
_a : str = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
_a : Dict = {
'unet': model.eval(),
'vae': vae.eval(),
'scheduler': scheduler,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
}
return components
def __lowercase ( self : str ,_UpperCAmelCase : Optional[int] ,_UpperCAmelCase : int=0 ):
if str(_UpperCAmelCase ).startswith('mps' ):
_a : List[str] = torch.manual_seed(_UpperCAmelCase )
else:
_a : List[Any] = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
_a : int = {
'prompt': 'A painting of a squirrel eating a burger',
'image': self.dummy_image.cpu(),
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def __lowercase ( self : int ):
_a : Tuple = 'cpu'
_a : Tuple = self.get_dummy_components()
_a : Any = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
_a : Dict = self.get_dummy_inputs(_UpperCAmelCase )
_a : List[Any] = pipe(**_UpperCAmelCase ).images
_a : int = image[0, -3:, -3:, -1]
self.assertEqual(image.shape ,(1, 256, 256, 3) )
_a : Union[str, Any] = np.array(
[0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55] )
_a : Tuple = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_UpperCAmelCase ,1E-3 )
def __lowercase ( self : Any ):
super().test_attention_slicing_forward_pass(expected_max_diff=7E-3 )
def __lowercase ( self : Any ):
super().test_cpu_offload_forward_pass(expected_max_diff=3E-3 )
def __lowercase ( self : str ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def __lowercase ( self : str ):
super().test_inference_batch_single_identical(expected_max_diff=7E-3 )
def __lowercase ( self : List[str] ):
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3 )
def __lowercase ( self : Dict ):
super().test_save_load_local(expected_max_difference=3E-3 )
def __lowercase ( self : Union[str, Any] ):
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def __lowercase ( self : Tuple ):
_a : int = [
'DDIMScheduler',
'DDPMScheduler',
'PNDMScheduler',
'HeunDiscreteScheduler',
'EulerAncestralDiscreteScheduler',
'KDPM2DiscreteScheduler',
'KDPM2AncestralDiscreteScheduler',
'DPMSolverSDEScheduler',
]
_a : Optional[int] = self.get_dummy_components()
_a : Optional[int] = self.pipeline_class(**_UpperCAmelCase )
# make sure that PNDM does not need warm-up
pipe.scheduler.register_to_config(skip_prk_steps=_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
_a : Union[str, Any] = self.get_dummy_inputs(_UpperCAmelCase )
_a : Tuple = 2
_a : int = []
for scheduler_enum in KarrasDiffusionSchedulers:
if scheduler_enum.name in skip_schedulers:
# no sigma schedulers are not supported
# no schedulers
continue
_a : int = getattr(_UpperCAmelCase ,scheduler_enum.name )
_a : int = scheduler_cls.from_config(pipe.scheduler.config )
_a : List[str] = pipe(**_UpperCAmelCase )[0]
outputs.append(_UpperCAmelCase )
assert check_same_shape(_UpperCAmelCase )
@require_torch_gpu
@slow
class __magic_name__ ( unittest.TestCase ):
def __lowercase ( self : Dict ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self : List[str] ):
_a : Dict = torch.manual_seed(33 )
_a : str = StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4' ,torch_dtype=torch.floataa )
pipe.to('cuda' )
_a : List[str] = StableDiffusionLatentUpscalePipeline.from_pretrained(
'stabilityai/sd-x2-latent-upscaler' ,torch_dtype=torch.floataa )
upscaler.to('cuda' )
_a : Optional[Any] = 'a photo of an astronaut high resolution, unreal engine, ultra realistic'
_a : Any = pipe(_UpperCAmelCase ,generator=_UpperCAmelCase ,output_type='latent' ).images
_a : Optional[int] = upscaler(
prompt=_UpperCAmelCase ,image=_UpperCAmelCase ,num_inference_steps=20 ,guidance_scale=0 ,generator=_UpperCAmelCase ,output_type='np' ,).images[0]
_a : Tuple = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy' )
assert np.abs((expected_image - image).mean() ) < 5E-2
def __lowercase ( self : Optional[int] ):
_a : Any = torch.manual_seed(33 )
_a : Optional[int] = StableDiffusionLatentUpscalePipeline.from_pretrained(
'stabilityai/sd-x2-latent-upscaler' ,torch_dtype=torch.floataa )
upscaler.to('cuda' )
_a : Tuple = 'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'
_a : Dict = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png' )
_a : Optional[Any] = upscaler(
prompt=_UpperCAmelCase ,image=_UpperCAmelCase ,num_inference_steps=20 ,guidance_scale=0 ,generator=_UpperCAmelCase ,output_type='np' ,).images[0]
_a : Dict = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy' )
assert np.abs((expected_image - image).max() ) < 5E-2
| 89
|
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
'''compression_format, is_archive''' , [
('''7z''', True),
('''bz2''', False),
('''gzip''', False),
('''lz4''', False),
('''tar''', True),
('''xz''', False),
('''zip''', True),
('''zstd''', False),
] , )
def a ( snake_case__: int , snake_case__: Tuple , snake_case__: Dict , snake_case__: Dict , snake_case__: List[Any] , snake_case__: int , snake_case__: List[Any] , snake_case__: Optional[int] , snake_case__: str , snake_case__: Union[str, Any] , snake_case__: List[str] , snake_case__: int , ):
'''simple docstring'''
lowercase_ = {
'''7z''': (seven_zip_file, SevenZipExtractor),
'''bz2''': (bza_file, BzipaExtractor),
'''gzip''': (gz_file, GzipExtractor),
'''lz4''': (lza_file, LzaExtractor),
'''tar''': (tar_file, TarExtractor),
'''xz''': (xz_file, XzExtractor),
'''zip''': (zip_file, ZipExtractor),
'''zstd''': (zstd_file, ZstdExtractor),
}
lowercase_ , lowercase_ = input_paths_and_base_extractors[compression_format]
if input_path is None:
lowercase_ = F'''for \'{compression_format}\' compression_format, '''
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(snake_case__ )
assert base_extractor.is_extractable(snake_case__ )
lowercase_ = tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
base_extractor.extract(snake_case__ , snake_case__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
lowercase_ = file_path.read_text(encoding='''utf-8''' )
else:
lowercase_ = output_path.read_text(encoding='''utf-8''' )
lowercase_ = text_file.read_text(encoding='''utf-8''' )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
'''compression_format, is_archive''' , [
('''7z''', True),
('''bz2''', False),
('''gzip''', False),
('''lz4''', False),
('''tar''', True),
('''xz''', False),
('''zip''', True),
('''zstd''', False),
] , )
def a ( snake_case__: List[Any] , snake_case__: int , snake_case__: Optional[int] , snake_case__: Union[str, Any] , snake_case__: List[Any] , snake_case__: Tuple , snake_case__: Optional[int] , snake_case__: List[str] , snake_case__: Union[str, Any] , snake_case__: Tuple , snake_case__: int , snake_case__: Optional[int] , ):
'''simple docstring'''
lowercase_ = {
'''7z''': seven_zip_file,
'''bz2''': bza_file,
'''gzip''': gz_file,
'''lz4''': lza_file,
'''tar''': tar_file,
'''xz''': xz_file,
'''zip''': zip_file,
'''zstd''': zstd_file,
}
lowercase_ = input_paths[compression_format]
if input_path is None:
lowercase_ = F'''for \'{compression_format}\' compression_format, '''
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(snake_case__ )
lowercase_ = Extractor.infer_extractor_format(snake_case__ )
assert extractor_format is not None
lowercase_ = tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
Extractor.extract(snake_case__ , snake_case__ , snake_case__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
lowercase_ = file_path.read_text(encoding='''utf-8''' )
else:
lowercase_ = output_path.read_text(encoding='''utf-8''' )
lowercase_ = text_file.read_text(encoding='''utf-8''' )
assert extracted_file_content == expected_file_content
@pytest.fixture
def a ( snake_case__: Union[str, Any] , snake_case__: List[Any] ):
'''simple docstring'''
import tarfile
lowercase_ = tmp_path / '''data_dot_dot'''
directory.mkdir()
lowercase_ = directory / '''tar_file_with_dot_dot.tar'''
with tarfile.TarFile(snake_case__ , '''w''' ) as f:
f.add(snake_case__ , arcname=os.path.join('''..''' , text_file.name ) )
return path
@pytest.fixture
def a ( snake_case__: int ):
'''simple docstring'''
import tarfile
lowercase_ = tmp_path / '''data_sym_link'''
directory.mkdir()
lowercase_ = directory / '''tar_file_with_sym_link.tar'''
os.symlink('''..''' , directory / '''subdir''' , target_is_directory=snake_case__ )
with tarfile.TarFile(snake_case__ , '''w''' ) as f:
f.add(str(directory / '''subdir''' ) , arcname='''subdir''' ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
'''insecure_tar_file, error_log''' , [('''tar_file_with_dot_dot''', '''illegal path'''), ('''tar_file_with_sym_link''', '''Symlink''')] , )
def a ( snake_case__: List[Any] , snake_case__: Optional[int] , snake_case__: List[str] , snake_case__: List[str] , snake_case__: int , snake_case__: Optional[Any] ):
'''simple docstring'''
lowercase_ = {
'''tar_file_with_dot_dot''': tar_file_with_dot_dot,
'''tar_file_with_sym_link''': tar_file_with_sym_link,
}
lowercase_ = insecure_tar_files[insecure_tar_file]
lowercase_ = tmp_path / '''extracted'''
TarExtractor.extract(snake_case__ , snake_case__ )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def a ( snake_case__: Optional[int] ):
'''simple docstring'''
# We should have less false positives than zipfile.is_zipfile
# We do that by checking only the magic number
lowercase_ = tmpdir / '''not_a_zip_file'''
# From: https://github.com/python/cpython/pull/5053
lowercase_ = (
B'''\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'''
B'''\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'''
B'''DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'''
B'''\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'''
)
with not_a_zip_file.open('''wb''' ) as f:
f.write(snake_case__ )
assert zipfile.is_zipfile(str(snake_case__ ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(snake_case__ ) # but we're right
| 30
| 0
|
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = BarthezTokenizer
snake_case_ = BarthezTokenizerFast
snake_case_ = True
snake_case_ = True
def lowercase_ ( self ) -> int:
'''simple docstring'''
super().setUp()
__lowerCamelCase = BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=lowerCamelCase__ )
__lowerCamelCase = tokenizer
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = '<pad>'
__lowerCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__ ) , lowerCamelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__ ) , lowerCamelCase__ )
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(lowerCamelCase__ ) , 101_122 )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 101_122 )
@require_torch
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
__lowerCamelCase = [0, 57, 3_018, 70_307, 91, 2]
__lowerCamelCase = self.tokenizer(
lowerCamelCase__ , max_length=len(lowerCamelCase__ ) , padding=lowerCamelCase__ , truncation=lowerCamelCase__ , return_tensors='pt' )
self.assertIsInstance(lowerCamelCase__ , lowerCamelCase__ )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
__lowerCamelCase = batch.input_ids.tolist()[0]
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = self.get_rust_tokenizer()
__lowerCamelCase = 'I was born in 92000, and this is falsé.'
__lowerCamelCase = tokenizer.tokenize(lowerCamelCase__ )
__lowerCamelCase = rust_tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
__lowerCamelCase = rust_tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = self.get_rust_tokenizer()
__lowerCamelCase = tokenizer.encode(lowerCamelCase__ )
__lowerCamelCase = rust_tokenizer.encode(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
@slow
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
# fmt: off
__lowerCamelCase = {'input_ids': [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
__lowerCamelCase = [
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=lowerCamelCase__ , model_name='moussaKam/mbarthez' , revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6' , sequences=lowerCamelCase__ , )
| 90
|
from __future__ import annotations
def a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''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 a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if length > 1:
lowercase_ = int(length / 2 )
for i in range(snake_case__ , low + middle ):
comp_and_swap(snake_case__ , snake_case__ , i + middle , snake_case__ )
bitonic_merge(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
bitonic_merge(snake_case__ , low + middle , snake_case__ , snake_case__ )
def a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if length > 1:
lowercase_ = int(length / 2 )
bitonic_sort(snake_case__ , snake_case__ , snake_case__ , 1 )
bitonic_sort(snake_case__ , low + middle , snake_case__ , 0 )
bitonic_merge(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
if __name__ == "__main__":
__a = input('Enter numbers separated by a comma:\n').strip()
__a = [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=', ')
| 30
| 0
|
"""simple docstring"""
import random
from typing import Any
def _A (__a ) -> list[Any]:
"""simple docstring"""
for _ in range(len(__a ) ):
SCREAMING_SNAKE_CASE_ : Optional[int] = random.randint(0 , len(__a ) - 1 )
SCREAMING_SNAKE_CASE_ : Tuple = random.randint(0 , len(__a ) - 1 )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Union[str, Any] = data[b], data[a]
return data
if __name__ == "__main__":
UpperCAmelCase_ : Dict = [0, 1, 2, 3, 4, 5, 6, 7]
UpperCAmelCase_ : Dict = ["""python""", """says""", """hello""", """!"""]
print("""Fisher-Yates Shuffle:""")
print("""List""", integers, strings)
print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
| 91
|
from __future__ import annotations
from collections.abc import MutableSequence
class lowercase__:
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : MutableSequence[float] ) -> None:
if len(SCREAMING_SNAKE_CASE_ ) != degree + 1:
raise ValueError(
'''The number of coefficients should be equal to the degree + 1.''' )
lowercase_ = list(SCREAMING_SNAKE_CASE_ )
lowercase_ = degree
def __add__( self : Any , SCREAMING_SNAKE_CASE_ : Polynomial ) -> Polynomial:
if self.degree > polynomial_a.degree:
lowercase_ = self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree , SCREAMING_SNAKE_CASE_ )
else:
lowercase_ = polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree , SCREAMING_SNAKE_CASE_ )
def __sub__( self : str , SCREAMING_SNAKE_CASE_ : Polynomial ) -> Polynomial:
return self + polynomial_a * Polynomial(0 , [-1] )
def __neg__( self : int ) -> Polynomial:
return Polynomial(self.degree , [-c for c in self.coefficients] )
def __mul__( self : Any , SCREAMING_SNAKE_CASE_ : Polynomial ) -> Polynomial:
lowercase_ = [0] * (self.degree + polynomial_a.degree + 1)
for i in range(self.degree + 1 ):
for j in range(polynomial_a.degree + 1 ):
coefficients[i + j] += (
self.coefficients[i] * polynomial_a.coefficients[j]
)
return Polynomial(self.degree + polynomial_a.degree , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Dict , SCREAMING_SNAKE_CASE_ : int | float ) -> int | float:
lowercase_ = 0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self : Tuple ) -> str:
lowercase_ = ''''''
for i in range(self.degree , -1 , -1 ):
if self.coefficients[i] == 0:
continue
elif self.coefficients[i] > 0:
if polynomial:
polynomial += " + "
else:
polynomial += " - "
if i == 0:
polynomial += str(abs(self.coefficients[i] ) )
elif i == 1:
polynomial += str(abs(self.coefficients[i] ) ) + "x"
else:
polynomial += str(abs(self.coefficients[i] ) ) + "x^" + str(SCREAMING_SNAKE_CASE_ )
return polynomial
def __repr__( self : Optional[Any] ) -> str:
return self.__str__()
def _lowercase ( self : int ) -> Polynomial:
lowercase_ = [0] * self.degree
for i in range(self.degree ):
lowercase_ = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : int | float = 0 ) -> Polynomial:
lowercase_ = [0] * (self.degree + 2)
lowercase_ = constant
for i in range(self.degree + 1 ):
lowercase_ = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 , SCREAMING_SNAKE_CASE_ )
def __eq__( self : str , SCREAMING_SNAKE_CASE_ : object ) -> bool:
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return False
if self.degree != polynomial_a.degree:
return False
for i in range(self.degree + 1 ):
if self.coefficients[i] != polynomial_a.coefficients[i]:
return False
return True
def __ne__( self : List[str] , SCREAMING_SNAKE_CASE_ : object ) -> bool:
return not self.__eq__(SCREAMING_SNAKE_CASE_ )
| 30
| 0
|
def _a ( SCREAMING_SNAKE_CASE_ : int ):
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_persistence() does not accept negative values" )
__lowerCAmelCase = 0
__lowerCAmelCase = str(SCREAMING_SNAKE_CASE_ )
while len(SCREAMING_SNAKE_CASE_ ) != 1:
__lowerCAmelCase = [int(SCREAMING_SNAKE_CASE_ ) for i in num_string]
__lowerCAmelCase = 1
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) ):
total *= numbers[i]
__lowerCAmelCase = str(SCREAMING_SNAKE_CASE_ )
steps += 1
return steps
def _a ( SCREAMING_SNAKE_CASE_ : int ):
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError("additive_persistence() only accepts integral values" )
if num < 0:
raise ValueError("additive_persistence() does not accept negative values" )
__lowerCAmelCase = 0
__lowerCAmelCase = str(SCREAMING_SNAKE_CASE_ )
while len(SCREAMING_SNAKE_CASE_ ) != 1:
__lowerCAmelCase = [int(SCREAMING_SNAKE_CASE_ ) for i in num_string]
__lowerCAmelCase = 0
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) ):
total += numbers[i]
__lowerCAmelCase = str(SCREAMING_SNAKE_CASE_ )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 92
|
import itertools
import math
def a ( snake_case__: int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(snake_case__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def a ( ):
'''simple docstring'''
lowercase_ = 2
while True:
if is_prime(snake_case__ ):
yield num
num += 1
def a ( snake_case__: int = 10_001 ):
'''simple docstring'''
return next(itertools.islice(prime_generator() , nth - 1 , snake_case__ ) )
if __name__ == "__main__":
print(f"{solution() = }")
| 30
| 0
|
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case_ ( __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Dict=None ):
"""simple docstring"""
assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
lowercase_ : int = nn.Parameter(__SCREAMING_SNAKE_CASE )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
lowercase_ : Any = nn.Parameter(__SCREAMING_SNAKE_CASE )
def snake_case_ ( __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : List[Any] ):
"""simple docstring"""
lowercase_ : Optional[int] = np.asarray(weights[0] )
lowercase_ : Optional[Any] = np.asarray(weights[1] )
lowercase_ : Optional[int] = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(__SCREAMING_SNAKE_CASE ).transpose(1 , 2 ).contiguous().view(-1 , __SCREAMING_SNAKE_CASE ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(__SCREAMING_SNAKE_CASE ).transpose(1 , 2 ).contiguous().view(-1 , __SCREAMING_SNAKE_CASE ) , )
set_param(
torch_layer.output.dense , torch.tensor(__SCREAMING_SNAKE_CASE ).view(-1 , __SCREAMING_SNAKE_CASE ).contiguous().transpose(0 , 1 ) , )
def snake_case_ ( __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
lowercase_ : Union[str, Any] = np.asarray(weights[0] )
lowercase_ : Any = np.asarray(weights[1] )
lowercase_ : Optional[int] = np.asarray(weights[2] )
lowercase_ : int = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(__SCREAMING_SNAKE_CASE ).transpose(1 , 2 ).contiguous().view(-1 , __SCREAMING_SNAKE_CASE ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(__SCREAMING_SNAKE_CASE ).transpose(1 , 2 ).contiguous().view(-1 , __SCREAMING_SNAKE_CASE ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(__SCREAMING_SNAKE_CASE ).transpose(1 , 2 ).contiguous().view(-1 , __SCREAMING_SNAKE_CASE ) , )
set_param(
torch_layer.output.dense , torch.tensor(__SCREAMING_SNAKE_CASE ).view(-1 , __SCREAMING_SNAKE_CASE ).contiguous().transpose(0 , 1 ) , )
def snake_case_ ( __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
lowercase_ : Union[str, Any] = weights[0][0][0]
lowercase_ : Optional[Any] = np.asarray(layer_norm_a[0] )
lowercase_ : List[Any] = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(__SCREAMING_SNAKE_CASE ) , torch.tensor(__SCREAMING_SNAKE_CASE ) , )
# lsh weights + output
lowercase_ : Dict = weights[0][1]
if len(__SCREAMING_SNAKE_CASE ) < 4:
set_layer_weights_in_torch_lsh(__SCREAMING_SNAKE_CASE , torch_block.attention , __SCREAMING_SNAKE_CASE )
else:
set_layer_weights_in_torch_local(__SCREAMING_SNAKE_CASE , torch_block.attention , __SCREAMING_SNAKE_CASE )
# intermediate weighs
lowercase_ : Dict = weights[2][0][1][2]
# Chunked Feed Forward
if len(__SCREAMING_SNAKE_CASE ) == 4:
lowercase_ : Any = intermediate_weights[2]
# layernorm 2
lowercase_ : List[Any] = np.asarray(intermediate_weights[0][0] )
lowercase_ : Any = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(__SCREAMING_SNAKE_CASE ) , torch.tensor(__SCREAMING_SNAKE_CASE ) , )
# intermediate dense
lowercase_ : List[str] = np.asarray(intermediate_weights[1][0] )
lowercase_ : List[str] = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(__SCREAMING_SNAKE_CASE ).transpose(0 , 1 ).contiguous() , torch.tensor(__SCREAMING_SNAKE_CASE ) , )
# intermediate out
lowercase_ : int = np.asarray(intermediate_weights[4][0] )
lowercase_ : Union[str, Any] = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(__SCREAMING_SNAKE_CASE ).transpose(0 , 1 ).contiguous() , torch.tensor(__SCREAMING_SNAKE_CASE ) , )
def snake_case_ ( __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[Any] ):
"""simple docstring"""
lowercase_ : Tuple = torch_model.reformer
# word embeds
lowercase_ : Optional[Any] = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(__SCREAMING_SNAKE_CASE ) , )
if isinstance(weights[3] , __SCREAMING_SNAKE_CASE ):
lowercase_ : Dict = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
lowercase_ : Tuple = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F'''{position_embeddings[emb_idx]} emb does not match'''
lowercase_ : Any = nn.Parameter(torch.tensor(__SCREAMING_SNAKE_CASE ) )
lowercase_ : Tuple = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
__SCREAMING_SNAKE_CASE ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
lowercase_ : Dict = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# output layer norm
lowercase_ : List[str] = np.asarray(weights[7][0] )
lowercase_ : Optional[Any] = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(__SCREAMING_SNAKE_CASE ) , torch.tensor(__SCREAMING_SNAKE_CASE ) , )
# output embeddings
lowercase_ : Optional[int] = np.asarray(weights[9][0] )
lowercase_ : Any = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(__SCREAMING_SNAKE_CASE ).transpose(0 , 1 ).contiguous() , torch.tensor(__SCREAMING_SNAKE_CASE ) , )
def snake_case_ ( __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
lowercase_ : int = ReformerConfig.from_json_file(__SCREAMING_SNAKE_CASE )
print(F'''Building PyTorch model from configuration: {config}''' )
lowercase_ : Optional[Any] = ReformerModelWithLMHead(__SCREAMING_SNAKE_CASE )
with open(__SCREAMING_SNAKE_CASE , '''rb''' ) as f:
lowercase_ : List[str] = pickle.load(__SCREAMING_SNAKE_CASE )['''weights''']
set_model_weights_in_torch(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , config.hidden_size )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , __SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
_lowercase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained Reformer model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
_lowercase : str = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 93
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 30
| 0
|
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
DYNAMO_BACKENDS,
_ask_field,
_ask_options,
_convert_dynamo_backend,
_convert_mixed_precision,
_convert_sagemaker_distributed_mode,
_convert_yes_no_to_bool,
)
if is_botoa_available():
import botoa # noqa: F401
def __lowerCamelCase ( UpperCAmelCase_ : Dict ):
"""simple docstring"""
a :Tuple = botoa.client('''iam''' )
a :List[Any] = {
'''Version''': '''2012-10-17''',
'''Statement''': [
{'''Effect''': '''Allow''', '''Principal''': {'''Service''': '''sagemaker.amazonaws.com'''}, '''Action''': '''sts:AssumeRole'''}
],
}
try:
# create the role, associated with the chosen trust policy
iam_client.create_role(
RoleName=UpperCAmelCase_ , AssumeRolePolicyDocument=json.dumps(UpperCAmelCase_ , indent=2 ) )
a :List[Any] = {
'''Version''': '''2012-10-17''',
'''Statement''': [
{
'''Effect''': '''Allow''',
'''Action''': [
'''sagemaker:*''',
'''ecr:GetDownloadUrlForLayer''',
'''ecr:BatchGetImage''',
'''ecr:BatchCheckLayerAvailability''',
'''ecr:GetAuthorizationToken''',
'''cloudwatch:PutMetricData''',
'''cloudwatch:GetMetricData''',
'''cloudwatch:GetMetricStatistics''',
'''cloudwatch:ListMetrics''',
'''logs:CreateLogGroup''',
'''logs:CreateLogStream''',
'''logs:DescribeLogStreams''',
'''logs:PutLogEvents''',
'''logs:GetLogEvents''',
'''s3:CreateBucket''',
'''s3:ListBucket''',
'''s3:GetBucketLocation''',
'''s3:GetObject''',
'''s3:PutObject''',
],
'''Resource''': '''*''',
}
],
}
# attach policy to role
iam_client.put_role_policy(
RoleName=UpperCAmelCase_ , PolicyName=F'''{role_name}_policy_permission''' , PolicyDocument=json.dumps(UpperCAmelCase_ , indent=2 ) , )
except iam_client.exceptions.EntityAlreadyExistsException:
print(F'''role {role_name} already exists. Using existing one''' )
def __lowerCamelCase ( UpperCAmelCase_ : Optional[int] ):
"""simple docstring"""
a :List[Any] = botoa.client('''iam''' )
return iam_client.get_role(RoleName=UpperCAmelCase_ )["Role"]["Arn"]
def __lowerCamelCase ( ):
"""simple docstring"""
a :int = _ask_options(
'''How do you want to authorize?''' , ['''AWS Profile''', '''Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) '''] , UpperCAmelCase_ , )
a :Union[str, Any] = None
if credentials_configuration == 0:
a :Optional[Any] = _ask_field('''Enter your AWS Profile name: [default] ''' , default='''default''' )
a :List[Any] = aws_profile
else:
print(
'''Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,'''
'''`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`''' )
a :Dict = _ask_field('''AWS Access Key ID: ''' )
a :Dict = aws_access_key_id
a :Union[str, Any] = _ask_field('''AWS Secret Access Key: ''' )
a :str = aws_secret_access_key
a :List[Any] = _ask_field('''Enter your AWS Region: [us-east-1]''' , default='''us-east-1''' )
a :List[str] = aws_region
a :Dict = _ask_options(
'''Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?''' , ['''Provide IAM Role name''', '''Create new IAM role using credentials'''] , UpperCAmelCase_ , )
if role_management == 0:
a :Optional[int] = _ask_field('''Enter your IAM role name: ''' )
else:
a :Tuple = '''accelerate_sagemaker_execution_role'''
print(F'''Accelerate will create an iam role "{iam_role_name}" using the provided credentials''' )
_create_iam_role_for_sagemaker(UpperCAmelCase_ )
a :List[Any] = _ask_field(
'''Do you want to use custom Docker image? [yes/NO]: ''' , _convert_yes_no_to_bool , default=UpperCAmelCase_ , error_message='''Please enter yes or no.''' , )
a :Dict = None
if is_custom_docker_image:
a :List[Any] = _ask_field('''Enter your Docker image: ''' , lambda UpperCAmelCase_ : str(UpperCAmelCase_ ).lower() )
a :str = _ask_field(
'''Do you want to provide SageMaker input channels with data locations? [yes/NO]: ''' , _convert_yes_no_to_bool , default=UpperCAmelCase_ , error_message='''Please enter yes or no.''' , )
a :int = None
if is_sagemaker_inputs_enabled:
a :Optional[int] = _ask_field(
'''Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): ''' , lambda UpperCAmelCase_ : str(UpperCAmelCase_ ).lower() , )
a :Dict = _ask_field(
'''Do you want to enable SageMaker metrics? [yes/NO]: ''' , _convert_yes_no_to_bool , default=UpperCAmelCase_ , error_message='''Please enter yes or no.''' , )
a :Optional[int] = None
if is_sagemaker_metrics_enabled:
a :Tuple = _ask_field(
'''Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): ''' , lambda UpperCAmelCase_ : str(UpperCAmelCase_ ).lower() , )
a :str = _ask_options(
'''What is the distributed mode?''' , ['''No distributed training''', '''Data parallelism'''] , _convert_sagemaker_distributed_mode , )
a :List[str] = {}
a :Optional[int] = _ask_field(
'''Do you wish to optimize your script with torch dynamo?[yes/NO]:''' , _convert_yes_no_to_bool , default=UpperCAmelCase_ , error_message='''Please enter yes or no.''' , )
if use_dynamo:
a :List[str] = '''dynamo_'''
a :Optional[Any] = _ask_options(
'''Which dynamo backend would you like to use?''' , [x.lower() for x in DYNAMO_BACKENDS] , _convert_dynamo_backend , default=2 , )
a :List[str] = _ask_field(
'''Do you want to customize the defaults sent to torch.compile? [yes/NO]: ''' , _convert_yes_no_to_bool , default=UpperCAmelCase_ , error_message='''Please enter yes or no.''' , )
if use_custom_options:
a :Optional[int] = _ask_options(
'''Which mode do you want to use?''' , UpperCAmelCase_ , lambda UpperCAmelCase_ : TORCH_DYNAMO_MODES[int(UpperCAmelCase_ )] , default='''default''' , )
a :Optional[Any] = _ask_field(
'''Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: ''' , _convert_yes_no_to_bool , default=UpperCAmelCase_ , error_message='''Please enter yes or no.''' , )
a :Union[str, Any] = _ask_field(
'''Do you want to enable dynamic shape tracing? [yes/NO]: ''' , _convert_yes_no_to_bool , default=UpperCAmelCase_ , error_message='''Please enter yes or no.''' , )
a :Tuple = '''Which EC2 instance type you want to use for your training?'''
if distributed_type != SageMakerDistributedType.NO:
a :int = _ask_options(
UpperCAmelCase_ , UpperCAmelCase_ , lambda UpperCAmelCase_ : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(UpperCAmelCase_ )] )
else:
eca_instance_query += "? [ml.p3.2xlarge]:"
a :List[str] = _ask_field(UpperCAmelCase_ , lambda UpperCAmelCase_ : str(UpperCAmelCase_ ).lower() , default='''ml.p3.2xlarge''' )
a :Dict = 1
if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
a :Union[str, Any] = _ask_field(
'''How many machines do you want use? [1]: ''' , UpperCAmelCase_ , default=1 , )
a :Optional[int] = _ask_options(
'''Do you wish to use FP16 or BF16 (mixed precision)?''' , ['''no''', '''fp16''', '''bf16''', '''fp8'''] , _convert_mixed_precision , )
if use_dynamo and mixed_precision == "no":
print(
'''Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.''' )
return SageMakerConfig(
image_uri=UpperCAmelCase_ , compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER , distributed_type=UpperCAmelCase_ , use_cpu=UpperCAmelCase_ , dynamo_config=UpperCAmelCase_ , eca_instance_type=UpperCAmelCase_ , profile=UpperCAmelCase_ , region=UpperCAmelCase_ , iam_role_name=UpperCAmelCase_ , mixed_precision=UpperCAmelCase_ , num_machines=UpperCAmelCase_ , sagemaker_inputs_file=UpperCAmelCase_ , sagemaker_metrics_file=UpperCAmelCase_ , )
| 94
|
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
__a = logging.get_logger(__name__)
__a = {
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
'constant': get_constant_schedule,
'constant_w_warmup': get_constant_schedule_with_warmup,
}
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[str]=None , SCREAMING_SNAKE_CASE_ : Optional[Any]=None , *SCREAMING_SNAKE_CASE_ : str , **SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> List[str]:
super().__init__(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if config is None:
assert isinstance(self.model , SCREAMING_SNAKE_CASE_ ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
f''' {self.model.__class__}'''
)
lowercase_ = self.model.config
else:
lowercase_ = config
lowercase_ = data_args
lowercase_ = self.config.tgt_vocab_size if isinstance(self.config , SCREAMING_SNAKE_CASE_ ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
f'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
''' padding..''' )
if self.args.label_smoothing == 0:
lowercase_ = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
lowercase_ = label_smoothed_nll_loss
def _lowercase ( self : int , SCREAMING_SNAKE_CASE_ : int ) -> Optional[Any]:
if self.optimizer is None:
lowercase_ = ['''bias''', '''LayerNorm.weight''']
lowercase_ = [
{
'''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
'''weight_decay''': self.args.weight_decay,
},
{
'''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
lowercase_ = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
lowercase_ = Adafactor
lowercase_ = {'''scale_parameter''': False, '''relative_step''': False}
else:
lowercase_ = AdamW
lowercase_ = {
'''betas''': (self.args.adam_betaa, self.args.adam_betaa),
'''eps''': self.args.adam_epsilon,
}
lowercase_ = self.args.learning_rate
if self.sharded_ddp:
lowercase_ = OSS(
params=SCREAMING_SNAKE_CASE_ , optim=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
else:
lowercase_ = optimizer_cls(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if self.lr_scheduler is None:
lowercase_ = self._get_lr_scheduler(SCREAMING_SNAKE_CASE_ )
else: # ignoring --lr_scheduler
logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.''' )
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : Dict ) -> Dict:
lowercase_ = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
lowercase_ = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
lowercase_ = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
lowercase_ = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=SCREAMING_SNAKE_CASE_ )
return scheduler
def _lowercase ( self : Tuple ) -> Optional[torch.utils.data.Sampler]:
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : List[Any] ) -> Any:
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
lowercase_ = model(**SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )[0]
lowercase_ = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
lowercase_ , lowercase_ = model(**SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )[:2]
else:
# compute label smoothed loss
lowercase_ = model(**SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )[0]
lowercase_ = torch.nn.functional.log_softmax(SCREAMING_SNAKE_CASE_ , dim=-1 )
lowercase_ , lowercase_ = self.loss_fn(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def _lowercase ( self : str , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> List[Any]:
lowercase_ = inputs.pop('''labels''' )
lowercase_ , lowercase_ = self._compute_loss(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return loss
def _lowercase ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : nn.Module , SCREAMING_SNAKE_CASE_ : Dict[str, Union[torch.Tensor, Any]] , SCREAMING_SNAKE_CASE_ : bool , SCREAMING_SNAKE_CASE_ : Optional[List[str]] = None , ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
lowercase_ = self._prepare_inputs(SCREAMING_SNAKE_CASE_ )
lowercase_ = {
'''max_length''': self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
'''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
lowercase_ = self.model.generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , **SCREAMING_SNAKE_CASE_ , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
lowercase_ = self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_ , gen_kwargs['''max_length'''] )
lowercase_ = inputs.pop('''labels''' )
with torch.no_grad():
# compute loss on predict data
lowercase_ , lowercase_ = self._compute_loss(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowercase_ = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
lowercase_ = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
lowercase_ = self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_ , gen_kwargs['''max_length'''] )
return (loss, logits, labels)
def _lowercase ( self : str , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : int ) -> Tuple:
# If PAD token is not defined at least EOS token has to be defined
lowercase_ = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
'''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
f''' padded to `max_length`={max_length}''' )
lowercase_ = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
lowercase_ = tensor
return padded_tensor
| 30
| 0
|
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase):
_lowercase : Dict = AutoencoderKL
_lowercase : Tuple = """sample"""
_lowercase : Optional[int] = 1E-2
@property
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
a__ : Union[str, Any] =4
a__ : List[str] =3
a__ : Union[str, Any] =(3_2, 3_2)
a__ : int =floats_tensor((batch_size, num_channels) + sizes ).to(lowerCAmelCase__ )
return {"sample": image}
@property
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
return (3, 3_2, 3_2)
@property
def _lowercase ( self ) -> List[Any]:
'''simple docstring'''
return (3, 3_2, 3_2)
def _lowercase ( self ) -> Union[str, Any]:
'''simple docstring'''
a__ : List[Any] ={
"block_out_channels": [3_2, 6_4],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 4,
}
a__ : List[str] =self.dummy_input
return init_dict, inputs_dict
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
pass
def _lowercase ( self ) -> List[Any]:
'''simple docstring'''
pass
@unittest.skipIf(torch_device == "mps" , "Gradient checkpointing skipped on MPS" )
def _lowercase ( self ) -> Optional[int]:
'''simple docstring'''
a__ , a__ : Union[str, Any] =self.prepare_init_args_and_inputs_for_common()
a__ : Tuple =self.model_class(**lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
assert not model.is_gradient_checkpointing and model.training
a__ : Optional[Any] =model(**lowerCAmelCase__ ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
a__ : str =torch.randn_like(lowerCAmelCase__ )
a__ : Tuple =(out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
a__ : Any =self.model_class(**lowerCAmelCase__ )
# clone model
model_a.load_state_dict(model.state_dict() )
model_a.to(lowerCAmelCase__ )
model_a.enable_gradient_checkpointing()
assert model_a.is_gradient_checkpointing and model_a.training
a__ : Dict =model_a(**lowerCAmelCase__ ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_a.zero_grad()
a__ : int =(out_a - labels).mean()
loss_a.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_a).abs() < 1E-5 )
a__ : Union[str, Any] =dict(model.named_parameters() )
a__ : int =dict(model_a.named_parameters() )
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data , named_params_a[name].grad.data , atol=5E-5 ) )
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
a__ , a__ : Optional[int] =AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy" , output_loading_info=lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertEqual(len(loading_info["missing_keys"] ) , 0 )
model.to(lowerCAmelCase__ )
a__ : Optional[Any] =model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def _lowercase ( self ) -> Optional[int]:
'''simple docstring'''
a__ : Tuple =AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy" )
a__ : str =model.to(lowerCAmelCase__ )
model.eval()
if torch_device == "mps":
a__ : Any =torch.manual_seed(0 )
else:
a__ : Union[str, Any] =torch.Generator(device=lowerCAmelCase__ ).manual_seed(0 )
a__ : Union[str, Any] =torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
a__ : Union[str, Any] =image.to(lowerCAmelCase__ )
with torch.no_grad():
a__ : Tuple =model(lowerCAmelCase__ , sample_posterior=lowerCAmelCase__ , generator=lowerCAmelCase__ ).sample
a__ : Any =output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
a__ : str =torch.tensor(
[
-4.0078E-01,
-3.8323E-04,
-1.2681E-01,
-1.1462E-01,
2.0095E-01,
1.0893E-01,
-8.8247E-02,
-3.0361E-01,
-9.8644E-03,
] )
elif torch_device == "cpu":
a__ : Dict =torch.tensor(
[-0.13_52, 0.08_78, 0.04_19, -0.08_18, -0.10_69, 0.06_88, -0.14_58, -0.44_46, -0.00_26] )
else:
a__ : Optional[int] =torch.tensor(
[-0.24_21, 0.46_42, 0.25_07, -0.04_38, 0.06_82, 0.31_60, -0.20_18, -0.07_27, 0.24_85] )
self.assertTrue(torch_all_close(lowerCAmelCase__ , lowerCAmelCase__ , rtol=1E-2 ) )
@slow
class __lowerCAmelCase ( unittest.TestCase):
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
return F'''gaussian_noise_s={seed}_shape={"_".join([str(lowerCAmelCase__ ) for s in shape] )}.npy'''
def _lowercase ( self ) -> List[str]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self , lowerCAmelCase__=0 , lowerCAmelCase__=(4, 3, 5_1_2, 5_1_2) , lowerCAmelCase__=False ) -> Optional[Any]:
'''simple docstring'''
a__ : List[str] =torch.floataa if fpaa else torch.floataa
a__ : Tuple =torch.from_numpy(load_hf_numpy(self.get_file_format(lowerCAmelCase__ , lowerCAmelCase__ ) ) ).to(lowerCAmelCase__ ).to(lowerCAmelCase__ )
return image
def _lowercase ( self , lowerCAmelCase__="CompVis/stable-diffusion-v1-4" , lowerCAmelCase__=False ) -> Any:
'''simple docstring'''
a__ : str ="fp16" if fpaa else None
a__ : str =torch.floataa if fpaa else torch.floataa
a__ : Dict =AutoencoderKL.from_pretrained(
lowerCAmelCase__ , subfolder="vae" , torch_dtype=lowerCAmelCase__ , revision=lowerCAmelCase__ , )
model.to(lowerCAmelCase__ ).eval()
return model
def _lowercase ( self , lowerCAmelCase__=0 ) -> Optional[int]:
'''simple docstring'''
if torch_device == "mps":
return torch.manual_seed(lowerCAmelCase__ )
return torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
@parameterized.expand(
[
# fmt: off
[3_3, [-0.16_03, 0.98_78, -0.04_95, -0.07_90, -0.27_09, 0.83_75, -0.20_60, -0.08_24], [-0.23_95, 0.00_98, 0.01_02, -0.07_09, -0.28_40, -0.02_74, -0.07_18, -0.18_24]],
[4_7, [-0.23_76, 0.11_68, 0.13_32, -0.48_40, -0.25_08, -0.07_91, -0.04_93, -0.40_89], [0.03_50, 0.08_47, 0.04_67, 0.03_44, -0.08_42, -0.05_47, -0.06_33, -0.11_31]],
# fmt: on
] )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> List[str]:
'''simple docstring'''
a__ : List[Any] =self.get_sd_vae_model()
a__ : Tuple =self.get_sd_image(lowerCAmelCase__ )
a__ : Tuple =self.get_generator(lowerCAmelCase__ )
with torch.no_grad():
a__ : Tuple =model(lowerCAmelCase__ , generator=lowerCAmelCase__ , sample_posterior=lowerCAmelCase__ ).sample
assert sample.shape == image.shape
a__ : Tuple =sample[-1, -2:, -2:, :2].flatten().float().cpu()
a__ : Tuple =torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice )
assert torch_all_close(lowerCAmelCase__ , lowerCAmelCase__ , atol=3E-3 )
@parameterized.expand(
[
# fmt: off
[3_3, [-0.05_13, 0.02_89, 1.37_99, 0.21_66, -0.25_73, -0.08_71, 0.51_03, -0.09_99]],
[4_7, [-0.41_28, -0.13_20, -0.37_04, 0.19_65, -0.41_16, -0.23_32, -0.33_40, 0.22_47]],
# fmt: on
] )
@require_torch_gpu
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
'''simple docstring'''
a__ : Optional[int] =self.get_sd_vae_model(fpaa=lowerCAmelCase__ )
a__ : Any =self.get_sd_image(lowerCAmelCase__ , fpaa=lowerCAmelCase__ )
a__ : Dict =self.get_generator(lowerCAmelCase__ )
with torch.no_grad():
a__ : Union[str, Any] =model(lowerCAmelCase__ , generator=lowerCAmelCase__ , sample_posterior=lowerCAmelCase__ ).sample
assert sample.shape == image.shape
a__ : int =sample[-1, -2:, :2, -2:].flatten().float().cpu()
a__ : List[str] =torch.tensor(lowerCAmelCase__ )
assert torch_all_close(lowerCAmelCase__ , lowerCAmelCase__ , atol=1E-2 )
@parameterized.expand(
[
# fmt: off
[3_3, [-0.16_09, 0.98_66, -0.04_87, -0.07_77, -0.27_16, 0.83_68, -0.20_55, -0.08_14], [-0.23_95, 0.00_98, 0.01_02, -0.07_09, -0.28_40, -0.02_74, -0.07_18, -0.18_24]],
[4_7, [-0.23_77, 0.11_47, 0.13_33, -0.48_41, -0.25_06, -0.08_05, -0.04_91, -0.40_85], [0.03_50, 0.08_47, 0.04_67, 0.03_44, -0.08_42, -0.05_47, -0.06_33, -0.11_31]],
# fmt: on
] )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> List[str]:
'''simple docstring'''
a__ : Any =self.get_sd_vae_model()
a__ : Optional[int] =self.get_sd_image(lowerCAmelCase__ )
with torch.no_grad():
a__ : Dict =model(lowerCAmelCase__ ).sample
assert sample.shape == image.shape
a__ : List[str] =sample[-1, -2:, -2:, :2].flatten().float().cpu()
a__ : List[str] =torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice )
assert torch_all_close(lowerCAmelCase__ , lowerCAmelCase__ , atol=3E-3 )
@parameterized.expand(
[
# fmt: off
[1_3, [-0.20_51, -0.18_03, -0.23_11, -0.21_14, -0.32_92, -0.35_74, -0.29_53, -0.33_23]],
[3_7, [-0.26_32, -0.26_25, -0.21_99, -0.27_41, -0.45_39, -0.49_90, -0.37_20, -0.49_25]],
# fmt: on
] )
@require_torch_gpu
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
a__ : List[Any] =self.get_sd_vae_model()
a__ : Optional[Any] =self.get_sd_image(lowerCAmelCase__ , shape=(3, 4, 6_4, 6_4) )
with torch.no_grad():
a__ : Optional[Any] =model.decode(lowerCAmelCase__ ).sample
assert list(sample.shape ) == [3, 3, 5_1_2, 5_1_2]
a__ : List[Any] =sample[-1, -2:, :2, -2:].flatten().cpu()
a__ : Dict =torch.tensor(lowerCAmelCase__ )
assert torch_all_close(lowerCAmelCase__ , lowerCAmelCase__ , atol=1E-3 )
@parameterized.expand(
[
# fmt: off
[2_7, [-0.03_69, 0.02_07, -0.07_76, -0.06_82, -0.17_47, -0.19_30, -0.14_65, -0.20_39]],
[1_6, [-0.16_28, -0.21_34, -0.27_47, -0.26_42, -0.37_74, -0.44_04, -0.36_87, -0.42_77]],
# fmt: on
] )
@require_torch_gpu
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
'''simple docstring'''
a__ : Optional[Any] =self.get_sd_vae_model(fpaa=lowerCAmelCase__ )
a__ : Tuple =self.get_sd_image(lowerCAmelCase__ , shape=(3, 4, 6_4, 6_4) , fpaa=lowerCAmelCase__ )
with torch.no_grad():
a__ : Union[str, Any] =model.decode(lowerCAmelCase__ ).sample
assert list(sample.shape ) == [3, 3, 5_1_2, 5_1_2]
a__ : Tuple =sample[-1, -2:, :2, -2:].flatten().float().cpu()
a__ : Dict =torch.tensor(lowerCAmelCase__ )
assert torch_all_close(lowerCAmelCase__ , lowerCAmelCase__ , atol=5E-3 )
@parameterized.expand([(1_3,), (1_6,), (2_7,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason="xformers is not required when using PyTorch 2.0." )
def _lowercase ( self , lowerCAmelCase__ ) -> Dict:
'''simple docstring'''
a__ : int =self.get_sd_vae_model(fpaa=lowerCAmelCase__ )
a__ : List[Any] =self.get_sd_image(lowerCAmelCase__ , shape=(3, 4, 6_4, 6_4) , fpaa=lowerCAmelCase__ )
with torch.no_grad():
a__ : Optional[int] =model.decode(lowerCAmelCase__ ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
a__ : Tuple =model.decode(lowerCAmelCase__ ).sample
assert list(sample.shape ) == [3, 3, 5_1_2, 5_1_2]
assert torch_all_close(lowerCAmelCase__ , lowerCAmelCase__ , atol=1E-1 )
@parameterized.expand([(1_3,), (1_6,), (3_7,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason="xformers is not required when using PyTorch 2.0." )
def _lowercase ( self , lowerCAmelCase__ ) -> List[Any]:
'''simple docstring'''
a__ : Dict =self.get_sd_vae_model()
a__ : str =self.get_sd_image(lowerCAmelCase__ , shape=(3, 4, 6_4, 6_4) )
with torch.no_grad():
a__ : List[str] =model.decode(lowerCAmelCase__ ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
a__ : Optional[int] =model.decode(lowerCAmelCase__ ).sample
assert list(sample.shape ) == [3, 3, 5_1_2, 5_1_2]
assert torch_all_close(lowerCAmelCase__ , lowerCAmelCase__ , atol=1E-2 )
@parameterized.expand(
[
# fmt: off
[3_3, [-0.30_01, 0.09_18, -2.69_84, -3.97_20, -3.20_99, -5.03_53, 1.73_38, -0.20_65, 3.42_67]],
[4_7, [-1.50_30, -4.38_71, -6.03_55, -9.11_57, -1.66_61, -2.78_53, 2.16_07, -5.08_23, 2.56_33]],
# fmt: on
] )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
a__ : Tuple =self.get_sd_vae_model()
a__ : int =self.get_sd_image(lowerCAmelCase__ )
a__ : Any =self.get_generator(lowerCAmelCase__ )
with torch.no_grad():
a__ : str =model.encode(lowerCAmelCase__ ).latent_dist
a__ : Union[str, Any] =dist.sample(generator=lowerCAmelCase__ )
assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
a__ : Optional[Any] =sample[0, -1, -3:, -3:].flatten().cpu()
a__ : List[str] =torch.tensor(lowerCAmelCase__ )
a__ : Union[str, Any] =3E-3 if torch_device != "mps" else 1E-2
assert torch_all_close(lowerCAmelCase__ , lowerCAmelCase__ , atol=lowerCAmelCase__ )
| 95
|
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : List[str] ) -> List[Any]:
lowercase_ = 1_0
def _lowercase ( self : int ) -> List[str]:
lowercase_ = [1, 2, 3, 4]
lowercase_ = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_ , self.block_size , 0 ) , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : int ) -> Optional[Any]:
lowercase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
lowercase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_ , self.block_size , 0 ) , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
lowercase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
lowercase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_ , self.block_size , 0 ) , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Any ) -> List[Any]:
lowercase_ = '''It was the year of Our Lord one thousand seven hundred and
seventy-five.\n\nSpiritual revelations were conceded to England at that
favoured period, as at this.'''
lowercase_ , lowercase_ = process_story(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , [] )
def _lowercase ( self : List[str] ) -> List[str]:
lowercase_ = ''''''
lowercase_ , lowercase_ = process_story(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , [] )
self.assertEqual(SCREAMING_SNAKE_CASE_ , [] )
def _lowercase ( self : Union[str, Any] ) -> Union[str, Any]:
lowercase_ = (
'''It was the year of Our Lord one thousand seven hundred and '''
'''seventy-five\n\nSpiritual revelations were conceded to England '''
'''at that favoured period, as at this.\n@highlight\n\nIt was the best of times'''
)
lowercase_ , lowercase_ = process_story(SCREAMING_SNAKE_CASE_ )
lowercase_ = [
'''It was the year of Our Lord one thousand seven hundred and seventy-five.''',
'''Spiritual revelations were conceded to England at that favoured period, as at this.''',
]
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowercase_ = ['''It was the best of times.''']
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Union[str, Any] ) -> Optional[Any]:
lowercase_ = torch.tensor([1, 2, 3, 4] )
lowercase_ = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_ , 0 ).numpy() , expected.numpy() )
def _lowercase ( self : List[Any] ) -> Tuple:
lowercase_ = torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3] )
lowercase_ = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_ , 2_3 ).numpy() , expected.numpy() )
def _lowercase ( self : int ) -> Dict:
lowercase_ = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
lowercase_ = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_ , 1 ).numpy() , expected.numpy() )
def _lowercase ( self : List[str] ) -> Tuple:
lowercase_ = 1_0_1
lowercase_ = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]] )
lowercase_ = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
lowercase_ = compute_token_type_ids(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
np.testing.assert_array_equal(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 30
| 0
|
"""simple docstring"""
import math
def _snake_case ( lowercase__ ):
if not isinstance(lowercase__ , lowercase__ ):
_lowerCamelCase : Any = f'''Input value of [number={number}] must be an integer'''
raise TypeError(lowercase__ )
if number < 1:
_lowerCamelCase : Tuple = f'''Input value of [number={number}] must be > 0'''
raise ValueError(lowercase__ )
elif number == 1:
return 3
elif number == 2:
return 5
else:
_lowerCamelCase : Optional[Any] = int(math.log(number // 3 , 2 ) ) + 2
_lowerCamelCase : int = [3, 5]
_lowerCamelCase : Dict = 2
_lowerCamelCase : Optional[int] = 3
for block in range(1 , lowercase__ ):
for _ in range(lowercase__ ):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(11):
lowercase__ = 0
try:
lowercase__ = proth(number)
except ValueError:
print(F"ValueError: there is no {number}th Proth number")
continue
print(F"The {number}th Proth number: {value}")
| 96
|
def a ( snake_case__: list ):
'''simple docstring'''
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
lowercase_ = []
def generate(snake_case__: int , snake_case__: list ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , snake_case__ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
lowercase_ , lowercase_ = arr[k - 1], arr[i]
else: # k is odd
lowercase_ , lowercase_ = arr[k - 1], arr[0]
generate(k - 1 , snake_case__ )
generate(len(snake_case__ ) , snake_case__ )
return res
if __name__ == "__main__":
__a = input('Enter numbers separated by a comma:\n').strip()
__a = [int(item) for item in user_input.split(',')]
print(heaps(arr))
| 30
| 0
|
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class lowercase ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = XLMRobertaModel.from_pretrained('''xlm-roberta-base''' )
UpperCamelCase__ :int = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] )
# The dog is cute and lives in the garden house
UpperCamelCase__ :str = torch.Size((1, 12, 768) ) # batch_size, sequence_length, embedding_vector_dim
UpperCamelCase__ :Union[str, Any] = torch.tensor(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
UpperCamelCase__ :Optional[int] = model(UpperCamelCase_ )['''last_hidden_state'''].detach()
self.assertEqual(output.shape , UpperCamelCase_ )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , UpperCamelCase_ , atol=1e-3 ) )
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[str] = XLMRobertaModel.from_pretrained('''xlm-roberta-large''' )
UpperCamelCase__ :Optional[int] = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] )
# The dog is cute and lives in the garden house
UpperCamelCase__ :Union[str, Any] = torch.Size((1, 12, 1024) ) # batch_size, sequence_length, embedding_vector_dim
UpperCamelCase__ :Dict = torch.tensor(
[[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
UpperCamelCase__ :Union[str, Any] = model(UpperCamelCase_ )['''last_hidden_state'''].detach()
self.assertEqual(output.shape , UpperCamelCase_ )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , UpperCamelCase_ , atol=1e-3 ) )
| 97
|
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def a ( ):
'''simple docstring'''
lowercase_ = argparse.ArgumentParser()
parser.add_argument(
'''-m''' , '''--pretrained_model_name_or_path''' , type=snake_case__ , default=snake_case__ , required=snake_case__ , help='''Path to pretrained model or model identifier from huggingface.co/models.''' , )
parser.add_argument(
'''-c''' , '''--caption''' , type=snake_case__ , default='''robotic cat with wings''' , help='''Text used to generate images.''' , )
parser.add_argument(
'''-n''' , '''--images_num''' , type=snake_case__ , default=4 , help='''How much images to generate.''' , )
parser.add_argument(
'''-s''' , '''--seed''' , type=snake_case__ , default=42 , help='''Seed for random process.''' , )
parser.add_argument(
'''-ci''' , '''--cuda_id''' , type=snake_case__ , default=0 , help='''cuda_id.''' , )
lowercase_ = parser.parse_args()
return args
def a ( snake_case__: Optional[Any] , snake_case__: Tuple , snake_case__: Union[str, Any] ):
'''simple docstring'''
if not len(snake_case__ ) == rows * cols:
raise ValueError('''The specified number of rows and columns are not correct.''' )
lowercase_ , lowercase_ = imgs[0].size
lowercase_ = Image.new('''RGB''' , size=(cols * w, rows * h) )
lowercase_ , lowercase_ = grid.size
for i, img in enumerate(snake_case__ ):
grid.paste(snake_case__ , box=(i % cols * w, i // cols * h) )
return grid
def a ( snake_case__: Tuple , snake_case__: Union[str, Any]="robotic cat with wings" , snake_case__: Union[str, Any]=7.5 , snake_case__: List[str]=50 , snake_case__: List[Any]=1 , snake_case__: Optional[int]=42 , ):
'''simple docstring'''
lowercase_ = torch.Generator(pipeline.device ).manual_seed(snake_case__ )
lowercase_ = pipeline(
snake_case__ , guidance_scale=snake_case__ , num_inference_steps=snake_case__ , generator=snake_case__ , num_images_per_prompt=snake_case__ , ).images
lowercase_ = int(math.sqrt(snake_case__ ) )
lowercase_ = image_grid(snake_case__ , rows=_rows , cols=num_images_per_prompt // _rows )
return grid, images
__a = parse_args()
# Load models and create wrapper for stable diffusion
__a = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
__a = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
__a = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
__a = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')
__a = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__a = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
__a = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, 'unet', unet)
else:
__a = unet.to(torch.device('cuda', args.cuda_id))
__a = pipeline.to(unet.device)
__a , __a = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '{}.png'.format('_'.join(args.caption.split()))))
__a = os.path.join(args.pretrained_model_name_or_path, '_'.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, '{}.png'.format(idx + 1)))
| 30
| 0
|
"""simple docstring"""
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def a_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=1_0_2_4 ):
UpperCAmelCase__ , UpperCAmelCase__ = [], []
UpperCAmelCase__ = list(zip(lowerCamelCase , lowerCamelCase ) )
UpperCAmelCase__ , UpperCAmelCase__ = sorted_examples[0]
def is_too_big(lowerCamelCase ):
return tok(lowerCamelCase , return_tensors='pt' ).input_ids.shape[1] > max_tokens
for src, tgt in tqdm(sorted_examples[1:] ):
UpperCAmelCase__ = new_src + ' ' + src
UpperCAmelCase__ = new_tgt + ' ' + tgt
if is_too_big(lowerCamelCase ) or is_too_big(lowerCamelCase ): # cant fit, finalize example
finished_src.append(lowerCamelCase )
finished_tgt.append(lowerCamelCase )
UpperCAmelCase__ , UpperCAmelCase__ = src, tgt
else: # can fit, keep adding
UpperCAmelCase__ , UpperCAmelCase__ = cand_src, cand_tgt
# cleanup
if new_src:
assert new_tgt
finished_src.append(lowerCamelCase )
finished_tgt.append(lowerCamelCase )
return finished_src, finished_tgt
def a_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = Path(lowerCamelCase )
save_path.mkdir(exist_ok=lowerCamelCase )
for split in ["train"]:
UpperCAmelCase__ , UpperCAmelCase__ = data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
UpperCAmelCase__ = [x.rstrip() for x in Path(lowerCamelCase ).open().readlines()]
UpperCAmelCase__ = [x.rstrip() for x in Path(lowerCamelCase ).open().readlines()]
UpperCAmelCase__ , UpperCAmelCase__ = pack_examples(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
print(f'''packed {split} split from {len(lowerCamelCase )} examples -> {len(lowerCamelCase )}.''' )
Path(save_path / f'''{split}.source''' ).open('w' ).write('\n'.join(lowerCamelCase ) )
Path(save_path / f'''{split}.target''' ).open('w' ).write('\n'.join(lowerCamelCase ) )
for split in ["val", "test"]:
UpperCAmelCase__ , UpperCAmelCase__ = data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
shutil.copyfile(lowerCamelCase , save_path / f'''{split}.source''' )
shutil.copyfile(lowerCamelCase , save_path / f'''{split}.target''' )
def a_ ( ):
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument('--tok_name' , type=lowerCamelCase , help='like facebook/bart-large-cnn,t5-base, etc.' )
parser.add_argument('--max_seq_len' , type=lowerCamelCase , default=1_2_8 )
parser.add_argument('--data_dir' , type=lowerCamelCase )
parser.add_argument('--save_path' , type=lowerCamelCase )
UpperCAmelCase__ = parser.parse_args()
UpperCAmelCase__ = AutoTokenizer.from_pretrained(args.tok_name )
return pack_data_dir(lowerCamelCase , Path(args.data_dir ) , args.max_seq_len , args.save_path )
if __name__ == "__main__":
packer_cli()
| 98
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['RemBertTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['RemBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RemBertForCausalLM',
'RemBertForMaskedLM',
'RemBertForMultipleChoice',
'RemBertForQuestionAnswering',
'RemBertForSequenceClassification',
'RemBertForTokenClassification',
'RemBertLayer',
'RemBertModel',
'RemBertPreTrainedModel',
'load_tf_weights_in_rembert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRemBertForCausalLM',
'TFRemBertForMaskedLM',
'TFRemBertForMultipleChoice',
'TFRemBertForQuestionAnswering',
'TFRemBertForSequenceClassification',
'TFRemBertForTokenClassification',
'TFRemBertLayer',
'TFRemBertModel',
'TFRemBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 30
| 0
|
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class A__ :
"""simple docstring"""
def __init__( self , lowercase , lowercase=13 , lowercase=30 , lowercase=2 , lowercase=3 , lowercase=True , lowercase=True , lowercase=32 , lowercase=5 , lowercase=4 , lowercase=37 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=10 , lowercase=0.02 , lowercase=3 , lowercase=None , lowercase=2 , ) -> Optional[int]:
'''simple docstring'''
a__ : List[Any] = parent
a__ : str = batch_size
a__ : Any = image_size
a__ : int = patch_size
a__ : Tuple = num_channels
a__ : Optional[int] = is_training
a__ : int = use_labels
a__ : Union[str, Any] = hidden_size
a__ : str = num_hidden_layers
a__ : List[Any] = num_attention_heads
a__ : Dict = intermediate_size
a__ : Any = hidden_act
a__ : Any = hidden_dropout_prob
a__ : List[str] = attention_probs_dropout_prob
a__ : Dict = type_sequence_label_size
a__ : List[Any] = initializer_range
a__ : Dict = scope
a__ : List[Any] = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
a__ : Any = (image_size // patch_size) ** 2
a__ : Any = num_patches + 2
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
a__ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
a__ : int = None
if self.use_labels:
a__ : str = ids_tensor([self.batch_size] , self.type_sequence_label_size)
a__ : Tuple = self.get_config()
return config, pixel_values, labels
def __lowercase ( self) -> Dict:
'''simple docstring'''
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowercase , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __lowercase ( self , lowercase , lowercase , lowercase) -> int:
'''simple docstring'''
a__ : int = DeiTModel(config=lowercase)
model.to(lowercase)
model.eval()
a__ : Union[str, Any] = model(lowercase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def __lowercase ( self , lowercase , lowercase , lowercase) -> str:
'''simple docstring'''
a__ : List[str] = DeiTForMaskedImageModeling(config=lowercase)
model.to(lowercase)
model.eval()
a__ : List[Any] = model(lowercase)
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size))
# test greyscale images
a__ : Tuple = 1
a__ : Optional[Any] = DeiTForMaskedImageModeling(lowercase)
model.to(lowercase)
model.eval()
a__ : Union[str, Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
a__ : Dict = model(lowercase)
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size))
def __lowercase ( self , lowercase , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
a__ : int = self.type_sequence_label_size
a__ : List[str] = DeiTForImageClassification(lowercase)
model.to(lowercase)
model.eval()
a__ : Tuple = model(lowercase , labels=lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
# test greyscale images
a__ : Union[str, Any] = 1
a__ : Optional[Any] = DeiTForImageClassification(lowercase)
model.to(lowercase)
model.eval()
a__ : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
a__ : str = model(lowercase , labels=lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
def __lowercase ( self) -> Dict:
'''simple docstring'''
a__ : Tuple = self.prepare_config_and_inputs()
(
(
a__
) , (
a__
) , (
a__
) ,
) : Union[str, Any] = config_and_inputs
a__ : Dict = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class A__ ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
__A : Dict = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
__A : str = (
{
'''feature-extraction''': DeiTModel,
'''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
__A : List[str] = False
__A : Union[str, Any] = False
__A : Optional[Any] = False
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
a__ : List[str] = DeiTModelTester(self)
a__ : str = ConfigTester(self , config_class=lowercase , has_text_modality=lowercase , hidden_size=37)
def __lowercase ( self) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='DeiT does not use inputs_embeds')
def __lowercase ( self) -> Tuple:
'''simple docstring'''
pass
def __lowercase ( self) -> List[str]:
'''simple docstring'''
a__ , a__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ : Union[str, Any] = model_class(lowercase)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
a__ : List[str] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase , nn.Linear))
def __lowercase ( self) -> Tuple:
'''simple docstring'''
a__ , a__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ : Union[str, Any] = model_class(lowercase)
a__ : List[str] = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a__ : Union[str, Any] = [*signature.parameters.keys()]
a__ : Any = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowercase)
def __lowercase ( self) -> int:
'''simple docstring'''
a__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase)
def __lowercase ( self) -> Any:
'''simple docstring'''
a__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*lowercase)
def __lowercase ( self) -> List[str]:
'''simple docstring'''
a__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase)
def __lowercase ( self , lowercase , lowercase , lowercase=False) -> Tuple:
'''simple docstring'''
a__ : Optional[int] = super()._prepare_for_class(lowercase , lowercase , return_labels=lowercase)
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
if not self.model_tester.is_training:
return
a__ , a__ : int = self.model_tester.prepare_config_and_inputs_for_common()
a__ : List[Any] = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(lowercase)
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
a__ : List[str] = model_class(lowercase)
model.to(lowercase)
model.train()
a__ : str = self._prepare_for_class(lowercase , lowercase , return_labels=lowercase)
a__ : str = model(**lowercase).loss
loss.backward()
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
a__ , a__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
a__ : List[str] = False
a__ : List[str] = True
for model_class in self.all_model_classes:
if model_class in get_values(lowercase) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
a__ : int = model_class(lowercase)
model.gradient_checkpointing_enable()
model.to(lowercase)
model.train()
a__ : List[Any] = self._prepare_for_class(lowercase , lowercase , return_labels=lowercase)
a__ : List[Any] = model(**lowercase).loss
loss.backward()
def __lowercase ( self) -> Dict:
'''simple docstring'''
a__ , a__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
a__ : Tuple = [
{'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
{'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
{'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(lowercase),
*get_values(lowercase),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F'Testing {model_class} with {problem_type["title"]}'):
a__ : List[str] = problem_type['title']
a__ : Any = problem_type['num_labels']
a__ : Union[str, Any] = model_class(lowercase)
model.to(lowercase)
model.train()
a__ : List[Any] = self._prepare_for_class(lowercase , lowercase , return_labels=lowercase)
if problem_type["num_labels"] > 1:
a__ : Any = inputs['labels'].unsqueeze(1).repeat(1 , problem_type['num_labels'])
a__ : Tuple = inputs['labels'].to(problem_type['dtype'])
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=lowercase) as warning_list:
a__ : Optional[Any] = model(**lowercase).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message):
raise ValueError(
F'Something is going wrong in the regression problem: intercepted {w.message}')
loss.backward()
@slow
def __lowercase ( self) -> Any:
'''simple docstring'''
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : Any = DeiTModel.from_pretrained(lowercase)
self.assertIsNotNone(lowercase)
def A_ ( ) -> Any:
a__ : Any = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class A__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowercase ( self) -> List[str]:
'''simple docstring'''
return (
DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224')
if is_vision_available()
else None
)
@slow
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
a__ : Optional[Any] = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224').to(
lowercase)
a__ : Optional[Any] = self.default_image_processor
a__ : str = prepare_img()
a__ : Optional[int] = image_processor(images=lowercase , return_tensors='pt').to(lowercase)
# forward pass
with torch.no_grad():
a__ : int = model(**lowercase)
# verify the logits
a__ : Tuple = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape , lowercase)
a__ : int = torch.tensor([-1.02_66, 0.19_12, -1.28_61]).to(lowercase)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase , atol=1e-4))
@slow
@require_accelerate
@require_torch_gpu
def __lowercase ( self) -> Any:
'''simple docstring'''
a__ : Dict = DeiTModel.from_pretrained(
'facebook/deit-base-distilled-patch16-224' , torch_dtype=torch.floataa , device_map='auto')
a__ : Union[str, Any] = self.default_image_processor
a__ : List[str] = prepare_img()
a__ : int = image_processor(images=lowercase , return_tensors='pt')
a__ : str = inputs.pixel_values.to(lowercase)
# forward pass to make sure inference works in fp16
with torch.no_grad():
a__ : Any = model(lowercase)
| 99
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['DeiTFeatureExtractor']
__a = ['DeiTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DeiTForImageClassification',
'DeiTForImageClassificationWithTeacher',
'DeiTForMaskedImageModeling',
'DeiTModel',
'DeiTPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDeiTForImageClassification',
'TFDeiTForImageClassificationWithTeacher',
'TFDeiTForMaskedImageModeling',
'TFDeiTModel',
'TFDeiTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 30
| 0
|
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from ...test_tokenization_common import TokenizerTesterMixin
__magic_name__ = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
__magic_name__ = 250004
__magic_name__ = 250020
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_ ( __a , unittest.TestCase ):
"""simple docstring"""
__lowercase : int = MBartaaTokenizer
__lowercase : str = MBartaaTokenizerFast
__lowercase : List[str] = True
__lowercase : Union[str, Any] = True
def snake_case_ ( self):
super().setUp()
# We have a SentencePiece fixture for testing
__SCREAMING_SNAKE_CASE = MBartaaTokenizer(lowerCAmelCase__ , src_lang="""en_XX""" , tgt_lang="""ro_RO""" , keep_accents=lowerCAmelCase__)
tokenizer.save_pretrained(self.tmpdirname)
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = """<s>"""
__SCREAMING_SNAKE_CASE = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__) , lowerCAmelCase__)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__) , lowerCAmelCase__)
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , """<s>""")
self.assertEqual(vocab_keys[1] , """<pad>""")
self.assertEqual(vocab_keys[-1] , """<mask>""")
self.assertEqual(len(lowerCAmelCase__) , 1_0_5_4)
def snake_case_ ( self):
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_5_4)
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = MBartaaTokenizer(lowerCAmelCase__ , src_lang="""en_XX""" , tgt_lang="""ro_RO""" , keep_accents=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = tokenizer.tokenize("""This is a test""")
self.assertListEqual(lowerCAmelCase__ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase__) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
__SCREAMING_SNAKE_CASE = 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""", """."""] , )
__SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(lowerCAmelCase__)
self.assertListEqual(
lowerCAmelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
] , )
__SCREAMING_SNAKE_CASE = 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>""", """."""] , )
@slow
def snake_case_ ( self):
# fmt: off
__SCREAMING_SNAKE_CASE = {"""input_ids""": [[2_5_0_0_0_4, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [2_5_0_0_0_4, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_5_0_0_0_4, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase__ , model_name="""facebook/mbart-large-50""" , revision="""d3913889c59cd5c9e456b269c376325eabad57e2""" , )
def snake_case_ ( self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
__SCREAMING_SNAKE_CASE = (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart50""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
__SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(lowerCAmelCase__)
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files))
__SCREAMING_SNAKE_CASE = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f)
self.assertSequenceEqual(lowerCAmelCase__ , lowerCAmelCase__)
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(lowerCAmelCase__)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCAmelCase__ , lowerCAmelCase__))
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowerCAmelCase__)
# Save tokenizer rust, legacy_format=True
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(lowerCAmelCase__ , legacy_format=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(lowerCAmelCase__)
# Checks it save with the same files
self.assertSequenceEqual(lowerCAmelCase__ , lowerCAmelCase__)
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(lowerCAmelCase__)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCAmelCase__ , lowerCAmelCase__))
shutil.rmtree(lowerCAmelCase__)
# Save tokenizer rust, legacy_format=False
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(lowerCAmelCase__ , legacy_format=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(lowerCAmelCase__)
# Checks it saved the tokenizer.json file
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files))
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(lowerCAmelCase__)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCAmelCase__ , lowerCAmelCase__))
shutil.rmtree(lowerCAmelCase__)
@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
__lowercase : str = '''facebook/mbart-large-50-one-to-many-mmt'''
__lowercase : Any = [
''' UN Chief Says There Is No Military Solution in Syria''',
''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
]
__lowercase : Union[str, Any] = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
'''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
]
__lowercase : int = [EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2]
@classmethod
def snake_case_ ( cls):
__SCREAMING_SNAKE_CASE = MBartaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="""en_XX""" , tgt_lang="""ro_RO""")
__SCREAMING_SNAKE_CASE = 1
return cls
def snake_case_ ( self):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""] , 2_5_0_0_0_1)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""] , 2_5_0_0_0_4)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""] , 2_5_0_0_2_0)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""mr_IN"""] , 2_5_0_0_3_8)
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
self.assertListEqual(self.expected_src_tokens , lowerCAmelCase__)
def snake_case_ ( self):
self.assertIn(lowerCAmelCase__ , self.tokenizer.all_special_ids)
__SCREAMING_SNAKE_CASE = [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
__SCREAMING_SNAKE_CASE = self.tokenizer.decode(lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=lowerCAmelCase__)
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__)
self.assertNotIn(self.tokenizer.eos_token , lowerCAmelCase__)
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = ["""this is gunna be a long sentence """ * 2_0]
assert isinstance(src_text[0] , lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = 1_0
__SCREAMING_SNAKE_CASE = self.tokenizer(lowerCAmelCase__ , max_length=lowerCAmelCase__ , truncation=lowerCAmelCase__).input_ids[0]
self.assertEqual(ids[0] , lowerCAmelCase__)
self.assertEqual(ids[-1] , 2)
self.assertEqual(len(lowerCAmelCase__) , lowerCAmelCase__)
def snake_case_ ( self):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]) , [2_5_0_0_5_3, 2_5_0_0_0_1])
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = MBartaaTokenizer.from_pretrained(lowerCAmelCase__)
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , lowerCAmelCase__)
@require_torch
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=lowerCAmelCase__ , return_tensors="""pt""")
__SCREAMING_SNAKE_CASE = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id)
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == RO_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]
@require_torch
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=len(self.expected_src_tokens) , return_tensors="""pt""" , )
__SCREAMING_SNAKE_CASE = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id)
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__)
self.assertEqual((2, 1_4) , batch.input_ids.shape)
self.assertEqual((2, 1_4) , batch.attention_mask.shape)
__SCREAMING_SNAKE_CASE = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , lowerCAmelCase__)
self.assertEqual(2 , batch.decoder_input_ids[0, 0]) # decoder_start_token_id
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE])
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id])
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = self.tokenizer(self.src_text , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=3 , return_tensors="""pt""")
__SCREAMING_SNAKE_CASE = self.tokenizer(
text_target=self.tgt_text , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=1_0 , return_tensors="""pt""")
__SCREAMING_SNAKE_CASE = targets["""input_ids"""]
__SCREAMING_SNAKE_CASE = shift_tokens_right(lowerCAmelCase__ , self.tokenizer.pad_token_id)
self.assertEqual(batch.input_ids.shape[1] , 3)
self.assertEqual(batch.decoder_input_ids.shape[1] , 1_0)
@require_torch
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = self.tokenizer._build_translation_inputs(
"""A test""" , return_tensors="""pt""" , src_lang="""en_XX""" , tgt_lang="""ar_AR""")
self.assertEqual(
nested_simplify(lowerCAmelCase__) , {
# en_XX, A, test, EOS
"""input_ids""": [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
"""attention_mask""": [[1, 1, 1, 1]],
# ar_AR
"""forced_bos_token_id""": 2_5_0_0_0_1,
} , )
| 100
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__a = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'processing_trocr': ['TrOCRProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
'TrOCRForCausalLM',
'TrOCRPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 30
| 0
|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class lowercase ( unittest.TestCase ):
def A__ ( self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def A__ ( self):
lowercase = 1
lowercase = 3
lowercase = (3_2, 3_2)
lowercase = floats_tensor((batch_size, num_channels) + sizes ,rng=random.Random(0)).to(A__)
return image
@property
def A__ ( self):
torch.manual_seed(0)
lowercase = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) ,layers_per_block=2 ,sample_size=3_2 ,in_channels=4 ,out_channels=4 ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,cross_attention_dim=3_2 ,)
return model
@property
def A__ ( self):
torch.manual_seed(0)
lowercase = AutoencoderKL(
block_out_channels=[3_2, 6_4] ,in_channels=3 ,out_channels=3 ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,latent_channels=4 ,)
return model
@property
def A__ ( self):
torch.manual_seed(0)
lowercase = RobertaSeriesConfig(
hidden_size=3_2 ,project_dim=3_2 ,intermediate_size=3_7 ,layer_norm_eps=1E-05 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=5_0_0_6 ,)
return RobertaSeriesModelWithTransformation(A__)
@property
def A__ ( self):
def extract(*A__ ,**A__):
class lowercase :
def __init__( self):
lowercase = torch.ones([0])
def A__ ( self ,A__):
self.pixel_values.to(A__)
return self
return Out()
return extract
def A__ ( self):
lowercase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
lowercase = self.dummy_cond_unet
lowercase = PNDMScheduler(skip_prk_steps=A__)
lowercase = self.dummy_vae
lowercase = self.dummy_text_encoder
lowercase = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''')
lowercase = 7_7
lowercase = self.dummy_image.to(A__)
lowercase = init_image / 2 + 0.5
# make sure here that pndm scheduler skips prk
lowercase = AltDiffusionImgaImgPipeline(
unet=A__ ,scheduler=A__ ,vae=A__ ,text_encoder=A__ ,tokenizer=A__ ,safety_checker=A__ ,feature_extractor=self.dummy_extractor ,)
lowercase = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor ,do_normalize=A__)
lowercase = alt_pipe.to(A__)
alt_pipe.set_progress_bar_config(disable=A__)
lowercase = '''A painting of a squirrel eating a burger'''
lowercase = torch.Generator(device=A__).manual_seed(0)
lowercase = alt_pipe(
[prompt] ,generator=A__ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type='''np''' ,image=A__ ,)
lowercase = output.images
lowercase = torch.Generator(device=A__).manual_seed(0)
lowercase = alt_pipe(
[prompt] ,generator=A__ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type='''np''' ,image=A__ ,return_dict=A__ ,)[0]
lowercase = image[0, -3:, -3:, -1]
lowercase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
lowercase = np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499])
assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-3
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5E-3
@unittest.skipIf(torch_device != '''cuda''' ,'''This test requires a GPU''')
def A__ ( self):
lowercase = self.dummy_cond_unet
lowercase = PNDMScheduler(skip_prk_steps=A__)
lowercase = self.dummy_vae
lowercase = self.dummy_text_encoder
lowercase = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''')
lowercase = 7_7
lowercase = self.dummy_image.to(A__)
# put models in fp16
lowercase = unet.half()
lowercase = vae.half()
lowercase = bert.half()
# make sure here that pndm scheduler skips prk
lowercase = AltDiffusionImgaImgPipeline(
unet=A__ ,scheduler=A__ ,vae=A__ ,text_encoder=A__ ,tokenizer=A__ ,safety_checker=A__ ,feature_extractor=self.dummy_extractor ,)
lowercase = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor ,do_normalize=A__)
lowercase = alt_pipe.to(A__)
alt_pipe.set_progress_bar_config(disable=A__)
lowercase = '''A painting of a squirrel eating a burger'''
lowercase = torch.manual_seed(0)
lowercase = alt_pipe(
[prompt] ,generator=A__ ,num_inference_steps=2 ,output_type='''np''' ,image=A__ ,).images
assert image.shape == (1, 3_2, 3_2, 3)
@unittest.skipIf(torch_device != '''cuda''' ,'''This test requires a GPU''')
def A__ ( self):
lowercase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/img2img/sketch-mountains-input.jpg''')
# resize to resolution that is divisible by 8 but not 16 or 32
lowercase = init_image.resize((7_6_0, 5_0_4))
lowercase = '''BAAI/AltDiffusion'''
lowercase = AltDiffusionImgaImgPipeline.from_pretrained(
A__ ,safety_checker=A__ ,)
pipe.to(A__)
pipe.set_progress_bar_config(disable=A__)
pipe.enable_attention_slicing()
lowercase = '''A fantasy landscape, trending on artstation'''
lowercase = torch.manual_seed(0)
lowercase = pipe(
prompt=A__ ,image=A__ ,strength=0.75 ,guidance_scale=7.5 ,generator=A__ ,output_type='''np''' ,)
lowercase = output.images[0]
lowercase = image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]
assert image.shape == (5_0_4, 7_6_0, 3)
lowercase = np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
@slow
@require_torch_gpu
class lowercase ( unittest.TestCase ):
def A__ ( self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ ( self):
lowercase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/img2img/sketch-mountains-input.jpg''')
lowercase = init_image.resize((7_6_8, 5_1_2))
lowercase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy''')
lowercase = '''BAAI/AltDiffusion'''
lowercase = AltDiffusionImgaImgPipeline.from_pretrained(
A__ ,safety_checker=A__ ,)
pipe.to(A__)
pipe.set_progress_bar_config(disable=A__)
pipe.enable_attention_slicing()
lowercase = '''A fantasy landscape, trending on artstation'''
lowercase = torch.manual_seed(0)
lowercase = pipe(
prompt=A__ ,image=A__ ,strength=0.75 ,guidance_scale=7.5 ,generator=A__ ,output_type='''np''' ,)
lowercase = output.images[0]
assert image.shape == (5_1_2, 7_6_8, 3)
# img2img is flaky across GPUs even in fp32, so using MAE here
assert np.abs(expected_image - image).max() < 1E-2
| 101
|
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
__a = logging.get_logger(__name__)
# General docstring
__a = 'RegNetConfig'
# Base docstring
__a = 'facebook/regnet-y-040'
__a = [1, 1_0_8_8, 7, 7]
# Image classification docstring
__a = 'facebook/regnet-y-040'
__a = 'tabby, tabby cat'
__a = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 3 , SCREAMING_SNAKE_CASE_ : int = 1 , SCREAMING_SNAKE_CASE_ : int = 1 , SCREAMING_SNAKE_CASE_ : Optional[str] = "relu" , **SCREAMING_SNAKE_CASE_ : Any , ) -> List[str]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
lowercase_ = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
lowercase_ = tf.keras.layers.ConvaD(
filters=SCREAMING_SNAKE_CASE_ , kernel_size=SCREAMING_SNAKE_CASE_ , strides=SCREAMING_SNAKE_CASE_ , padding='''VALID''' , groups=SCREAMING_SNAKE_CASE_ , use_bias=SCREAMING_SNAKE_CASE_ , name='''convolution''' , )
lowercase_ = tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name='''normalization''' )
lowercase_ = ACTaFN[activation] if activation is not None else tf.identity
def _lowercase ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Any:
lowercase_ = self.convolution(self.padding(SCREAMING_SNAKE_CASE_ ) )
lowercase_ = self.normalization(SCREAMING_SNAKE_CASE_ )
lowercase_ = self.activation(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : str , SCREAMING_SNAKE_CASE_ : RegNetConfig , **SCREAMING_SNAKE_CASE_ : str ) -> Any:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = config.num_channels
lowercase_ = TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name='''embedder''' , )
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : str ) -> Optional[int]:
lowercase_ = shape_list(SCREAMING_SNAKE_CASE_ )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
'''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''' )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
lowercase_ = tf.transpose(SCREAMING_SNAKE_CASE_ , perm=(0, 2, 3, 1) )
lowercase_ = self.embedder(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 2 , **SCREAMING_SNAKE_CASE_ : List[str] ) -> Union[str, Any]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = tf.keras.layers.ConvaD(
filters=SCREAMING_SNAKE_CASE_ , kernel_size=1 , strides=SCREAMING_SNAKE_CASE_ , use_bias=SCREAMING_SNAKE_CASE_ , name='''convolution''' )
lowercase_ = tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name='''normalization''' )
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : tf.Tensor , SCREAMING_SNAKE_CASE_ : bool = False ) -> tf.Tensor:
return self.normalization(self.convolution(SCREAMING_SNAKE_CASE_ ) , training=SCREAMING_SNAKE_CASE_ )
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , **SCREAMING_SNAKE_CASE_ : int ) -> Optional[Any]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = tf.keras.layers.GlobalAveragePoolingaD(keepdims=SCREAMING_SNAKE_CASE_ , name='''pooler''' )
lowercase_ = [
tf.keras.layers.ConvaD(filters=SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation='''relu''' , name='''attention.0''' ),
tf.keras.layers.ConvaD(filters=SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation='''sigmoid''' , name='''attention.2''' ),
]
def _lowercase ( self : str , SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> Dict:
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
lowercase_ = self.pooler(SCREAMING_SNAKE_CASE_ )
for layer_module in self.attention:
lowercase_ = layer_module(SCREAMING_SNAKE_CASE_ )
lowercase_ = hidden_state * pooled
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : str , SCREAMING_SNAKE_CASE_ : RegNetConfig , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 1 , **SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> List[str]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = in_channels != out_channels or stride != 1
lowercase_ = max(1 , out_channels // config.groups_width )
lowercase_ = (
TFRegNetShortCut(SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , name='''shortcut''' )
if should_apply_shortcut
else tf.keras.layers.Activation('''linear''' , name='''shortcut''' )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
lowercase_ = [
TFRegNetConvLayer(SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation=config.hidden_act , name='''layer.0''' ),
TFRegNetConvLayer(
SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , groups=SCREAMING_SNAKE_CASE_ , activation=config.hidden_act , name='''layer.1''' ),
TFRegNetConvLayer(SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation=SCREAMING_SNAKE_CASE_ , name='''layer.2''' ),
]
lowercase_ = ACTaFN[config.hidden_act]
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Any:
lowercase_ = hidden_state
for layer_module in self.layers:
lowercase_ = layer_module(SCREAMING_SNAKE_CASE_ )
lowercase_ = self.shortcut(SCREAMING_SNAKE_CASE_ )
hidden_state += residual
lowercase_ = self.activation(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Any , SCREAMING_SNAKE_CASE_ : RegNetConfig , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 1 , **SCREAMING_SNAKE_CASE_ : List[str] ) -> int:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = in_channels != out_channels or stride != 1
lowercase_ = max(1 , out_channels // config.groups_width )
lowercase_ = (
TFRegNetShortCut(SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , name='''shortcut''' )
if should_apply_shortcut
else tf.keras.layers.Activation('''linear''' , name='''shortcut''' )
)
lowercase_ = [
TFRegNetConvLayer(SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation=config.hidden_act , name='''layer.0''' ),
TFRegNetConvLayer(
SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , groups=SCREAMING_SNAKE_CASE_ , activation=config.hidden_act , name='''layer.1''' ),
TFRegNetSELayer(SCREAMING_SNAKE_CASE_ , reduced_channels=int(round(in_channels / 4 ) ) , name='''layer.2''' ),
TFRegNetConvLayer(SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation=SCREAMING_SNAKE_CASE_ , name='''layer.3''' ),
]
lowercase_ = ACTaFN[config.hidden_act]
def _lowercase ( self : int , SCREAMING_SNAKE_CASE_ : Dict ) -> Optional[Any]:
lowercase_ = hidden_state
for layer_module in self.layers:
lowercase_ = layer_module(SCREAMING_SNAKE_CASE_ )
lowercase_ = self.shortcut(SCREAMING_SNAKE_CASE_ )
hidden_state += residual
lowercase_ = self.activation(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : str , SCREAMING_SNAKE_CASE_ : RegNetConfig , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 2 , SCREAMING_SNAKE_CASE_ : int = 2 , **SCREAMING_SNAKE_CASE_ : List[Any] ) -> List[str]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = TFRegNetXLayer if config.layer_type == '''x''' else TFRegNetYLayer
lowercase_ = [
# downsampling is done in the first layer with stride of 2
layer(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , name='''layers.0''' ),
*[layer(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , name=f'''layers.{i+1}''' ) for i in range(depth - 1 )],
]
def _lowercase ( self : Tuple , SCREAMING_SNAKE_CASE_ : int ) -> int:
for layer_module in self.layers:
lowercase_ = layer_module(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : RegNetConfig , **SCREAMING_SNAKE_CASE_ : Dict ) -> Tuple:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
SCREAMING_SNAKE_CASE_ , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name='''stages.0''' , ) )
lowercase_ = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(SCREAMING_SNAKE_CASE_ , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , depth=SCREAMING_SNAKE_CASE_ , name=f'''stages.{i+1}''' ) )
def _lowercase ( self : Dict , SCREAMING_SNAKE_CASE_ : tf.Tensor , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : bool = True ) -> TFBaseModelOutputWithNoAttention:
lowercase_ = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowercase_ = hidden_states + (hidden_state,)
lowercase_ = stage_module(SCREAMING_SNAKE_CASE_ )
if output_hidden_states:
lowercase_ = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE_ , hidden_states=SCREAMING_SNAKE_CASE_ )
@keras_serializable
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
a :str = RegNetConfig
def __init__( self : str , SCREAMING_SNAKE_CASE_ : Dict , **SCREAMING_SNAKE_CASE_ : List[str] ) -> Any:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = config
lowercase_ = TFRegNetEmbeddings(SCREAMING_SNAKE_CASE_ , name='''embedder''' )
lowercase_ = TFRegNetEncoder(SCREAMING_SNAKE_CASE_ , name='''encoder''' )
lowercase_ = tf.keras.layers.GlobalAveragePoolingaD(keepdims=SCREAMING_SNAKE_CASE_ , name='''pooler''' )
@unpack_inputs
def _lowercase ( self : Dict , SCREAMING_SNAKE_CASE_ : tf.Tensor , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : bool = False , ) -> TFBaseModelOutputWithPoolingAndNoAttention:
lowercase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ = self.embedder(SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
lowercase_ = self.encoder(
SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
lowercase_ = encoder_outputs[0]
lowercase_ = self.pooler(SCREAMING_SNAKE_CASE_ )
# Change to NCHW output format have uniformity in the modules
lowercase_ = tf.transpose(SCREAMING_SNAKE_CASE_ , perm=(0, 3, 1, 2) )
lowercase_ = tf.transpose(SCREAMING_SNAKE_CASE_ , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
lowercase_ = tuple([tf.transpose(SCREAMING_SNAKE_CASE_ , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=SCREAMING_SNAKE_CASE_ , pooler_output=SCREAMING_SNAKE_CASE_ , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Tuple = RegNetConfig
a :Any = 'regnet'
a :List[str] = 'pixel_values'
@property
def _lowercase ( self : List[str] ) -> str:
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4) , dtype=tf.floataa )}
__a = r'\n Parameters:\n This model is a Tensorflow\n [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n behavior.\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'
__a = r'\n Args:\n pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConveNextImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
'The bare RegNet model outputting raw features without any specific head on top.' , UpperCAmelCase , )
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : RegNetConfig , *SCREAMING_SNAKE_CASE_ : Any , **SCREAMING_SNAKE_CASE_ : str ) -> List[str]:
super().__init__(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowercase_ = TFRegNetMainLayer(SCREAMING_SNAKE_CASE_ , name='''regnet''' )
@unpack_inputs
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=SCREAMING_SNAKE_CASE_ , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : tf.Tensor , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : Optional[Any]=False , ) -> Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
lowercase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ = self.regnet(
pixel_values=SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
'\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , UpperCAmelCase , )
class lowercase__( UpperCAmelCase , UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : RegNetConfig , *SCREAMING_SNAKE_CASE_ : str , **SCREAMING_SNAKE_CASE_ : int ) -> Union[str, Any]:
super().__init__(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowercase_ = config.num_labels
lowercase_ = TFRegNetMainLayer(SCREAMING_SNAKE_CASE_ , name='''regnet''' )
# classification head
lowercase_ = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name='''classifier.1''' ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=SCREAMING_SNAKE_CASE_ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : tf.Tensor = None , SCREAMING_SNAKE_CASE_ : tf.Tensor = None , SCREAMING_SNAKE_CASE_ : bool = None , SCREAMING_SNAKE_CASE_ : bool = None , SCREAMING_SNAKE_CASE_ : Dict=False , ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
lowercase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ = self.regnet(
SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
lowercase_ = outputs.pooler_output if return_dict else outputs[1]
lowercase_ = self.classifier[0](SCREAMING_SNAKE_CASE_ )
lowercase_ = self.classifier[1](SCREAMING_SNAKE_CASE_ )
lowercase_ = None if labels is None else self.hf_compute_loss(labels=SCREAMING_SNAKE_CASE_ , logits=SCREAMING_SNAKE_CASE_ )
if not return_dict:
lowercase_ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=SCREAMING_SNAKE_CASE_ , logits=SCREAMING_SNAKE_CASE_ , hidden_states=outputs.hidden_states )
| 30
| 0
|
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _UpperCAmelCase ( __snake_case, unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =DDIMPipeline
lowerCamelCase__ =UNCONDITIONAL_IMAGE_GENERATION_PARAMS
lowerCamelCase__ =PipelineTesterMixin.required_optional_params - {
'num_images_per_prompt',
'latents',
'callback',
'callback_steps',
}
lowerCamelCase__ =UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
lowerCamelCase__ =False
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Any = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
__snake_case : Optional[Any] = DDIMScheduler()
__snake_case : Dict = {'''unet''': unet, '''scheduler''': scheduler}
return components
def SCREAMING_SNAKE_CASE (self , a_ , a_=0 ):
'''simple docstring'''
if str(a_ ).startswith('''mps''' ):
__snake_case : Any = torch.manual_seed(a_ )
else:
__snake_case : Union[str, Any] = torch.Generator(device=a_ ).manual_seed(a_ )
__snake_case : Dict = {
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Tuple = '''cpu'''
__snake_case : Optional[int] = self.get_dummy_components()
__snake_case : List[Any] = self.pipeline_class(**a_ )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
__snake_case : Tuple = self.get_dummy_inputs(a_ )
__snake_case : List[Any] = pipe(**a_ ).images
__snake_case : int = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
__snake_case : Optional[Any] = np.array(
[1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04] )
__snake_case : List[str] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(a_ , 1E-3 )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
super().test_save_load_local(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Union[str, Any] = '''google/ddpm-cifar10-32'''
__snake_case : str = UNetaDModel.from_pretrained(a_ )
__snake_case : List[Any] = DDIMScheduler()
__snake_case : Optional[Any] = DDIMPipeline(unet=a_ , scheduler=a_ )
ddim.to(a_ )
ddim.set_progress_bar_config(disable=a_ )
__snake_case : List[str] = torch.manual_seed(0 )
__snake_case : int = ddim(generator=a_ , eta=0.0 , output_type='''numpy''' ).images
__snake_case : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__snake_case : Union[str, Any] = np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Dict = '''google/ddpm-ema-bedroom-256'''
__snake_case : Optional[Any] = UNetaDModel.from_pretrained(a_ )
__snake_case : List[str] = DDIMScheduler.from_pretrained(a_ )
__snake_case : Tuple = DDIMPipeline(unet=a_ , scheduler=a_ )
ddpm.to(a_ )
ddpm.set_progress_bar_config(disable=a_ )
__snake_case : List[str] = torch.manual_seed(0 )
__snake_case : List[str] = ddpm(generator=a_ , output_type='''numpy''' ).images
__snake_case : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
__snake_case : Tuple = np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 102
|
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
from ..pytorch_utils import ConvaD
if is_accelerate_available():
from accelerate import init_empty_weights
from accelerate.utils import find_tied_parameters
__a = logging.get_logger(__name__)
def a ( snake_case__: Optional[int] , snake_case__: Dict , snake_case__: int , snake_case__: List[str]=None , snake_case__: List[Any]=None ):
'''simple docstring'''
# Recurse if needed
if "." in tensor_name:
lowercase_ = tensor_name.split('''.''' )
for split in splits[:-1]:
lowercase_ = getattr(snake_case__ , snake_case__ )
if new_module is None:
raise ValueError(F'''{module} has no attribute {split}.''' )
lowercase_ = new_module
lowercase_ = splits[-1]
if tensor_name not in module._parameters and tensor_name not in module._buffers:
raise ValueError(F'''{module} does not have a parameter or a buffer named {tensor_name}.''' )
lowercase_ = tensor_name in module._buffers
lowercase_ = getattr(snake_case__ , snake_case__ )
if old_value.device == torch.device('''meta''' ) and device not in ["meta", torch.device('''meta''' )] and value is None:
raise ValueError(F'''{tensor_name} is on the meta device, we need a `value` to put in on {device}.''' )
lowercase_ = False
lowercase_ = False
if is_buffer or not is_bitsandbytes_available():
lowercase_ = False
lowercase_ = False
else:
lowercase_ = hasattr(bnb.nn , '''Params4bit''' ) and isinstance(module._parameters[tensor_name] , bnb.nn.Paramsabit )
lowercase_ = isinstance(module._parameters[tensor_name] , bnb.nn.IntaParams )
if is_abit or is_abit:
lowercase_ = module._parameters[tensor_name]
if param.device.type != "cuda":
if value is None:
lowercase_ = old_value.to(snake_case__ )
elif isinstance(snake_case__ , torch.Tensor ):
lowercase_ = value.to('''cpu''' )
if value.dtype == torch.inta:
lowercase_ = version.parse(importlib.metadata.version('''bitsandbytes''' ) ) > version.parse(
'''0.37.2''' )
if not is_abit_serializable:
raise ValueError(
'''Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. '''
'''Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.''' )
else:
lowercase_ = torch.tensor(snake_case__ , device='''cpu''' )
# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
# Since weights are saved in the correct "orientation", we skip transposing when loading.
if issubclass(module.source_cls , snake_case__ ) and fpaa_statistics is None:
lowercase_ = new_value.T
lowercase_ = old_value.__dict__
if is_abit:
lowercase_ = bnb.nn.IntaParams(snake_case__ , requires_grad=snake_case__ , **snake_case__ ).to(snake_case__ )
elif is_abit:
lowercase_ = bnb.nn.Paramsabit(snake_case__ , requires_grad=snake_case__ , **snake_case__ ).to(snake_case__ )
lowercase_ = new_value
if fpaa_statistics is not None:
setattr(module.weight , '''SCB''' , fpaa_statistics.to(snake_case__ ) )
else:
if value is None:
lowercase_ = old_value.to(snake_case__ )
elif isinstance(snake_case__ , torch.Tensor ):
lowercase_ = value.to(snake_case__ )
else:
lowercase_ = torch.tensor(snake_case__ , device=snake_case__ )
if is_buffer:
lowercase_ = new_value
else:
lowercase_ = nn.Parameter(snake_case__ , requires_grad=old_value.requires_grad )
lowercase_ = new_value
def a ( snake_case__: str , snake_case__: Union[str, Any]=None , snake_case__: Any=None , snake_case__: List[str]=None , snake_case__: Optional[Any]=False ):
'''simple docstring'''
for name, module in model.named_children():
if current_key_name is None:
lowercase_ = []
current_key_name.append(snake_case__ )
if (isinstance(snake_case__ , nn.Linear ) or isinstance(snake_case__ , snake_case__ )) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
if not any(key in '''.'''.join(snake_case__ ) for key in modules_to_not_convert ):
with init_empty_weights():
if isinstance(snake_case__ , snake_case__ ):
lowercase_ , lowercase_ = module.weight.shape
else:
lowercase_ = module.in_features
lowercase_ = module.out_features
if quantization_config.quantization_method() == "llm_int8":
lowercase_ = bnb.nn.LinearabitLt(
snake_case__ , snake_case__ , module.bias is not None , has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight , threshold=quantization_config.llm_inta_threshold , )
lowercase_ = True
else:
if (
quantization_config.llm_inta_skip_modules is not None
and name in quantization_config.llm_inta_skip_modules
):
pass
else:
lowercase_ = bnb.nn.Linearabit(
snake_case__ , snake_case__ , module.bias is not None , quantization_config.bnb_abit_compute_dtype , compress_statistics=quantization_config.bnb_abit_use_double_quant , quant_type=quantization_config.bnb_abit_quant_type , )
lowercase_ = True
# Store the module class in case we need to transpose the weight later
lowercase_ = type(snake_case__ )
# Force requires grad to False to avoid unexpected errors
model._modules[name].requires_grad_(snake_case__ )
if len(list(module.children() ) ) > 0:
lowercase_ , lowercase_ = _replace_with_bnb_linear(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , has_been_replaced=snake_case__ , )
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def a ( snake_case__: Any , snake_case__: Any=None , snake_case__: Union[str, Any]=None , snake_case__: str=None ):
'''simple docstring'''
lowercase_ = ['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
lowercase_ , lowercase_ = _replace_with_bnb_linear(
snake_case__ , snake_case__ , snake_case__ , snake_case__ )
if not has_been_replaced:
logger.warning(
'''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
''' Please double check your model architecture, or submit an issue on github if you think this is'''
''' a bug.''' )
return model
def a ( *snake_case__: str , **snake_case__: Dict ):
'''simple docstring'''
warnings.warn(
'''`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead''' , snake_case__ , )
return replace_with_bnb_linear(*snake_case__ , **snake_case__ )
def a ( *snake_case__: Any , **snake_case__: List[Any] ):
'''simple docstring'''
warnings.warn(
'''`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead''' , snake_case__ , )
return set_module_quantized_tensor_to_device(*snake_case__ , **snake_case__ )
def a ( snake_case__: Optional[Any] ):
'''simple docstring'''
lowercase_ = deepcopy(snake_case__ ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
tied_model.tie_weights()
lowercase_ = find_tied_parameters(snake_case__ )
# For compatibility with Accelerate < 0.18
if isinstance(snake_case__ , snake_case__ ):
lowercase_ = sum(list(tied_params.values() ) , [] ) + list(tied_params.keys() )
else:
lowercase_ = sum(snake_case__ , [] )
lowercase_ = len(snake_case__ ) > 0
# Check if it is a base model
lowercase_ = not hasattr(snake_case__ , model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
lowercase_ = list(model.named_children() )
lowercase_ = [list_modules[-1][0]]
# add last module together with tied weights
lowercase_ = set(snake_case__ ) - set(snake_case__ )
lowercase_ = list(set(snake_case__ ) ) + list(snake_case__ )
# remove ".weight" from the keys
lowercase_ = ['''.weight''', '''.bias''']
lowercase_ = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
lowercase_ = name.replace(snake_case__ , '''''' )
filtered_module_names.append(snake_case__ )
return filtered_module_names
| 30
| 0
|
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase_ )
class __snake_case ( UpperCamelCase_ ):
_a = field(default='''language-modeling''' ,metadata={'''include_in_asdict_even_if_is_default''': True} )
_a = Features({'''text''': Value('''string''' )} )
_a = Features({} )
_a = "text"
@property
def UpperCAmelCase__ ( self : str):
return {self.text_column: "text"}
| 103
|
import argparse
import os
import re
__a = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__a = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
__a = re.compile(r'\s*\(\s*"(\S[^"]+)"')
def a ( snake_case__: str , snake_case__: bool = False ):
'''simple docstring'''
with open(snake_case__ , '''r''' , encoding='''utf-8''' ) as f:
lowercase_ = f.read()
lowercase_ = content.split('''\n''' )
lowercase_ = []
lowercase_ = 0
while line_idx < len(snake_case__ ):
if _re_intro_mapping.search(lines[line_idx] ) is not None:
lowercase_ = len(re.search(r'''^(\s*)\S''' , lines[line_idx] ).groups()[0] ) + 8
# Start of a new mapping!
while not lines[line_idx].startswith(''' ''' * indent + '''(''' ):
new_lines.append(lines[line_idx] )
line_idx += 1
lowercase_ = []
while lines[line_idx].strip() != "]":
# Blocks either fit in one line or not
if lines[line_idx].strip() == "(":
lowercase_ = line_idx
while not lines[line_idx].startswith(''' ''' * indent + ''')''' ):
line_idx += 1
blocks.append('''\n'''.join(lines[start_idx : line_idx + 1] ) )
else:
blocks.append(lines[line_idx] )
line_idx += 1
# Sort blocks by their identifiers
lowercase_ = sorted(snake_case__ , key=lambda snake_case__ : _re_identifier.search(snake_case__ ).groups()[0] )
new_lines += blocks
else:
new_lines.append(lines[line_idx] )
line_idx += 1
if overwrite:
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as f:
f.write('''\n'''.join(snake_case__ ) )
elif "\n".join(snake_case__ ) != content:
return True
def a ( snake_case__: bool = False ):
'''simple docstring'''
lowercase_ = [os.path.join(snake_case__ , snake_case__ ) for f in os.listdir(snake_case__ ) if f.endswith('''.py''' )]
lowercase_ = [sort_auto_mapping(snake_case__ , overwrite=snake_case__ ) for fname in fnames]
if not overwrite and any(snake_case__ ):
lowercase_ = [f for f, d in zip(snake_case__ , snake_case__ ) if d]
raise ValueError(
F'''The following files have auto mappings that need sorting: {', '.join(snake_case__ )}. Run `make style` to fix'''
''' this.''' )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
__a = parser.parse_args()
sort_all_auto_mappings(not args.check_only)
| 30
| 0
|
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowercase_ :
"""simple docstring"""
def __init__( self : str ,lowercase__ : Tuple ,lowercase__ : Dict=1_3 ,lowercase__ : List[str]=3_0 ,lowercase__ : Tuple=2 ,lowercase__ : Optional[int]=3 ,lowercase__ : List[str]=True ,lowercase__ : Tuple=True ,lowercase__ : int=3_2 ,lowercase__ : List[str]=5 ,lowercase__ : Tuple=4 ,lowercase__ : Any=3_7 ,lowercase__ : Any="gelu" ,lowercase__ : Union[str, Any]=0.1 ,lowercase__ : Optional[int]=0.1 ,lowercase__ : str=1_0 ,lowercase__ : Optional[int]=0.0_2 ,lowercase__ : Union[str, Any]=3 ,lowercase__ : Optional[int]=0.6 ,lowercase__ : List[Any]=None ,):
__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 = mask_ratio
__lowercase = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
__lowercase = (image_size // patch_size) ** 2
__lowercase = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
__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 SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
return ViTMAEConfig(
image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,is_decoder=lowercase__ ,initializer_range=self.initializer_range ,mask_ratio=self.mask_ratio ,)
def SCREAMING_SNAKE_CASE ( self : List[Any] ,lowercase__ : str ,lowercase__ : Optional[int] ,lowercase__ : List[str] ):
__lowercase = ViTMAEModel(config=lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = model(lowercase__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE ( self : Any ,lowercase__ : List[Any] ,lowercase__ : int ,lowercase__ : Optional[Any] ):
__lowercase = ViTMAEForPreTraining(lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = model(lowercase__ )
__lowercase = (self.image_size // self.patch_size) ** 2
__lowercase = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape ,(self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
__lowercase = 1
__lowercase = ViTMAEForPreTraining(lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowercase = model(lowercase__ )
__lowercase = self.patch_size**2
self.parent.assertEqual(result.logits.shape ,(self.batch_size, num_patches, expected_num_channels) )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowercase_ (lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
SCREAMING_SNAKE_CASE : Dict = {'feature-extraction': ViTMAEModel} if is_torch_available() else {}
SCREAMING_SNAKE_CASE : Any = False
SCREAMING_SNAKE_CASE : Optional[int] = False
SCREAMING_SNAKE_CASE : List[str] = False
SCREAMING_SNAKE_CASE : Union[str, Any] = False
def SCREAMING_SNAKE_CASE ( self : int ):
__lowercase = ViTMAEModelTester(self )
__lowercase = ConfigTester(self ,config_class=lowercase__ ,has_text_modality=lowercase__ ,hidden_size=3_7 )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMAE does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE ( self : Any ):
pass
def SCREAMING_SNAKE_CASE ( self : Tuple ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(lowercase__ )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
__lowercase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase__ ,nn.Linear ) )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(lowercase__ )
__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] ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Any ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Any ,lowercase__ : Optional[int] ,lowercase__ : List[str] ,lowercase__ : Optional[Any] ):
# make masks reproducible
np.random.seed(2 )
__lowercase = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
__lowercase = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
__lowercase = torch.from_numpy(lowercase__ )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
__lowercase = pt_noise
super().check_pt_tf_models(lowercase__ ,lowercase__ ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(lowercase__ )
model.to(lowercase__ )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(lowercase__ ,lowercase__ ) )
__lowercase = outputs[0].cpu().numpy()
__lowercase = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase__ )
__lowercase = model_class.from_pretrained(lowercase__ )
model.to(lowercase__ )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(lowercase__ ,lowercase__ ) )
# Make sure we don't have nans
__lowercase = after_outputs[0].cpu().numpy()
__lowercase = 0
__lowercase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase__ ,1e-5 )
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def SCREAMING_SNAKE_CASE ( self : Tuple ):
pass
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def SCREAMING_SNAKE_CASE ( self : int ):
pass
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def SCREAMING_SNAKE_CASE ( self : Any ):
pass
@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''' )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE ( self : Any ):
pass
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = ViTMAEModel.from_pretrained(lowercase__ )
self.assertIsNotNone(lowercase__ )
def _A ( ):
"""simple docstring"""
__lowercase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowercase_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def SCREAMING_SNAKE_CASE ( self : str ):
return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''' ) if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE ( self : str ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
__lowercase = ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''' ).to(lowercase__ )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=lowercase__ ,return_tensors='''pt''' ).to(lowercase__ )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
__lowercase = ViTMAEConfig()
__lowercase = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
__lowercase = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
__lowercase = model(**lowercase__ ,noise=torch.from_numpy(lowercase__ ).to(device=lowercase__ ) )
# verify the logits
__lowercase = torch.Size((1, 1_9_6, 7_6_8) )
self.assertEqual(outputs.logits.shape ,lowercase__ )
__lowercase = torch.tensor(
[[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] ,expected_slice.to(lowercase__ ) ,atol=1e-4 ) )
| 104
|
def a ( snake_case__: list , snake_case__: list , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if index == number_of_items:
return 0
lowercase_ = 0
lowercase_ = 0
lowercase_ = knapsack(snake_case__ , snake_case__ , snake_case__ , snake_case__ , index + 1 )
if weights[index] <= max_weight:
lowercase_ = values[index] + knapsack(
snake_case__ , snake_case__ , snake_case__ , max_weight - weights[index] , index + 1 )
return max(snake_case__ , snake_case__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 30
| 0
|
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _SCREAMING_SNAKE_CASE ( _lowercase : List[Any] ) ->str:
'''simple docstring'''
a : Union[str, Any] = os.path.join(args.tf_model_dir , "parameters.json" )
a : str = json.loads(open(_lowercase ).read() )
if not params:
raise ValueError(
F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.""" )
if not args.output.endswith(".pt" ):
a : str = args.output + ".pt"
a : Dict = OrderedDict()
with tf.device("/CPU:0" ):
a : Optional[int] = tf.train.load_checkpoint(args.tf_model_dir )
a : Optional[Any] = reader.get_variable_to_shape_map()
for key_name in shapes.keys():
a : Dict = reader.get_tensor(_lowercase ).astype(np.floataa )
if key_name.endswith("/adam_m" ) or key_name.endswith("/adam_v" ):
continue
if key_name.startswith("pasts/" ):
if key_name.startswith("pasts/mlp" ):
a : Union[str, Any] = int(key_name[9] )
elif key_name.startswith("pasts/out" ):
a : Optional[int] = 8
a : Dict = "model.sqout.%d.weight" % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
a : int = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
a : Dict = torch.tensor(_lowercase )
elif key_name.startswith("model/moe" ):
a : List[str] = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/switch_gating/kernel" ):
a : str = "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
a : Tuple = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
a : List[str] = torch.tensor(_lowercase )
elif key_name.endswith("/softmlp/kernel" ):
a : Optional[int] = "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
a : Tuple = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
a : List[Any] = torch.tensor(_lowercase )
elif key_name.endswith("/wo/kernel" ) or key_name.endswith("/wi/kernel" ):
a : Any = key_name[-9:-7]
for i in range(16 ):
a : List[Any] = "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
a : str = (
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
a : Dict = torch.tensor(_lowercase )
elif key_name.startswith("model/mlp" ):
a : Union[str, Any] = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/p1/kernel" ):
a : str = "model.blocks.%d.feed_forward.mlp.wi.weight" % player
a : Dict = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
a : Optional[int] = torch.tensor(_lowercase )
elif key_name.endswith("/p1/bias" ):
a : str = "model.blocks.%d.feed_forward.mlp.wi.bias" % player
a : List[Any] = vnp.copy() # same because it is one dimensional
a : Tuple = torch.tensor(_lowercase )
elif key_name.endswith("/p2/kernel" ):
a : Union[str, Any] = "model.blocks.%d.feed_forward.mlp.wo.weight" % player
a : Tuple = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
a : List[Any] = torch.tensor(_lowercase )
elif key_name.endswith("/p2/bias" ):
a : Dict = "model.blocks.%d.feed_forward.mlp.wo.bias" % player
a : List[str] = vnp.copy() # same because it is one dimensional
a : str = torch.tensor(_lowercase )
elif key_name.startswith("model/ln" ):
a : List[str] = int(key_name[8:].split("/" )[0] )
if key_name.endswith("/b" ):
a : Optional[Any] = "model.blocks.%d.feed_forward.norm.bias" % player
a : Tuple = vnp.copy() # same because it is one dimensional
a : int = torch.tensor(_lowercase )
elif key_name.endswith("/g" ):
a : Optional[Any] = "model.blocks.%d.feed_forward.norm.weight" % player
a : List[str] = vnp.copy() # same because it is one dimensional
a : Tuple = torch.tensor(_lowercase )
elif key_name.startswith("model/att" ):
a : Optional[Any] = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/qkv/kernel" ):
a : Union[str, Any] = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
a : List[str] = state[:, 0, :, :]
a : Dict = state[:, 1, :, :]
a : Union[str, Any] = state[:, 2, :, :]
a : str = (
state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
a : List[str] = (
state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
a : Dict = (
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
a : List[Any] = "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
a : Union[str, Any] = torch.tensor(_lowercase )
a : Any = "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
a : List[str] = torch.tensor(_lowercase )
a : Optional[Any] = "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
a : Optional[Any] = torch.tensor(_lowercase )
elif key_name.endswith("/o/kernel" ):
a : Any = "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
a : Optional[int] = (
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
a : Tuple = torch.tensor(_lowercase )
elif key_name.startswith("model/an" ):
a : List[str] = int(key_name[8:].split("/" )[0] )
if key_name.endswith("/b" ):
a : Optional[int] = "model.blocks.%d.self_attn.norm.bias" % player
a : Union[str, Any] = vnp.copy() # same because it is one dimensional
a : List[Any] = torch.tensor(_lowercase )
elif key_name.endswith("/g" ):
a : Any = "model.blocks.%d.self_attn.norm.weight" % player
a : str = vnp.copy() # same because it is one dimensional
a : Any = torch.tensor(_lowercase )
elif (
key_name.startswith("model/wte" )
or key_name.startswith("model/wpe" )
or key_name.startswith("model/ete" )
):
a : Optional[int] = {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
key_name[-3:]
]
a : Tuple = "model.%s.weight" % nlayer
a : Any = vnp.copy() # same in embedded
a : Tuple = torch.tensor(_lowercase )
if key_name.startswith("model/wte" ):
a : Optional[int] = "lm_head.weight"
a : Optional[int] = vnp.copy() # same in embedded
a : Optional[int] = torch.tensor(_lowercase )
elif key_name.startswith("model/wob" ):
a : Optional[int] = "final_logits_bias"
a : Optional[Any] = vnp.copy() # same in embedded
a : Optional[int] = state.reshape((1, -1) )
a : List[Any] = torch.tensor(_lowercase )
elif key_name == "model/dense/kernel":
a : Optional[int] = "model.last_project.weight"
a : Union[str, Any] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
a : List[Any] = torch.tensor(_lowercase )
elif key_name == "model/dense_1/bias":
a : Dict = "model.last_project.bias"
a : Optional[Any] = vnp.copy() # same because it is one dimensional
a : Any = torch.tensor(_lowercase )
torch.save(_lowercase , args.output )
if __name__ == "__main__":
a : Optional[int] = argparse.ArgumentParser(
description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
a : Tuple = parser.parse_args()
convert_tf_gptsan_to_pt(args)
| 105
|
import argparse
from collections import defaultdict
import yaml
__a = 'docs/source/en/_toctree.yml'
def a ( snake_case__: Dict ):
'''simple docstring'''
lowercase_ = defaultdict(snake_case__ )
for doc in model_doc:
counts[doc["local"]] += 1
lowercase_ = [key for key, value in counts.items() if value > 1]
lowercase_ = []
for duplicate_key in duplicates:
lowercase_ = list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key} )
if len(snake_case__ ) > 1:
raise ValueError(
F'''{duplicate_key} is present several times in the documentation table of content at '''
'''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
'''others.''' )
# Only add this once
new_doc.append({'''local''': duplicate_key, '''title''': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1] )
# Sort
return sorted(snake_case__ , key=lambda snake_case__ : s["title"].lower() )
def a ( snake_case__: List[Any]=False ):
'''simple docstring'''
with open(snake_case__ , encoding='''utf-8''' ) as f:
lowercase_ = yaml.safe_load(f.read() )
# Get to the API doc
lowercase_ = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase_ = content[api_idx]['''sections''']
# Then to the model doc
lowercase_ = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
lowercase_ = api_doc[model_idx]['''sections''']
lowercase_ = [(idx, section) for idx, section in enumerate(snake_case__ ) if '''sections''' in section]
lowercase_ = False
for idx, modality_doc in modalities_docs:
lowercase_ = modality_doc['''sections''']
lowercase_ = clean_model_doc_toc(snake_case__ )
if old_modality_doc != new_modality_doc:
lowercase_ = True
if overwrite:
lowercase_ = new_modality_doc
if diff:
if overwrite:
lowercase_ = model_doc
lowercase_ = api_doc
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(snake_case__ , allow_unicode=snake_case__ ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
__a = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 30
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|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : List[Any] = {
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowercase__ = "trocr"
lowercase__ = ["past_key_values"]
lowercase__ = {
"num_attention_heads": "decoder_attention_heads",
"hidden_size": "d_model",
"num_hidden_layers": "decoder_layers",
}
def __init__( self : str ,lowercase_ : Optional[int]=5_0_2_6_5 ,lowercase_ : Dict=1_0_2_4 ,lowercase_ : int=1_2 ,lowercase_ : Tuple=1_6 ,lowercase_ : Any=4_0_9_6 ,lowercase_ : Any="gelu" ,lowercase_ : Tuple=5_1_2 ,lowercase_ : Optional[Any]=0.1 ,lowercase_ : Optional[int]=0.0 ,lowercase_ : Any=0.0 ,lowercase_ : Optional[Any]=2 ,lowercase_ : Any=0.02 ,lowercase_ : str=0.0 ,lowercase_ : Optional[int]=True ,lowercase_ : str=False ,lowercase_ : Optional[int]=True ,lowercase_ : Union[str, Any]=True ,lowercase_ : str=1 ,lowercase_ : Dict=0 ,lowercase_ : List[str]=2 ,**lowercase_ : Union[str, Any] ,):
lowerCAmelCase__ : Dict = vocab_size
lowerCAmelCase__ : List[Any] = d_model
lowerCAmelCase__ : Dict = decoder_layers
lowerCAmelCase__ : List[Any] = decoder_attention_heads
lowerCAmelCase__ : Optional[Any] = decoder_ffn_dim
lowerCAmelCase__ : str = activation_function
lowerCAmelCase__ : List[str] = max_position_embeddings
lowerCAmelCase__ : int = dropout
lowerCAmelCase__ : Union[str, Any] = attention_dropout
lowerCAmelCase__ : str = activation_dropout
lowerCAmelCase__ : int = init_std
lowerCAmelCase__ : int = decoder_layerdrop
lowerCAmelCase__ : Optional[Any] = use_cache
lowerCAmelCase__ : Any = scale_embedding
lowerCAmelCase__ : List[Any] = use_learned_position_embeddings
lowerCAmelCase__ : Any = layernorm_embedding
super().__init__(
pad_token_id=lowercase_ ,bos_token_id=lowercase_ ,eos_token_id=lowercase_ ,decoder_start_token_id=lowercase_ ,**lowercase_ ,)
| 106
|
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Union[str, Any] = 'upernet'
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : Dict=None , SCREAMING_SNAKE_CASE_ : str=5_1_2 , SCREAMING_SNAKE_CASE_ : Tuple=0.02 , SCREAMING_SNAKE_CASE_ : Optional[Any]=[1, 2, 3, 6] , SCREAMING_SNAKE_CASE_ : Optional[int]=True , SCREAMING_SNAKE_CASE_ : Tuple=0.4 , SCREAMING_SNAKE_CASE_ : Optional[int]=3_8_4 , SCREAMING_SNAKE_CASE_ : str=2_5_6 , SCREAMING_SNAKE_CASE_ : Dict=1 , SCREAMING_SNAKE_CASE_ : Tuple=False , SCREAMING_SNAKE_CASE_ : str=2_5_5 , **SCREAMING_SNAKE_CASE_ : str , ) -> int:
super().__init__(**SCREAMING_SNAKE_CASE_ )
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowercase_ = CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase_ = backbone_config.get('''model_type''' )
lowercase_ = CONFIG_MAPPING[backbone_model_type]
lowercase_ = config_class.from_dict(SCREAMING_SNAKE_CASE_ )
lowercase_ = backbone_config
lowercase_ = hidden_size
lowercase_ = initializer_range
lowercase_ = pool_scales
lowercase_ = use_auxiliary_head
lowercase_ = auxiliary_loss_weight
lowercase_ = auxiliary_in_channels
lowercase_ = auxiliary_channels
lowercase_ = auxiliary_num_convs
lowercase_ = auxiliary_concat_input
lowercase_ = loss_ignore_index
def _lowercase ( self : List[str] ) -> List[str]:
lowercase_ = copy.deepcopy(self.__dict__ )
lowercase_ = self.backbone_config.to_dict()
lowercase_ = self.__class__.model_type
return output
| 30
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|
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
__lowerCAmelCase : Optional[List[str]] = None
__lowerCAmelCase : Dict = '<' if sys.byteorder == 'little' else '>'
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__lowerCAmelCase : List[str] = [
np.dtype('|b1'),
np.dtype('|u1'),
np.dtype('<u2'),
np.dtype('>u2'),
np.dtype('<i2'),
np.dtype('>i2'),
np.dtype('<u4'),
np.dtype('>u4'),
np.dtype('<i4'),
np.dtype('>i4'),
np.dtype('<f4'),
np.dtype('>f4'),
np.dtype('<f8'),
np.dtype('>f8'),
]
@dataclass
class snake_case__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : bool = True
SCREAMING_SNAKE_CASE_ : Optional[str] = None
# Automatically constructed
SCREAMING_SNAKE_CASE_ : ClassVar[str] = "PIL.Image.Image"
SCREAMING_SNAKE_CASE_ : ClassVar[Any] = pa.struct({"""bytes""": pa.binary(), """path""": pa.string()} )
SCREAMING_SNAKE_CASE_ : str = field(default="""Image""" , init=_UpperCamelCase , repr=_UpperCamelCase )
def __call__( self : Any ) -> List[str]:
return self.pa_type
def __UpperCAmelCase ( self : Dict , __lowerCamelCase : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'." )
if isinstance(__lowerCamelCase , __lowerCamelCase ):
a = np.array(__lowerCamelCase )
if isinstance(__lowerCamelCase , __lowerCamelCase ):
return {"path": value, "bytes": None}
elif isinstance(__lowerCamelCase , __lowerCamelCase ):
return {"path": None, "bytes": value}
elif isinstance(__lowerCamelCase , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(__lowerCamelCase )
elif isinstance(__lowerCamelCase , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(__lowerCamelCase )
elif value.get("path" ) is not None and os.path.isfile(value["path"] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("path" )}
elif value.get("bytes" ) is not None or value.get("path" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("bytes" ), "path": value.get("path" )}
else:
raise ValueError(
f"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""" )
def __UpperCAmelCase ( self : Any , __lowerCamelCase : dict , __lowerCamelCase : Tuple=None ) -> "PIL.Image.Image":
if not self.decode:
raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead." )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support decoding images, please install 'Pillow'." )
if token_per_repo_id is None:
a = {}
a , a = value["path"], value["bytes"]
if bytes_ is None:
if path is None:
raise ValueError(f"""An image should have one of 'path' or 'bytes' but both are None in {value}.""" )
else:
if is_local_path(__lowerCamelCase ):
a = PIL.Image.open(__lowerCamelCase )
else:
a = path.split("::" )[-1]
try:
a = string_to_dict(__lowerCamelCase , config.HUB_DATASETS_URL )["repo_id"]
a = token_per_repo_id.get(__lowerCamelCase )
except ValueError:
a = None
with xopen(__lowerCamelCase , "rb" , use_auth_token=__lowerCamelCase ) as f:
a = BytesIO(f.read() )
a = PIL.Image.open(bytes_ )
else:
a = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def __UpperCAmelCase ( self : Optional[int] ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("binary" ),
"path": Value("string" ),
}
)
def __UpperCAmelCase ( self : List[str] , __lowerCamelCase : Union[pa.StringArray, pa.StructArray, pa.ListArray] ) -> pa.StructArray:
if pa.types.is_string(storage.type ):
a = pa.array([None] * len(__lowerCamelCase ) , type=pa.binary() )
a = pa.StructArray.from_arrays([bytes_array, storage] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
a = pa.array([None] * len(__lowerCamelCase ) , type=pa.string() )
a = pa.StructArray.from_arrays([storage, path_array] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("bytes" ) >= 0:
a = storage.field("bytes" )
else:
a = pa.array([None] * len(__lowerCamelCase ) , type=pa.binary() )
if storage.type.get_field_index("path" ) >= 0:
a = storage.field("path" )
else:
a = pa.array([None] * len(__lowerCamelCase ) , type=pa.string() )
a = pa.StructArray.from_arrays([bytes_array, path_array] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
a = pa.array(
[encode_np_array(np.array(__lowerCamelCase ) )["bytes"] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
a = pa.array([None] * len(__lowerCamelCase ) , type=pa.string() )
a = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["bytes", "path"] , mask=bytes_array.is_null() )
return array_cast(__lowerCamelCase , self.pa_type )
def __UpperCAmelCase ( self : int , __lowerCamelCase : pa.StructArray ) -> pa.StructArray:
@no_op_if_value_is_null
def path_to_bytes(__lowerCamelCase : str ):
with xopen(__lowerCamelCase , "rb" ) as f:
a = f.read()
return bytes_
a = pa.array(
[
(path_to_bytes(x["path"] ) if x["bytes"] is None else x["bytes"]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
a = pa.array(
[os.path.basename(__lowerCamelCase ) if path is not None else None for path in storage.field("path" ).to_pylist()] , type=pa.string() , )
a = pa.StructArray.from_arrays([bytes_array, path_array] , ["bytes", "path"] , mask=bytes_array.is_null() )
return array_cast(__lowerCamelCase , self.pa_type )
def __magic_name__ ( ):
'''simple docstring'''
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'." )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
a = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def __magic_name__ ( A : "PIL.Image.Image" ):
'''simple docstring'''
a = BytesIO()
if image.format in list_image_compression_formats():
a = image.format
else:
a = "PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
image.save(A, format=A )
return buffer.getvalue()
def __magic_name__ ( A : "PIL.Image.Image" ):
'''simple docstring'''
if hasattr(A, "filename" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(A )}
def __magic_name__ ( A : np.ndarray ):
'''simple docstring'''
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'." )
a = array.dtype
a = dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
a = dtype.kind
a = dtype.itemsize
a = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
a = np.dtype("|u1" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
F"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.""" )
if dtype is not dest_dtype:
warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
a = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
a = dtype_byteorder + dtype_kind + str(A )
a = np.dtype(A )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
F"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}""" )
a = PIL.Image.fromarray(array.astype(A ) )
return {"path": None, "bytes": image_to_bytes(A )}
def __magic_name__ ( A : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ):
'''simple docstring'''
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'." )
if objs:
a , a = first_non_null_value(A )
if isinstance(A, A ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(A, np.ndarray ):
a = no_op_if_value_is_null(A )
return [obj_to_image_dict_func(A ) for obj in objs]
elif isinstance(A, PIL.Image.Image ):
a = no_op_if_value_is_null(A )
return [obj_to_image_dict_func(A ) for obj in objs]
else:
return objs
else:
return objs
| 107
|
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
__a = 'sshleifer/bart-tiny-random'
__a = 'patrickvonplaten/t5-tiny-random'
@require_torch
class lowercase__( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _lowercase ( self : Any ) -> Tuple:
return AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : int ) -> List[Any]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.num_hidden_layers , 1 )
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : List[str] ) -> List[Any]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=SCREAMING_SNAKE_CASE_ )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , self.teacher_config.encoder_layers )
def _lowercase ( self : List[Any] ) -> List[Any]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , 1 )
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=SCREAMING_SNAKE_CASE_ , d=SCREAMING_SNAKE_CASE_ )
| 30
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|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/config.json''',
'''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/config.json''',
'''xlm-roberta-large-finetuned-conll02-dutch''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'''
),
'''xlm-roberta-large-finetuned-conll02-spanish''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'''
),
'''xlm-roberta-large-finetuned-conll03-english''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'''
),
'''xlm-roberta-large-finetuned-conll03-german''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'''
),
}
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : Any ="xlm-roberta"
def __init__( self , snake_case__=30_522 , snake_case__=768 , snake_case__=12 , snake_case__=12 , snake_case__=3_072 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=512 , snake_case__=2 , snake_case__=0.02 , snake_case__=1e-12 , snake_case__=1 , snake_case__=0 , snake_case__=2 , snake_case__="absolute" , snake_case__=True , snake_case__=None , **snake_case__ , ):
"""simple docstring"""
super().__init__(pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , **snake_case__ )
lowerCAmelCase : int = vocab_size
lowerCAmelCase : str = hidden_size
lowerCAmelCase : List[Any] = num_hidden_layers
lowerCAmelCase : Union[str, Any] = num_attention_heads
lowerCAmelCase : List[Any] = hidden_act
lowerCAmelCase : Any = intermediate_size
lowerCAmelCase : List[str] = hidden_dropout_prob
lowerCAmelCase : Dict = attention_probs_dropout_prob
lowerCAmelCase : str = max_position_embeddings
lowerCAmelCase : str = type_vocab_size
lowerCAmelCase : List[Any] = initializer_range
lowerCAmelCase : int = layer_norm_eps
lowerCAmelCase : Union[str, Any] = position_embedding_type
lowerCAmelCase : int = use_cache
lowerCAmelCase : int = classifier_dropout
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
@property
def lowercase__ ( self ):
"""simple docstring"""
if self.task == "multiple-choice":
lowerCAmelCase : Tuple = {0: "batch", 1: "choice", 2: "sequence"}
else:
lowerCAmelCase : Any = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 108
|
def a ( snake_case__: int = 100 ):
'''simple docstring'''
lowercase_ = (n * (n + 1) // 2) ** 2
lowercase_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"{solution() = }")
| 30
| 0
|
"""simple docstring"""
import torch
from transformers import AutoModel
class SCREAMING_SNAKE_CASE__ ( torch.nn.Module ):
def __init__( self , _SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased" ) -> Tuple:
'''simple docstring'''
super(_SCREAMING_SNAKE_CASE , self ).__init__()
UpperCAmelCase : Any = AutoModel.from_pretrained(_SCREAMING_SNAKE_CASE , return_dict=_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Tuple = torch.nn.CosineSimilarity(3 , 1E-08 )
UpperCAmelCase : List[str] = torch.nn.Softmax(dim=1 )
def SCREAMING_SNAKE_CASE ( self , **_SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
return self.bert(**_SCREAMING_SNAKE_CASE ).last_hidden_state
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
return token_embeddings.sum(2 , keepdim=_SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=1 ) -> Union[str, Any]:
'''simple docstring'''
return self.softmax(T * self.cos(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Dict = W_supports["""sizes"""].tolist()
UpperCAmelCase : Optional[Any] = W_supports["""start_token_id"""].item()
UpperCAmelCase : Any = W_supports["""end_token_id"""].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
UpperCAmelCase : str = self.BERT(**_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Union[str, Any] = self.BERT(**_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Dict = None
UpperCAmelCase : Dict = None
UpperCAmelCase : Optional[int] = W_supports["""input_ids"""] == start_token_id
UpperCAmelCase : List[str] = W_supports["""input_ids"""] == end_token_id
for i, size in enumerate(_SCREAMING_SNAKE_CASE ):
if i == 0:
UpperCAmelCase : List[str] = 0
else:
UpperCAmelCase : Optional[Any] = support_sizes[i - 1]
UpperCAmelCase : List[str] = S[s : s + size][start_token_masks[s : s + size]]
UpperCAmelCase : Optional[Any] = S[s : s + size][end_token_masks[s : s + size]]
UpperCAmelCase : Dict = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
UpperCAmelCase : Optional[int] = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
UpperCAmelCase : List[Any] = torch.vstack((p_starts, p_start) )
UpperCAmelCase : Any = torch.vstack((p_ends, p_end) )
else:
UpperCAmelCase : str = p_start
UpperCAmelCase : Optional[int] = p_end
return p_starts, p_ends
| 109
|
import logging
from transformers.configuration_utils import PretrainedConfig
__a = logging.getLogger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Optional[int] = 'masked_bert'
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : List[Any]=3_0_5_2_2 , SCREAMING_SNAKE_CASE_ : List[str]=7_6_8 , SCREAMING_SNAKE_CASE_ : Optional[int]=1_2 , SCREAMING_SNAKE_CASE_ : Any=1_2 , SCREAMING_SNAKE_CASE_ : str=3_0_7_2 , SCREAMING_SNAKE_CASE_ : Union[str, Any]="gelu" , SCREAMING_SNAKE_CASE_ : Optional[int]=0.1 , SCREAMING_SNAKE_CASE_ : List[str]=0.1 , SCREAMING_SNAKE_CASE_ : Tuple=5_1_2 , SCREAMING_SNAKE_CASE_ : str=2 , SCREAMING_SNAKE_CASE_ : Dict=0.02 , SCREAMING_SNAKE_CASE_ : Any=1e-12 , SCREAMING_SNAKE_CASE_ : Any=0 , SCREAMING_SNAKE_CASE_ : Optional[int]="topK" , SCREAMING_SNAKE_CASE_ : Dict="constant" , SCREAMING_SNAKE_CASE_ : Optional[Any]=0.0 , **SCREAMING_SNAKE_CASE_ : Optional[int] , ) -> Optional[Any]:
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
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_ = type_vocab_size
lowercase_ = initializer_range
lowercase_ = layer_norm_eps
lowercase_ = pruning_method
lowercase_ = mask_init
lowercase_ = mask_scale
| 30
| 0
|
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def _a ( ):
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 110
|
import os
def a ( ):
'''simple docstring'''
lowercase_ = os.path.join(os.path.dirname(snake_case__ ) , '''num.txt''' )
with open(snake_case__ ) as file_hand:
return str(sum(int(snake_case__ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 30
| 0
|
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(snake_case__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 82
|
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
'''compression_format, is_archive''' , [
('''7z''', True),
('''bz2''', False),
('''gzip''', False),
('''lz4''', False),
('''tar''', True),
('''xz''', False),
('''zip''', True),
('''zstd''', False),
] , )
def a ( snake_case__: int , snake_case__: Tuple , snake_case__: Dict , snake_case__: Dict , snake_case__: List[Any] , snake_case__: int , snake_case__: List[Any] , snake_case__: Optional[int] , snake_case__: str , snake_case__: Union[str, Any] , snake_case__: List[str] , snake_case__: int , ):
'''simple docstring'''
lowercase_ = {
'''7z''': (seven_zip_file, SevenZipExtractor),
'''bz2''': (bza_file, BzipaExtractor),
'''gzip''': (gz_file, GzipExtractor),
'''lz4''': (lza_file, LzaExtractor),
'''tar''': (tar_file, TarExtractor),
'''xz''': (xz_file, XzExtractor),
'''zip''': (zip_file, ZipExtractor),
'''zstd''': (zstd_file, ZstdExtractor),
}
lowercase_ , lowercase_ = input_paths_and_base_extractors[compression_format]
if input_path is None:
lowercase_ = F'''for \'{compression_format}\' compression_format, '''
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(snake_case__ )
assert base_extractor.is_extractable(snake_case__ )
lowercase_ = tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
base_extractor.extract(snake_case__ , snake_case__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
lowercase_ = file_path.read_text(encoding='''utf-8''' )
else:
lowercase_ = output_path.read_text(encoding='''utf-8''' )
lowercase_ = text_file.read_text(encoding='''utf-8''' )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
'''compression_format, is_archive''' , [
('''7z''', True),
('''bz2''', False),
('''gzip''', False),
('''lz4''', False),
('''tar''', True),
('''xz''', False),
('''zip''', True),
('''zstd''', False),
] , )
def a ( snake_case__: List[Any] , snake_case__: int , snake_case__: Optional[int] , snake_case__: Union[str, Any] , snake_case__: List[Any] , snake_case__: Tuple , snake_case__: Optional[int] , snake_case__: List[str] , snake_case__: Union[str, Any] , snake_case__: Tuple , snake_case__: int , snake_case__: Optional[int] , ):
'''simple docstring'''
lowercase_ = {
'''7z''': seven_zip_file,
'''bz2''': bza_file,
'''gzip''': gz_file,
'''lz4''': lza_file,
'''tar''': tar_file,
'''xz''': xz_file,
'''zip''': zip_file,
'''zstd''': zstd_file,
}
lowercase_ = input_paths[compression_format]
if input_path is None:
lowercase_ = F'''for \'{compression_format}\' compression_format, '''
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(snake_case__ )
lowercase_ = Extractor.infer_extractor_format(snake_case__ )
assert extractor_format is not None
lowercase_ = tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
Extractor.extract(snake_case__ , snake_case__ , snake_case__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
lowercase_ = file_path.read_text(encoding='''utf-8''' )
else:
lowercase_ = output_path.read_text(encoding='''utf-8''' )
lowercase_ = text_file.read_text(encoding='''utf-8''' )
assert extracted_file_content == expected_file_content
@pytest.fixture
def a ( snake_case__: Union[str, Any] , snake_case__: List[Any] ):
'''simple docstring'''
import tarfile
lowercase_ = tmp_path / '''data_dot_dot'''
directory.mkdir()
lowercase_ = directory / '''tar_file_with_dot_dot.tar'''
with tarfile.TarFile(snake_case__ , '''w''' ) as f:
f.add(snake_case__ , arcname=os.path.join('''..''' , text_file.name ) )
return path
@pytest.fixture
def a ( snake_case__: int ):
'''simple docstring'''
import tarfile
lowercase_ = tmp_path / '''data_sym_link'''
directory.mkdir()
lowercase_ = directory / '''tar_file_with_sym_link.tar'''
os.symlink('''..''' , directory / '''subdir''' , target_is_directory=snake_case__ )
with tarfile.TarFile(snake_case__ , '''w''' ) as f:
f.add(str(directory / '''subdir''' ) , arcname='''subdir''' ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
'''insecure_tar_file, error_log''' , [('''tar_file_with_dot_dot''', '''illegal path'''), ('''tar_file_with_sym_link''', '''Symlink''')] , )
def a ( snake_case__: List[Any] , snake_case__: Optional[int] , snake_case__: List[str] , snake_case__: List[str] , snake_case__: int , snake_case__: Optional[Any] ):
'''simple docstring'''
lowercase_ = {
'''tar_file_with_dot_dot''': tar_file_with_dot_dot,
'''tar_file_with_sym_link''': tar_file_with_sym_link,
}
lowercase_ = insecure_tar_files[insecure_tar_file]
lowercase_ = tmp_path / '''extracted'''
TarExtractor.extract(snake_case__ , snake_case__ )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def a ( snake_case__: Optional[int] ):
'''simple docstring'''
# We should have less false positives than zipfile.is_zipfile
# We do that by checking only the magic number
lowercase_ = tmpdir / '''not_a_zip_file'''
# From: https://github.com/python/cpython/pull/5053
lowercase_ = (
B'''\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'''
B'''\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'''
B'''DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'''
B'''\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'''
)
with not_a_zip_file.open('''wb''' ) as f:
f.write(snake_case__ )
assert zipfile.is_zipfile(str(snake_case__ ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(snake_case__ ) # but we're right
| 30
| 0
|
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : str ):
return "".join(sorted(snake_case__ ) )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : str ):
return word_by_signature[signature(snake_case__ )]
_A = Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
_A = sorted({word.strip().lower() for word in data.splitlines()})
_A = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
_A = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open('anagrams.txt', 'w') as file:
file.write('all_anagrams = \n ')
file.write(pprint.pformat(all_anagrams))
| 62
|
from __future__ import annotations
def a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''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 a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if length > 1:
lowercase_ = int(length / 2 )
for i in range(snake_case__ , low + middle ):
comp_and_swap(snake_case__ , snake_case__ , i + middle , snake_case__ )
bitonic_merge(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
bitonic_merge(snake_case__ , low + middle , snake_case__ , snake_case__ )
def a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if length > 1:
lowercase_ = int(length / 2 )
bitonic_sort(snake_case__ , snake_case__ , snake_case__ , 1 )
bitonic_sort(snake_case__ , low + middle , snake_case__ , 0 )
bitonic_merge(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
if __name__ == "__main__":
__a = input('Enter numbers separated by a comma:\n').strip()
__a = [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=', ')
| 30
| 0
|
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class A__ ( __snake_case , __snake_case , __snake_case ):
@register_to_config
def __init__( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ = False , ):
'''simple docstring'''
super().__init__()
UpperCamelCase : Dict = nn.Embedding(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = nn.Embedding(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = False
UpperCamelCase : List[str] = nn.Dropout(p=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = TaConfig(
vocab_size=SCREAMING_SNAKE_CASE_ , d_model=SCREAMING_SNAKE_CASE_ , num_heads=SCREAMING_SNAKE_CASE_ , d_kv=SCREAMING_SNAKE_CASE_ , d_ff=SCREAMING_SNAKE_CASE_ , dropout_rate=SCREAMING_SNAKE_CASE_ , feed_forward_proj=SCREAMING_SNAKE_CASE_ , is_decoder=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Optional[int] = nn.ModuleList()
for lyr_num in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = TaBlock(SCREAMING_SNAKE_CASE_ )
self.encoders.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = TaLayerNorm(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = nn.Dropout(p=SCREAMING_SNAKE_CASE_ )
def __UpperCamelCase( self , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : Tuple = self.token_embedder(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = encoder_input_tokens.shape[1]
UpperCamelCase : Union[str, Any] = torch.arange(SCREAMING_SNAKE_CASE_ , device=encoder_input_tokens.device )
x += self.position_encoding(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = self.dropout_pre(SCREAMING_SNAKE_CASE_ )
# inverted the attention mask
UpperCamelCase : Optional[Any] = encoder_input_tokens.size()
UpperCamelCase : Optional[int] = self.get_extended_attention_mask(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for lyr in self.encoders:
UpperCamelCase : Any = lyr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : List[Any] = self.layer_norm(SCREAMING_SNAKE_CASE_ )
return self.dropout_post(SCREAMING_SNAKE_CASE_ ), encoder_inputs_mask
| 52
|
from __future__ import annotations
from collections.abc import MutableSequence
class lowercase__:
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : MutableSequence[float] ) -> None:
if len(SCREAMING_SNAKE_CASE_ ) != degree + 1:
raise ValueError(
'''The number of coefficients should be equal to the degree + 1.''' )
lowercase_ = list(SCREAMING_SNAKE_CASE_ )
lowercase_ = degree
def __add__( self : Any , SCREAMING_SNAKE_CASE_ : Polynomial ) -> Polynomial:
if self.degree > polynomial_a.degree:
lowercase_ = self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree , SCREAMING_SNAKE_CASE_ )
else:
lowercase_ = polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree , SCREAMING_SNAKE_CASE_ )
def __sub__( self : str , SCREAMING_SNAKE_CASE_ : Polynomial ) -> Polynomial:
return self + polynomial_a * Polynomial(0 , [-1] )
def __neg__( self : int ) -> Polynomial:
return Polynomial(self.degree , [-c for c in self.coefficients] )
def __mul__( self : Any , SCREAMING_SNAKE_CASE_ : Polynomial ) -> Polynomial:
lowercase_ = [0] * (self.degree + polynomial_a.degree + 1)
for i in range(self.degree + 1 ):
for j in range(polynomial_a.degree + 1 ):
coefficients[i + j] += (
self.coefficients[i] * polynomial_a.coefficients[j]
)
return Polynomial(self.degree + polynomial_a.degree , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Dict , SCREAMING_SNAKE_CASE_ : int | float ) -> int | float:
lowercase_ = 0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self : Tuple ) -> str:
lowercase_ = ''''''
for i in range(self.degree , -1 , -1 ):
if self.coefficients[i] == 0:
continue
elif self.coefficients[i] > 0:
if polynomial:
polynomial += " + "
else:
polynomial += " - "
if i == 0:
polynomial += str(abs(self.coefficients[i] ) )
elif i == 1:
polynomial += str(abs(self.coefficients[i] ) ) + "x"
else:
polynomial += str(abs(self.coefficients[i] ) ) + "x^" + str(SCREAMING_SNAKE_CASE_ )
return polynomial
def __repr__( self : Optional[Any] ) -> str:
return self.__str__()
def _lowercase ( self : int ) -> Polynomial:
lowercase_ = [0] * self.degree
for i in range(self.degree ):
lowercase_ = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : int | float = 0 ) -> Polynomial:
lowercase_ = [0] * (self.degree + 2)
lowercase_ = constant
for i in range(self.degree + 1 ):
lowercase_ = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 , SCREAMING_SNAKE_CASE_ )
def __eq__( self : str , SCREAMING_SNAKE_CASE_ : object ) -> bool:
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return False
if self.degree != polynomial_a.degree:
return False
for i in range(self.degree + 1 ):
if self.coefficients[i] != polynomial_a.coefficients[i]:
return False
return True
def __ne__( self : List[str] , SCREAMING_SNAKE_CASE_ : object ) -> bool:
return not self.__eq__(SCREAMING_SNAKE_CASE_ )
| 30
| 0
|
"""simple docstring"""
class __lowerCamelCase :
'''simple docstring'''
def __init__( self , __UpperCAmelCase ) -> None:
_a = size
_a = [0] * size
_a = [0] * size
@staticmethod
def _UpperCAmelCase ( __UpperCAmelCase ) -> int:
return index | (index + 1)
@staticmethod
def _UpperCAmelCase ( __UpperCAmelCase ) -> int:
return (index & (index + 1)) - 1
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase ) -> None:
_a = value
while index < self.size:
_a = self.get_prev(SCREAMING_SNAKE_CASE_ ) + 1
if current_left_border == index:
_a = value
else:
_a = max(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_a = self.get_next(SCREAMING_SNAKE_CASE_ )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase ) -> int:
right -= 1 # Because of right is exclusive
_a = 0
while left <= right:
_a = self.get_prev(SCREAMING_SNAKE_CASE_ )
if left <= current_left:
_a = max(SCREAMING_SNAKE_CASE_ , self.tree[right] )
_a = current_left
else:
_a = max(SCREAMING_SNAKE_CASE_ , self.arr[right] )
right -= 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 320
|
import itertools
import math
def a ( snake_case__: int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(snake_case__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def a ( ):
'''simple docstring'''
lowercase_ = 2
while True:
if is_prime(snake_case__ ):
yield num
num += 1
def a ( snake_case__: int = 10_001 ):
'''simple docstring'''
return next(itertools.islice(prime_generator() , nth - 1 , snake_case__ ) )
if __name__ == "__main__":
print(f"{solution() = }")
| 30
| 0
|
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging
if is_vision_available():
import PIL
if is_torch_available():
import torch
a = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( _a ):
_a = ['pixel_values']
def __init__( self : Any , lowerCAmelCase : bool = True , lowerCAmelCase : Dict[str, int] = None , lowerCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , lowerCAmelCase : bool = True , lowerCAmelCase : Union[int, float] = 1 / 255 , lowerCAmelCase : bool = True , lowerCAmelCase : Dict[str, int] = None , lowerCAmelCase : bool = True , **lowerCAmelCase : Dict , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = size if size is not None else {"""shortest_edge""": 224}
lowerCAmelCase = get_size_dict(SCREAMING_SNAKE_CASE_ , default_to_square=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
lowerCAmelCase = get_size_dict(SCREAMING_SNAKE_CASE_ , param_name="""crop_size""" )
lowerCAmelCase = do_resize
lowerCAmelCase = size
lowerCAmelCase = resample
lowerCAmelCase = do_rescale
lowerCAmelCase = rescale_factor
lowerCAmelCase = do_center_crop
lowerCAmelCase = crop_size
lowerCAmelCase = do_flip_channel_order
def __lowercase ( self : List[str] , lowerCAmelCase : np.ndarray , lowerCAmelCase : Dict[str, int] , lowerCAmelCase : PILImageResampling = PIL.Image.BILINEAR , lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase : str , ):
lowerCAmelCase = get_size_dict(SCREAMING_SNAKE_CASE_ , default_to_square=SCREAMING_SNAKE_CASE_ )
if "shortest_edge" not in size:
raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}''' )
lowerCAmelCase = get_resize_output_image_size(SCREAMING_SNAKE_CASE_ , size=size["""shortest_edge"""] , default_to_square=SCREAMING_SNAKE_CASE_ )
return resize(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def __lowercase ( self : Any , lowerCAmelCase : np.ndarray , lowerCAmelCase : Dict[str, int] , lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase : Optional[Any] , ):
lowerCAmelCase = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''' )
return center_crop(SCREAMING_SNAKE_CASE_ , size=(size["""height"""], size["""width"""]) , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def __lowercase ( self : Union[str, Any] , lowerCAmelCase : np.ndarray , lowerCAmelCase : Union[int, float] , lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase : Union[str, Any] , ):
return rescale(SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def __lowercase ( self : Tuple , lowerCAmelCase : np.ndarray , lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None ):
return flip_channel_order(SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ )
def __lowercase ( self : int , lowerCAmelCase : ImageInput , lowerCAmelCase : bool = None , lowerCAmelCase : Dict[str, int] = None , lowerCAmelCase : PILImageResampling = None , lowerCAmelCase : bool = None , lowerCAmelCase : float = None , lowerCAmelCase : bool = None , lowerCAmelCase : Dict[str, int] = None , lowerCAmelCase : bool = None , lowerCAmelCase : Optional[Union[str, TensorType]] = None , lowerCAmelCase : ChannelDimension = ChannelDimension.FIRST , **lowerCAmelCase : Dict , ):
lowerCAmelCase = do_resize if do_resize is not None else self.do_resize
lowerCAmelCase = resample if resample is not None else self.resample
lowerCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
lowerCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCAmelCase = (
do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
)
lowerCAmelCase = size if size is not None else self.size
lowerCAmelCase = get_size_dict(SCREAMING_SNAKE_CASE_ , default_to_square=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = crop_size if crop_size is not None else self.crop_size
lowerCAmelCase = get_size_dict(SCREAMING_SNAKE_CASE_ , param_name="""crop_size""" )
lowerCAmelCase = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
# All transformations expect numpy arrays.
lowerCAmelCase = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
lowerCAmelCase = [self.resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_center_crop:
lowerCAmelCase = [self.center_crop(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
lowerCAmelCase = [self.rescale(image=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ ) for image in images]
# the pretrained checkpoints assume images are BGR, not RGB
if do_flip_channel_order:
lowerCAmelCase = [self.flip_channel_order(image=SCREAMING_SNAKE_CASE_ ) for image in images]
lowerCAmelCase = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for image in images]
lowerCAmelCase = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def __lowercase ( self : Union[str, Any] , lowerCAmelCase : str , lowerCAmelCase : List[Tuple] = None ):
lowerCAmelCase = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
lowerCAmelCase = target_sizes.numpy()
lowerCAmelCase = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
lowerCAmelCase = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
lowerCAmelCase = logits.argmax(dim=1 )
lowerCAmelCase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 155
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 30
| 0
|
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
_lowerCamelCase : Dict = logging.get_logger(__name__)
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Any, *__A : Optional[Any], **__A : Dict ):
warnings.warn(
'''The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use VideoMAEImageProcessor instead.''', SCREAMING_SNAKE_CASE_, )
super().__init__(*SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
| 336
|
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
__a = logging.get_logger(__name__)
__a = {
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
'constant': get_constant_schedule,
'constant_w_warmup': get_constant_schedule_with_warmup,
}
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[str]=None , SCREAMING_SNAKE_CASE_ : Optional[Any]=None , *SCREAMING_SNAKE_CASE_ : str , **SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> List[str]:
super().__init__(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if config is None:
assert isinstance(self.model , SCREAMING_SNAKE_CASE_ ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
f''' {self.model.__class__}'''
)
lowercase_ = self.model.config
else:
lowercase_ = config
lowercase_ = data_args
lowercase_ = self.config.tgt_vocab_size if isinstance(self.config , SCREAMING_SNAKE_CASE_ ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
f'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
''' padding..''' )
if self.args.label_smoothing == 0:
lowercase_ = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
lowercase_ = label_smoothed_nll_loss
def _lowercase ( self : int , SCREAMING_SNAKE_CASE_ : int ) -> Optional[Any]:
if self.optimizer is None:
lowercase_ = ['''bias''', '''LayerNorm.weight''']
lowercase_ = [
{
'''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
'''weight_decay''': self.args.weight_decay,
},
{
'''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
lowercase_ = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
lowercase_ = Adafactor
lowercase_ = {'''scale_parameter''': False, '''relative_step''': False}
else:
lowercase_ = AdamW
lowercase_ = {
'''betas''': (self.args.adam_betaa, self.args.adam_betaa),
'''eps''': self.args.adam_epsilon,
}
lowercase_ = self.args.learning_rate
if self.sharded_ddp:
lowercase_ = OSS(
params=SCREAMING_SNAKE_CASE_ , optim=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
else:
lowercase_ = optimizer_cls(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if self.lr_scheduler is None:
lowercase_ = self._get_lr_scheduler(SCREAMING_SNAKE_CASE_ )
else: # ignoring --lr_scheduler
logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.''' )
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : Dict ) -> Dict:
lowercase_ = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
lowercase_ = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
lowercase_ = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
lowercase_ = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=SCREAMING_SNAKE_CASE_ )
return scheduler
def _lowercase ( self : Tuple ) -> Optional[torch.utils.data.Sampler]:
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : List[Any] ) -> Any:
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
lowercase_ = model(**SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )[0]
lowercase_ = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
lowercase_ , lowercase_ = model(**SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )[:2]
else:
# compute label smoothed loss
lowercase_ = model(**SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )[0]
lowercase_ = torch.nn.functional.log_softmax(SCREAMING_SNAKE_CASE_ , dim=-1 )
lowercase_ , lowercase_ = self.loss_fn(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def _lowercase ( self : str , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> List[Any]:
lowercase_ = inputs.pop('''labels''' )
lowercase_ , lowercase_ = self._compute_loss(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return loss
def _lowercase ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : nn.Module , SCREAMING_SNAKE_CASE_ : Dict[str, Union[torch.Tensor, Any]] , SCREAMING_SNAKE_CASE_ : bool , SCREAMING_SNAKE_CASE_ : Optional[List[str]] = None , ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
lowercase_ = self._prepare_inputs(SCREAMING_SNAKE_CASE_ )
lowercase_ = {
'''max_length''': self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
'''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
lowercase_ = self.model.generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , **SCREAMING_SNAKE_CASE_ , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
lowercase_ = self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_ , gen_kwargs['''max_length'''] )
lowercase_ = inputs.pop('''labels''' )
with torch.no_grad():
# compute loss on predict data
lowercase_ , lowercase_ = self._compute_loss(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowercase_ = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
lowercase_ = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
lowercase_ = self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_ , gen_kwargs['''max_length'''] )
return (loss, logits, labels)
def _lowercase ( self : str , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : int ) -> Tuple:
# If PAD token is not defined at least EOS token has to be defined
lowercase_ = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
'''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
f''' padded to `max_length`={max_length}''' )
lowercase_ = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
lowercase_ = tensor
return padded_tensor
| 30
| 0
|
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
def __init__( self , __lowercase=0.01 , __lowercase=1_000) -> Tuple:
__UpperCamelCase :Optional[int] = p_stop
__UpperCamelCase :List[Any] = max_length
def __iter__( self) -> Dict:
__UpperCamelCase :Union[str, Any] = 0
__UpperCamelCase :List[str] = False
while not stop and count < self.max_length:
yield count
count += 1
__UpperCamelCase :Optional[int] = random.random() < self.p_stop
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase=False , __lowercase=True) -> List[str]:
__UpperCamelCase :Optional[int] = [
BatchSamplerShard(SCREAMING_SNAKE_CASE_ , 2 , SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
for i in range(2)
]
__UpperCamelCase :int = [list(SCREAMING_SNAKE_CASE_) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(SCREAMING_SNAKE_CASE_) for shard in batch_sampler_shards] , [len(SCREAMING_SNAKE_CASE_) for e in expected])
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
def UpperCamelCase__ ( self) -> Optional[Any]:
# Check the shards when the dataset is a round multiple of total batch size.
__UpperCamelCase :Optional[int] = BatchSampler(range(24) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Union[str, Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Optional[int] = BatchSampler(range(24) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
# Expected shouldn't change
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
__UpperCamelCase :List[str] = BatchSampler(range(21) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Any = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
__UpperCamelCase :str = BatchSampler(range(21) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :str = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
__UpperCamelCase :Dict = BatchSampler(range(22) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[Any] = BatchSampler(range(22) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[str] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
__UpperCamelCase :int = BatchSampler(range(20) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[str] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
__UpperCamelCase :int = BatchSampler(range(20) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :str = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
# Check the shards when the dataset is very small.
__UpperCamelCase :Tuple = BatchSampler(range(2) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Dict = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
__UpperCamelCase :int = BatchSampler(range(2) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Any = [[], []]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
def UpperCamelCase__ ( self) -> List[str]:
# Check the shards when the dataset is a round multiple of batch size.
__UpperCamelCase :int = BatchSampler(range(24) , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Optional[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[str] = BatchSampler(range(24) , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_)
# Expected shouldn't change
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_)
# Check the shards when the dataset is not a round multiple of batch size.
__UpperCamelCase :Dict = BatchSampler(range(22) , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[Any] = BatchSampler(range(22) , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_)
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
__UpperCamelCase :List[str] = BatchSampler(range(21) , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Any = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Any = BatchSampler(range(21) , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_)
# Check the shards when the dataset is very small.
__UpperCamelCase :int = BatchSampler(range(2) , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Optional[Any] = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Union[str, Any] = BatchSampler(range(2) , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Optional[Any] = [[], []]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_)
def UpperCamelCase__ ( self) -> List[Any]:
# Check the shards when the dataset is a round multiple of total batch size.
__UpperCamelCase :Any = BatchSampler(range(24) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[str] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Union[str, Any] = BatchSampler(range(24) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
# Expected shouldn't change
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
__UpperCamelCase :List[str] = BatchSampler(range(21) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Any = BatchSampler(range(21) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Union[str, Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
__UpperCamelCase :int = BatchSampler(range(22) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Optional[Any] = BatchSampler(range(22) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[str] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
__UpperCamelCase :int = BatchSampler(range(20) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Union[str, Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Dict = BatchSampler(range(20) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
# Check the shards when the dataset is very small.
__UpperCamelCase :str = BatchSampler(range(2) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Optional[Any] = [[[0, 1]], []]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Any = BatchSampler(range(2) , batch_size=3 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[str] = [[], []]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
def UpperCamelCase__ ( self) -> Optional[int]:
# Check the shards when the dataset is a round multiple of batch size.
__UpperCamelCase :str = BatchSampler(range(24) , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :int = BatchSampler(range(24) , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_)
# Expected shouldn't change
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
# Check the shards when the dataset is not a round multiple of batch size.
__UpperCamelCase :Tuple = BatchSampler(range(22) , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Any = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :str = BatchSampler(range(22) , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Dict = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
__UpperCamelCase :Union[str, Any] = BatchSampler(range(21) , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Union[str, Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Tuple = BatchSampler(range(21) , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Dict = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
# Check the shards when the dataset is very small.
__UpperCamelCase :List[str] = BatchSampler(range(2) , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[Any] = [[[0, 1]], []]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :int = BatchSampler(range(2) , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Tuple = [[], []]
self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_)
def UpperCamelCase__ ( self) -> str:
__UpperCamelCase :Optional[Any] = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
__UpperCamelCase :Union[str, Any] = [BatchSamplerShard(SCREAMING_SNAKE_CASE_ , 2 , SCREAMING_SNAKE_CASE_ , even_batches=SCREAMING_SNAKE_CASE_) for i in range(2)]
self.assertEqual(len(batch_sampler_shards[0]) , 3)
self.assertEqual(len(batch_sampler_shards[1]) , 2)
self.assertListEqual(list(batch_sampler_shards[0]) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]])
self.assertListEqual(list(batch_sampler_shards[1]) , [[3, 4], [9, 10, 11]])
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase , __lowercase=False , __lowercase=2 , __lowercase=False) -> Any:
random.seed(SCREAMING_SNAKE_CASE_)
__UpperCamelCase :str = list(SCREAMING_SNAKE_CASE_)
__UpperCamelCase :int = [
IterableDatasetShard(
SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ , drop_last=SCREAMING_SNAKE_CASE_ , num_processes=SCREAMING_SNAKE_CASE_ , process_index=SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_ , )
for i in range(SCREAMING_SNAKE_CASE_)
]
__UpperCamelCase :int = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(SCREAMING_SNAKE_CASE_)
iterable_dataset_lists.append(list(SCREAMING_SNAKE_CASE_))
__UpperCamelCase :Optional[int] = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
__UpperCamelCase :Dict = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(SCREAMING_SNAKE_CASE_) , len(SCREAMING_SNAKE_CASE_))
self.assertTrue(len(SCREAMING_SNAKE_CASE_) % shard_batch_size == 0)
__UpperCamelCase :Union[str, Any] = []
for idx in range(0 , len(SCREAMING_SNAKE_CASE_) , SCREAMING_SNAKE_CASE_):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(SCREAMING_SNAKE_CASE_) < len(SCREAMING_SNAKE_CASE_):
reference += reference
self.assertListEqual(SCREAMING_SNAKE_CASE_ , reference[: len(SCREAMING_SNAKE_CASE_)])
def UpperCamelCase__ ( self) -> Union[str, Any]:
__UpperCamelCase :Tuple = 42
__UpperCamelCase :int = RandomIterableDataset()
self.check_iterable_dataset_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_)
self.check_iterable_dataset_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_)
self.check_iterable_dataset_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_)
self.check_iterable_dataset_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_)
# Edge case with a very small dataset
__UpperCamelCase :Union[str, Any] = RandomIterableDataset(max_length=2)
self.check_iterable_dataset_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_)
self.check_iterable_dataset_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_)
self.check_iterable_dataset_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_)
self.check_iterable_dataset_shards(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_)
def UpperCamelCase__ ( self) -> List[Any]:
__UpperCamelCase :Dict = BatchSampler(range(16) , batch_size=4 , drop_last=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Tuple = SkipBatchSampler(SCREAMING_SNAKE_CASE_ , 2)
self.assertListEqual(list(SCREAMING_SNAKE_CASE_) , [[8, 9, 10, 11], [12, 13, 14, 15]])
def UpperCamelCase__ ( self) -> Any:
__UpperCamelCase :List[str] = SkipDataLoader(list(range(16)) , batch_size=4 , skip_batches=2)
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]])
def UpperCamelCase__ ( self) -> Optional[int]:
__UpperCamelCase :Union[str, Any] = DataLoader(list(range(16)) , batch_size=4)
__UpperCamelCase :Any = skip_first_batches(SCREAMING_SNAKE_CASE_ , num_batches=2)
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]])
def UpperCamelCase__ ( self) -> Optional[int]:
__UpperCamelCase :str = DataLoaderShard(list(range(16)) , batch_size=4)
for idx, _ in enumerate(SCREAMING_SNAKE_CASE_):
self.assertEqual(dataloader.end_of_dataloader , idx == 3)
# Test it also works on the second iteration
for idx, _ in enumerate(SCREAMING_SNAKE_CASE_):
self.assertEqual(dataloader.end_of_dataloader , idx == 3)
def UpperCamelCase__ ( self) -> Any:
Accelerator()
__UpperCamelCase :Any = DataLoaderDispatcher(range(16) , batch_size=4)
for idx, _ in enumerate(SCREAMING_SNAKE_CASE_):
self.assertEqual(dataloader.end_of_dataloader , idx == 3)
# Test it also works on the second iteration
for idx, _ in enumerate(SCREAMING_SNAKE_CASE_):
self.assertEqual(dataloader.end_of_dataloader , idx == 3)
| 43
|
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : List[str] ) -> List[Any]:
lowercase_ = 1_0
def _lowercase ( self : int ) -> List[str]:
lowercase_ = [1, 2, 3, 4]
lowercase_ = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_ , self.block_size , 0 ) , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : int ) -> Optional[Any]:
lowercase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
lowercase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_ , self.block_size , 0 ) , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
lowercase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
lowercase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_ , self.block_size , 0 ) , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Any ) -> List[Any]:
lowercase_ = '''It was the year of Our Lord one thousand seven hundred and
seventy-five.\n\nSpiritual revelations were conceded to England at that
favoured period, as at this.'''
lowercase_ , lowercase_ = process_story(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , [] )
def _lowercase ( self : List[str] ) -> List[str]:
lowercase_ = ''''''
lowercase_ , lowercase_ = process_story(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , [] )
self.assertEqual(SCREAMING_SNAKE_CASE_ , [] )
def _lowercase ( self : Union[str, Any] ) -> Union[str, Any]:
lowercase_ = (
'''It was the year of Our Lord one thousand seven hundred and '''
'''seventy-five\n\nSpiritual revelations were conceded to England '''
'''at that favoured period, as at this.\n@highlight\n\nIt was the best of times'''
)
lowercase_ , lowercase_ = process_story(SCREAMING_SNAKE_CASE_ )
lowercase_ = [
'''It was the year of Our Lord one thousand seven hundred and seventy-five.''',
'''Spiritual revelations were conceded to England at that favoured period, as at this.''',
]
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowercase_ = ['''It was the best of times.''']
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Union[str, Any] ) -> Optional[Any]:
lowercase_ = torch.tensor([1, 2, 3, 4] )
lowercase_ = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_ , 0 ).numpy() , expected.numpy() )
def _lowercase ( self : List[Any] ) -> Tuple:
lowercase_ = torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3] )
lowercase_ = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_ , 2_3 ).numpy() , expected.numpy() )
def _lowercase ( self : int ) -> Dict:
lowercase_ = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
lowercase_ = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_ , 1 ).numpy() , expected.numpy() )
def _lowercase ( self : List[str] ) -> Tuple:
lowercase_ = 1_0_1
lowercase_ = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]] )
lowercase_ = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
lowercase_ = compute_token_type_ids(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
np.testing.assert_array_equal(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 30
| 0
|
def _A ( _lowercase , _lowercase ) -> List[str]:
"""simple docstring"""
return number | (1 << position)
def _A ( _lowercase , _lowercase ) -> Union[str, Any]:
"""simple docstring"""
return number & ~(1 << position)
def _A ( _lowercase , _lowercase ) -> List[str]:
"""simple docstring"""
return number ^ (1 << position)
def _A ( _lowercase , _lowercase ) -> Tuple:
"""simple docstring"""
return ((number >> position) & 1) == 1
def _A ( _lowercase , _lowercase ) -> Dict:
"""simple docstring"""
return int((number & (1 << position)) != 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 310
|
def a ( snake_case__: list ):
'''simple docstring'''
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
lowercase_ = []
def generate(snake_case__: int , snake_case__: list ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , snake_case__ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
lowercase_ , lowercase_ = arr[k - 1], arr[i]
else: # k is odd
lowercase_ , lowercase_ = arr[k - 1], arr[0]
generate(k - 1 , snake_case__ )
generate(len(snake_case__ ) , snake_case__ )
return res
if __name__ == "__main__":
__a = input('Enter numbers separated by a comma:\n').strip()
__a = [int(item) for item in user_input.split(',')]
print(heaps(arr))
| 30
| 0
|
"""simple docstring"""
def __lowerCAmelCase (_UpperCamelCase ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 86
|
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def a ( ):
'''simple docstring'''
lowercase_ = argparse.ArgumentParser()
parser.add_argument(
'''-m''' , '''--pretrained_model_name_or_path''' , type=snake_case__ , default=snake_case__ , required=snake_case__ , help='''Path to pretrained model or model identifier from huggingface.co/models.''' , )
parser.add_argument(
'''-c''' , '''--caption''' , type=snake_case__ , default='''robotic cat with wings''' , help='''Text used to generate images.''' , )
parser.add_argument(
'''-n''' , '''--images_num''' , type=snake_case__ , default=4 , help='''How much images to generate.''' , )
parser.add_argument(
'''-s''' , '''--seed''' , type=snake_case__ , default=42 , help='''Seed for random process.''' , )
parser.add_argument(
'''-ci''' , '''--cuda_id''' , type=snake_case__ , default=0 , help='''cuda_id.''' , )
lowercase_ = parser.parse_args()
return args
def a ( snake_case__: Optional[Any] , snake_case__: Tuple , snake_case__: Union[str, Any] ):
'''simple docstring'''
if not len(snake_case__ ) == rows * cols:
raise ValueError('''The specified number of rows and columns are not correct.''' )
lowercase_ , lowercase_ = imgs[0].size
lowercase_ = Image.new('''RGB''' , size=(cols * w, rows * h) )
lowercase_ , lowercase_ = grid.size
for i, img in enumerate(snake_case__ ):
grid.paste(snake_case__ , box=(i % cols * w, i // cols * h) )
return grid
def a ( snake_case__: Tuple , snake_case__: Union[str, Any]="robotic cat with wings" , snake_case__: Union[str, Any]=7.5 , snake_case__: List[str]=50 , snake_case__: List[Any]=1 , snake_case__: Optional[int]=42 , ):
'''simple docstring'''
lowercase_ = torch.Generator(pipeline.device ).manual_seed(snake_case__ )
lowercase_ = pipeline(
snake_case__ , guidance_scale=snake_case__ , num_inference_steps=snake_case__ , generator=snake_case__ , num_images_per_prompt=snake_case__ , ).images
lowercase_ = int(math.sqrt(snake_case__ ) )
lowercase_ = image_grid(snake_case__ , rows=_rows , cols=num_images_per_prompt // _rows )
return grid, images
__a = parse_args()
# Load models and create wrapper for stable diffusion
__a = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
__a = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
__a = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
__a = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')
__a = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__a = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
__a = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, 'unet', unet)
else:
__a = unet.to(torch.device('cuda', args.cuda_id))
__a = pipeline.to(unet.device)
__a , __a = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '{}.png'.format('_'.join(args.caption.split()))))
__a = os.path.join(args.pretrained_model_name_or_path, '_'.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, '{}.png'.format(idx + 1)))
| 30
| 0
|
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = ''
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'hf-legacy' # "hf://"" is reserved for hffs
def __init__( self , __lowercase = None , __lowercase = None , **__lowercase , ) -> int:
super().__init__(self , **SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Dict = repo_info
lowerCAmelCase_ : Union[str, Any] = token
lowerCAmelCase_ : str = None
def lowercase_ ( self ) -> Optional[Any]:
if self.dir_cache is None:
lowerCAmelCase_ : List[str] = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
lowerCAmelCase_ : Tuple = {
'''name''': hf_file.rfilename,
'''size''': None,
'''type''': '''file''',
}
self.dir_cache.update(
{
str(SCREAMING_SNAKE_CASE_ ): {'''name''': str(SCREAMING_SNAKE_CASE_ ), '''size''': None, '''type''': '''directory'''}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def lowercase_ ( self , __lowercase , __lowercase = "rb" , **__lowercase , ) -> Optional[Any]:
if not isinstance(self.repo_info , SCREAMING_SNAKE_CASE_ ):
raise NotImplementedError(f"""Open is only implemented for dataset repositories, but got {self.repo_info}""" )
lowerCAmelCase_ : int = hf_hub_url(self.repo_info.id , SCREAMING_SNAKE_CASE_ , revision=self.repo_info.sha )
return fsspec.open(
SCREAMING_SNAKE_CASE_ , mode=SCREAMING_SNAKE_CASE_ , headers=get_authentication_headers_for_url(SCREAMING_SNAKE_CASE_ , use_auth_token=self.token ) , client_kwargs={'''trust_env''': True} , ).open()
def lowercase_ ( self , __lowercase , **__lowercase ) -> int:
self._get_dirs()
lowerCAmelCase_ : Tuple = self._strip_protocol(SCREAMING_SNAKE_CASE_ )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self , __lowercase , __lowercase=False , **__lowercase ) -> Optional[int]:
self._get_dirs()
lowerCAmelCase_ : Optional[Any] = PurePosixPath(path.strip('''/''' ) )
lowerCAmelCase_ : List[str] = {}
for p, f in self.dir_cache.items():
lowerCAmelCase_ : Dict = PurePosixPath(p.strip('''/''' ) )
lowerCAmelCase_ : List[Any] = p.parent
if root == path:
lowerCAmelCase_ : int = f
lowerCAmelCase_ : Optional[Any] = list(paths.values() )
if detail:
return out
else:
return sorted(f['''name'''] for f in out )
| 262
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['RemBertTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['RemBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RemBertForCausalLM',
'RemBertForMaskedLM',
'RemBertForMultipleChoice',
'RemBertForQuestionAnswering',
'RemBertForSequenceClassification',
'RemBertForTokenClassification',
'RemBertLayer',
'RemBertModel',
'RemBertPreTrainedModel',
'load_tf_weights_in_rembert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRemBertForCausalLM',
'TFRemBertForMaskedLM',
'TFRemBertForMultipleChoice',
'TFRemBertForQuestionAnswering',
'TFRemBertForSequenceClassification',
'TFRemBertForTokenClassification',
'TFRemBertLayer',
'TFRemBertModel',
'TFRemBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 30
| 0
|
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float , ) -> List[str]:
__a = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError('''All input parameters must be positive''' )
if any(p > 1 for p in parameters[1:4] ):
raise ValueError('''Relative densities cannot be greater than one''' )
else:
__a = 1 - (matter_density + radiation_density + dark_energy)
__a = (
radiation_density * (redshift + 1) ** 4
+ matter_density * (redshift + 1) ** 3
+ curvature * (redshift + 1) ** 2
+ dark_energy
)
__a = hubble_constant * e_a ** (1 / 2)
return hubble
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo LCDM approximation
lowercase_ = 0.3
print(
hubble_parameter(
hubble_constant=68.3,
radiation_density=1e-4,
matter_density=matter_density,
dark_energy=1 - matter_density,
redshift=0,
)
)
| 45
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['DeiTFeatureExtractor']
__a = ['DeiTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DeiTForImageClassification',
'DeiTForImageClassificationWithTeacher',
'DeiTForMaskedImageModeling',
'DeiTModel',
'DeiTPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDeiTForImageClassification',
'TFDeiTForImageClassificationWithTeacher',
'TFDeiTForMaskedImageModeling',
'TFDeiTModel',
'TFDeiTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 30
| 0
|
class __lowerCAmelCase :
def __init__( self , _snake_case , _snake_case=None , _snake_case=None ):
"""simple docstring"""
_lowerCAmelCase = data
_lowerCAmelCase = previous
_lowerCAmelCase = next_node
def __str__( self ):
"""simple docstring"""
return F'{self.data}'
def snake_case ( self ):
"""simple docstring"""
return self.data
def snake_case ( self ):
"""simple docstring"""
return self.next
def snake_case ( self ):
"""simple docstring"""
return self.previous
class __lowerCAmelCase :
def __init__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = head
def __iter__( self ):
"""simple docstring"""
return self
def snake_case ( self ):
"""simple docstring"""
if not self.current:
raise StopIteration
else:
_lowerCAmelCase = self.current.get_data()
_lowerCAmelCase = self.current.get_next()
return value
class __lowerCAmelCase :
def __init__( self ):
"""simple docstring"""
_lowerCAmelCase = None # First node in list
_lowerCAmelCase = None # Last node in list
def __str__( self ):
"""simple docstring"""
_lowerCAmelCase = self.head
_lowerCAmelCase = []
while current is not None:
nodes.append(current.get_data() )
_lowerCAmelCase = current.get_next()
return " ".join(str(SCREAMING_SNAKE_CASE_ ) for node in nodes )
def __contains__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.head
while current:
if current.get_data() == value:
return True
_lowerCAmelCase = current.get_next()
return False
def __iter__( self ):
"""simple docstring"""
return LinkedListIterator(self.head )
def snake_case ( self ):
"""simple docstring"""
if self.head:
return self.head.get_data()
return None
def snake_case ( self ):
"""simple docstring"""
if self.tail:
return self.tail.get_data()
return None
def snake_case ( self , _snake_case ):
"""simple docstring"""
if self.head is None:
_lowerCAmelCase = node
_lowerCAmelCase = node
else:
self.insert_before_node(self.head , SCREAMING_SNAKE_CASE_ )
def snake_case ( self , _snake_case ):
"""simple docstring"""
if self.head is None:
self.set_head(SCREAMING_SNAKE_CASE_ )
else:
self.insert_after_node(self.tail , SCREAMING_SNAKE_CASE_ )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = Node(SCREAMING_SNAKE_CASE_ )
if self.head is None:
self.set_head(SCREAMING_SNAKE_CASE_ )
else:
self.set_tail(SCREAMING_SNAKE_CASE_ )
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = node
_lowerCAmelCase = node.previous
if node.get_previous() is None:
_lowerCAmelCase = node_to_insert
else:
_lowerCAmelCase = node_to_insert
_lowerCAmelCase = node_to_insert
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = node
_lowerCAmelCase = node.next
if node.get_next() is None:
_lowerCAmelCase = node_to_insert
else:
_lowerCAmelCase = node_to_insert
_lowerCAmelCase = node_to_insert
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = 1
_lowerCAmelCase = Node(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = self.head
while node:
if current_position == position:
self.insert_before_node(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return
current_position += 1
_lowerCAmelCase = node.next
self.insert_after_node(self.tail , SCREAMING_SNAKE_CASE_ )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.head
while node:
if node.get_data() == item:
return node
_lowerCAmelCase = node.get_next()
raise Exception("""Node not found""" )
def snake_case ( self , _snake_case ):
"""simple docstring"""
if (node := self.get_node(SCREAMING_SNAKE_CASE_ )) is not None:
if node == self.head:
_lowerCAmelCase = self.head.get_next()
if node == self.tail:
_lowerCAmelCase = self.tail.get_previous()
self.remove_node_pointers(SCREAMING_SNAKE_CASE_ )
@staticmethod
def snake_case ( _snake_case ):
"""simple docstring"""
if node.get_next():
_lowerCAmelCase = node.previous
if node.get_previous():
_lowerCAmelCase = node.next
_lowerCAmelCase = None
_lowerCAmelCase = None
def snake_case ( self ):
"""simple docstring"""
return self.head is None
def _UpperCAmelCase ( ):
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 82
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__a = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'processing_trocr': ['TrOCRProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
'TrOCRForCausalLM',
'TrOCRPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 30
| 0
|
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : list ):
for i in range(len(snake_case__ ) - 1 , 0 , -1 ):
__UpperCamelCase =False
for j in range(snake_case__ , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
__UpperCamelCase , __UpperCamelCase =unsorted[j - 1], unsorted[j]
__UpperCamelCase =True
for j in range(snake_case__ ):
if unsorted[j] > unsorted[j + 1]:
__UpperCamelCase , __UpperCamelCase =unsorted[j + 1], unsorted[j]
__UpperCamelCase =True
if not swapped:
break
return unsorted
if __name__ == "__main__":
import doctest
doctest.testmod()
_A = input('Enter numbers separated by a comma:\n').strip()
_A = [int(item) for item in user_input.split(',')]
print(f"""{cocktail_shaker_sort(unsorted) = }""")
| 62
|
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
__a = logging.get_logger(__name__)
# General docstring
__a = 'RegNetConfig'
# Base docstring
__a = 'facebook/regnet-y-040'
__a = [1, 1_0_8_8, 7, 7]
# Image classification docstring
__a = 'facebook/regnet-y-040'
__a = 'tabby, tabby cat'
__a = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 3 , SCREAMING_SNAKE_CASE_ : int = 1 , SCREAMING_SNAKE_CASE_ : int = 1 , SCREAMING_SNAKE_CASE_ : Optional[str] = "relu" , **SCREAMING_SNAKE_CASE_ : Any , ) -> List[str]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
lowercase_ = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
lowercase_ = tf.keras.layers.ConvaD(
filters=SCREAMING_SNAKE_CASE_ , kernel_size=SCREAMING_SNAKE_CASE_ , strides=SCREAMING_SNAKE_CASE_ , padding='''VALID''' , groups=SCREAMING_SNAKE_CASE_ , use_bias=SCREAMING_SNAKE_CASE_ , name='''convolution''' , )
lowercase_ = tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name='''normalization''' )
lowercase_ = ACTaFN[activation] if activation is not None else tf.identity
def _lowercase ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Any:
lowercase_ = self.convolution(self.padding(SCREAMING_SNAKE_CASE_ ) )
lowercase_ = self.normalization(SCREAMING_SNAKE_CASE_ )
lowercase_ = self.activation(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : str , SCREAMING_SNAKE_CASE_ : RegNetConfig , **SCREAMING_SNAKE_CASE_ : str ) -> Any:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = config.num_channels
lowercase_ = TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name='''embedder''' , )
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : str ) -> Optional[int]:
lowercase_ = shape_list(SCREAMING_SNAKE_CASE_ )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
'''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''' )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
lowercase_ = tf.transpose(SCREAMING_SNAKE_CASE_ , perm=(0, 2, 3, 1) )
lowercase_ = self.embedder(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 2 , **SCREAMING_SNAKE_CASE_ : List[str] ) -> Union[str, Any]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = tf.keras.layers.ConvaD(
filters=SCREAMING_SNAKE_CASE_ , kernel_size=1 , strides=SCREAMING_SNAKE_CASE_ , use_bias=SCREAMING_SNAKE_CASE_ , name='''convolution''' )
lowercase_ = tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name='''normalization''' )
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : tf.Tensor , SCREAMING_SNAKE_CASE_ : bool = False ) -> tf.Tensor:
return self.normalization(self.convolution(SCREAMING_SNAKE_CASE_ ) , training=SCREAMING_SNAKE_CASE_ )
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , **SCREAMING_SNAKE_CASE_ : int ) -> Optional[Any]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = tf.keras.layers.GlobalAveragePoolingaD(keepdims=SCREAMING_SNAKE_CASE_ , name='''pooler''' )
lowercase_ = [
tf.keras.layers.ConvaD(filters=SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation='''relu''' , name='''attention.0''' ),
tf.keras.layers.ConvaD(filters=SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation='''sigmoid''' , name='''attention.2''' ),
]
def _lowercase ( self : str , SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> Dict:
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
lowercase_ = self.pooler(SCREAMING_SNAKE_CASE_ )
for layer_module in self.attention:
lowercase_ = layer_module(SCREAMING_SNAKE_CASE_ )
lowercase_ = hidden_state * pooled
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : str , SCREAMING_SNAKE_CASE_ : RegNetConfig , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 1 , **SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> List[str]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = in_channels != out_channels or stride != 1
lowercase_ = max(1 , out_channels // config.groups_width )
lowercase_ = (
TFRegNetShortCut(SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , name='''shortcut''' )
if should_apply_shortcut
else tf.keras.layers.Activation('''linear''' , name='''shortcut''' )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
lowercase_ = [
TFRegNetConvLayer(SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation=config.hidden_act , name='''layer.0''' ),
TFRegNetConvLayer(
SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , groups=SCREAMING_SNAKE_CASE_ , activation=config.hidden_act , name='''layer.1''' ),
TFRegNetConvLayer(SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation=SCREAMING_SNAKE_CASE_ , name='''layer.2''' ),
]
lowercase_ = ACTaFN[config.hidden_act]
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Any:
lowercase_ = hidden_state
for layer_module in self.layers:
lowercase_ = layer_module(SCREAMING_SNAKE_CASE_ )
lowercase_ = self.shortcut(SCREAMING_SNAKE_CASE_ )
hidden_state += residual
lowercase_ = self.activation(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Any , SCREAMING_SNAKE_CASE_ : RegNetConfig , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 1 , **SCREAMING_SNAKE_CASE_ : List[str] ) -> int:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = in_channels != out_channels or stride != 1
lowercase_ = max(1 , out_channels // config.groups_width )
lowercase_ = (
TFRegNetShortCut(SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , name='''shortcut''' )
if should_apply_shortcut
else tf.keras.layers.Activation('''linear''' , name='''shortcut''' )
)
lowercase_ = [
TFRegNetConvLayer(SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation=config.hidden_act , name='''layer.0''' ),
TFRegNetConvLayer(
SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , groups=SCREAMING_SNAKE_CASE_ , activation=config.hidden_act , name='''layer.1''' ),
TFRegNetSELayer(SCREAMING_SNAKE_CASE_ , reduced_channels=int(round(in_channels / 4 ) ) , name='''layer.2''' ),
TFRegNetConvLayer(SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation=SCREAMING_SNAKE_CASE_ , name='''layer.3''' ),
]
lowercase_ = ACTaFN[config.hidden_act]
def _lowercase ( self : int , SCREAMING_SNAKE_CASE_ : Dict ) -> Optional[Any]:
lowercase_ = hidden_state
for layer_module in self.layers:
lowercase_ = layer_module(SCREAMING_SNAKE_CASE_ )
lowercase_ = self.shortcut(SCREAMING_SNAKE_CASE_ )
hidden_state += residual
lowercase_ = self.activation(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : str , SCREAMING_SNAKE_CASE_ : RegNetConfig , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 2 , SCREAMING_SNAKE_CASE_ : int = 2 , **SCREAMING_SNAKE_CASE_ : List[Any] ) -> List[str]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = TFRegNetXLayer if config.layer_type == '''x''' else TFRegNetYLayer
lowercase_ = [
# downsampling is done in the first layer with stride of 2
layer(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , name='''layers.0''' ),
*[layer(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , name=f'''layers.{i+1}''' ) for i in range(depth - 1 )],
]
def _lowercase ( self : Tuple , SCREAMING_SNAKE_CASE_ : int ) -> int:
for layer_module in self.layers:
lowercase_ = layer_module(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : RegNetConfig , **SCREAMING_SNAKE_CASE_ : Dict ) -> Tuple:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
SCREAMING_SNAKE_CASE_ , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name='''stages.0''' , ) )
lowercase_ = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(SCREAMING_SNAKE_CASE_ , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , depth=SCREAMING_SNAKE_CASE_ , name=f'''stages.{i+1}''' ) )
def _lowercase ( self : Dict , SCREAMING_SNAKE_CASE_ : tf.Tensor , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : bool = True ) -> TFBaseModelOutputWithNoAttention:
lowercase_ = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowercase_ = hidden_states + (hidden_state,)
lowercase_ = stage_module(SCREAMING_SNAKE_CASE_ )
if output_hidden_states:
lowercase_ = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE_ , hidden_states=SCREAMING_SNAKE_CASE_ )
@keras_serializable
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
a :str = RegNetConfig
def __init__( self : str , SCREAMING_SNAKE_CASE_ : Dict , **SCREAMING_SNAKE_CASE_ : List[str] ) -> Any:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = config
lowercase_ = TFRegNetEmbeddings(SCREAMING_SNAKE_CASE_ , name='''embedder''' )
lowercase_ = TFRegNetEncoder(SCREAMING_SNAKE_CASE_ , name='''encoder''' )
lowercase_ = tf.keras.layers.GlobalAveragePoolingaD(keepdims=SCREAMING_SNAKE_CASE_ , name='''pooler''' )
@unpack_inputs
def _lowercase ( self : Dict , SCREAMING_SNAKE_CASE_ : tf.Tensor , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : bool = False , ) -> TFBaseModelOutputWithPoolingAndNoAttention:
lowercase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ = self.embedder(SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
lowercase_ = self.encoder(
SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
lowercase_ = encoder_outputs[0]
lowercase_ = self.pooler(SCREAMING_SNAKE_CASE_ )
# Change to NCHW output format have uniformity in the modules
lowercase_ = tf.transpose(SCREAMING_SNAKE_CASE_ , perm=(0, 3, 1, 2) )
lowercase_ = tf.transpose(SCREAMING_SNAKE_CASE_ , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
lowercase_ = tuple([tf.transpose(SCREAMING_SNAKE_CASE_ , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=SCREAMING_SNAKE_CASE_ , pooler_output=SCREAMING_SNAKE_CASE_ , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Tuple = RegNetConfig
a :Any = 'regnet'
a :List[str] = 'pixel_values'
@property
def _lowercase ( self : List[str] ) -> str:
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4) , dtype=tf.floataa )}
__a = r'\n Parameters:\n This model is a Tensorflow\n [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n behavior.\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'
__a = r'\n Args:\n pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConveNextImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
'The bare RegNet model outputting raw features without any specific head on top.' , UpperCAmelCase , )
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : RegNetConfig , *SCREAMING_SNAKE_CASE_ : Any , **SCREAMING_SNAKE_CASE_ : str ) -> List[str]:
super().__init__(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowercase_ = TFRegNetMainLayer(SCREAMING_SNAKE_CASE_ , name='''regnet''' )
@unpack_inputs
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=SCREAMING_SNAKE_CASE_ , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : tf.Tensor , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : Optional[Any]=False , ) -> Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
lowercase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ = self.regnet(
pixel_values=SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
'\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , UpperCAmelCase , )
class lowercase__( UpperCAmelCase , UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : RegNetConfig , *SCREAMING_SNAKE_CASE_ : str , **SCREAMING_SNAKE_CASE_ : int ) -> Union[str, Any]:
super().__init__(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowercase_ = config.num_labels
lowercase_ = TFRegNetMainLayer(SCREAMING_SNAKE_CASE_ , name='''regnet''' )
# classification head
lowercase_ = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name='''classifier.1''' ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=SCREAMING_SNAKE_CASE_ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : tf.Tensor = None , SCREAMING_SNAKE_CASE_ : tf.Tensor = None , SCREAMING_SNAKE_CASE_ : bool = None , SCREAMING_SNAKE_CASE_ : bool = None , SCREAMING_SNAKE_CASE_ : Dict=False , ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
lowercase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ = self.regnet(
SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
lowercase_ = outputs.pooler_output if return_dict else outputs[1]
lowercase_ = self.classifier[0](SCREAMING_SNAKE_CASE_ )
lowercase_ = self.classifier[1](SCREAMING_SNAKE_CASE_ )
lowercase_ = None if labels is None else self.hf_compute_loss(labels=SCREAMING_SNAKE_CASE_ , logits=SCREAMING_SNAKE_CASE_ )
if not return_dict:
lowercase_ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=SCREAMING_SNAKE_CASE_ , logits=SCREAMING_SNAKE_CASE_ , hidden_states=outputs.hidden_states )
| 30
| 0
|
def A_ ( _lowerCAmelCase ) -> List[Any]:
def merge(_lowerCAmelCase , _lowerCAmelCase ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(snake_case__ ) <= 1:
return collection
UpperCamelCase : Optional[int] = len(snake_case__ ) // 2
return merge(merge_sort(collection[:mid] ) , merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
__lowerCamelCase : Optional[int] = input("""Enter numbers separated by a comma:\n""").strip()
__lowerCamelCase : Dict = [int(item) for item in user_input.split(""",""")]
print(*merge_sort(unsorted), sep=""",""")
| 52
|
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
from ..pytorch_utils import ConvaD
if is_accelerate_available():
from accelerate import init_empty_weights
from accelerate.utils import find_tied_parameters
__a = logging.get_logger(__name__)
def a ( snake_case__: Optional[int] , snake_case__: Dict , snake_case__: int , snake_case__: List[str]=None , snake_case__: List[Any]=None ):
'''simple docstring'''
# Recurse if needed
if "." in tensor_name:
lowercase_ = tensor_name.split('''.''' )
for split in splits[:-1]:
lowercase_ = getattr(snake_case__ , snake_case__ )
if new_module is None:
raise ValueError(F'''{module} has no attribute {split}.''' )
lowercase_ = new_module
lowercase_ = splits[-1]
if tensor_name not in module._parameters and tensor_name not in module._buffers:
raise ValueError(F'''{module} does not have a parameter or a buffer named {tensor_name}.''' )
lowercase_ = tensor_name in module._buffers
lowercase_ = getattr(snake_case__ , snake_case__ )
if old_value.device == torch.device('''meta''' ) and device not in ["meta", torch.device('''meta''' )] and value is None:
raise ValueError(F'''{tensor_name} is on the meta device, we need a `value` to put in on {device}.''' )
lowercase_ = False
lowercase_ = False
if is_buffer or not is_bitsandbytes_available():
lowercase_ = False
lowercase_ = False
else:
lowercase_ = hasattr(bnb.nn , '''Params4bit''' ) and isinstance(module._parameters[tensor_name] , bnb.nn.Paramsabit )
lowercase_ = isinstance(module._parameters[tensor_name] , bnb.nn.IntaParams )
if is_abit or is_abit:
lowercase_ = module._parameters[tensor_name]
if param.device.type != "cuda":
if value is None:
lowercase_ = old_value.to(snake_case__ )
elif isinstance(snake_case__ , torch.Tensor ):
lowercase_ = value.to('''cpu''' )
if value.dtype == torch.inta:
lowercase_ = version.parse(importlib.metadata.version('''bitsandbytes''' ) ) > version.parse(
'''0.37.2''' )
if not is_abit_serializable:
raise ValueError(
'''Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. '''
'''Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.''' )
else:
lowercase_ = torch.tensor(snake_case__ , device='''cpu''' )
# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
# Since weights are saved in the correct "orientation", we skip transposing when loading.
if issubclass(module.source_cls , snake_case__ ) and fpaa_statistics is None:
lowercase_ = new_value.T
lowercase_ = old_value.__dict__
if is_abit:
lowercase_ = bnb.nn.IntaParams(snake_case__ , requires_grad=snake_case__ , **snake_case__ ).to(snake_case__ )
elif is_abit:
lowercase_ = bnb.nn.Paramsabit(snake_case__ , requires_grad=snake_case__ , **snake_case__ ).to(snake_case__ )
lowercase_ = new_value
if fpaa_statistics is not None:
setattr(module.weight , '''SCB''' , fpaa_statistics.to(snake_case__ ) )
else:
if value is None:
lowercase_ = old_value.to(snake_case__ )
elif isinstance(snake_case__ , torch.Tensor ):
lowercase_ = value.to(snake_case__ )
else:
lowercase_ = torch.tensor(snake_case__ , device=snake_case__ )
if is_buffer:
lowercase_ = new_value
else:
lowercase_ = nn.Parameter(snake_case__ , requires_grad=old_value.requires_grad )
lowercase_ = new_value
def a ( snake_case__: str , snake_case__: Union[str, Any]=None , snake_case__: Any=None , snake_case__: List[str]=None , snake_case__: Optional[Any]=False ):
'''simple docstring'''
for name, module in model.named_children():
if current_key_name is None:
lowercase_ = []
current_key_name.append(snake_case__ )
if (isinstance(snake_case__ , nn.Linear ) or isinstance(snake_case__ , snake_case__ )) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
if not any(key in '''.'''.join(snake_case__ ) for key in modules_to_not_convert ):
with init_empty_weights():
if isinstance(snake_case__ , snake_case__ ):
lowercase_ , lowercase_ = module.weight.shape
else:
lowercase_ = module.in_features
lowercase_ = module.out_features
if quantization_config.quantization_method() == "llm_int8":
lowercase_ = bnb.nn.LinearabitLt(
snake_case__ , snake_case__ , module.bias is not None , has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight , threshold=quantization_config.llm_inta_threshold , )
lowercase_ = True
else:
if (
quantization_config.llm_inta_skip_modules is not None
and name in quantization_config.llm_inta_skip_modules
):
pass
else:
lowercase_ = bnb.nn.Linearabit(
snake_case__ , snake_case__ , module.bias is not None , quantization_config.bnb_abit_compute_dtype , compress_statistics=quantization_config.bnb_abit_use_double_quant , quant_type=quantization_config.bnb_abit_quant_type , )
lowercase_ = True
# Store the module class in case we need to transpose the weight later
lowercase_ = type(snake_case__ )
# Force requires grad to False to avoid unexpected errors
model._modules[name].requires_grad_(snake_case__ )
if len(list(module.children() ) ) > 0:
lowercase_ , lowercase_ = _replace_with_bnb_linear(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , has_been_replaced=snake_case__ , )
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def a ( snake_case__: Any , snake_case__: Any=None , snake_case__: Union[str, Any]=None , snake_case__: str=None ):
'''simple docstring'''
lowercase_ = ['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
lowercase_ , lowercase_ = _replace_with_bnb_linear(
snake_case__ , snake_case__ , snake_case__ , snake_case__ )
if not has_been_replaced:
logger.warning(
'''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
''' Please double check your model architecture, or submit an issue on github if you think this is'''
''' a bug.''' )
return model
def a ( *snake_case__: str , **snake_case__: Dict ):
'''simple docstring'''
warnings.warn(
'''`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead''' , snake_case__ , )
return replace_with_bnb_linear(*snake_case__ , **snake_case__ )
def a ( *snake_case__: Any , **snake_case__: List[Any] ):
'''simple docstring'''
warnings.warn(
'''`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead''' , snake_case__ , )
return set_module_quantized_tensor_to_device(*snake_case__ , **snake_case__ )
def a ( snake_case__: Optional[Any] ):
'''simple docstring'''
lowercase_ = deepcopy(snake_case__ ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
tied_model.tie_weights()
lowercase_ = find_tied_parameters(snake_case__ )
# For compatibility with Accelerate < 0.18
if isinstance(snake_case__ , snake_case__ ):
lowercase_ = sum(list(tied_params.values() ) , [] ) + list(tied_params.keys() )
else:
lowercase_ = sum(snake_case__ , [] )
lowercase_ = len(snake_case__ ) > 0
# Check if it is a base model
lowercase_ = not hasattr(snake_case__ , model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
lowercase_ = list(model.named_children() )
lowercase_ = [list_modules[-1][0]]
# add last module together with tied weights
lowercase_ = set(snake_case__ ) - set(snake_case__ )
lowercase_ = list(set(snake_case__ ) ) + list(snake_case__ )
# remove ".weight" from the keys
lowercase_ = ['''.weight''', '''.bias''']
lowercase_ = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
lowercase_ = name.replace(snake_case__ , '''''' )
filtered_module_names.append(snake_case__ )
return filtered_module_names
| 30
| 0
|
"""simple docstring"""
def A_ ( _lowerCAmelCase : int, _lowerCAmelCase : list ):
"""simple docstring"""
_enforce_args(snake_case__, snake_case__ )
if n == 0:
return 0
_a = float('''-inf''' )
for i in range(1, n + 1 ):
_a = max(
snake_case__, prices[i - 1] + naive_cut_rod_recursive(n - i, snake_case__ ) )
return max_revue
def A_ ( _lowerCAmelCase : int, _lowerCAmelCase : list ):
"""simple docstring"""
_enforce_args(snake_case__, snake_case__ )
_a = [float('''-inf''' ) for _ in range(n + 1 )]
return _top_down_cut_rod_recursive(snake_case__, snake_case__, snake_case__ )
def A_ ( _lowerCAmelCase : int, _lowerCAmelCase : list, _lowerCAmelCase : list ):
"""simple docstring"""
if max_rev[n] >= 0:
return max_rev[n]
elif n == 0:
return 0
else:
_a = float('''-inf''' )
for i in range(1, n + 1 ):
_a = max(
snake_case__, prices[i - 1] + _top_down_cut_rod_recursive(n - i, snake_case__, snake_case__ ), )
_a = max_revenue
return max_rev[n]
def A_ ( _lowerCAmelCase : int, _lowerCAmelCase : list ):
"""simple docstring"""
_enforce_args(snake_case__, snake_case__ )
# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
# length 0.
_a = [float('''-inf''' ) for _ in range(n + 1 )]
_a = 0
for i in range(1, n + 1 ):
_a = max_rev[i]
for j in range(1, i + 1 ):
_a = max(snake_case__, prices[j - 1] + max_rev[i - j] )
_a = max_revenue_i
return max_rev[n]
def A_ ( _lowerCAmelCase : int, _lowerCAmelCase : list ):
"""simple docstring"""
if n < 0:
_a = f'n must be greater than or equal to 0. Got n = {n}'
raise ValueError(snake_case__ )
if n > len(snake_case__ ):
_a = (
'''Each integral piece of rod must have a corresponding price. '''
f'Got n = {n} but length of prices = {len(snake_case__ )}'
)
raise ValueError(snake_case__ )
def A_ ( ):
"""simple docstring"""
_a = [6, 10, 12, 15, 20, 23]
_a = len(snake_case__ )
# the best revenue comes from cutting the rod into 6 pieces, each
# of length 1 resulting in a revenue of 6 * 6 = 36.
_a = 36
_a = top_down_cut_rod(snake_case__, snake_case__ )
_a = bottom_up_cut_rod(snake_case__, snake_case__ )
_a = naive_cut_rod_recursive(snake_case__, snake_case__ )
assert expected_max_revenue == max_rev_top_down
assert max_rev_top_down == max_rev_bottom_up
assert max_rev_bottom_up == max_rev_naive
if __name__ == "__main__":
main()
| 320
|
import argparse
import os
import re
__a = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__a = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
__a = re.compile(r'\s*\(\s*"(\S[^"]+)"')
def a ( snake_case__: str , snake_case__: bool = False ):
'''simple docstring'''
with open(snake_case__ , '''r''' , encoding='''utf-8''' ) as f:
lowercase_ = f.read()
lowercase_ = content.split('''\n''' )
lowercase_ = []
lowercase_ = 0
while line_idx < len(snake_case__ ):
if _re_intro_mapping.search(lines[line_idx] ) is not None:
lowercase_ = len(re.search(r'''^(\s*)\S''' , lines[line_idx] ).groups()[0] ) + 8
# Start of a new mapping!
while not lines[line_idx].startswith(''' ''' * indent + '''(''' ):
new_lines.append(lines[line_idx] )
line_idx += 1
lowercase_ = []
while lines[line_idx].strip() != "]":
# Blocks either fit in one line or not
if lines[line_idx].strip() == "(":
lowercase_ = line_idx
while not lines[line_idx].startswith(''' ''' * indent + ''')''' ):
line_idx += 1
blocks.append('''\n'''.join(lines[start_idx : line_idx + 1] ) )
else:
blocks.append(lines[line_idx] )
line_idx += 1
# Sort blocks by their identifiers
lowercase_ = sorted(snake_case__ , key=lambda snake_case__ : _re_identifier.search(snake_case__ ).groups()[0] )
new_lines += blocks
else:
new_lines.append(lines[line_idx] )
line_idx += 1
if overwrite:
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as f:
f.write('''\n'''.join(snake_case__ ) )
elif "\n".join(snake_case__ ) != content:
return True
def a ( snake_case__: bool = False ):
'''simple docstring'''
lowercase_ = [os.path.join(snake_case__ , snake_case__ ) for f in os.listdir(snake_case__ ) if f.endswith('''.py''' )]
lowercase_ = [sort_auto_mapping(snake_case__ , overwrite=snake_case__ ) for fname in fnames]
if not overwrite and any(snake_case__ ):
lowercase_ = [f for f, d in zip(snake_case__ , snake_case__ ) if d]
raise ValueError(
F'''The following files have auto mappings that need sorting: {', '.join(snake_case__ )}. Run `make style` to fix'''
''' this.''' )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
__a = parser.parse_args()
sort_all_auto_mappings(not args.check_only)
| 30
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|
"""simple docstring"""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE__ :
_a = 42
_a = None
_a = None
def lowercase (snake_case__ : TreeNode | None ) -> str:
'''simple docstring'''
def is_valid_tree(snake_case__ : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(snake_case__ , snake_case__ ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(snake_case__ ):
raise ValueError(
"""Each node should be type of TreeNode and data should be float.""" )
def is_binary_search_tree_recursive_check(
snake_case__ : TreeNode | None , snake_case__ : float , snake_case__ : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , snake_case__ , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , snake_case__ )
)
return is_binary_search_tree_recursive_check(snake_case__ , -float("""inf""" ) , float("""inf""" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 155
|
def a ( snake_case__: list , snake_case__: list , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if index == number_of_items:
return 0
lowercase_ = 0
lowercase_ = 0
lowercase_ = knapsack(snake_case__ , snake_case__ , snake_case__ , snake_case__ , index + 1 )
if weights[index] <= max_weight:
lowercase_ = values[index] + knapsack(
snake_case__ , snake_case__ , snake_case__ , max_weight - weights[index] , index + 1 )
return max(snake_case__ , snake_case__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 30
| 0
|
def a__ ( UpperCAmelCase : list[int] , UpperCAmelCase : str ) -> List[str]:
UpperCAmelCase : Dict = int(snake_case__ )
# Initialize Result
UpperCAmelCase : Optional[Any] = []
# Traverse through all denomination
for denomination in reversed(snake_case__ ):
# Find denominations
while int(snake_case__ ) >= int(snake_case__ ):
total_value -= int(snake_case__ )
answer.append(snake_case__ ) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
_lowerCamelCase : Any = []
_lowerCamelCase : List[str] = "0"
if (
input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
== "y"
):
_lowerCamelCase : Optional[int] = int(input("Enter the number of denominations you want to add: ").strip())
for i in range(0, n):
denominations.append(int(input(f"""Denomination {i}: """).strip()))
_lowerCamelCase : Optional[Any] = input("Enter the change you want to make in Indian Currency: ").strip()
else:
# All denominations of Indian Currency if user does not enter
_lowerCamelCase : Optional[int] = [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 5_0_0, 2_0_0_0]
_lowerCamelCase : List[Any] = input("Enter the change you want to make: ").strip()
if int(value) == 0 or int(value) < 0:
print("The total value cannot be zero or negative.")
else:
print(f"""Following is minimal change for {value}: """)
_lowerCamelCase : int = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=" ")
| 336
|
import argparse
from collections import defaultdict
import yaml
__a = 'docs/source/en/_toctree.yml'
def a ( snake_case__: Dict ):
'''simple docstring'''
lowercase_ = defaultdict(snake_case__ )
for doc in model_doc:
counts[doc["local"]] += 1
lowercase_ = [key for key, value in counts.items() if value > 1]
lowercase_ = []
for duplicate_key in duplicates:
lowercase_ = list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key} )
if len(snake_case__ ) > 1:
raise ValueError(
F'''{duplicate_key} is present several times in the documentation table of content at '''
'''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
'''others.''' )
# Only add this once
new_doc.append({'''local''': duplicate_key, '''title''': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1] )
# Sort
return sorted(snake_case__ , key=lambda snake_case__ : s["title"].lower() )
def a ( snake_case__: List[Any]=False ):
'''simple docstring'''
with open(snake_case__ , encoding='''utf-8''' ) as f:
lowercase_ = yaml.safe_load(f.read() )
# Get to the API doc
lowercase_ = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase_ = content[api_idx]['''sections''']
# Then to the model doc
lowercase_ = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
lowercase_ = api_doc[model_idx]['''sections''']
lowercase_ = [(idx, section) for idx, section in enumerate(snake_case__ ) if '''sections''' in section]
lowercase_ = False
for idx, modality_doc in modalities_docs:
lowercase_ = modality_doc['''sections''']
lowercase_ = clean_model_doc_toc(snake_case__ )
if old_modality_doc != new_modality_doc:
lowercase_ = True
if overwrite:
lowercase_ = new_modality_doc
if diff:
if overwrite:
lowercase_ = model_doc
lowercase_ = api_doc
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(snake_case__ , allow_unicode=snake_case__ ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
__a = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 30
| 0
|
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
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''salesforce/blip2-opt-2.7b''': '''https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json''',
}
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : List[Any] = 'blip_2_vision_model'
def __init__( self , __lowercase=1_408 , __lowercase=6_144 , __lowercase=39 , __lowercase=16 , __lowercase=224 , __lowercase=14 , __lowercase="gelu" , __lowercase=0.0_00_01 , __lowercase=0.0 , __lowercase=1E-1_0 , __lowercase=True , **__lowercase , ) -> List[Any]:
super().__init__(**SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Optional[int] = hidden_size
__UpperCamelCase :Any = intermediate_size
__UpperCamelCase :List[Any] = num_hidden_layers
__UpperCamelCase :List[Any] = num_attention_heads
__UpperCamelCase :Optional[Any] = patch_size
__UpperCamelCase :str = image_size
__UpperCamelCase :Dict = initializer_range
__UpperCamelCase :str = attention_dropout
__UpperCamelCase :Optional[int] = layer_norm_eps
__UpperCamelCase :Union[str, Any] = hidden_act
__UpperCamelCase :List[str] = qkv_bias
@classmethod
def UpperCamelCase__ ( cls , __lowercase , **__lowercase) -> "PretrainedConfig":
cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE_)
__UpperCamelCase , __UpperCamelCase :Tuple = cls.get_config_dict(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_)
# get the vision config dict if we are loading from Blip2Config
if config_dict.get('''model_type''') == "blip-2":
__UpperCamelCase :Dict = 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(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_)
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : Dict = 'blip_2_qformer'
def __init__( self , __lowercase=30_522 , __lowercase=768 , __lowercase=12 , __lowercase=12 , __lowercase=3_072 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=512 , __lowercase=0.02 , __lowercase=1E-1_2 , __lowercase=0 , __lowercase="absolute" , __lowercase=2 , __lowercase=1_408 , **__lowercase , ) -> List[Any]:
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_)
__UpperCamelCase :str = vocab_size
__UpperCamelCase :Optional[int] = hidden_size
__UpperCamelCase :Any = num_hidden_layers
__UpperCamelCase :Any = num_attention_heads
__UpperCamelCase :Optional[int] = hidden_act
__UpperCamelCase :str = intermediate_size
__UpperCamelCase :Tuple = hidden_dropout_prob
__UpperCamelCase :Tuple = attention_probs_dropout_prob
__UpperCamelCase :List[Any] = max_position_embeddings
__UpperCamelCase :Optional[int] = initializer_range
__UpperCamelCase :Any = layer_norm_eps
__UpperCamelCase :Union[str, Any] = position_embedding_type
__UpperCamelCase :str = cross_attention_frequency
__UpperCamelCase :List[str] = encoder_hidden_size
@classmethod
def UpperCamelCase__ ( cls , __lowercase , **__lowercase) -> "PretrainedConfig":
cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE_)
__UpperCamelCase , __UpperCamelCase :str = cls.get_config_dict(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_)
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get('''model_type''') == "blip-2":
__UpperCamelCase :Optional[int] = 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(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_)
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : str = 'blip-2'
a__ : int = True
def __init__( self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=32 , **__lowercase) -> Tuple:
super().__init__(**SCREAMING_SNAKE_CASE_)
if vision_config is None:
__UpperCamelCase :Dict = {}
logger.info('''vision_config is None. initializing the Blip2VisionConfig with default values.''')
if qformer_config is None:
__UpperCamelCase :str = {}
logger.info('''qformer_config is None. Initializing the Blip2QFormerConfig with default values.''')
if text_config is None:
__UpperCamelCase :str = {}
logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''')
__UpperCamelCase :Optional[int] = BlipaVisionConfig(**SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Optional[Any] = BlipaQFormerConfig(**SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Any = text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
__UpperCamelCase :Tuple = CONFIG_MAPPING[text_model_type](**SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Union[str, Any] = self.text_config.tie_word_embeddings
__UpperCamelCase :Dict = self.text_config.is_encoder_decoder
__UpperCamelCase :Optional[Any] = num_query_tokens
__UpperCamelCase :List[Any] = self.vision_config.hidden_size
__UpperCamelCase :Any = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
__UpperCamelCase :Optional[int] = 1.0
__UpperCamelCase :int = 0.02
@classmethod
def UpperCamelCase__ ( cls , __lowercase , __lowercase , __lowercase , **__lowercase , ) -> Dict:
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **SCREAMING_SNAKE_CASE_ , )
def UpperCamelCase__ ( self) -> str:
__UpperCamelCase :Optional[Any] = copy.deepcopy(self.__dict__)
__UpperCamelCase :str = self.vision_config.to_dict()
__UpperCamelCase :int = self.qformer_config.to_dict()
__UpperCamelCase :Dict = self.text_config.to_dict()
__UpperCamelCase :List[Any] = self.__class__.model_type
return output
| 43
|
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Union[str, Any] = 'upernet'
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : Dict=None , SCREAMING_SNAKE_CASE_ : str=5_1_2 , SCREAMING_SNAKE_CASE_ : Tuple=0.02 , SCREAMING_SNAKE_CASE_ : Optional[Any]=[1, 2, 3, 6] , SCREAMING_SNAKE_CASE_ : Optional[int]=True , SCREAMING_SNAKE_CASE_ : Tuple=0.4 , SCREAMING_SNAKE_CASE_ : Optional[int]=3_8_4 , SCREAMING_SNAKE_CASE_ : str=2_5_6 , SCREAMING_SNAKE_CASE_ : Dict=1 , SCREAMING_SNAKE_CASE_ : Tuple=False , SCREAMING_SNAKE_CASE_ : str=2_5_5 , **SCREAMING_SNAKE_CASE_ : str , ) -> int:
super().__init__(**SCREAMING_SNAKE_CASE_ )
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowercase_ = CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase_ = backbone_config.get('''model_type''' )
lowercase_ = CONFIG_MAPPING[backbone_model_type]
lowercase_ = config_class.from_dict(SCREAMING_SNAKE_CASE_ )
lowercase_ = backbone_config
lowercase_ = hidden_size
lowercase_ = initializer_range
lowercase_ = pool_scales
lowercase_ = use_auxiliary_head
lowercase_ = auxiliary_loss_weight
lowercase_ = auxiliary_in_channels
lowercase_ = auxiliary_channels
lowercase_ = auxiliary_num_convs
lowercase_ = auxiliary_concat_input
lowercase_ = loss_ignore_index
def _lowercase ( self : List[str] ) -> List[str]:
lowercase_ = copy.deepcopy(self.__dict__ )
lowercase_ = self.backbone_config.to_dict()
lowercase_ = self.__class__.model_type
return output
| 30
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import os
def _A ( _lowercase = "input.txt" ) -> Any:
"""simple docstring"""
with open(os.path.join(os.path.dirname(snake_case__ ) , snake_case__ ) ) as input_file:
__UpperCamelCase = [
[int(snake_case__ ) for element in line.split(',' )]
for line in input_file.readlines()
]
__UpperCamelCase = len(snake_case__ )
__UpperCamelCase = len(matrix[0] )
__UpperCamelCase = [[-1 for _ in range(snake_case__ )] for _ in range(snake_case__ )]
for i in range(snake_case__ ):
__UpperCamelCase = matrix[i][0]
for j in range(1 , snake_case__ ):
for i in range(snake_case__ ):
__UpperCamelCase = minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1 , snake_case__ ):
__UpperCamelCase = min(
minimal_path_sums[i][j] , minimal_path_sums[i - 1][j] + matrix[i][j] )
for i in range(rows - 2 , -1 , -1 ):
__UpperCamelCase = min(
minimal_path_sums[i][j] , minimal_path_sums[i + 1][j] + matrix[i][j] )
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 310
|
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
__a = 'sshleifer/bart-tiny-random'
__a = 'patrickvonplaten/t5-tiny-random'
@require_torch
class lowercase__( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _lowercase ( self : Any ) -> Tuple:
return AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : int ) -> List[Any]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.num_hidden_layers , 1 )
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : List[str] ) -> List[Any]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=SCREAMING_SNAKE_CASE_ )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , self.teacher_config.encoder_layers )
def _lowercase ( self : List[Any] ) -> List[Any]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , 1 )
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=SCREAMING_SNAKE_CASE_ , d=SCREAMING_SNAKE_CASE_ )
| 30
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"""simple docstring"""
def __lowerCAmelCase (_UpperCamelCase = 100 ):
__lowerCAmelCase : Tuple = (n * (n + 1) // 2) ** 2
__lowerCAmelCase : Optional[Any] = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'{solution() = }')
| 86
|
def a ( snake_case__: int = 100 ):
'''simple docstring'''
lowercase_ = (n * (n + 1) // 2) ** 2
lowercase_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"{solution() = }")
| 30
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|
from itertools import product
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[Any]:
lowerCAmelCase_ : List[Any] = sides_number
lowerCAmelCase_ : Any = max_face_number * dice_number
lowerCAmelCase_ : Dict = [0] * (max_total + 1)
lowerCAmelCase_ : Optional[int] = 1
lowerCAmelCase_ : Dict = range(snake_case__ , max_face_number + 1 )
for dice_numbers in product(snake_case__ , repeat=snake_case__ ):
lowerCAmelCase_ : Optional[Any] = sum(snake_case__ )
totals_frequencies[total] += 1
return totals_frequencies
def lowerCAmelCase ( )-> Any:
lowerCAmelCase_ : Optional[Any] = total_frequency_distribution(
sides_number=4 , dice_number=9 )
lowerCAmelCase_ : Tuple = total_frequency_distribution(
sides_number=6 , dice_number=6 )
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : List[Any] = 9
lowerCAmelCase_ : Any = 4 * 9
lowerCAmelCase_ : Optional[Any] = 6
for peter_total in range(snake_case__ , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
lowerCAmelCase_ : Optional[Any] = (4**9) * (6**6)
lowerCAmelCase_ : str = peter_wins_count / total_games_number
lowerCAmelCase_ : Tuple = round(snake_case__ , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(f"""{solution() = }""")
| 262
|
import logging
from transformers.configuration_utils import PretrainedConfig
__a = logging.getLogger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Optional[int] = 'masked_bert'
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : List[Any]=3_0_5_2_2 , SCREAMING_SNAKE_CASE_ : List[str]=7_6_8 , SCREAMING_SNAKE_CASE_ : Optional[int]=1_2 , SCREAMING_SNAKE_CASE_ : Any=1_2 , SCREAMING_SNAKE_CASE_ : str=3_0_7_2 , SCREAMING_SNAKE_CASE_ : Union[str, Any]="gelu" , SCREAMING_SNAKE_CASE_ : Optional[int]=0.1 , SCREAMING_SNAKE_CASE_ : List[str]=0.1 , SCREAMING_SNAKE_CASE_ : Tuple=5_1_2 , SCREAMING_SNAKE_CASE_ : str=2 , SCREAMING_SNAKE_CASE_ : Dict=0.02 , SCREAMING_SNAKE_CASE_ : Any=1e-12 , SCREAMING_SNAKE_CASE_ : Any=0 , SCREAMING_SNAKE_CASE_ : Optional[int]="topK" , SCREAMING_SNAKE_CASE_ : Dict="constant" , SCREAMING_SNAKE_CASE_ : Optional[Any]=0.0 , **SCREAMING_SNAKE_CASE_ : Optional[int] , ) -> Optional[Any]:
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
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_ = type_vocab_size
lowercase_ = initializer_range
lowercase_ = layer_norm_eps
lowercase_ = pruning_method
lowercase_ = mask_init
lowercase_ = mask_scale
| 30
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|
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def lowercase ( lowerCAmelCase__ : Dict[str, torch.Tensor] ) -> List[Any]:
__a = []
__a = []
__a = []
for rt in rc.restypes:
__a = rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names] )
__a = {name: i for i, name in enumerate(snake_case__ )}
restype_atomaa_to_atomaa_list.append(
[(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types] )
restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names] )
# Add dummy mapping for restype 'UNK'
restype_atomaa_to_atomaa_list.append([0] * 14 )
restype_atomaa_to_atomaa_list.append([0] * 37 )
restype_atomaa_mask_list.append([0.0] * 14 )
__a = torch.tensor(
snake_case__ , dtype=torch.intaa , device=protein['''aatype'''].device , )
__a = torch.tensor(
snake_case__ , dtype=torch.intaa , device=protein['''aatype'''].device , )
__a = torch.tensor(
snake_case__ , dtype=torch.floataa , device=protein['''aatype'''].device , )
__a = protein['''aatype'''].to(torch.long )
# create the mapping for (residx, atom14) --> atom37, i.e. an array
# with shape (num_res, 14) containing the atom37 indices for this protein
__a = restype_atomaa_to_atomaa[protein_aatype]
__a = restype_atomaa_mask[protein_aatype]
__a = residx_atomaa_mask
__a = residx_atomaa_to_atomaa.long()
# create the gather indices for mapping back
__a = restype_atomaa_to_atomaa[protein_aatype]
__a = residx_atomaa_to_atomaa.long()
# create the corresponding mask
__a = torch.zeros([21, 37] , dtype=torch.floataa , device=protein['''aatype'''].device )
for restype, restype_letter in enumerate(rc.restypes ):
__a = rc.restype_atoa[restype_letter]
__a = rc.residue_atoms[restype_name]
for atom_name in atom_names:
__a = rc.atom_order[atom_name]
__a = 1
__a = restype_atomaa_mask[protein_aatype]
__a = residx_atomaa_mask
return protein
def lowercase ( lowerCAmelCase__ : Dict[str, torch.Tensor] ) -> Any:
__a = tree_map(lambda lowerCAmelCase__ : torch.tensor(snake_case__ , device=batch['''aatype'''].device ) , snake_case__ , np.ndarray )
__a = tensor_tree_map(lambda lowerCAmelCase__ : np.array(snake_case__ ) , make_atomaa_masks(snake_case__ ) )
return out
| 45
|
import os
def a ( ):
'''simple docstring'''
lowercase_ = os.path.join(os.path.dirname(snake_case__ ) , '''num.txt''' )
with open(snake_case__ ) as file_hand:
return str(sum(int(snake_case__ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
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|
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class __lowerCAmelCase ( lowerCamelCase__ ):
@staticmethod
def snake_case ( _snake_case ):
"""simple docstring"""
_lowerCAmelCase = parser.add_parser("""download""" )
download_parser.add_argument(
"""--cache-dir""" , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ , help="""Path to location to store the models""" )
download_parser.add_argument(
"""--force""" , action="""store_true""" , help="""Force the model to be download even if already in cache-dir""" )
download_parser.add_argument(
"""--trust-remote-code""" , action="""store_true""" , help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine""" , )
download_parser.add_argument("""model""" , type=SCREAMING_SNAKE_CASE_ , help="""Name of the model to download""" )
download_parser.set_defaults(func=SCREAMING_SNAKE_CASE_ )
def __init__( self , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = model
_lowerCAmelCase = cache
_lowerCAmelCase = force
_lowerCAmelCase = trust_remote_code
def snake_case ( self ):
"""simple docstring"""
from ..models.auto import AutoModel, AutoTokenizer
AutoModel.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
AutoTokenizer.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
| 82
|
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
'''compression_format, is_archive''' , [
('''7z''', True),
('''bz2''', False),
('''gzip''', False),
('''lz4''', False),
('''tar''', True),
('''xz''', False),
('''zip''', True),
('''zstd''', False),
] , )
def a ( snake_case__: int , snake_case__: Tuple , snake_case__: Dict , snake_case__: Dict , snake_case__: List[Any] , snake_case__: int , snake_case__: List[Any] , snake_case__: Optional[int] , snake_case__: str , snake_case__: Union[str, Any] , snake_case__: List[str] , snake_case__: int , ):
'''simple docstring'''
lowercase_ = {
'''7z''': (seven_zip_file, SevenZipExtractor),
'''bz2''': (bza_file, BzipaExtractor),
'''gzip''': (gz_file, GzipExtractor),
'''lz4''': (lza_file, LzaExtractor),
'''tar''': (tar_file, TarExtractor),
'''xz''': (xz_file, XzExtractor),
'''zip''': (zip_file, ZipExtractor),
'''zstd''': (zstd_file, ZstdExtractor),
}
lowercase_ , lowercase_ = input_paths_and_base_extractors[compression_format]
if input_path is None:
lowercase_ = F'''for \'{compression_format}\' compression_format, '''
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(snake_case__ )
assert base_extractor.is_extractable(snake_case__ )
lowercase_ = tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
base_extractor.extract(snake_case__ , snake_case__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
lowercase_ = file_path.read_text(encoding='''utf-8''' )
else:
lowercase_ = output_path.read_text(encoding='''utf-8''' )
lowercase_ = text_file.read_text(encoding='''utf-8''' )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
'''compression_format, is_archive''' , [
('''7z''', True),
('''bz2''', False),
('''gzip''', False),
('''lz4''', False),
('''tar''', True),
('''xz''', False),
('''zip''', True),
('''zstd''', False),
] , )
def a ( snake_case__: List[Any] , snake_case__: int , snake_case__: Optional[int] , snake_case__: Union[str, Any] , snake_case__: List[Any] , snake_case__: Tuple , snake_case__: Optional[int] , snake_case__: List[str] , snake_case__: Union[str, Any] , snake_case__: Tuple , snake_case__: int , snake_case__: Optional[int] , ):
'''simple docstring'''
lowercase_ = {
'''7z''': seven_zip_file,
'''bz2''': bza_file,
'''gzip''': gz_file,
'''lz4''': lza_file,
'''tar''': tar_file,
'''xz''': xz_file,
'''zip''': zip_file,
'''zstd''': zstd_file,
}
lowercase_ = input_paths[compression_format]
if input_path is None:
lowercase_ = F'''for \'{compression_format}\' compression_format, '''
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(snake_case__ )
lowercase_ = Extractor.infer_extractor_format(snake_case__ )
assert extractor_format is not None
lowercase_ = tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
Extractor.extract(snake_case__ , snake_case__ , snake_case__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
lowercase_ = file_path.read_text(encoding='''utf-8''' )
else:
lowercase_ = output_path.read_text(encoding='''utf-8''' )
lowercase_ = text_file.read_text(encoding='''utf-8''' )
assert extracted_file_content == expected_file_content
@pytest.fixture
def a ( snake_case__: Union[str, Any] , snake_case__: List[Any] ):
'''simple docstring'''
import tarfile
lowercase_ = tmp_path / '''data_dot_dot'''
directory.mkdir()
lowercase_ = directory / '''tar_file_with_dot_dot.tar'''
with tarfile.TarFile(snake_case__ , '''w''' ) as f:
f.add(snake_case__ , arcname=os.path.join('''..''' , text_file.name ) )
return path
@pytest.fixture
def a ( snake_case__: int ):
'''simple docstring'''
import tarfile
lowercase_ = tmp_path / '''data_sym_link'''
directory.mkdir()
lowercase_ = directory / '''tar_file_with_sym_link.tar'''
os.symlink('''..''' , directory / '''subdir''' , target_is_directory=snake_case__ )
with tarfile.TarFile(snake_case__ , '''w''' ) as f:
f.add(str(directory / '''subdir''' ) , arcname='''subdir''' ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
'''insecure_tar_file, error_log''' , [('''tar_file_with_dot_dot''', '''illegal path'''), ('''tar_file_with_sym_link''', '''Symlink''')] , )
def a ( snake_case__: List[Any] , snake_case__: Optional[int] , snake_case__: List[str] , snake_case__: List[str] , snake_case__: int , snake_case__: Optional[Any] ):
'''simple docstring'''
lowercase_ = {
'''tar_file_with_dot_dot''': tar_file_with_dot_dot,
'''tar_file_with_sym_link''': tar_file_with_sym_link,
}
lowercase_ = insecure_tar_files[insecure_tar_file]
lowercase_ = tmp_path / '''extracted'''
TarExtractor.extract(snake_case__ , snake_case__ )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def a ( snake_case__: Optional[int] ):
'''simple docstring'''
# We should have less false positives than zipfile.is_zipfile
# We do that by checking only the magic number
lowercase_ = tmpdir / '''not_a_zip_file'''
# From: https://github.com/python/cpython/pull/5053
lowercase_ = (
B'''\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'''
B'''\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'''
B'''DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'''
B'''\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'''
)
with not_a_zip_file.open('''wb''' ) as f:
f.write(snake_case__ )
assert zipfile.is_zipfile(str(snake_case__ ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(snake_case__ ) # but we're right
| 30
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|
import os
import pytest
from attr import dataclass
_A = 'us-east-1' # defaults region
@dataclass
class UpperCAmelCase__ :
"""simple docstring"""
UpperCAmelCase__ : str
UpperCAmelCase__ : List[str] = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
UpperCAmelCase__ : Optional[int] = {
'task_name': 'mnli',
'per_device_train_batch_size': 1_6,
'per_device_eval_batch_size': 1_6,
'do_train': True,
'do_eval': True,
'do_predict': True,
'output_dir': '/opt/ml/model',
'overwrite_output_dir': True,
'max_steps': 5_0_0,
'save_steps': 5_5_0_0,
}
UpperCAmelCase__ : Any = {**hyperparameters, 'max_steps': 1_0_0_0}
@property
def _a ( self ) -> str:
if self.framework == "pytorch":
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
{"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
]
else:
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
{"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
]
@property
def _a ( self ) -> str:
return f'{self.framework}-transfromers-test'
@property
def _a ( self ) -> str:
return f'./tests/sagemaker/scripts/{self.framework}'
@property
def _a ( self ) -> str:
if self.framework == "pytorch":
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
else:
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"
@pytest.fixture(scope='class' )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Optional[int] ):
__UpperCamelCase =SageMakerTestEnvironment(framework=request.cls.framework )
| 62
|
from __future__ import annotations
def a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''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 a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if length > 1:
lowercase_ = int(length / 2 )
for i in range(snake_case__ , low + middle ):
comp_and_swap(snake_case__ , snake_case__ , i + middle , snake_case__ )
bitonic_merge(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
bitonic_merge(snake_case__ , low + middle , snake_case__ , snake_case__ )
def a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if length > 1:
lowercase_ = int(length / 2 )
bitonic_sort(snake_case__ , snake_case__ , snake_case__ , 1 )
bitonic_sort(snake_case__ , low + middle , snake_case__ , 0 )
bitonic_merge(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
if __name__ == "__main__":
__a = input('Enter numbers separated by a comma:\n').strip()
__a = [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=', ')
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| 0
|
def A_ ( _lowerCAmelCase ) -> Any:
UpperCamelCase : Optional[int] = [0] * len(snake_case__ )
UpperCamelCase : Any = []
UpperCamelCase : Optional[Any] = [1] * len(snake_case__ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(snake_case__ ) ):
if indegree[i] == 0:
queue.append(snake_case__ )
while queue:
UpperCamelCase : Optional[int] = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
UpperCamelCase : Tuple = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(snake_case__ )
print(max(snake_case__ ) )
# Adjacency list of Graph
__lowerCamelCase : Optional[Any] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 52
|
from __future__ import annotations
from collections.abc import MutableSequence
class lowercase__:
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : MutableSequence[float] ) -> None:
if len(SCREAMING_SNAKE_CASE_ ) != degree + 1:
raise ValueError(
'''The number of coefficients should be equal to the degree + 1.''' )
lowercase_ = list(SCREAMING_SNAKE_CASE_ )
lowercase_ = degree
def __add__( self : Any , SCREAMING_SNAKE_CASE_ : Polynomial ) -> Polynomial:
if self.degree > polynomial_a.degree:
lowercase_ = self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree , SCREAMING_SNAKE_CASE_ )
else:
lowercase_ = polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree , SCREAMING_SNAKE_CASE_ )
def __sub__( self : str , SCREAMING_SNAKE_CASE_ : Polynomial ) -> Polynomial:
return self + polynomial_a * Polynomial(0 , [-1] )
def __neg__( self : int ) -> Polynomial:
return Polynomial(self.degree , [-c for c in self.coefficients] )
def __mul__( self : Any , SCREAMING_SNAKE_CASE_ : Polynomial ) -> Polynomial:
lowercase_ = [0] * (self.degree + polynomial_a.degree + 1)
for i in range(self.degree + 1 ):
for j in range(polynomial_a.degree + 1 ):
coefficients[i + j] += (
self.coefficients[i] * polynomial_a.coefficients[j]
)
return Polynomial(self.degree + polynomial_a.degree , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Dict , SCREAMING_SNAKE_CASE_ : int | float ) -> int | float:
lowercase_ = 0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self : Tuple ) -> str:
lowercase_ = ''''''
for i in range(self.degree , -1 , -1 ):
if self.coefficients[i] == 0:
continue
elif self.coefficients[i] > 0:
if polynomial:
polynomial += " + "
else:
polynomial += " - "
if i == 0:
polynomial += str(abs(self.coefficients[i] ) )
elif i == 1:
polynomial += str(abs(self.coefficients[i] ) ) + "x"
else:
polynomial += str(abs(self.coefficients[i] ) ) + "x^" + str(SCREAMING_SNAKE_CASE_ )
return polynomial
def __repr__( self : Optional[Any] ) -> str:
return self.__str__()
def _lowercase ( self : int ) -> Polynomial:
lowercase_ = [0] * self.degree
for i in range(self.degree ):
lowercase_ = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : int | float = 0 ) -> Polynomial:
lowercase_ = [0] * (self.degree + 2)
lowercase_ = constant
for i in range(self.degree + 1 ):
lowercase_ = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 , SCREAMING_SNAKE_CASE_ )
def __eq__( self : str , SCREAMING_SNAKE_CASE_ : object ) -> bool:
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return False
if self.degree != polynomial_a.degree:
return False
for i in range(self.degree + 1 ):
if self.coefficients[i] != polynomial_a.coefficients[i]:
return False
return True
def __ne__( self : List[str] , SCREAMING_SNAKE_CASE_ : object ) -> bool:
return not self.__eq__(SCREAMING_SNAKE_CASE_ )
| 30
| 0
|
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
__snake_case = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def A_ ( ):
"""simple docstring"""
_a = os.path.dirname(os.path.realpath(snake_case__ ) )
_a = os.path.join(snake_case__, '''words.txt''' )
_a = ''''''
with open(snake_case__ ) as f:
_a = f.readline()
_a = [word.strip('''"''' ) for word in words.strip('''\r\n''' ).split(''',''' )]
_a = [
word
for word in [sum(ord(snake_case__ ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(snake_case__ )
if __name__ == "__main__":
print(solution())
| 320
|
import itertools
import math
def a ( snake_case__: int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(snake_case__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def a ( ):
'''simple docstring'''
lowercase_ = 2
while True:
if is_prime(snake_case__ ):
yield num
num += 1
def a ( snake_case__: int = 10_001 ):
'''simple docstring'''
return next(itertools.islice(prime_generator() , nth - 1 , snake_case__ ) )
if __name__ == "__main__":
print(f"{solution() = }")
| 30
| 0
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageVariationPipeline,
UnCLIPScheduler,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps
from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class SCREAMING_SNAKE_CASE__ ( _a , unittest.TestCase ):
_a = UnCLIPImageVariationPipeline
_a = IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
_a = IMAGE_VARIATION_BATCH_PARAMS
_a = [
'generator',
'return_dict',
'decoder_num_inference_steps',
'super_res_num_inference_steps',
]
_a = False
@property
def __lowercase ( self : Optional[Any] ):
return 32
@property
def __lowercase ( self : Optional[Any] ):
return 32
@property
def __lowercase ( self : Any ):
return self.time_input_dim
@property
def __lowercase ( self : Dict ):
return self.time_input_dim * 4
@property
def __lowercase ( self : Optional[Any] ):
return 100
@property
def __lowercase ( self : Dict ):
lowerCAmelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
return tokenizer
@property
def __lowercase ( self : Optional[Any] ):
torch.manual_seed(0 )
lowerCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE_ )
@property
def __lowercase ( self : int ):
torch.manual_seed(0 )
lowerCAmelCase = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , )
return CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE_ )
@property
def __lowercase ( self : Any ):
torch.manual_seed(0 )
lowerCAmelCase = {
"""clip_embeddings_dim""": self.text_embedder_hidden_size,
"""time_embed_dim""": self.time_embed_dim,
"""cross_attention_dim""": self.cross_attention_dim,
}
lowerCAmelCase = UnCLIPTextProjModel(**SCREAMING_SNAKE_CASE_ )
return model
@property
def __lowercase ( self : List[Any] ):
torch.manual_seed(0 )
lowerCAmelCase = {
"""sample_size""": 32,
# RGB in channels
"""in_channels""": 3,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 6,
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": """identity""",
}
lowerCAmelCase = UNetaDConditionModel(**SCREAMING_SNAKE_CASE_ )
return model
@property
def __lowercase ( self : Tuple ):
return {
"sample_size": 64,
"layers_per_block": 1,
"down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
"up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"in_channels": 6,
"out_channels": 3,
}
@property
def __lowercase ( self : Union[str, Any] ):
torch.manual_seed(0 )
lowerCAmelCase = UNetaDModel(**self.dummy_super_res_kwargs )
return model
@property
def __lowercase ( self : Dict ):
# seeded differently to get different unet than `self.dummy_super_res_first`
torch.manual_seed(1 )
lowerCAmelCase = UNetaDModel(**self.dummy_super_res_kwargs )
return model
def __lowercase ( self : int ):
lowerCAmelCase = self.dummy_decoder
lowerCAmelCase = self.dummy_text_proj
lowerCAmelCase = self.dummy_text_encoder
lowerCAmelCase = self.dummy_tokenizer
lowerCAmelCase = self.dummy_super_res_first
lowerCAmelCase = self.dummy_super_res_last
lowerCAmelCase = UnCLIPScheduler(
variance_type="""learned_range""" , prediction_type="""epsilon""" , num_train_timesteps=1000 , )
lowerCAmelCase = UnCLIPScheduler(
variance_type="""fixed_small_log""" , prediction_type="""epsilon""" , num_train_timesteps=1000 , )
lowerCAmelCase = CLIPImageProcessor(crop_size=32 , size=32 )
lowerCAmelCase = self.dummy_image_encoder
return {
"decoder": decoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_proj": text_proj,
"feature_extractor": feature_extractor,
"image_encoder": image_encoder,
"super_res_first": super_res_first,
"super_res_last": super_res_last,
"decoder_scheduler": decoder_scheduler,
"super_res_scheduler": super_res_scheduler,
}
def __lowercase ( self : int , lowerCAmelCase : str , lowerCAmelCase : Optional[Any]=0 , lowerCAmelCase : Any=True ):
lowerCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
lowerCAmelCase = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
lowerCAmelCase = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
if pil_image:
lowerCAmelCase = input_image * 0.5 + 0.5
lowerCAmelCase = input_image.clamp(0 , 1 )
lowerCAmelCase = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
lowerCAmelCase = DiffusionPipeline.numpy_to_pil(SCREAMING_SNAKE_CASE_ )[0]
return {
"image": input_image,
"generator": generator,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "np",
}
def __lowercase ( self : Optional[int] ):
lowerCAmelCase = """cpu"""
lowerCAmelCase = self.get_dummy_components()
lowerCAmelCase = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ , pil_image=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = pipe(**SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = output.images
lowerCAmelCase = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ , pil_image=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = pipe(
**SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
lowerCAmelCase = image[0, -3:, -3:, -1]
lowerCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCAmelCase = np.array(
[
0.9997,
0.0002,
0.9997,
0.9997,
0.9969,
0.0023,
0.9997,
0.9969,
0.9970,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __lowercase ( self : str ):
lowerCAmelCase = """cpu"""
lowerCAmelCase = self.get_dummy_components()
lowerCAmelCase = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ , pil_image=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = pipe(**SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = output.images
lowerCAmelCase = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ , pil_image=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = pipe(
**SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
lowerCAmelCase = image[0, -3:, -3:, -1]
lowerCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCAmelCase = np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __lowercase ( self : str ):
lowerCAmelCase = """cpu"""
lowerCAmelCase = self.get_dummy_components()
lowerCAmelCase = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ , pil_image=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = [
pipeline_inputs["""image"""],
pipeline_inputs["""image"""],
]
lowerCAmelCase = pipe(**SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = output.images
lowerCAmelCase = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ , pil_image=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = [
tuple_pipeline_inputs["""image"""],
tuple_pipeline_inputs["""image"""],
]
lowerCAmelCase = pipe(
**SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
lowerCAmelCase = image[0, -3:, -3:, -1]
lowerCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (2, 64, 64, 3)
lowerCAmelCase = np.array(
[
0.9997,
0.9989,
0.0008,
0.0021,
0.9960,
0.0018,
0.0014,
0.0002,
0.9933,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __lowercase ( self : Any ):
lowerCAmelCase = torch.device("""cpu""" )
class SCREAMING_SNAKE_CASE__ :
_a = 1
lowerCAmelCase = self.get_dummy_components()
lowerCAmelCase = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(0 )
lowerCAmelCase = pipe.decoder.dtype
lowerCAmelCase = 1
lowerCAmelCase = (
batch_size,
pipe.decoder.config.in_channels,
pipe.decoder.config.sample_size,
pipe.decoder.config.sample_size,
)
lowerCAmelCase = pipe.prepare_latents(
SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ , device=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , latents=SCREAMING_SNAKE_CASE_ , scheduler=DummyScheduler() )
lowerCAmelCase = (
batch_size,
pipe.super_res_first.config.in_channels // 2,
pipe.super_res_first.config.sample_size,
pipe.super_res_first.config.sample_size,
)
lowerCAmelCase = pipe.prepare_latents(
SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ , device=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , latents=SCREAMING_SNAKE_CASE_ , scheduler=DummyScheduler() )
lowerCAmelCase = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ , pil_image=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = pipe(
**SCREAMING_SNAKE_CASE_ , decoder_latents=SCREAMING_SNAKE_CASE_ , super_res_latents=SCREAMING_SNAKE_CASE_ ).images
lowerCAmelCase = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ , pil_image=SCREAMING_SNAKE_CASE_ )
# Don't pass image, instead pass embedding
lowerCAmelCase = pipeline_inputs.pop("""image""" )
lowerCAmelCase = pipe.image_encoder(SCREAMING_SNAKE_CASE_ ).image_embeds
lowerCAmelCase = pipe(
**SCREAMING_SNAKE_CASE_ , decoder_latents=SCREAMING_SNAKE_CASE_ , super_res_latents=SCREAMING_SNAKE_CASE_ , image_embeddings=SCREAMING_SNAKE_CASE_ , ).images
# make sure passing text embeddings manually is identical
assert np.abs(img_out_a - img_out_a ).max() < 1e-4
@skip_mps
def __lowercase ( self : Tuple ):
lowerCAmelCase = torch_device == """cpu"""
# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
lowerCAmelCase = 1e-2
self._test_attention_slicing_forward_pass(
test_max_difference=SCREAMING_SNAKE_CASE_ , expected_max_diff=SCREAMING_SNAKE_CASE_ )
@skip_mps
def __lowercase ( self : List[str] ):
lowerCAmelCase = torch_device == """cpu"""
lowerCAmelCase = True
lowerCAmelCase = [
"""decoder_num_inference_steps""",
"""super_res_num_inference_steps""",
]
self._test_inference_batch_single_identical(
test_max_difference=SCREAMING_SNAKE_CASE_ , relax_max_difference=SCREAMING_SNAKE_CASE_ , additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE_ , )
def __lowercase ( self : Optional[int] ):
lowerCAmelCase = [
"""decoder_num_inference_steps""",
"""super_res_num_inference_steps""",
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
lowerCAmelCase = [2, 3]
self._test_inference_batch_consistent(
batch_sizes=SCREAMING_SNAKE_CASE_ , additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE_ , )
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE_ )
@skip_mps
def __lowercase ( self : Optional[int] ):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def __lowercase ( self : str ):
return super().test_save_load_local()
@skip_mps
def __lowercase ( self : str ):
return super().test_save_load_optional_components()
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowercase ( self : Optional[int] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self : Optional[int] ):
lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png""" )
lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/unclip/karlo_v1_alpha_cat_variation_fp16.npy""" )
lowerCAmelCase = UnCLIPImageVariationPipeline.from_pretrained(
"""kakaobrain/karlo-v1-alpha-image-variations""" , torch_dtype=torch.floataa )
lowerCAmelCase = pipeline.to(SCREAMING_SNAKE_CASE_ )
pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowerCAmelCase = pipeline(
SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , output_type="""np""" , )
lowerCAmelCase = output.images[0]
assert image.shape == (256, 256, 3)
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , 15 )
| 155
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 30
| 0
|
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __UpperCAmelCase ( unittest.TestCase ):
@property
def __magic_name__ ( self : Tuple ):
torch.manual_seed(0 )
UpperCAmelCase : Any = UNetaDModel(
block_out_channels=(3_2, 6_4), layers_per_block=2, sample_size=3_2, in_channels=3, out_channels=3, down_block_types=('''DownBlock2D''', '''AttnDownBlock2D'''), up_block_types=('''AttnUpBlock2D''', '''UpBlock2D'''), )
return model
@property
def __magic_name__ ( self : int ):
torch.manual_seed(0 )
UpperCAmelCase : Optional[int] = VQModel(
block_out_channels=[3_2, 6_4], in_channels=3, out_channels=3, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''], up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''], latent_channels=3, )
return model
@property
def __magic_name__ ( self : str ):
torch.manual_seed(0 )
UpperCAmelCase : Any = CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=3_2, intermediate_size=3_7, layer_norm_eps=1E-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_0_0_0, )
return CLIPTextModel(SCREAMING_SNAKE_CASE_ )
def __magic_name__ ( self : str ):
UpperCAmelCase : Optional[int] = self.dummy_uncond_unet
UpperCAmelCase : List[str] = DDIMScheduler()
UpperCAmelCase : Any = self.dummy_vq_model
UpperCAmelCase : List[str] = LDMPipeline(unet=SCREAMING_SNAKE_CASE_, vqvae=SCREAMING_SNAKE_CASE_, scheduler=SCREAMING_SNAKE_CASE_ )
ldm.to(SCREAMING_SNAKE_CASE_ )
ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase : List[str] = torch.manual_seed(0 )
UpperCAmelCase : Tuple = ldm(generator=SCREAMING_SNAKE_CASE_, num_inference_steps=2, output_type='''numpy''' ).images
UpperCAmelCase : Dict = torch.manual_seed(0 )
UpperCAmelCase : Dict = ldm(generator=SCREAMING_SNAKE_CASE_, num_inference_steps=2, output_type='''numpy''', return_dict=SCREAMING_SNAKE_CASE_ )[0]
UpperCAmelCase : Any = image[0, -3:, -3:, -1]
UpperCAmelCase : Optional[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
UpperCAmelCase : Optional[int] = np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2] )
UpperCAmelCase : Optional[Any] = 1E-2 if torch_device != '''mps''' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class __UpperCAmelCase ( unittest.TestCase ):
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : List[Any] = LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256''' )
ldm.to(SCREAMING_SNAKE_CASE_ )
ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase : Optional[Any] = torch.manual_seed(0 )
UpperCAmelCase : str = ldm(generator=SCREAMING_SNAKE_CASE_, num_inference_steps=5, output_type='''numpy''' ).images
UpperCAmelCase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_5_6, 2_5_6, 3)
UpperCAmelCase : Union[str, Any] = np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7] )
UpperCAmelCase : Dict = 1E-2 if torch_device != '''mps''' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
| 336
|
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
__a = logging.get_logger(__name__)
__a = {
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
'constant': get_constant_schedule,
'constant_w_warmup': get_constant_schedule_with_warmup,
}
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[str]=None , SCREAMING_SNAKE_CASE_ : Optional[Any]=None , *SCREAMING_SNAKE_CASE_ : str , **SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> List[str]:
super().__init__(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if config is None:
assert isinstance(self.model , SCREAMING_SNAKE_CASE_ ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
f''' {self.model.__class__}'''
)
lowercase_ = self.model.config
else:
lowercase_ = config
lowercase_ = data_args
lowercase_ = self.config.tgt_vocab_size if isinstance(self.config , SCREAMING_SNAKE_CASE_ ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
f'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
''' padding..''' )
if self.args.label_smoothing == 0:
lowercase_ = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
lowercase_ = label_smoothed_nll_loss
def _lowercase ( self : int , SCREAMING_SNAKE_CASE_ : int ) -> Optional[Any]:
if self.optimizer is None:
lowercase_ = ['''bias''', '''LayerNorm.weight''']
lowercase_ = [
{
'''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
'''weight_decay''': self.args.weight_decay,
},
{
'''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
lowercase_ = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
lowercase_ = Adafactor
lowercase_ = {'''scale_parameter''': False, '''relative_step''': False}
else:
lowercase_ = AdamW
lowercase_ = {
'''betas''': (self.args.adam_betaa, self.args.adam_betaa),
'''eps''': self.args.adam_epsilon,
}
lowercase_ = self.args.learning_rate
if self.sharded_ddp:
lowercase_ = OSS(
params=SCREAMING_SNAKE_CASE_ , optim=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
else:
lowercase_ = optimizer_cls(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if self.lr_scheduler is None:
lowercase_ = self._get_lr_scheduler(SCREAMING_SNAKE_CASE_ )
else: # ignoring --lr_scheduler
logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.''' )
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : Dict ) -> Dict:
lowercase_ = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
lowercase_ = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
lowercase_ = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
lowercase_ = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=SCREAMING_SNAKE_CASE_ )
return scheduler
def _lowercase ( self : Tuple ) -> Optional[torch.utils.data.Sampler]:
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : List[Any] ) -> Any:
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
lowercase_ = model(**SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )[0]
lowercase_ = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
lowercase_ , lowercase_ = model(**SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )[:2]
else:
# compute label smoothed loss
lowercase_ = model(**SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )[0]
lowercase_ = torch.nn.functional.log_softmax(SCREAMING_SNAKE_CASE_ , dim=-1 )
lowercase_ , lowercase_ = self.loss_fn(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def _lowercase ( self : str , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> List[Any]:
lowercase_ = inputs.pop('''labels''' )
lowercase_ , lowercase_ = self._compute_loss(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return loss
def _lowercase ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : nn.Module , SCREAMING_SNAKE_CASE_ : Dict[str, Union[torch.Tensor, Any]] , SCREAMING_SNAKE_CASE_ : bool , SCREAMING_SNAKE_CASE_ : Optional[List[str]] = None , ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
lowercase_ = self._prepare_inputs(SCREAMING_SNAKE_CASE_ )
lowercase_ = {
'''max_length''': self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
'''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
lowercase_ = self.model.generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , **SCREAMING_SNAKE_CASE_ , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
lowercase_ = self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_ , gen_kwargs['''max_length'''] )
lowercase_ = inputs.pop('''labels''' )
with torch.no_grad():
# compute loss on predict data
lowercase_ , lowercase_ = self._compute_loss(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowercase_ = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
lowercase_ = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
lowercase_ = self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_ , gen_kwargs['''max_length'''] )
return (loss, logits, labels)
def _lowercase ( self : str , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : int ) -> Tuple:
# If PAD token is not defined at least EOS token has to be defined
lowercase_ = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
'''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
f''' padded to `max_length`={max_length}''' )
lowercase_ = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
lowercase_ = tensor
return padded_tensor
| 30
| 0
|
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self , __lowercase , __lowercase) -> Tuple:
__UpperCamelCase :List[Any] = jnp.ones((batch_size, length)) / length
return scores
def UpperCamelCase__ ( self) -> Optional[int]:
__UpperCamelCase :Dict = None
__UpperCamelCase :Dict = 20
__UpperCamelCase :int = self._get_uniform_logits(batch_size=2 , length=SCREAMING_SNAKE_CASE_)
# tweak scores to not be uniform anymore
__UpperCamelCase :int = scores.at[1, 5].set((1 / length) + 0.1) # peak, 1st batch
__UpperCamelCase :List[Any] = scores.at[1, 10].set((1 / length) - 0.4) # valley, 1st batch
# compute softmax
__UpperCamelCase :Optional[Any] = jax.nn.softmax(SCREAMING_SNAKE_CASE_ , axis=-1)
__UpperCamelCase :Tuple = FlaxTemperatureLogitsWarper(temperature=0.5)
__UpperCamelCase :int = FlaxTemperatureLogitsWarper(temperature=1.3)
__UpperCamelCase :int = jax.nn.softmax(temp_dist_warper_sharper(SCREAMING_SNAKE_CASE_ , scores.copy() , cur_len=SCREAMING_SNAKE_CASE_) , axis=-1)
__UpperCamelCase :Union[str, Any] = jax.nn.softmax(temp_dist_warper_smoother(SCREAMING_SNAKE_CASE_ , scores.copy() , cur_len=SCREAMING_SNAKE_CASE_) , axis=-1)
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_sharp[0, :] , atol=1E-3))
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_smooth[0, :] , atol=1E-3))
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() , warped_prob_sharp[1, :].max())
self.assertGreater(probs[1, :].min() , warped_prob_sharp[1, :].min())
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() , warped_prob_smooth[1, :].max())
self.assertLess(probs[1, :].min() , warped_prob_smooth[1, :].min())
def UpperCamelCase__ ( self) -> Union[str, Any]:
__UpperCamelCase :Optional[Any] = None
__UpperCamelCase :Optional[int] = 10
__UpperCamelCase :Optional[Any] = 2
# create ramp distribution
__UpperCamelCase :List[str] = np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE_)[None, :] , (batch_size, vocab_size)).copy()
__UpperCamelCase :List[Any] = ramp_logits[1:, : vocab_size // 2] + vocab_size
__UpperCamelCase :int = FlaxTopKLogitsWarper(3)
__UpperCamelCase :Any = top_k_warp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0]).tolist() , 7 * [True] + 3 * [False])
self.assertListEqual(jnp.isinf(scores[1]).tolist() , 2 * [True] + 3 * [False] + 5 * [True])
# check special case
__UpperCamelCase :List[Any] = 5
__UpperCamelCase :Optional[int] = FlaxTopKLogitsWarper(top_k=1 , filter_value=0.0 , min_tokens_to_keep=3)
__UpperCamelCase :int = np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE_)[None, :] , (batch_size, length)).copy()
__UpperCamelCase :List[str] = top_k_warp_safety_check(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1).tolist() , [2, 2])
def UpperCamelCase__ ( self) -> List[str]:
__UpperCamelCase :List[Any] = None
__UpperCamelCase :Union[str, Any] = 10
__UpperCamelCase :List[Any] = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
__UpperCamelCase :List[str] = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]))
__UpperCamelCase :Optional[int] = FlaxTopPLogitsWarper(0.8)
__UpperCamelCase :Tuple = np.exp(top_p_warp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_))
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
__UpperCamelCase :Union[str, Any] = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]])
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1E-3))
# check edge cases with negative and extreme logits
__UpperCamelCase :int = np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE_)[None, :] , (batch_size, vocab_size)).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
__UpperCamelCase :Optional[Any] = ramp_logits[1] * 100.0
# make sure at least 2 tokens are kept
__UpperCamelCase :Any = FlaxTopPLogitsWarper(0.9 , min_tokens_to_keep=2 , filter_value=0.0)
__UpperCamelCase :List[str] = top_p_warp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1).tolist() , [3, 2])
def UpperCamelCase__ ( self) -> List[str]:
__UpperCamelCase :Union[str, Any] = 20
__UpperCamelCase :Optional[int] = 4
__UpperCamelCase :str = 0
__UpperCamelCase :Optional[Any] = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=SCREAMING_SNAKE_CASE_)
# check that min length is applied at length 5
__UpperCamelCase :List[Any] = ids_tensor((batch_size, 20) , vocab_size=20)
__UpperCamelCase :Tuple = 5
__UpperCamelCase :Any = self._get_uniform_logits(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Dict = min_dist_processor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() , 4 * [-float('''inf''')])
# check that min length is not applied anymore at length 15
__UpperCamelCase :Any = self._get_uniform_logits(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Any = 15
__UpperCamelCase :List[str] = min_dist_processor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE_).any())
def UpperCamelCase__ ( self) -> int:
__UpperCamelCase :int = 20
__UpperCamelCase :Union[str, Any] = 4
__UpperCamelCase :List[Any] = 0
__UpperCamelCase :List[Any] = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE_)
# check that all scores are -inf except the bos_token_id score
__UpperCamelCase :Dict = ids_tensor((batch_size, 1) , vocab_size=20)
__UpperCamelCase :Optional[int] = 1
__UpperCamelCase :Union[str, Any] = self._get_uniform_logits(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Dict = logits_processor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]).all())
self.assertListEqual(scores[:, bos_token_id].tolist() , 4 * [0]) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
__UpperCamelCase :Union[str, Any] = 3
__UpperCamelCase :Dict = self._get_uniform_logits(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Dict = logits_processor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE_).any())
def UpperCamelCase__ ( self) -> List[Any]:
__UpperCamelCase :Optional[Any] = 20
__UpperCamelCase :Any = 4
__UpperCamelCase :List[str] = 0
__UpperCamelCase :Optional[Any] = 5
__UpperCamelCase :Tuple = FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_)
# check that all scores are -inf except the eos_token_id when max_length is reached
__UpperCamelCase :str = ids_tensor((batch_size, 4) , vocab_size=20)
__UpperCamelCase :Any = 4
__UpperCamelCase :Optional[Any] = self._get_uniform_logits(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Any = logits_processor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]).all())
self.assertListEqual(scores[:, eos_token_id].tolist() , 4 * [0]) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
__UpperCamelCase :Dict = 3
__UpperCamelCase :int = self._get_uniform_logits(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Any = logits_processor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE_).any())
def UpperCamelCase__ ( self) -> List[str]:
__UpperCamelCase :Optional[Any] = 4
__UpperCamelCase :List[Any] = 10
__UpperCamelCase :Union[str, Any] = 15
__UpperCamelCase :Union[str, Any] = 2
__UpperCamelCase :List[Any] = 1
__UpperCamelCase :List[Any] = 15
# dummy input_ids and scores
__UpperCamelCase :int = ids_tensor((batch_size, sequence_length) , SCREAMING_SNAKE_CASE_)
__UpperCamelCase :str = input_ids.copy()
__UpperCamelCase :str = self._get_uniform_logits(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Any = scores.copy()
# instantiate all dist processors
__UpperCamelCase :Dict = FlaxTemperatureLogitsWarper(temperature=0.5)
__UpperCamelCase :Optional[int] = FlaxTopKLogitsWarper(3)
__UpperCamelCase :int = FlaxTopPLogitsWarper(0.8)
# instantiate all logits processors
__UpperCamelCase :int = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :int = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :str = FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :str = 10
# no processor list
__UpperCamelCase :Optional[Any] = temp_dist_warp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Tuple = top_k_warp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Tuple = top_p_warp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Any = min_dist_proc(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Optional[int] = bos_dist_proc(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Optional[Any] = eos_dist_proc(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
# with processor list
__UpperCamelCase :Any = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
__UpperCamelCase :Union[str, Any] = processor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
# scores should be equal
self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1E-3))
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist())
def UpperCamelCase__ ( self) -> int:
__UpperCamelCase :str = 4
__UpperCamelCase :str = 10
__UpperCamelCase :Optional[int] = 15
__UpperCamelCase :int = 2
__UpperCamelCase :Optional[int] = 1
__UpperCamelCase :Dict = 15
# dummy input_ids and scores
__UpperCamelCase :List[str] = ids_tensor((batch_size, sequence_length) , SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Dict = input_ids.copy()
__UpperCamelCase :Any = self._get_uniform_logits(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Any = scores.copy()
# instantiate all dist processors
__UpperCamelCase :str = FlaxTemperatureLogitsWarper(temperature=0.5)
__UpperCamelCase :Any = FlaxTopKLogitsWarper(3)
__UpperCamelCase :Any = FlaxTopPLogitsWarper(0.8)
# instantiate all logits processors
__UpperCamelCase :Optional[Any] = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Dict = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[Any] = FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Any = 10
# no processor list
def run_no_processor_list(__lowercase , __lowercase , __lowercase):
__UpperCamelCase :Dict = temp_dist_warp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Dict = top_k_warp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Any = top_p_warp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[str] = min_dist_proc(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[str] = bos_dist_proc(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[str] = eos_dist_proc(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
return scores
# with processor list
def run_processor_list(__lowercase , __lowercase , __lowercase):
__UpperCamelCase :str = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
__UpperCamelCase :Tuple = processor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , cur_len=SCREAMING_SNAKE_CASE_)
return scores
__UpperCamelCase :List[Any] = jax.jit(SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Optional[Any] = jax.jit(SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Tuple = jitted_run_no_processor_list(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
__UpperCamelCase :str = jitted_run_processor_list(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
# scores should be equal
self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1E-3))
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist())
| 43
|
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : List[str] ) -> List[Any]:
lowercase_ = 1_0
def _lowercase ( self : int ) -> List[str]:
lowercase_ = [1, 2, 3, 4]
lowercase_ = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_ , self.block_size , 0 ) , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : int ) -> Optional[Any]:
lowercase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
lowercase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_ , self.block_size , 0 ) , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
lowercase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
lowercase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_ , self.block_size , 0 ) , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Any ) -> List[Any]:
lowercase_ = '''It was the year of Our Lord one thousand seven hundred and
seventy-five.\n\nSpiritual revelations were conceded to England at that
favoured period, as at this.'''
lowercase_ , lowercase_ = process_story(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , [] )
def _lowercase ( self : List[str] ) -> List[str]:
lowercase_ = ''''''
lowercase_ , lowercase_ = process_story(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , [] )
self.assertEqual(SCREAMING_SNAKE_CASE_ , [] )
def _lowercase ( self : Union[str, Any] ) -> Union[str, Any]:
lowercase_ = (
'''It was the year of Our Lord one thousand seven hundred and '''
'''seventy-five\n\nSpiritual revelations were conceded to England '''
'''at that favoured period, as at this.\n@highlight\n\nIt was the best of times'''
)
lowercase_ , lowercase_ = process_story(SCREAMING_SNAKE_CASE_ )
lowercase_ = [
'''It was the year of Our Lord one thousand seven hundred and seventy-five.''',
'''Spiritual revelations were conceded to England at that favoured period, as at this.''',
]
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowercase_ = ['''It was the best of times.''']
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Union[str, Any] ) -> Optional[Any]:
lowercase_ = torch.tensor([1, 2, 3, 4] )
lowercase_ = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_ , 0 ).numpy() , expected.numpy() )
def _lowercase ( self : List[Any] ) -> Tuple:
lowercase_ = torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3] )
lowercase_ = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_ , 2_3 ).numpy() , expected.numpy() )
def _lowercase ( self : int ) -> Dict:
lowercase_ = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
lowercase_ = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_ , 1 ).numpy() , expected.numpy() )
def _lowercase ( self : List[str] ) -> Tuple:
lowercase_ = 1_0_1
lowercase_ = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]] )
lowercase_ = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
lowercase_ = compute_token_type_ids(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
np.testing.assert_array_equal(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 30
| 0
|
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class __lowerCamelCase (unittest.TestCase ):
def __init__( self: Any,A_: Optional[int],A_: Optional[Any]=7,A_: Tuple=3,A_: Optional[Any]=18,A_: Any=30,A_: Dict=400,A_: Dict=True,A_: Optional[Any]=None,A_: Tuple=True,A_: Tuple=None,A_: Optional[Any]=True,A_: str=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],A_: Optional[int]=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],A_: Optional[int]=True,):
'''simple docstring'''
__UpperCamelCase = size if size is not None else {'height': 224, 'width': 224}
__UpperCamelCase = crop_size if crop_size is not None else {'height': 18, 'width': 18}
__UpperCamelCase = parent
__UpperCamelCase = batch_size
__UpperCamelCase = num_channels
__UpperCamelCase = image_size
__UpperCamelCase = min_resolution
__UpperCamelCase = max_resolution
__UpperCamelCase = do_resize
__UpperCamelCase = size
__UpperCamelCase = do_center_crop
__UpperCamelCase = crop_size
__UpperCamelCase = do_normalize
__UpperCamelCase = image_mean
__UpperCamelCase = image_std
__UpperCamelCase = do_convert_rgb
def snake_case_ ( self: Any ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def snake_case_ ( self: Optional[Any],A_: Optional[int]=False,A_: List[Any]=False,A_: Dict=False ):
'''simple docstring'''
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
__UpperCamelCase = []
for i in range(self.batch_size ):
image_inputs.append(
np.random.randint(
255,size=(self.num_channels, self.max_resolution, self.max_resolution),dtype=np.uinta ) )
else:
__UpperCamelCase = []
for i in range(self.batch_size ):
__UpperCamelCase, __UpperCamelCase = np.random.choice(np.arange(self.min_resolution,self.max_resolution ),2 )
image_inputs.append(np.random.randint(255,size=(self.num_channels, width, height),dtype=np.uinta ) )
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
__UpperCamelCase = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_,0,-1 ) ) for x in image_inputs]
if torchify:
__UpperCamelCase = [torch.from_numpy(SCREAMING_SNAKE_CASE_ ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class __lowerCamelCase (_a , unittest.TestCase ):
_lowercase = ChineseCLIPImageProcessor if is_vision_available() else None
def snake_case_ ( self: Union[str, Any] ):
'''simple docstring'''
__UpperCamelCase = ChineseCLIPImageProcessingTester(self,do_center_crop=SCREAMING_SNAKE_CASE_ )
@property
def snake_case_ ( self: Any ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case_ ( self: str ):
'''simple docstring'''
__UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,'do_resize' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,'size' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,'do_center_crop' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,'center_crop' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,'do_normalize' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,'image_mean' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,'image_std' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,'do_convert_rgb' ) )
def snake_case_ ( self: int ):
'''simple docstring'''
__UpperCamelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size,{'height': 224, 'width': 224} )
self.assertEqual(image_processor.crop_size,{'height': 18, 'width': 18} )
__UpperCamelCase = self.image_processing_class.from_dict(self.image_processor_dict,size=42,crop_size=84 )
self.assertEqual(image_processor.size,{'shortest_edge': 42} )
self.assertEqual(image_processor.crop_size,{'height': 84, 'width': 84} )
def snake_case_ ( self: List[str] ):
'''simple docstring'''
pass
def snake_case_ ( self: List[Any] ):
'''simple docstring'''
__UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__UpperCamelCase = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_,Image.Image )
# Test not batched input
__UpperCamelCase = image_processing(image_inputs[0],return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
),)
# Test batched
__UpperCamelCase = image_processing(SCREAMING_SNAKE_CASE_,return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
),)
def snake_case_ ( self: int ):
'''simple docstring'''
__UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__UpperCamelCase = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_,numpify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_,np.ndarray )
# Test not batched input
__UpperCamelCase = image_processing(image_inputs[0],return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
),)
# Test batched
__UpperCamelCase = image_processing(SCREAMING_SNAKE_CASE_,return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
),)
def snake_case_ ( self: Union[str, Any] ):
'''simple docstring'''
__UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__UpperCamelCase = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_,torchify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_,torch.Tensor )
# Test not batched input
__UpperCamelCase = image_processing(image_inputs[0],return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
),)
# Test batched
__UpperCamelCase = image_processing(SCREAMING_SNAKE_CASE_,return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
),)
@require_torch
@require_vision
class __lowerCamelCase (_a , unittest.TestCase ):
_lowercase = ChineseCLIPImageProcessor if is_vision_available() else None
def snake_case_ ( self: int ):
'''simple docstring'''
__UpperCamelCase = ChineseCLIPImageProcessingTester(self,num_channels=4,do_center_crop=SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = 3
@property
def snake_case_ ( self: Optional[int] ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case_ ( self: Optional[int] ):
'''simple docstring'''
__UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,'do_resize' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,'size' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,'do_center_crop' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,'center_crop' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,'do_normalize' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,'image_mean' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,'image_std' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,'do_convert_rgb' ) )
def snake_case_ ( self: Dict ):
'''simple docstring'''
pass
def snake_case_ ( self: Optional[Any] ):
'''simple docstring'''
__UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__UpperCamelCase = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_,Image.Image )
# Test not batched input
__UpperCamelCase = image_processing(image_inputs[0],return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape,(
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
),)
# Test batched
__UpperCamelCase = image_processing(SCREAMING_SNAKE_CASE_,return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape,(
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
),)
| 310
|
def a ( snake_case__: list ):
'''simple docstring'''
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
lowercase_ = []
def generate(snake_case__: int , snake_case__: list ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , snake_case__ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
lowercase_ , lowercase_ = arr[k - 1], arr[i]
else: # k is odd
lowercase_ , lowercase_ = arr[k - 1], arr[0]
generate(k - 1 , snake_case__ )
generate(len(snake_case__ ) , snake_case__ )
return res
if __name__ == "__main__":
__a = input('Enter numbers separated by a comma:\n').strip()
__a = [int(item) for item in user_input.split(',')]
print(heaps(arr))
| 30
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase__ = {"""configuration_unispeech""": ["""UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP""", """UniSpeechConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
"""UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""UniSpeechForCTC""",
"""UniSpeechForPreTraining""",
"""UniSpeechForSequenceClassification""",
"""UniSpeechModel""",
"""UniSpeechPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 86
|
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def a ( ):
'''simple docstring'''
lowercase_ = argparse.ArgumentParser()
parser.add_argument(
'''-m''' , '''--pretrained_model_name_or_path''' , type=snake_case__ , default=snake_case__ , required=snake_case__ , help='''Path to pretrained model or model identifier from huggingface.co/models.''' , )
parser.add_argument(
'''-c''' , '''--caption''' , type=snake_case__ , default='''robotic cat with wings''' , help='''Text used to generate images.''' , )
parser.add_argument(
'''-n''' , '''--images_num''' , type=snake_case__ , default=4 , help='''How much images to generate.''' , )
parser.add_argument(
'''-s''' , '''--seed''' , type=snake_case__ , default=42 , help='''Seed for random process.''' , )
parser.add_argument(
'''-ci''' , '''--cuda_id''' , type=snake_case__ , default=0 , help='''cuda_id.''' , )
lowercase_ = parser.parse_args()
return args
def a ( snake_case__: Optional[Any] , snake_case__: Tuple , snake_case__: Union[str, Any] ):
'''simple docstring'''
if not len(snake_case__ ) == rows * cols:
raise ValueError('''The specified number of rows and columns are not correct.''' )
lowercase_ , lowercase_ = imgs[0].size
lowercase_ = Image.new('''RGB''' , size=(cols * w, rows * h) )
lowercase_ , lowercase_ = grid.size
for i, img in enumerate(snake_case__ ):
grid.paste(snake_case__ , box=(i % cols * w, i // cols * h) )
return grid
def a ( snake_case__: Tuple , snake_case__: Union[str, Any]="robotic cat with wings" , snake_case__: Union[str, Any]=7.5 , snake_case__: List[str]=50 , snake_case__: List[Any]=1 , snake_case__: Optional[int]=42 , ):
'''simple docstring'''
lowercase_ = torch.Generator(pipeline.device ).manual_seed(snake_case__ )
lowercase_ = pipeline(
snake_case__ , guidance_scale=snake_case__ , num_inference_steps=snake_case__ , generator=snake_case__ , num_images_per_prompt=snake_case__ , ).images
lowercase_ = int(math.sqrt(snake_case__ ) )
lowercase_ = image_grid(snake_case__ , rows=_rows , cols=num_images_per_prompt // _rows )
return grid, images
__a = parse_args()
# Load models and create wrapper for stable diffusion
__a = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
__a = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
__a = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
__a = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')
__a = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__a = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
__a = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, 'unet', unet)
else:
__a = unet.to(torch.device('cuda', args.cuda_id))
__a = pipeline.to(unet.device)
__a , __a = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '{}.png'.format('_'.join(args.caption.split()))))
__a = os.path.join(args.pretrained_model_name_or_path, '_'.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, '{}.png'.format(idx + 1)))
| 30
| 0
|
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_UpperCAmelCase : Optional[Any] =pytest.mark.integration
@pytest.mark.parametrize('''path''' , ['''paws''', '''csv'''] )
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Any:
inspect_dataset(snake_case__ , snake_case__ )
lowerCAmelCase_ : Optional[int] = path + '''.py'''
assert script_name in os.listdir(snake_case__ )
assert "__pycache__" not in os.listdir(snake_case__ )
@pytest.mark.filterwarnings('''ignore:inspect_metric is deprecated:FutureWarning''' )
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning''' )
@pytest.mark.parametrize('''path''' , ['''accuracy'''] )
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Any:
inspect_metric(snake_case__ , snake_case__ )
lowerCAmelCase_ : List[Any] = path + '''.py'''
assert script_name in os.listdir(snake_case__ )
assert "__pycache__" not in os.listdir(snake_case__ )
@pytest.mark.parametrize(
'''path, config_name, expected_splits''' , [
('''squad''', '''plain_text''', ['''train''', '''validation''']),
('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
] , )
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Union[str, Any]:
lowerCAmelCase_ : int = get_dataset_config_info(snake_case__ , config_name=snake_case__ )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
'''path, config_name, expected_exception''' , [
('''paws''', None, ValueError),
] , )
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
with pytest.raises(snake_case__ ):
get_dataset_config_info(snake_case__ , config_name=snake_case__ )
@pytest.mark.parametrize(
'''path, expected''' , [
('''squad''', '''plain_text'''),
('''acronym_identification''', '''default'''),
('''lhoestq/squad''', '''plain_text'''),
('''lhoestq/test''', '''default'''),
('''lhoestq/demo1''', '''lhoestq--demo1'''),
('''dalle-mini/wit''', '''dalle-mini--wit'''),
] , )
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[str]:
lowerCAmelCase_ : Union[str, Any] = get_dataset_config_names(snake_case__ )
assert expected in config_names
@pytest.mark.parametrize(
'''path, expected_configs, expected_splits_in_first_config''' , [
('''squad''', ['''plain_text'''], ['''train''', '''validation''']),
('''dalle-mini/wit''', ['''dalle-mini--wit'''], ['''train''']),
('''paws''', ['''labeled_final''', '''labeled_swap''', '''unlabeled_final'''], ['''train''', '''test''', '''validation''']),
] , )
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Any:
lowerCAmelCase_ : int = get_dataset_infos(snake_case__ )
assert list(infos.keys() ) == expected_configs
lowerCAmelCase_ : List[Any] = expected_configs[0]
assert expected_config in infos
lowerCAmelCase_ : Any = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
'''path, expected_config, expected_splits''' , [
('''squad''', '''plain_text''', ['''train''', '''validation''']),
('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
] , )
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Tuple:
lowerCAmelCase_ : Dict = get_dataset_infos(snake_case__ )
assert expected_config in infos
lowerCAmelCase_ : str = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
'''path, config_name, expected_exception''' , [
('''paws''', None, ValueError),
] , )
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Tuple:
with pytest.raises(snake_case__ ):
get_dataset_split_names(snake_case__ , config_name=snake_case__ )
| 262
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['RemBertTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['RemBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RemBertForCausalLM',
'RemBertForMaskedLM',
'RemBertForMultipleChoice',
'RemBertForQuestionAnswering',
'RemBertForSequenceClassification',
'RemBertForTokenClassification',
'RemBertLayer',
'RemBertModel',
'RemBertPreTrainedModel',
'load_tf_weights_in_rembert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRemBertForCausalLM',
'TFRemBertForMaskedLM',
'TFRemBertForMultipleChoice',
'TFRemBertForQuestionAnswering',
'TFRemBertForSequenceClassification',
'TFRemBertForTokenClassification',
'TFRemBertLayer',
'TFRemBertModel',
'TFRemBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 30
| 0
|
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : int ) -> List[Any]:
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
__a = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
__a = 1
if upper_limit > 0:
__a = 1
# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
for i in range(2 , upper_limit + 1 ):
for j in range(snake_case__ ):
catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]
return catalan_list
if __name__ == "__main__":
print("\n********* Catalan Numbers Using Dynamic Programming ************\n")
print("\n*** Enter -1 at any time to quit ***")
print("\nEnter the upper limit (≥ 0) for the Catalan number sequence: ", end="")
try:
while True:
lowercase_ = int(input().strip())
if N < 0:
print("\n********* Goodbye!! ************")
break
else:
print(F'''The Catalan numbers from 0 through {N} are:''')
print(catalan_numbers(N))
print("Try another upper limit for the sequence: ", end="")
except (NameError, ValueError):
print("\n********* Invalid input, goodbye! ************\n")
import doctest
doctest.testmod()
| 45
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['DeiTFeatureExtractor']
__a = ['DeiTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DeiTForImageClassification',
'DeiTForImageClassificationWithTeacher',
'DeiTForMaskedImageModeling',
'DeiTModel',
'DeiTPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDeiTForImageClassification',
'TFDeiTForImageClassificationWithTeacher',
'TFDeiTForMaskedImageModeling',
'TFDeiTModel',
'TFDeiTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 30
| 0
|
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __lowerCAmelCase :
def __init__( self , _snake_case , _snake_case=3 , _snake_case=32 , _snake_case=3 , _snake_case=10 , _snake_case=[10, 20, 30, 40] , _snake_case=[1, 1, 2, 1] , _snake_case=True , _snake_case=True , _snake_case="relu" , _snake_case=3 , _snake_case=None , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = image_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = embeddings_size
_lowerCAmelCase = hidden_sizes
_lowerCAmelCase = depths
_lowerCAmelCase = is_training
_lowerCAmelCase = use_labels
_lowerCAmelCase = hidden_act
_lowerCAmelCase = num_labels
_lowerCAmelCase = scope
_lowerCAmelCase = len(SCREAMING_SNAKE_CASE_ )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
_lowerCAmelCase = self.get_config()
return config, pixel_values, labels
def snake_case ( self ):
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = TFRegNetModel(config=SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = model(SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = TFRegNetForImageClassification(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = config_and_inputs
_lowerCAmelCase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
__lowerCamelCase = (
{'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
if is_tf_available()
else {}
)
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFRegNetModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , has_text_modality=SCREAMING_SNAKE_CASE_ )
def snake_case ( self ):
"""simple docstring"""
return
@unittest.skip(reason="""RegNet does not use inputs_embeds""" )
def snake_case ( self ):
"""simple docstring"""
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , reason="""TF does not support backprop for grouped convolutions on CPU.""" , )
@slow
def snake_case ( self ):
"""simple docstring"""
super().test_keras_fit()
@unittest.skip(reason="""RegNet does not support input and output embeddings""" )
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCAmelCase = [*signature.parameters.keys()]
_lowerCAmelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE_ )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def snake_case ( self ):
"""simple docstring"""
def check_hidden_states_output(_snake_case , _snake_case , _snake_case ):
_lowerCAmelCase = model_class(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , training=SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_lowerCAmelCase = self.model_tester.num_stages
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCAmelCase = ["""basic""", """bottleneck"""]
for model_class in self.all_model_classes:
for layer_type in layers_type:
_lowerCAmelCase = layer_type
_lowerCAmelCase = True
check_hidden_states_output(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCAmelCase = True
check_hidden_states_output(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(_snake_case , _snake_case , _snake_case , _snake_case={} ):
_lowerCAmelCase = model(SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = model(SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ).to_tuple()
def recursive_check(_snake_case , _snake_case ):
if isinstance(SCREAMING_SNAKE_CASE_ , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
recursive_check(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ) , msg=(
"""Tuple and dict output are not equal. Difference:"""
F' {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}'
) , )
recursive_check(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
check_equivalence(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
check_equivalence(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
check_equivalence(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , {"""output_hidden_states""": True} )
_lowerCAmelCase = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
check_equivalence(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , {"""output_hidden_states""": True} )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def snake_case ( self ):
"""simple docstring"""
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase = TFRegNetModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def snake_case ( self ):
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=SCREAMING_SNAKE_CASE_ , return_tensors="""tf""" )
# forward pass
_lowerCAmelCase = model(**SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
# verify the logits
_lowerCAmelCase = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = tf.constant([-0.4180, -1.5051, -3.4836] )
tf.debugging.assert_near(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 )
| 82
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__a = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'processing_trocr': ['TrOCRProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
'TrOCRForCausalLM',
'TrOCRPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 30
| 0
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 62
|
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
__a = logging.get_logger(__name__)
# General docstring
__a = 'RegNetConfig'
# Base docstring
__a = 'facebook/regnet-y-040'
__a = [1, 1_0_8_8, 7, 7]
# Image classification docstring
__a = 'facebook/regnet-y-040'
__a = 'tabby, tabby cat'
__a = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 3 , SCREAMING_SNAKE_CASE_ : int = 1 , SCREAMING_SNAKE_CASE_ : int = 1 , SCREAMING_SNAKE_CASE_ : Optional[str] = "relu" , **SCREAMING_SNAKE_CASE_ : Any , ) -> List[str]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
lowercase_ = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
lowercase_ = tf.keras.layers.ConvaD(
filters=SCREAMING_SNAKE_CASE_ , kernel_size=SCREAMING_SNAKE_CASE_ , strides=SCREAMING_SNAKE_CASE_ , padding='''VALID''' , groups=SCREAMING_SNAKE_CASE_ , use_bias=SCREAMING_SNAKE_CASE_ , name='''convolution''' , )
lowercase_ = tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name='''normalization''' )
lowercase_ = ACTaFN[activation] if activation is not None else tf.identity
def _lowercase ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Any:
lowercase_ = self.convolution(self.padding(SCREAMING_SNAKE_CASE_ ) )
lowercase_ = self.normalization(SCREAMING_SNAKE_CASE_ )
lowercase_ = self.activation(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : str , SCREAMING_SNAKE_CASE_ : RegNetConfig , **SCREAMING_SNAKE_CASE_ : str ) -> Any:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = config.num_channels
lowercase_ = TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name='''embedder''' , )
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : str ) -> Optional[int]:
lowercase_ = shape_list(SCREAMING_SNAKE_CASE_ )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
'''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''' )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
lowercase_ = tf.transpose(SCREAMING_SNAKE_CASE_ , perm=(0, 2, 3, 1) )
lowercase_ = self.embedder(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 2 , **SCREAMING_SNAKE_CASE_ : List[str] ) -> Union[str, Any]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = tf.keras.layers.ConvaD(
filters=SCREAMING_SNAKE_CASE_ , kernel_size=1 , strides=SCREAMING_SNAKE_CASE_ , use_bias=SCREAMING_SNAKE_CASE_ , name='''convolution''' )
lowercase_ = tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name='''normalization''' )
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : tf.Tensor , SCREAMING_SNAKE_CASE_ : bool = False ) -> tf.Tensor:
return self.normalization(self.convolution(SCREAMING_SNAKE_CASE_ ) , training=SCREAMING_SNAKE_CASE_ )
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , **SCREAMING_SNAKE_CASE_ : int ) -> Optional[Any]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = tf.keras.layers.GlobalAveragePoolingaD(keepdims=SCREAMING_SNAKE_CASE_ , name='''pooler''' )
lowercase_ = [
tf.keras.layers.ConvaD(filters=SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation='''relu''' , name='''attention.0''' ),
tf.keras.layers.ConvaD(filters=SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation='''sigmoid''' , name='''attention.2''' ),
]
def _lowercase ( self : str , SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> Dict:
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
lowercase_ = self.pooler(SCREAMING_SNAKE_CASE_ )
for layer_module in self.attention:
lowercase_ = layer_module(SCREAMING_SNAKE_CASE_ )
lowercase_ = hidden_state * pooled
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : str , SCREAMING_SNAKE_CASE_ : RegNetConfig , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 1 , **SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> List[str]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = in_channels != out_channels or stride != 1
lowercase_ = max(1 , out_channels // config.groups_width )
lowercase_ = (
TFRegNetShortCut(SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , name='''shortcut''' )
if should_apply_shortcut
else tf.keras.layers.Activation('''linear''' , name='''shortcut''' )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
lowercase_ = [
TFRegNetConvLayer(SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation=config.hidden_act , name='''layer.0''' ),
TFRegNetConvLayer(
SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , groups=SCREAMING_SNAKE_CASE_ , activation=config.hidden_act , name='''layer.1''' ),
TFRegNetConvLayer(SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation=SCREAMING_SNAKE_CASE_ , name='''layer.2''' ),
]
lowercase_ = ACTaFN[config.hidden_act]
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Any:
lowercase_ = hidden_state
for layer_module in self.layers:
lowercase_ = layer_module(SCREAMING_SNAKE_CASE_ )
lowercase_ = self.shortcut(SCREAMING_SNAKE_CASE_ )
hidden_state += residual
lowercase_ = self.activation(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Any , SCREAMING_SNAKE_CASE_ : RegNetConfig , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 1 , **SCREAMING_SNAKE_CASE_ : List[str] ) -> int:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = in_channels != out_channels or stride != 1
lowercase_ = max(1 , out_channels // config.groups_width )
lowercase_ = (
TFRegNetShortCut(SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , name='''shortcut''' )
if should_apply_shortcut
else tf.keras.layers.Activation('''linear''' , name='''shortcut''' )
)
lowercase_ = [
TFRegNetConvLayer(SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation=config.hidden_act , name='''layer.0''' ),
TFRegNetConvLayer(
SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , groups=SCREAMING_SNAKE_CASE_ , activation=config.hidden_act , name='''layer.1''' ),
TFRegNetSELayer(SCREAMING_SNAKE_CASE_ , reduced_channels=int(round(in_channels / 4 ) ) , name='''layer.2''' ),
TFRegNetConvLayer(SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation=SCREAMING_SNAKE_CASE_ , name='''layer.3''' ),
]
lowercase_ = ACTaFN[config.hidden_act]
def _lowercase ( self : int , SCREAMING_SNAKE_CASE_ : Dict ) -> Optional[Any]:
lowercase_ = hidden_state
for layer_module in self.layers:
lowercase_ = layer_module(SCREAMING_SNAKE_CASE_ )
lowercase_ = self.shortcut(SCREAMING_SNAKE_CASE_ )
hidden_state += residual
lowercase_ = self.activation(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : str , SCREAMING_SNAKE_CASE_ : RegNetConfig , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 2 , SCREAMING_SNAKE_CASE_ : int = 2 , **SCREAMING_SNAKE_CASE_ : List[Any] ) -> List[str]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = TFRegNetXLayer if config.layer_type == '''x''' else TFRegNetYLayer
lowercase_ = [
# downsampling is done in the first layer with stride of 2
layer(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , name='''layers.0''' ),
*[layer(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , name=f'''layers.{i+1}''' ) for i in range(depth - 1 )],
]
def _lowercase ( self : Tuple , SCREAMING_SNAKE_CASE_ : int ) -> int:
for layer_module in self.layers:
lowercase_ = layer_module(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : RegNetConfig , **SCREAMING_SNAKE_CASE_ : Dict ) -> Tuple:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
SCREAMING_SNAKE_CASE_ , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name='''stages.0''' , ) )
lowercase_ = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(SCREAMING_SNAKE_CASE_ , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , depth=SCREAMING_SNAKE_CASE_ , name=f'''stages.{i+1}''' ) )
def _lowercase ( self : Dict , SCREAMING_SNAKE_CASE_ : tf.Tensor , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : bool = True ) -> TFBaseModelOutputWithNoAttention:
lowercase_ = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowercase_ = hidden_states + (hidden_state,)
lowercase_ = stage_module(SCREAMING_SNAKE_CASE_ )
if output_hidden_states:
lowercase_ = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE_ , hidden_states=SCREAMING_SNAKE_CASE_ )
@keras_serializable
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
a :str = RegNetConfig
def __init__( self : str , SCREAMING_SNAKE_CASE_ : Dict , **SCREAMING_SNAKE_CASE_ : List[str] ) -> Any:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = config
lowercase_ = TFRegNetEmbeddings(SCREAMING_SNAKE_CASE_ , name='''embedder''' )
lowercase_ = TFRegNetEncoder(SCREAMING_SNAKE_CASE_ , name='''encoder''' )
lowercase_ = tf.keras.layers.GlobalAveragePoolingaD(keepdims=SCREAMING_SNAKE_CASE_ , name='''pooler''' )
@unpack_inputs
def _lowercase ( self : Dict , SCREAMING_SNAKE_CASE_ : tf.Tensor , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : bool = False , ) -> TFBaseModelOutputWithPoolingAndNoAttention:
lowercase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ = self.embedder(SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
lowercase_ = self.encoder(
SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
lowercase_ = encoder_outputs[0]
lowercase_ = self.pooler(SCREAMING_SNAKE_CASE_ )
# Change to NCHW output format have uniformity in the modules
lowercase_ = tf.transpose(SCREAMING_SNAKE_CASE_ , perm=(0, 3, 1, 2) )
lowercase_ = tf.transpose(SCREAMING_SNAKE_CASE_ , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
lowercase_ = tuple([tf.transpose(SCREAMING_SNAKE_CASE_ , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=SCREAMING_SNAKE_CASE_ , pooler_output=SCREAMING_SNAKE_CASE_ , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Tuple = RegNetConfig
a :Any = 'regnet'
a :List[str] = 'pixel_values'
@property
def _lowercase ( self : List[str] ) -> str:
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4) , dtype=tf.floataa )}
__a = r'\n Parameters:\n This model is a Tensorflow\n [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n behavior.\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'
__a = r'\n Args:\n pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConveNextImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
'The bare RegNet model outputting raw features without any specific head on top.' , UpperCAmelCase , )
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : RegNetConfig , *SCREAMING_SNAKE_CASE_ : Any , **SCREAMING_SNAKE_CASE_ : str ) -> List[str]:
super().__init__(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowercase_ = TFRegNetMainLayer(SCREAMING_SNAKE_CASE_ , name='''regnet''' )
@unpack_inputs
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=SCREAMING_SNAKE_CASE_ , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : tf.Tensor , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : Optional[Any]=False , ) -> Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
lowercase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ = self.regnet(
pixel_values=SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
'\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , UpperCAmelCase , )
class lowercase__( UpperCAmelCase , UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : RegNetConfig , *SCREAMING_SNAKE_CASE_ : str , **SCREAMING_SNAKE_CASE_ : int ) -> Union[str, Any]:
super().__init__(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowercase_ = config.num_labels
lowercase_ = TFRegNetMainLayer(SCREAMING_SNAKE_CASE_ , name='''regnet''' )
# classification head
lowercase_ = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name='''classifier.1''' ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=SCREAMING_SNAKE_CASE_ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : tf.Tensor = None , SCREAMING_SNAKE_CASE_ : tf.Tensor = None , SCREAMING_SNAKE_CASE_ : bool = None , SCREAMING_SNAKE_CASE_ : bool = None , SCREAMING_SNAKE_CASE_ : Dict=False , ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
lowercase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ = self.regnet(
SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
lowercase_ = outputs.pooler_output if return_dict else outputs[1]
lowercase_ = self.classifier[0](SCREAMING_SNAKE_CASE_ )
lowercase_ = self.classifier[1](SCREAMING_SNAKE_CASE_ )
lowercase_ = None if labels is None else self.hf_compute_loss(labels=SCREAMING_SNAKE_CASE_ , logits=SCREAMING_SNAKE_CASE_ )
if not return_dict:
lowercase_ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=SCREAMING_SNAKE_CASE_ , logits=SCREAMING_SNAKE_CASE_ , hidden_states=outputs.hidden_states )
| 30
| 0
|
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Optional[Any] = DownBlockaD # noqa F405
_UpperCAmelCase :Any = 'down'
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int = [-0.02_32, -0.98_69, 0.80_54, -0.06_37, -0.16_88, -1.42_64, 0.44_70, -1.33_94, 0.09_04]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :int = ResnetDownsampleBlockaD # noqa F405
_UpperCAmelCase :Dict = 'down'
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = [0.07_10, 0.24_10, -0.73_20, -1.07_57, -1.13_43, 0.35_40, -0.01_33, -0.25_76, 0.09_48]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :int = AttnDownBlockaD # noqa F405
_UpperCAmelCase :Tuple = 'down'
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[int] = [0.06_36, 0.89_64, -0.62_34, -1.01_31, 0.08_44, 0.49_35, 0.34_37, 0.09_11, -0.29_57]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :str = CrossAttnDownBlockaD # noqa F405
_UpperCAmelCase :str = 'down'
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : Tuple = super().prepare_init_args_and_inputs_for_common()
UpperCamelCase : Union[str, Any] = 32
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : str = [0.22_38, -0.73_96, -0.22_55, -0.38_29, 0.19_25, 1.16_65, 0.06_03, -0.72_95, 0.19_83]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :List[str] = SimpleCrossAttnDownBlockaD # noqa F405
_UpperCAmelCase :List[Any] = 'down'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_encoder_hidden_states=SCREAMING_SNAKE_CASE_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : Optional[Any] = super().prepare_init_args_and_inputs_for_common()
UpperCamelCase : int = 32
return init_dict, inputs_dict
@unittest.skipIf(torch_device == "mps" , "MPS result is not consistent" )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = [0.79_21, -0.09_92, -0.19_62, -0.76_95, -0.42_42, 0.78_04, 0.47_37, 0.27_65, 0.33_38]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Dict = SkipDownBlockaD # noqa F405
_UpperCAmelCase :str = 'down'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_skip_sample=SCREAMING_SNAKE_CASE_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[str] = [-0.08_45, -0.20_87, -0.24_65, 0.09_71, 0.19_00, -0.04_84, 0.26_64, 0.41_79, 0.50_69]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Optional[Any] = AttnSkipDownBlockaD # noqa F405
_UpperCAmelCase :Optional[Any] = 'down'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_skip_sample=SCREAMING_SNAKE_CASE_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = [0.55_39, 0.16_09, 0.49_24, 0.05_37, -0.19_95, 0.40_50, 0.09_79, -0.27_21, -0.06_42]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Dict = DownEncoderBlockaD # noqa F405
_UpperCAmelCase :Tuple = 'down'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_temb=SCREAMING_SNAKE_CASE_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : str = {
"in_channels": 32,
"out_channels": 32,
}
UpperCamelCase : Any = self.dummy_input
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : str = [1.11_02, 0.53_02, 0.48_72, -0.00_23, -0.80_42, 0.04_83, -0.34_89, -0.56_32, 0.76_26]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Optional[int] = AttnDownEncoderBlockaD # noqa F405
_UpperCAmelCase :Optional[Any] = 'down'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_temb=SCREAMING_SNAKE_CASE_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[int] = {
"in_channels": 32,
"out_channels": 32,
}
UpperCamelCase : Optional[Any] = self.dummy_input
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int = [0.89_66, -0.14_86, 0.85_68, 0.81_41, -0.90_46, -0.13_42, -0.09_72, -0.74_17, 0.15_38]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Dict = UNetMidBlockaD # noqa F405
_UpperCAmelCase :str = 'mid'
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Dict = {
"in_channels": 32,
"temb_channels": 128,
}
UpperCamelCase : Tuple = self.dummy_input
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = [-0.10_62, 1.72_48, 0.34_94, 1.45_69, -0.09_10, -1.24_21, -0.99_84, 0.67_36, 1.00_28]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :List[Any] = UNetMidBlockaDCrossAttn # noqa F405
_UpperCAmelCase :str = 'mid'
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : Dict = super().prepare_init_args_and_inputs_for_common()
UpperCamelCase : Any = 32
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Tuple = [0.01_87, 2.42_20, 0.44_84, 1.12_03, -0.61_21, -1.51_22, -0.82_70, 0.78_51, 1.83_35]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :str = UNetMidBlockaDSimpleCrossAttn # noqa F405
_UpperCAmelCase :List[str] = 'mid'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_encoder_hidden_states=SCREAMING_SNAKE_CASE_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : Optional[int] = super().prepare_init_args_and_inputs_for_common()
UpperCamelCase : List[str] = 32
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Any = [0.71_43, 1.99_74, 0.54_48, 1.39_77, 0.12_82, -1.12_37, -1.42_38, 0.55_30, 0.88_80]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :str = UpBlockaD # noqa F405
_UpperCAmelCase :Optional[int] = 'up'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = [-0.20_41, -0.41_65, -0.30_22, 0.00_41, -0.66_28, -0.70_53, 0.19_28, -0.03_25, 0.05_23]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Optional[Any] = ResnetUpsampleBlockaD # noqa F405
_UpperCAmelCase :Tuple = 'up'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : str = [0.22_87, 0.35_49, -0.13_46, 0.47_97, -0.17_15, -0.96_49, 0.73_05, -0.58_64, -0.62_44]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Any = CrossAttnUpBlockaD # noqa F405
_UpperCAmelCase :Optional[Any] = 'up'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : str = super().prepare_init_args_and_inputs_for_common()
UpperCamelCase : Optional[int] = 32
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Tuple = [-0.14_03, -0.35_15, -0.04_20, -0.14_25, 0.31_67, 0.50_94, -0.21_81, 0.59_31, 0.55_82]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Union[str, Any] = SimpleCrossAttnUpBlockaD # noqa F405
_UpperCAmelCase :List[str] = 'up'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_ , include_encoder_hidden_states=SCREAMING_SNAKE_CASE_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : List[str] = super().prepare_init_args_and_inputs_for_common()
UpperCamelCase : Tuple = 32
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : str = [0.26_45, 0.14_80, 0.09_09, 0.80_44, -0.97_58, -0.90_83, 0.09_94, -1.14_53, -0.74_02]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Optional[int] = AttnUpBlockaD # noqa F405
_UpperCAmelCase :Tuple = 'up'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_ )
@unittest.skipIf(torch_device == "mps" , "MPS result is not consistent" )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[int] = [0.09_79, 0.13_26, 0.00_21, 0.06_59, 0.22_49, 0.00_59, 0.11_32, 0.59_52, 0.10_33]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Optional[int] = SkipUpBlockaD # noqa F405
_UpperCAmelCase :Tuple = 'up'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = [-0.08_93, -0.12_34, -0.15_06, -0.03_32, 0.01_23, -0.02_11, 0.05_66, 0.01_43, 0.03_62]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Union[str, Any] = AttnSkipUpBlockaD # noqa F405
_UpperCAmelCase :List[Any] = 'up'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=SCREAMING_SNAKE_CASE_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = [0.03_61, 0.06_17, 0.27_87, -0.03_50, 0.03_42, 0.34_21, -0.08_43, 0.09_13, 0.30_15]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Any = UpDecoderBlockaD # noqa F405
_UpperCAmelCase :Optional[Any] = 'up'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_temb=SCREAMING_SNAKE_CASE_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int = {"in_channels": 32, "out_channels": 32}
UpperCamelCase : int = self.dummy_input
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = [0.44_04, 0.19_98, -0.98_86, -0.33_20, -0.31_28, -0.70_34, -0.69_55, -0.23_38, -0.31_37]
super().test_output(SCREAMING_SNAKE_CASE_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :List[Any] = AttnUpDecoderBlockaD # noqa F405
_UpperCAmelCase :List[str] = 'up'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_temb=SCREAMING_SNAKE_CASE_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Dict = {"in_channels": 32, "out_channels": 32}
UpperCamelCase : Optional[Any] = self.dummy_input
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Dict = [0.67_38, 0.44_91, 0.10_55, 1.07_10, 0.73_16, 0.33_39, 0.33_52, 0.10_23, 0.35_68]
super().test_output(SCREAMING_SNAKE_CASE_ )
| 52
|
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
from ..pytorch_utils import ConvaD
if is_accelerate_available():
from accelerate import init_empty_weights
from accelerate.utils import find_tied_parameters
__a = logging.get_logger(__name__)
def a ( snake_case__: Optional[int] , snake_case__: Dict , snake_case__: int , snake_case__: List[str]=None , snake_case__: List[Any]=None ):
'''simple docstring'''
# Recurse if needed
if "." in tensor_name:
lowercase_ = tensor_name.split('''.''' )
for split in splits[:-1]:
lowercase_ = getattr(snake_case__ , snake_case__ )
if new_module is None:
raise ValueError(F'''{module} has no attribute {split}.''' )
lowercase_ = new_module
lowercase_ = splits[-1]
if tensor_name not in module._parameters and tensor_name not in module._buffers:
raise ValueError(F'''{module} does not have a parameter or a buffer named {tensor_name}.''' )
lowercase_ = tensor_name in module._buffers
lowercase_ = getattr(snake_case__ , snake_case__ )
if old_value.device == torch.device('''meta''' ) and device not in ["meta", torch.device('''meta''' )] and value is None:
raise ValueError(F'''{tensor_name} is on the meta device, we need a `value` to put in on {device}.''' )
lowercase_ = False
lowercase_ = False
if is_buffer or not is_bitsandbytes_available():
lowercase_ = False
lowercase_ = False
else:
lowercase_ = hasattr(bnb.nn , '''Params4bit''' ) and isinstance(module._parameters[tensor_name] , bnb.nn.Paramsabit )
lowercase_ = isinstance(module._parameters[tensor_name] , bnb.nn.IntaParams )
if is_abit or is_abit:
lowercase_ = module._parameters[tensor_name]
if param.device.type != "cuda":
if value is None:
lowercase_ = old_value.to(snake_case__ )
elif isinstance(snake_case__ , torch.Tensor ):
lowercase_ = value.to('''cpu''' )
if value.dtype == torch.inta:
lowercase_ = version.parse(importlib.metadata.version('''bitsandbytes''' ) ) > version.parse(
'''0.37.2''' )
if not is_abit_serializable:
raise ValueError(
'''Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. '''
'''Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.''' )
else:
lowercase_ = torch.tensor(snake_case__ , device='''cpu''' )
# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
# Since weights are saved in the correct "orientation", we skip transposing when loading.
if issubclass(module.source_cls , snake_case__ ) and fpaa_statistics is None:
lowercase_ = new_value.T
lowercase_ = old_value.__dict__
if is_abit:
lowercase_ = bnb.nn.IntaParams(snake_case__ , requires_grad=snake_case__ , **snake_case__ ).to(snake_case__ )
elif is_abit:
lowercase_ = bnb.nn.Paramsabit(snake_case__ , requires_grad=snake_case__ , **snake_case__ ).to(snake_case__ )
lowercase_ = new_value
if fpaa_statistics is not None:
setattr(module.weight , '''SCB''' , fpaa_statistics.to(snake_case__ ) )
else:
if value is None:
lowercase_ = old_value.to(snake_case__ )
elif isinstance(snake_case__ , torch.Tensor ):
lowercase_ = value.to(snake_case__ )
else:
lowercase_ = torch.tensor(snake_case__ , device=snake_case__ )
if is_buffer:
lowercase_ = new_value
else:
lowercase_ = nn.Parameter(snake_case__ , requires_grad=old_value.requires_grad )
lowercase_ = new_value
def a ( snake_case__: str , snake_case__: Union[str, Any]=None , snake_case__: Any=None , snake_case__: List[str]=None , snake_case__: Optional[Any]=False ):
'''simple docstring'''
for name, module in model.named_children():
if current_key_name is None:
lowercase_ = []
current_key_name.append(snake_case__ )
if (isinstance(snake_case__ , nn.Linear ) or isinstance(snake_case__ , snake_case__ )) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
if not any(key in '''.'''.join(snake_case__ ) for key in modules_to_not_convert ):
with init_empty_weights():
if isinstance(snake_case__ , snake_case__ ):
lowercase_ , lowercase_ = module.weight.shape
else:
lowercase_ = module.in_features
lowercase_ = module.out_features
if quantization_config.quantization_method() == "llm_int8":
lowercase_ = bnb.nn.LinearabitLt(
snake_case__ , snake_case__ , module.bias is not None , has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight , threshold=quantization_config.llm_inta_threshold , )
lowercase_ = True
else:
if (
quantization_config.llm_inta_skip_modules is not None
and name in quantization_config.llm_inta_skip_modules
):
pass
else:
lowercase_ = bnb.nn.Linearabit(
snake_case__ , snake_case__ , module.bias is not None , quantization_config.bnb_abit_compute_dtype , compress_statistics=quantization_config.bnb_abit_use_double_quant , quant_type=quantization_config.bnb_abit_quant_type , )
lowercase_ = True
# Store the module class in case we need to transpose the weight later
lowercase_ = type(snake_case__ )
# Force requires grad to False to avoid unexpected errors
model._modules[name].requires_grad_(snake_case__ )
if len(list(module.children() ) ) > 0:
lowercase_ , lowercase_ = _replace_with_bnb_linear(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , has_been_replaced=snake_case__ , )
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def a ( snake_case__: Any , snake_case__: Any=None , snake_case__: Union[str, Any]=None , snake_case__: str=None ):
'''simple docstring'''
lowercase_ = ['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
lowercase_ , lowercase_ = _replace_with_bnb_linear(
snake_case__ , snake_case__ , snake_case__ , snake_case__ )
if not has_been_replaced:
logger.warning(
'''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
''' Please double check your model architecture, or submit an issue on github if you think this is'''
''' a bug.''' )
return model
def a ( *snake_case__: str , **snake_case__: Dict ):
'''simple docstring'''
warnings.warn(
'''`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead''' , snake_case__ , )
return replace_with_bnb_linear(*snake_case__ , **snake_case__ )
def a ( *snake_case__: Any , **snake_case__: List[Any] ):
'''simple docstring'''
warnings.warn(
'''`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead''' , snake_case__ , )
return set_module_quantized_tensor_to_device(*snake_case__ , **snake_case__ )
def a ( snake_case__: Optional[Any] ):
'''simple docstring'''
lowercase_ = deepcopy(snake_case__ ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
tied_model.tie_weights()
lowercase_ = find_tied_parameters(snake_case__ )
# For compatibility with Accelerate < 0.18
if isinstance(snake_case__ , snake_case__ ):
lowercase_ = sum(list(tied_params.values() ) , [] ) + list(tied_params.keys() )
else:
lowercase_ = sum(snake_case__ , [] )
lowercase_ = len(snake_case__ ) > 0
# Check if it is a base model
lowercase_ = not hasattr(snake_case__ , model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
lowercase_ = list(model.named_children() )
lowercase_ = [list_modules[-1][0]]
# add last module together with tied weights
lowercase_ = set(snake_case__ ) - set(snake_case__ )
lowercase_ = list(set(snake_case__ ) ) + list(snake_case__ )
# remove ".weight" from the keys
lowercase_ = ['''.weight''', '''.bias''']
lowercase_ = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
lowercase_ = name.replace(snake_case__ , '''''' )
filtered_module_names.append(snake_case__ )
return filtered_module_names
| 30
| 0
|
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __lowerCamelCase ( a__ , a__ , a__ ):
'''simple docstring'''
A_ : Optional[int] = [r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']
@register_to_config
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = 50257 , __UpperCAmelCase = 1024 , __UpperCAmelCase = 768 , __UpperCAmelCase = 12 , __UpperCAmelCase = 12 , __UpperCAmelCase = None , __UpperCAmelCase = "gelu_new" , __UpperCAmelCase = 0.1 , __UpperCAmelCase = 0.1 , __UpperCAmelCase = 0.1 , __UpperCAmelCase = 1e-5 , __UpperCAmelCase = 0.02 , __UpperCAmelCase = True , __UpperCAmelCase = True , __UpperCAmelCase = False , __UpperCAmelCase = False , ) -> List[str]:
super().__init__()
_a = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
F'`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'
F' `n_embd`: {n_embd} are not equal.' )
_a = prefix_inner_dim
_a = prefix_hidden_dim
_a = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
_a = (
nn.Linear(self.prefix_hidden_dim , SCREAMING_SNAKE_CASE_ ) if self.prefix_hidden_dim is not None else nn.Identity()
)
_a = GPTaConfig(
vocab_size=SCREAMING_SNAKE_CASE_ , n_positions=SCREAMING_SNAKE_CASE_ , n_embd=SCREAMING_SNAKE_CASE_ , n_layer=SCREAMING_SNAKE_CASE_ , n_head=SCREAMING_SNAKE_CASE_ , n_inner=SCREAMING_SNAKE_CASE_ , activation_function=SCREAMING_SNAKE_CASE_ , resid_pdrop=SCREAMING_SNAKE_CASE_ , embd_pdrop=SCREAMING_SNAKE_CASE_ , attn_pdrop=SCREAMING_SNAKE_CASE_ , layer_norm_epsilon=SCREAMING_SNAKE_CASE_ , initializer_range=SCREAMING_SNAKE_CASE_ , scale_attn_weights=SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ , scale_attn_by_inverse_layer_idx=SCREAMING_SNAKE_CASE_ , reorder_and_upcast_attn=SCREAMING_SNAKE_CASE_ , )
_a = GPTaLMHeadModel(SCREAMING_SNAKE_CASE_ )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , ) -> Union[str, Any]:
_a = self.transformer.transformer.wte(SCREAMING_SNAKE_CASE_ )
_a = self.encode_prefix(SCREAMING_SNAKE_CASE_ )
_a = self.decode_prefix(SCREAMING_SNAKE_CASE_ )
_a = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
_a = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
_a = torch.cat((dummy_token, input_ids) , dim=1 )
_a = self.transformer(inputs_embeds=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase ) -> torch.Tensor:
return torch.zeros(SCREAMING_SNAKE_CASE_ , self.prefix_length , dtype=torch.intaa , device=SCREAMING_SNAKE_CASE_ )
def _UpperCAmelCase ( self , __UpperCAmelCase ) -> List[str]:
return self.encode_prefix(SCREAMING_SNAKE_CASE_ )
@torch.no_grad()
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Optional[int]:
_a = torch.split(SCREAMING_SNAKE_CASE_ , 1 , dim=0 )
_a = []
_a = []
for feature in features:
_a = self.decode_prefix(feature.to(SCREAMING_SNAKE_CASE_ ) ) # back to the clip feature
# Only support beam search for now
_a , _a = self.generate_beam(
input_embeds=SCREAMING_SNAKE_CASE_ , device=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
_a = torch.stack(SCREAMING_SNAKE_CASE_ )
_a = torch.stack(SCREAMING_SNAKE_CASE_ )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def _UpperCAmelCase ( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase = 5 , __UpperCAmelCase = 67 , __UpperCAmelCase = 1.0 , __UpperCAmelCase = None , ) -> List[str]:
_a = eos_token_id
_a = None
_a = None
_a = torch.ones(SCREAMING_SNAKE_CASE_ , device=SCREAMING_SNAKE_CASE_ , dtype=torch.int )
_a = torch.zeros(SCREAMING_SNAKE_CASE_ , device=SCREAMING_SNAKE_CASE_ , dtype=torch.bool )
if input_embeds is not None:
_a = input_embeds
else:
_a = self.transformer.transformer.wte(SCREAMING_SNAKE_CASE_ )
for i in range(SCREAMING_SNAKE_CASE_ ):
_a = self.transformer(inputs_embeds=SCREAMING_SNAKE_CASE_ )
_a = outputs.logits
_a = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
_a = logits.softmax(-1 ).log()
if scores is None:
_a , _a = logits.topk(SCREAMING_SNAKE_CASE_ , -1 )
_a = generated.expand(SCREAMING_SNAKE_CASE_ , *generated.shape[1:] )
_a , _a = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
_a = next_tokens
else:
_a = tokens.expand(SCREAMING_SNAKE_CASE_ , *tokens.shape[1:] )
_a = torch.cat((tokens, next_tokens) , dim=1 )
else:
_a = -float(np.inf )
_a = 0
_a = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
_a = scores_sum / seq_lengths[:, None]
_a , _a = scores_sum_average.view(-1 ).topk(SCREAMING_SNAKE_CASE_ , -1 )
_a = next_tokens // scores_sum.shape[1]
_a = seq_lengths[next_tokens_source]
_a = next_tokens % scores_sum.shape[1]
_a = next_tokens.unsqueeze(1 )
_a = tokens[next_tokens_source]
_a = torch.cat((tokens, next_tokens) , dim=1 )
_a = generated[next_tokens_source]
_a = scores_sum_average * seq_lengths
_a = is_stopped[next_tokens_source]
_a = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
_a = torch.cat((generated, next_token_embed) , dim=1 )
_a = is_stopped + next_tokens.eq(SCREAMING_SNAKE_CASE_ ).squeeze()
if is_stopped.all():
break
_a = scores / seq_lengths
_a = scores.argsort(descending=SCREAMING_SNAKE_CASE_ )
# tokens tensors are already padded to max_seq_length
_a = [tokens[i] for i in order]
_a = torch.stack(SCREAMING_SNAKE_CASE_ , dim=0 )
_a = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 320
|
import argparse
import os
import re
__a = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__a = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
__a = re.compile(r'\s*\(\s*"(\S[^"]+)"')
def a ( snake_case__: str , snake_case__: bool = False ):
'''simple docstring'''
with open(snake_case__ , '''r''' , encoding='''utf-8''' ) as f:
lowercase_ = f.read()
lowercase_ = content.split('''\n''' )
lowercase_ = []
lowercase_ = 0
while line_idx < len(snake_case__ ):
if _re_intro_mapping.search(lines[line_idx] ) is not None:
lowercase_ = len(re.search(r'''^(\s*)\S''' , lines[line_idx] ).groups()[0] ) + 8
# Start of a new mapping!
while not lines[line_idx].startswith(''' ''' * indent + '''(''' ):
new_lines.append(lines[line_idx] )
line_idx += 1
lowercase_ = []
while lines[line_idx].strip() != "]":
# Blocks either fit in one line or not
if lines[line_idx].strip() == "(":
lowercase_ = line_idx
while not lines[line_idx].startswith(''' ''' * indent + ''')''' ):
line_idx += 1
blocks.append('''\n'''.join(lines[start_idx : line_idx + 1] ) )
else:
blocks.append(lines[line_idx] )
line_idx += 1
# Sort blocks by their identifiers
lowercase_ = sorted(snake_case__ , key=lambda snake_case__ : _re_identifier.search(snake_case__ ).groups()[0] )
new_lines += blocks
else:
new_lines.append(lines[line_idx] )
line_idx += 1
if overwrite:
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as f:
f.write('''\n'''.join(snake_case__ ) )
elif "\n".join(snake_case__ ) != content:
return True
def a ( snake_case__: bool = False ):
'''simple docstring'''
lowercase_ = [os.path.join(snake_case__ , snake_case__ ) for f in os.listdir(snake_case__ ) if f.endswith('''.py''' )]
lowercase_ = [sort_auto_mapping(snake_case__ , overwrite=snake_case__ ) for fname in fnames]
if not overwrite and any(snake_case__ ):
lowercase_ = [f for f, d in zip(snake_case__ , snake_case__ ) if d]
raise ValueError(
F'''The following files have auto mappings that need sorting: {', '.join(snake_case__ )}. Run `make style` to fix'''
''' this.''' )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
__a = parser.parse_args()
sort_all_auto_mappings(not args.check_only)
| 30
| 0
|
"""simple docstring"""
from __future__ import annotations
def lowercase (snake_case__ : list , snake_case__ : int , snake_case__ : int , snake_case__ : int ) -> Tuple:
'''simple docstring'''
lowerCAmelCase = []
lowerCAmelCase , lowerCAmelCase = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((left if left[0] <= right[0] else right).pop(0 ) )
lowerCAmelCase = result + left + right
return input_list
def lowercase (snake_case__ : list ) -> Any:
'''simple docstring'''
if len(snake_case__ ) <= 1:
return input_list
lowerCAmelCase = list(snake_case__ )
# iteration for two-way merging
lowerCAmelCase = 2
while p <= len(snake_case__ ):
# getting low, high and middle value for merge-sort of single list
for i in range(0 , len(snake_case__ ) , snake_case__ ):
lowerCAmelCase = i
lowerCAmelCase = i + p - 1
lowerCAmelCase = (low + high + 1) // 2
lowerCAmelCase = merge(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
# final merge of last two parts
if p * 2 >= len(snake_case__ ):
lowerCAmelCase = i
lowerCAmelCase = merge(snake_case__ , 0 , snake_case__ , len(snake_case__ ) - 1 )
break
p *= 2
return input_list
if __name__ == "__main__":
a = input('Enter numbers separated by a comma:\n').strip()
if user_input == "":
a = []
else:
a = [int(item.strip()) for item in user_input.split(',')]
print(iter_merge_sort(unsorted))
| 155
|
def a ( snake_case__: list , snake_case__: list , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if index == number_of_items:
return 0
lowercase_ = 0
lowercase_ = 0
lowercase_ = knapsack(snake_case__ , snake_case__ , snake_case__ , snake_case__ , index + 1 )
if weights[index] <= max_weight:
lowercase_ = values[index] + knapsack(
snake_case__ , snake_case__ , snake_case__ , max_weight - weights[index] , index + 1 )
return max(snake_case__ , snake_case__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 30
| 0
|
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
_lowerCamelCase : Union[str, Any] = "2.13.1"
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("3.7"):
raise ImportWarning(
"To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition."
)
if version.parse(pyarrow.__version__).major < 8:
raise ImportWarning(
"To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n"
"If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."
)
del platform
del pyarrow
del version
from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
list_datasets,
list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
NamedSplit,
NamedSplitAll,
Split,
SplitBase,
SplitDict,
SplitGenerator,
SplitInfo,
SubSplitInfo,
percent,
)
from .tasks import *
from .utils import *
from .utils import logging
# deprecated modules
from datasets import arrow_dataset as _arrow_dataset # isort:skip
from datasets import utils as _utils # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager # isort:skip
_lowerCamelCase : int = concatenate_datasets
_lowerCamelCase : List[Any] = DownloadConfig
_lowerCamelCase : Optional[int] = DownloadManager
_lowerCamelCase : Dict = DownloadMode
_lowerCamelCase : Optional[Any] = DownloadConfig
_lowerCamelCase : Optional[int] = DownloadMode
_lowerCamelCase : Optional[Any] = DownloadManager
del _arrow_dataset, _utils, _deprecated_download_manager
| 336
|
import argparse
from collections import defaultdict
import yaml
__a = 'docs/source/en/_toctree.yml'
def a ( snake_case__: Dict ):
'''simple docstring'''
lowercase_ = defaultdict(snake_case__ )
for doc in model_doc:
counts[doc["local"]] += 1
lowercase_ = [key for key, value in counts.items() if value > 1]
lowercase_ = []
for duplicate_key in duplicates:
lowercase_ = list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key} )
if len(snake_case__ ) > 1:
raise ValueError(
F'''{duplicate_key} is present several times in the documentation table of content at '''
'''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
'''others.''' )
# Only add this once
new_doc.append({'''local''': duplicate_key, '''title''': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1] )
# Sort
return sorted(snake_case__ , key=lambda snake_case__ : s["title"].lower() )
def a ( snake_case__: List[Any]=False ):
'''simple docstring'''
with open(snake_case__ , encoding='''utf-8''' ) as f:
lowercase_ = yaml.safe_load(f.read() )
# Get to the API doc
lowercase_ = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase_ = content[api_idx]['''sections''']
# Then to the model doc
lowercase_ = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
lowercase_ = api_doc[model_idx]['''sections''']
lowercase_ = [(idx, section) for idx, section in enumerate(snake_case__ ) if '''sections''' in section]
lowercase_ = False
for idx, modality_doc in modalities_docs:
lowercase_ = modality_doc['''sections''']
lowercase_ = clean_model_doc_toc(snake_case__ )
if old_modality_doc != new_modality_doc:
lowercase_ = True
if overwrite:
lowercase_ = new_modality_doc
if diff:
if overwrite:
lowercase_ = model_doc
lowercase_ = api_doc
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(snake_case__ , allow_unicode=snake_case__ ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
__a = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 30
| 0
|
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowercase = '''src/transformers'''
# This is to make sure the transformers module imported is the one in the repo.
__lowercase = direct_transformers_import(PATH_TO_TRANSFORMERS)
__lowercase = transformers.models.auto.configuration_auto.CONFIG_MAPPING
__lowercase = {
# used to compute the property `self.chunk_length`
'''EncodecConfig''': ['''overlap'''],
# used as `self.bert_model = BertModel(config, ...)`
'''DPRConfig''': True,
# not used in modeling files, but it's an important information
'''FSMTConfig''': ['''langs'''],
# used internally in the configuration class file
'''GPTNeoConfig''': ['''attention_types'''],
# used internally in the configuration class file
'''EsmConfig''': ['''is_folding_model'''],
# used during training (despite we don't have training script for these models yet)
'''Mask2FormerConfig''': ['''ignore_value'''],
# `ignore_value` used during training (despite we don't have training script for these models yet)
# `norm` used in conversion script (despite not using in the modeling file)
'''OneFormerConfig''': ['''ignore_value''', '''norm'''],
# used during preprocessing and collation, see `collating_graphormer.py`
'''GraphormerConfig''': ['''spatial_pos_max'''],
# used internally in the configuration class file
'''T5Config''': ['''feed_forward_proj'''],
# used internally in the configuration class file
# `tokenizer_class` get default value `T5Tokenizer` intentionally
'''MT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
'''UMT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
# used internally in the configuration class file
'''LongT5Config''': ['''feed_forward_proj'''],
# used internally in the configuration class file
'''SwitchTransformersConfig''': ['''feed_forward_proj'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''BioGptConfig''': ['''layer_norm_eps'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''GLPNConfig''': ['''layer_norm_eps'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''SegformerConfig''': ['''layer_norm_eps'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''CvtConfig''': ['''layer_norm_eps'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''PerceiverConfig''': ['''layer_norm_eps'''],
# used internally to calculate the feature size
'''InformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
# used internally to calculate the feature size
'''TimeSeriesTransformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
# used internally to calculate the feature size
'''AutoformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
# used internally to calculate `mlp_dim`
'''SamVisionConfig''': ['''mlp_ratio'''],
# For (head) training, but so far not implemented
'''ClapAudioConfig''': ['''num_classes'''],
# Not used, but providing useful information to users
'''SpeechT5HifiGanConfig''': ['''sampling_rate'''],
}
# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
{
'''CLIPSegConfig''': True,
'''DeformableDetrConfig''': True,
'''DetaConfig''': True,
'''DinatConfig''': True,
'''DonutSwinConfig''': True,
'''EfficientFormerConfig''': True,
'''FSMTConfig''': True,
'''JukeboxConfig''': True,
'''LayoutLMv2Config''': True,
'''MaskFormerSwinConfig''': True,
'''MT5Config''': True,
'''NatConfig''': True,
'''OneFormerConfig''': True,
'''PerceiverConfig''': True,
'''RagConfig''': True,
'''SpeechT5Config''': True,
'''SwinConfig''': True,
'''Swin2SRConfig''': True,
'''Swinv2Config''': True,
'''SwitchTransformersConfig''': True,
'''TableTransformerConfig''': True,
'''TapasConfig''': True,
'''TransfoXLConfig''': True,
'''UniSpeechConfig''': True,
'''UniSpeechSatConfig''': True,
'''WavLMConfig''': True,
'''WhisperConfig''': True,
# TODO: @Arthur (for `alignment_head` and `alignment_layer`)
'''JukeboxPriorConfig''': True,
# TODO: @Younes (for `is_decoder`)
'''Pix2StructTextConfig''': True,
}
)
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Dict = False
for attribute in attributes:
for modeling_source in source_strings:
# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
if (
f"""config.{attribute}""" in modeling_source
or f"""getattr(config, \"{attribute}\"""" in modeling_source
or f"""getattr(self.config, \"{attribute}\"""" in modeling_source
):
__UpperCamelCase :List[Any] = True
# Deal with multi-line cases
elif (
re.search(
Rf"""getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\"""" , snake_case__ , )
is not None
):
__UpperCamelCase :List[str] = True
# `SequenceSummary` is called with `SequenceSummary(config)`
elif attribute in [
"summary_type",
"summary_use_proj",
"summary_activation",
"summary_last_dropout",
"summary_proj_to_labels",
"summary_first_dropout",
]:
if "SequenceSummary" in modeling_source:
__UpperCamelCase :int = True
if attribute_used:
break
if attribute_used:
break
# common and important attributes, even if they do not always appear in the modeling files
__UpperCamelCase :Any = [
'''bos_index''',
'''eos_index''',
'''pad_index''',
'''unk_index''',
'''mask_index''',
'''image_size''',
'''use_cache''',
'''out_features''',
'''out_indices''',
]
__UpperCamelCase :Optional[Any] = ['''encoder_no_repeat_ngram_size''']
# Special cases to be allowed
__UpperCamelCase :Union[str, Any] = True
if not attribute_used:
__UpperCamelCase :int = False
for attribute in attributes:
# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
if attribute in ["is_encoder_decoder"] and default_value is True:
__UpperCamelCase :Optional[Any] = True
elif attribute in ["tie_word_embeddings"] and default_value is False:
__UpperCamelCase :List[str] = True
# Allow cases without checking the default value in the configuration class
elif attribute in attributes_to_allow + attributes_used_in_generation:
__UpperCamelCase :Union[str, Any] = True
elif attribute.endswith('''_token_id''' ):
__UpperCamelCase :int = True
# configuration class specific cases
if not case_allowed:
__UpperCamelCase :Tuple = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__ , [] )
__UpperCamelCase :Optional[Any] = allowed_cases is True or attribute in allowed_cases
return attribute_used or case_allowed
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Optional[int] = dict(inspect.signature(config_class.__init__ ).parameters )
__UpperCamelCase :Any = [x for x in list(signature.keys() ) if x not in ['''self''', '''kwargs''']]
__UpperCamelCase :Union[str, Any] = [signature[param].default for param in parameter_names]
# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
# as one variant is used, the test should pass
__UpperCamelCase :int = {}
if len(config_class.attribute_map ) > 0:
__UpperCamelCase :Any = {v: k for k, v in config_class.attribute_map.items()}
# Get the path to modeling source files
__UpperCamelCase :Tuple = inspect.getsourcefile(snake_case__ )
__UpperCamelCase :Union[str, Any] = os.path.dirname(snake_case__ )
# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
__UpperCamelCase :List[str] = [os.path.join(snake_case__ , snake_case__ ) for fn in os.listdir(snake_case__ ) if fn.startswith('''modeling_''' )]
# Get the source code strings
__UpperCamelCase :List[Any] = []
for path in modeling_paths:
if os.path.isfile(snake_case__ ):
with open(snake_case__ ) as fp:
modeling_sources.append(fp.read() )
__UpperCamelCase :Union[str, Any] = []
for config_param, default_value in zip(snake_case__ , snake_case__ ):
# `attributes` here is all the variant names for `config_param`
__UpperCamelCase :Union[str, Any] = [config_param]
# some configuration classes have non-empty `attribute_map`, and both names could be used in the
# corresponding modeling files. As long as one of them appears, it is fine.
if config_param in reversed_attribute_map:
attributes.append(reversed_attribute_map[config_param] )
if not check_attribute_being_used(snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
unused_attributes.append(attributes[0] )
return sorted(snake_case__ )
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Union[str, Any] = {}
for _config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in _config_class.__module__:
continue
# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
__UpperCamelCase :List[str] = [
cls
for name, cls in inspect.getmembers(
inspect.getmodule(_config_class ) , lambda SCREAMING_SNAKE_CASE : inspect.isclass(snake_case__ )
and issubclass(snake_case__ , snake_case__ )
and inspect.getmodule(snake_case__ ) == inspect.getmodule(_config_class ) , )
]
for config_class in config_classes_in_module:
__UpperCamelCase :Tuple = check_config_attributes_being_used(snake_case__ )
if len(snake_case__ ) > 0:
__UpperCamelCase :List[Any] = unused_attributes
if len(snake_case__ ) > 0:
__UpperCamelCase :int = '''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
for name, attributes in configs_with_unused_attributes.items():
error += f"""{name}: {attributes}\n"""
raise ValueError(snake_case__ )
if __name__ == "__main__":
check_config_attributes()
| 43
|
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Union[str, Any] = 'upernet'
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : Dict=None , SCREAMING_SNAKE_CASE_ : str=5_1_2 , SCREAMING_SNAKE_CASE_ : Tuple=0.02 , SCREAMING_SNAKE_CASE_ : Optional[Any]=[1, 2, 3, 6] , SCREAMING_SNAKE_CASE_ : Optional[int]=True , SCREAMING_SNAKE_CASE_ : Tuple=0.4 , SCREAMING_SNAKE_CASE_ : Optional[int]=3_8_4 , SCREAMING_SNAKE_CASE_ : str=2_5_6 , SCREAMING_SNAKE_CASE_ : Dict=1 , SCREAMING_SNAKE_CASE_ : Tuple=False , SCREAMING_SNAKE_CASE_ : str=2_5_5 , **SCREAMING_SNAKE_CASE_ : str , ) -> int:
super().__init__(**SCREAMING_SNAKE_CASE_ )
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowercase_ = CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase_ = backbone_config.get('''model_type''' )
lowercase_ = CONFIG_MAPPING[backbone_model_type]
lowercase_ = config_class.from_dict(SCREAMING_SNAKE_CASE_ )
lowercase_ = backbone_config
lowercase_ = hidden_size
lowercase_ = initializer_range
lowercase_ = pool_scales
lowercase_ = use_auxiliary_head
lowercase_ = auxiliary_loss_weight
lowercase_ = auxiliary_in_channels
lowercase_ = auxiliary_channels
lowercase_ = auxiliary_num_convs
lowercase_ = auxiliary_concat_input
lowercase_ = loss_ignore_index
def _lowercase ( self : List[str] ) -> List[str]:
lowercase_ = copy.deepcopy(self.__dict__ )
lowercase_ = self.backbone_config.to_dict()
lowercase_ = self.__class__.model_type
return output
| 30
| 0
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowerCamelCase (unittest.TestCase ):
@slow
def snake_case_ ( self: Tuple ):
'''simple docstring'''
__UpperCamelCase = AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small',return_dict=SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = AutoTokenizer.from_pretrained('google/mt5-small' )
__UpperCamelCase = tokenizer('Hello there',return_tensors='pt' ).input_ids
__UpperCamelCase = tokenizer('Hi I am',return_tensors='pt' ).input_ids
__UpperCamelCase = model(input_ids.to(SCREAMING_SNAKE_CASE_ ),labels=labels.to(SCREAMING_SNAKE_CASE_ ) ).loss
__UpperCamelCase = -(labels.shape[-1] * loss.item())
__UpperCamelCase = -8_4.9_1_2_7
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 310
|
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
__a = 'sshleifer/bart-tiny-random'
__a = 'patrickvonplaten/t5-tiny-random'
@require_torch
class lowercase__( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _lowercase ( self : Any ) -> Tuple:
return AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : int ) -> List[Any]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.num_hidden_layers , 1 )
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : List[str] ) -> List[Any]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=SCREAMING_SNAKE_CASE_ )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , self.teacher_config.encoder_layers )
def _lowercase ( self : List[Any] ) -> List[Any]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , 1 )
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=SCREAMING_SNAKE_CASE_ , d=SCREAMING_SNAKE_CASE_ )
| 30
| 0
|
"""simple docstring"""
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : Tuple = len(snake_case__ )
__lowerCAmelCase : Union[str, Any] = len(snake_case__ )
__lowerCAmelCase : List[Any] = (
first_str_length if first_str_length > second_str_length else second_str_length
)
__lowerCAmelCase : Optional[Any] = []
for char_count in range(snake_case__ ):
if char_count < first_str_length:
output_list.append(first_str[char_count] )
if char_count < second_str_length:
output_list.append(second_str[char_count] )
return "".join(snake_case__ )
if __name__ == "__main__":
print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)
| 86
|
def a ( snake_case__: int = 100 ):
'''simple docstring'''
lowercase_ = (n * (n + 1) // 2) ** 2
lowercase_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"{solution() = }")
| 30
| 0
|
import torch
from torch import nn
class snake_case__( nn.Module ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase=1 , __lowercase=False ) -> Any:
super().__init__()
lowerCAmelCase_ : str = n_token
lowerCAmelCase_ : List[str] = d_embed
lowerCAmelCase_ : Optional[int] = d_proj
lowerCAmelCase_ : Dict = cutoffs + [n_token]
lowerCAmelCase_ : Dict = [0] + self.cutoffs
lowerCAmelCase_ : Union[str, Any] = div_val
lowerCAmelCase_ : Tuple = self.cutoffs[0]
lowerCAmelCase_ : Optional[int] = len(self.cutoffs ) - 1
lowerCAmelCase_ : Dict = self.shortlist_size + self.n_clusters
if self.n_clusters > 0:
lowerCAmelCase_ : Union[str, Any] = nn.Parameter(torch.zeros(self.n_clusters , self.d_embed ) )
lowerCAmelCase_ : Dict = nn.Parameter(torch.zeros(self.n_clusters ) )
lowerCAmelCase_ : Dict = nn.ModuleList()
lowerCAmelCase_ : Optional[int] = nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs ) ):
if d_proj != d_embed:
self.out_projs.append(nn.Parameter(torch.FloatTensor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ) )
else:
self.out_projs.append(SCREAMING_SNAKE_CASE_ )
self.out_layers.append(nn.Linear(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
else:
for i in range(len(self.cutoffs ) ):
lowerCAmelCase_ , lowerCAmelCase_ : List[str] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowerCAmelCase_ : Union[str, Any] = d_embed // (div_val**i)
self.out_projs.append(nn.Parameter(torch.FloatTensor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ) )
self.out_layers.append(nn.Linear(SCREAMING_SNAKE_CASE_ , r_idx - l_idx ) )
lowerCAmelCase_ : int = keep_order
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase ) -> Tuple:
if proj is None:
lowerCAmelCase_ : int = nn.functional.linear(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , bias=SCREAMING_SNAKE_CASE_ )
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
lowerCAmelCase_ : Tuple = nn.functional.linear(SCREAMING_SNAKE_CASE_ , proj.t().contiguous() )
lowerCAmelCase_ : List[Any] = nn.functional.linear(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , bias=SCREAMING_SNAKE_CASE_ )
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def lowercase_ ( self , __lowercase , __lowercase=None , __lowercase=False ) -> Optional[Any]:
if labels is not None:
# Shift so that tokens < n predict n
lowerCAmelCase_ : Dict = hidden[..., :-1, :].contiguous()
lowerCAmelCase_ : Union[str, Any] = labels[..., 1:].contiguous()
lowerCAmelCase_ : List[str] = hidden.view(-1 , hidden.size(-1 ) )
lowerCAmelCase_ : Union[str, Any] = labels.view(-1 )
if hidden.size(0 ) != labels.size(0 ):
raise RuntimeError('''Input and labels should have the same size in the batch dimension.''' )
else:
lowerCAmelCase_ : Dict = hidden.view(-1 , hidden.size(-1 ) )
if self.n_clusters == 0:
lowerCAmelCase_ : List[Any] = self._compute_logit(SCREAMING_SNAKE_CASE_ , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
if labels is not None:
lowerCAmelCase_ : Tuple = labels != -1_0_0
lowerCAmelCase_ : Union[str, Any] = torch.zeros_like(SCREAMING_SNAKE_CASE_ , dtype=hidden.dtype , device=hidden.device )
lowerCAmelCase_ : Dict = (
-nn.functional.log_softmax(SCREAMING_SNAKE_CASE_ , dim=-1 )[mask].gather(1 , labels[mask].unsqueeze(1 ) ).squeeze(1 )
)
else:
lowerCAmelCase_ : Optional[int] = nn.functional.log_softmax(SCREAMING_SNAKE_CASE_ , dim=-1 )
else:
# construct weights and biases
lowerCAmelCase_ , lowerCAmelCase_ : List[str] = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
lowerCAmelCase_ , lowerCAmelCase_ : Dict = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowerCAmelCase_ : Optional[Any] = self.out_layers[0].weight[l_idx:r_idx]
lowerCAmelCase_ : Tuple = self.out_layers[0].bias[l_idx:r_idx]
else:
lowerCAmelCase_ : List[Any] = self.out_layers[i].weight
lowerCAmelCase_ : Union[str, Any] = self.out_layers[i].bias
if i == 0:
lowerCAmelCase_ : int = torch.cat([weight_i, self.cluster_weight] , dim=0 )
lowerCAmelCase_ : Dict = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(SCREAMING_SNAKE_CASE_ )
biases.append(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = weights[0], biases[0], self.out_projs[0]
lowerCAmelCase_ : List[str] = self._compute_logit(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : int = nn.functional.log_softmax(SCREAMING_SNAKE_CASE_ , dim=1 )
if labels is None:
lowerCAmelCase_ : Optional[int] = hidden.new_empty((head_logit.size(0 ), self.n_token) )
else:
lowerCAmelCase_ : str = torch.zeros_like(SCREAMING_SNAKE_CASE_ , dtype=hidden.dtype , device=hidden.device )
lowerCAmelCase_ : Union[str, Any] = 0
lowerCAmelCase_ : Dict = [0] + self.cutoffs
for i in range(len(SCREAMING_SNAKE_CASE_ ) - 1 ):
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = cutoff_values[i], cutoff_values[i + 1]
if labels is not None:
lowerCAmelCase_ : Dict = (labels >= l_idx) & (labels < r_idx)
lowerCAmelCase_ : str = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
lowerCAmelCase_ : List[str] = labels.index_select(0 , SCREAMING_SNAKE_CASE_ ) - l_idx
lowerCAmelCase_ : Optional[int] = head_logprob.index_select(0 , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Union[str, Any] = hidden.index_select(0 , SCREAMING_SNAKE_CASE_ )
else:
lowerCAmelCase_ : str = hidden
if i == 0:
if labels is not None:
lowerCAmelCase_ : Optional[Any] = head_logprob_i.gather(1 , target_i[:, None] ).squeeze(1 )
else:
lowerCAmelCase_ : Dict = head_logprob[:, : self.cutoffs[0]]
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Tuple = weights[i], biases[i], self.out_projs[i]
lowerCAmelCase_ : Union[str, Any] = self._compute_logit(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Union[str, Any] = nn.functional.log_softmax(SCREAMING_SNAKE_CASE_ , dim=1 )
lowerCAmelCase_ : List[Any] = self.cutoffs[0] + i - 1 # No probability for the head cluster
if labels is not None:
lowerCAmelCase_ : Any = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
1 , target_i[:, None] ).squeeze(1 )
else:
lowerCAmelCase_ : Optional[Any] = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
lowerCAmelCase_ : int = logprob_i
if labels is not None:
if (hasattr(self , '''keep_order''' ) and self.keep_order) or keep_order:
out.index_copy_(0 , SCREAMING_SNAKE_CASE_ , -logprob_i )
else:
out[offset : offset + logprob_i.size(0 )].copy_(-logprob_i )
offset += logprob_i.size(0 )
return out
def lowercase_ ( self , __lowercase ) -> Optional[int]:
if self.n_clusters == 0:
lowerCAmelCase_ : List[str] = self._compute_logit(SCREAMING_SNAKE_CASE_ , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
return nn.functional.log_softmax(SCREAMING_SNAKE_CASE_ , dim=-1 )
else:
# construct weights and biases
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
lowerCAmelCase_ , lowerCAmelCase_ : str = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowerCAmelCase_ : int = self.out_layers[0].weight[l_idx:r_idx]
lowerCAmelCase_ : Optional[Any] = self.out_layers[0].bias[l_idx:r_idx]
else:
lowerCAmelCase_ : List[str] = self.out_layers[i].weight
lowerCAmelCase_ : List[Any] = self.out_layers[i].bias
if i == 0:
lowerCAmelCase_ : Optional[int] = torch.cat([weight_i, self.cluster_weight] , dim=0 )
lowerCAmelCase_ : Union[str, Any] = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(SCREAMING_SNAKE_CASE_ )
biases.append(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = weights[0], biases[0], self.out_projs[0]
lowerCAmelCase_ : Tuple = self._compute_logit(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : List[str] = hidden.new_empty((head_logit.size(0 ), self.n_token) )
lowerCAmelCase_ : Dict = nn.functional.log_softmax(SCREAMING_SNAKE_CASE_ , dim=1 )
lowerCAmelCase_ : Union[str, Any] = [0] + self.cutoffs
for i in range(len(SCREAMING_SNAKE_CASE_ ) - 1 ):
lowerCAmelCase_ , lowerCAmelCase_ : int = cutoff_values[i], cutoff_values[i + 1]
if i == 0:
lowerCAmelCase_ : Optional[Any] = head_logprob[:, : self.cutoffs[0]]
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Tuple = weights[i], biases[i], self.out_projs[i]
lowerCAmelCase_ : Optional[Any] = self._compute_logit(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Dict = nn.functional.log_softmax(SCREAMING_SNAKE_CASE_ , dim=1 )
lowerCAmelCase_ : Any = head_logprob[:, -i] + tail_logprob_i
lowerCAmelCase_ : List[Any] = logprob_i
return out
| 262
|
import logging
from transformers.configuration_utils import PretrainedConfig
__a = logging.getLogger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Optional[int] = 'masked_bert'
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : List[Any]=3_0_5_2_2 , SCREAMING_SNAKE_CASE_ : List[str]=7_6_8 , SCREAMING_SNAKE_CASE_ : Optional[int]=1_2 , SCREAMING_SNAKE_CASE_ : Any=1_2 , SCREAMING_SNAKE_CASE_ : str=3_0_7_2 , SCREAMING_SNAKE_CASE_ : Union[str, Any]="gelu" , SCREAMING_SNAKE_CASE_ : Optional[int]=0.1 , SCREAMING_SNAKE_CASE_ : List[str]=0.1 , SCREAMING_SNAKE_CASE_ : Tuple=5_1_2 , SCREAMING_SNAKE_CASE_ : str=2 , SCREAMING_SNAKE_CASE_ : Dict=0.02 , SCREAMING_SNAKE_CASE_ : Any=1e-12 , SCREAMING_SNAKE_CASE_ : Any=0 , SCREAMING_SNAKE_CASE_ : Optional[int]="topK" , SCREAMING_SNAKE_CASE_ : Dict="constant" , SCREAMING_SNAKE_CASE_ : Optional[Any]=0.0 , **SCREAMING_SNAKE_CASE_ : Optional[int] , ) -> Optional[Any]:
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
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_ = type_vocab_size
lowercase_ = initializer_range
lowercase_ = layer_norm_eps
lowercase_ = pruning_method
lowercase_ = mask_init
lowercase_ = mask_scale
| 30
| 0
|
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : float , lowerCAmelCase__ : int ) -> Dict:
if digit_amount > 0:
return round(number - int(snake_case__ ) , snake_case__ )
return number - int(snake_case__ )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 45
|
import os
def a ( ):
'''simple docstring'''
lowercase_ = os.path.join(os.path.dirname(snake_case__ ) , '''num.txt''' )
with open(snake_case__ ) as file_hand:
return str(sum(int(snake_case__ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 30
| 0
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""andreasmadsen/efficient_mlm_m0.40""": (
"""https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
),
}
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = 'roberta-prelayernorm'
def __init__( self , _snake_case=50265 , _snake_case=768 , _snake_case=12 , _snake_case=12 , _snake_case=3072 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=512 , _snake_case=2 , _snake_case=0.02 , _snake_case=1e-12 , _snake_case=1 , _snake_case=0 , _snake_case=2 , _snake_case="absolute" , _snake_case=True , _snake_case=None , **_snake_case , ):
"""simple docstring"""
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = hidden_act
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = type_vocab_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = position_embedding_type
_lowerCAmelCase = use_cache
_lowerCAmelCase = classifier_dropout
class __lowerCAmelCase ( lowerCamelCase__ ):
@property
def snake_case ( self ):
"""simple docstring"""
if self.task == "multiple-choice":
_lowerCAmelCase = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_lowerCAmelCase = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 82
|
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
'''compression_format, is_archive''' , [
('''7z''', True),
('''bz2''', False),
('''gzip''', False),
('''lz4''', False),
('''tar''', True),
('''xz''', False),
('''zip''', True),
('''zstd''', False),
] , )
def a ( snake_case__: int , snake_case__: Tuple , snake_case__: Dict , snake_case__: Dict , snake_case__: List[Any] , snake_case__: int , snake_case__: List[Any] , snake_case__: Optional[int] , snake_case__: str , snake_case__: Union[str, Any] , snake_case__: List[str] , snake_case__: int , ):
'''simple docstring'''
lowercase_ = {
'''7z''': (seven_zip_file, SevenZipExtractor),
'''bz2''': (bza_file, BzipaExtractor),
'''gzip''': (gz_file, GzipExtractor),
'''lz4''': (lza_file, LzaExtractor),
'''tar''': (tar_file, TarExtractor),
'''xz''': (xz_file, XzExtractor),
'''zip''': (zip_file, ZipExtractor),
'''zstd''': (zstd_file, ZstdExtractor),
}
lowercase_ , lowercase_ = input_paths_and_base_extractors[compression_format]
if input_path is None:
lowercase_ = F'''for \'{compression_format}\' compression_format, '''
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(snake_case__ )
assert base_extractor.is_extractable(snake_case__ )
lowercase_ = tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
base_extractor.extract(snake_case__ , snake_case__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
lowercase_ = file_path.read_text(encoding='''utf-8''' )
else:
lowercase_ = output_path.read_text(encoding='''utf-8''' )
lowercase_ = text_file.read_text(encoding='''utf-8''' )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
'''compression_format, is_archive''' , [
('''7z''', True),
('''bz2''', False),
('''gzip''', False),
('''lz4''', False),
('''tar''', True),
('''xz''', False),
('''zip''', True),
('''zstd''', False),
] , )
def a ( snake_case__: List[Any] , snake_case__: int , snake_case__: Optional[int] , snake_case__: Union[str, Any] , snake_case__: List[Any] , snake_case__: Tuple , snake_case__: Optional[int] , snake_case__: List[str] , snake_case__: Union[str, Any] , snake_case__: Tuple , snake_case__: int , snake_case__: Optional[int] , ):
'''simple docstring'''
lowercase_ = {
'''7z''': seven_zip_file,
'''bz2''': bza_file,
'''gzip''': gz_file,
'''lz4''': lza_file,
'''tar''': tar_file,
'''xz''': xz_file,
'''zip''': zip_file,
'''zstd''': zstd_file,
}
lowercase_ = input_paths[compression_format]
if input_path is None:
lowercase_ = F'''for \'{compression_format}\' compression_format, '''
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(snake_case__ )
lowercase_ = Extractor.infer_extractor_format(snake_case__ )
assert extractor_format is not None
lowercase_ = tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
Extractor.extract(snake_case__ , snake_case__ , snake_case__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
lowercase_ = file_path.read_text(encoding='''utf-8''' )
else:
lowercase_ = output_path.read_text(encoding='''utf-8''' )
lowercase_ = text_file.read_text(encoding='''utf-8''' )
assert extracted_file_content == expected_file_content
@pytest.fixture
def a ( snake_case__: Union[str, Any] , snake_case__: List[Any] ):
'''simple docstring'''
import tarfile
lowercase_ = tmp_path / '''data_dot_dot'''
directory.mkdir()
lowercase_ = directory / '''tar_file_with_dot_dot.tar'''
with tarfile.TarFile(snake_case__ , '''w''' ) as f:
f.add(snake_case__ , arcname=os.path.join('''..''' , text_file.name ) )
return path
@pytest.fixture
def a ( snake_case__: int ):
'''simple docstring'''
import tarfile
lowercase_ = tmp_path / '''data_sym_link'''
directory.mkdir()
lowercase_ = directory / '''tar_file_with_sym_link.tar'''
os.symlink('''..''' , directory / '''subdir''' , target_is_directory=snake_case__ )
with tarfile.TarFile(snake_case__ , '''w''' ) as f:
f.add(str(directory / '''subdir''' ) , arcname='''subdir''' ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
'''insecure_tar_file, error_log''' , [('''tar_file_with_dot_dot''', '''illegal path'''), ('''tar_file_with_sym_link''', '''Symlink''')] , )
def a ( snake_case__: List[Any] , snake_case__: Optional[int] , snake_case__: List[str] , snake_case__: List[str] , snake_case__: int , snake_case__: Optional[Any] ):
'''simple docstring'''
lowercase_ = {
'''tar_file_with_dot_dot''': tar_file_with_dot_dot,
'''tar_file_with_sym_link''': tar_file_with_sym_link,
}
lowercase_ = insecure_tar_files[insecure_tar_file]
lowercase_ = tmp_path / '''extracted'''
TarExtractor.extract(snake_case__ , snake_case__ )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def a ( snake_case__: Optional[int] ):
'''simple docstring'''
# We should have less false positives than zipfile.is_zipfile
# We do that by checking only the magic number
lowercase_ = tmpdir / '''not_a_zip_file'''
# From: https://github.com/python/cpython/pull/5053
lowercase_ = (
B'''\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'''
B'''\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'''
B'''DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'''
B'''\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'''
)
with not_a_zip_file.open('''wb''' ) as f:
f.write(snake_case__ )
assert zipfile.is_zipfile(str(snake_case__ ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(snake_case__ ) # but we're right
| 30
| 0
|
import operator as op
_A = 'scaler.pt'
_A = 'pytorch_model'
_A = 'random_states'
_A = 'optimizer'
_A = 'scheduler'
_A = 'pytorch_model.bin'
_A = 'pytorch_model.bin.index.json'
_A = 'model.safetensors'
_A = 'model.safetensors.index.json'
_A = '1.10.2'
_A = 'py38'
_A = '4.17.0'
_A = ['ml.p3.16xlarge', 'ml.p3dn.24xlarge', 'ml.p4dn.24xlarge']
_A = ['FULL_SHARD', 'SHARD_GRAD_OP', 'NO_SHARD', 'HYBRID_SHARD', 'HYBRID_SHARD_ZERO2']
_A = ['TRANSFORMER_BASED_WRAP', 'SIZE_BASED_WRAP', 'NO_WRAP']
_A = ['BACKWARD_PRE', 'BACKWARD_POST', 'NO_PREFETCH']
_A = ['FULL_STATE_DICT', 'LOCAL_STATE_DICT', 'SHARDED_STATE_DICT']
_A = '2.0.1'
_A = ['pdsh', 'standard', 'openmpi', 'mvapich']
_A = ['default', 'reduce-overhead', 'max-autotune']
_A = {'>': op.gt, '>=': op.ge, '==': op.eq, '!=': op.ne, '<=': op.le, '<': op.lt}
# These are the args for `torch.distributed.launch` for pytorch < 1.9
_A = [
'nnodes',
'nproc_per_node',
'rdzv_backend',
'rdzv_endpoint',
'rdzv_id',
'rdzv_conf',
'standalone',
'max_restarts',
'monitor_interval',
'start_method',
'role',
'module',
'm',
'no_python',
'run_path',
'log_dir',
'r',
'redirects',
't',
'tee',
'node_rank',
'master_addr',
'master_port',
]
_A = ['DEEPSPEED', 'MULTI_GPU', 'FSDP', 'MEGATRON_LM']
_A = ['DEEPSPEED', 'MULTI_XPU', 'FSDP']
| 62
|
from __future__ import annotations
def a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''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 a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if length > 1:
lowercase_ = int(length / 2 )
for i in range(snake_case__ , low + middle ):
comp_and_swap(snake_case__ , snake_case__ , i + middle , snake_case__ )
bitonic_merge(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
bitonic_merge(snake_case__ , low + middle , snake_case__ , snake_case__ )
def a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if length > 1:
lowercase_ = int(length / 2 )
bitonic_sort(snake_case__ , snake_case__ , snake_case__ , 1 )
bitonic_sort(snake_case__ , low + middle , snake_case__ , 0 )
bitonic_merge(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
if __name__ == "__main__":
__a = input('Enter numbers separated by a comma:\n').strip()
__a = [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=', ')
| 30
| 0
|
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
__lowerCamelCase : Tuple = logging.get_logger(__name__)
class A__ ( __snake_case ):
_UpperCAmelCase :Optional[int] = 'vision-encoder-decoder'
_UpperCAmelCase :Any = True
def __init__( self , **A_ ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
F"""A configuraton of type {self.model_type} cannot be instantiated because """
F"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}""" )
UpperCamelCase : List[str] = kwargs.pop("encoder" )
UpperCamelCase : Optional[int] = encoder_config.pop("model_type" )
UpperCamelCase : str = kwargs.pop("decoder" )
UpperCamelCase : str = decoder_config.pop("model_type" )
UpperCamelCase : Optional[int] = AutoConfig.for_model(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = AutoConfig.for_model(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = True
@classmethod
def __UpperCamelCase( cls , A_ , A_ , **A_ ):
'''simple docstring'''
logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" )
UpperCamelCase : List[Any] = True
UpperCamelCase : Dict = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **SCREAMING_SNAKE_CASE_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = copy.deepcopy(self.__dict__ )
UpperCamelCase : List[Any] = self.encoder.to_dict()
UpperCamelCase : List[Any] = self.decoder.to_dict()
UpperCamelCase : Any = self.__class__.model_type
return output
class A__ ( __snake_case ):
_UpperCAmelCase :Any = version.parse('1.11' )
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return 1e-4
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}} )
class A__ ( __snake_case ):
@property
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = OrderedDict()
UpperCamelCase : Dict = {0: "batch", 1: "past_decoder_sequence + sequence"}
UpperCamelCase : List[Any] = {0: "batch", 1: "past_decoder_sequence + sequence"}
UpperCamelCase : Union[str, Any] = {0: "batch", 1: "encoder_sequence"}
return common_inputs
def __UpperCamelCase( self , A_ , A_ = -1 , A_ = -1 , A_ = False , A_ = None , ):
'''simple docstring'''
import torch
UpperCamelCase : Dict = OrderedDict()
UpperCamelCase : Dict = super().generate_dummy_inputs(
SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ , seq_length=SCREAMING_SNAKE_CASE_ , is_pair=SCREAMING_SNAKE_CASE_ , framework=SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase : List[str] = dummy_input["input_ids"].shape
UpperCamelCase : Any = (batch, encoder_sequence, self._config.encoder_hidden_size)
UpperCamelCase : Tuple = dummy_input.pop("input_ids" )
UpperCamelCase : List[Any] = dummy_input.pop("attention_mask" )
UpperCamelCase : List[Any] = torch.zeros(SCREAMING_SNAKE_CASE_ )
return common_inputs
class A__ ( __snake_case ):
@property
def __UpperCamelCase( self ):
'''simple docstring'''
pass
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
return VisionEncoderDecoderEncoderOnnxConfig(SCREAMING_SNAKE_CASE_ )
def __UpperCamelCase( self , A_ , A_ , A_ = "default" ):
'''simple docstring'''
UpperCamelCase : List[Any] = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 52
|
from __future__ import annotations
from collections.abc import MutableSequence
class lowercase__:
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : MutableSequence[float] ) -> None:
if len(SCREAMING_SNAKE_CASE_ ) != degree + 1:
raise ValueError(
'''The number of coefficients should be equal to the degree + 1.''' )
lowercase_ = list(SCREAMING_SNAKE_CASE_ )
lowercase_ = degree
def __add__( self : Any , SCREAMING_SNAKE_CASE_ : Polynomial ) -> Polynomial:
if self.degree > polynomial_a.degree:
lowercase_ = self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree , SCREAMING_SNAKE_CASE_ )
else:
lowercase_ = polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree , SCREAMING_SNAKE_CASE_ )
def __sub__( self : str , SCREAMING_SNAKE_CASE_ : Polynomial ) -> Polynomial:
return self + polynomial_a * Polynomial(0 , [-1] )
def __neg__( self : int ) -> Polynomial:
return Polynomial(self.degree , [-c for c in self.coefficients] )
def __mul__( self : Any , SCREAMING_SNAKE_CASE_ : Polynomial ) -> Polynomial:
lowercase_ = [0] * (self.degree + polynomial_a.degree + 1)
for i in range(self.degree + 1 ):
for j in range(polynomial_a.degree + 1 ):
coefficients[i + j] += (
self.coefficients[i] * polynomial_a.coefficients[j]
)
return Polynomial(self.degree + polynomial_a.degree , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Dict , SCREAMING_SNAKE_CASE_ : int | float ) -> int | float:
lowercase_ = 0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self : Tuple ) -> str:
lowercase_ = ''''''
for i in range(self.degree , -1 , -1 ):
if self.coefficients[i] == 0:
continue
elif self.coefficients[i] > 0:
if polynomial:
polynomial += " + "
else:
polynomial += " - "
if i == 0:
polynomial += str(abs(self.coefficients[i] ) )
elif i == 1:
polynomial += str(abs(self.coefficients[i] ) ) + "x"
else:
polynomial += str(abs(self.coefficients[i] ) ) + "x^" + str(SCREAMING_SNAKE_CASE_ )
return polynomial
def __repr__( self : Optional[Any] ) -> str:
return self.__str__()
def _lowercase ( self : int ) -> Polynomial:
lowercase_ = [0] * self.degree
for i in range(self.degree ):
lowercase_ = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : int | float = 0 ) -> Polynomial:
lowercase_ = [0] * (self.degree + 2)
lowercase_ = constant
for i in range(self.degree + 1 ):
lowercase_ = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 , SCREAMING_SNAKE_CASE_ )
def __eq__( self : str , SCREAMING_SNAKE_CASE_ : object ) -> bool:
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return False
if self.degree != polynomial_a.degree:
return False
for i in range(self.degree + 1 ):
if self.coefficients[i] != polynomial_a.coefficients[i]:
return False
return True
def __ne__( self : List[str] , SCREAMING_SNAKE_CASE_ : object ) -> bool:
return not self.__eq__(SCREAMING_SNAKE_CASE_ )
| 30
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {
'''configuration_whisper''': ['''WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WhisperConfig''', '''WhisperOnnxConfig'''],
'''feature_extraction_whisper''': ['''WhisperFeatureExtractor'''],
'''processing_whisper''': ['''WhisperProcessor'''],
'''tokenization_whisper''': ['''WhisperTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''WhisperTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''WhisperForConditionalGeneration''',
'''WhisperModel''',
'''WhisperPreTrainedModel''',
'''WhisperForAudioClassification''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWhisperForConditionalGeneration''',
'''TFWhisperModel''',
'''TFWhisperPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''FlaxWhisperForConditionalGeneration''',
'''FlaxWhisperModel''',
'''FlaxWhisperPreTrainedModel''',
'''FlaxWhisperForAudioClassification''',
]
if TYPE_CHECKING:
from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
from .feature_extraction_whisper import WhisperFeatureExtractor
from .processing_whisper import WhisperProcessor
from .tokenization_whisper import WhisperTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_whisper_fast import WhisperTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_whisper import (
WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
WhisperForAudioClassification,
WhisperForConditionalGeneration,
WhisperModel,
WhisperPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_whisper import (
TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWhisperForConditionalGeneration,
TFWhisperModel,
TFWhisperPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_whisper import (
FlaxWhisperForAudioClassification,
FlaxWhisperForConditionalGeneration,
FlaxWhisperModel,
FlaxWhisperPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 320
|
import itertools
import math
def a ( snake_case__: int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(snake_case__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def a ( ):
'''simple docstring'''
lowercase_ = 2
while True:
if is_prime(snake_case__ ):
yield num
num += 1
def a ( snake_case__: int = 10_001 ):
'''simple docstring'''
return next(itertools.islice(prime_generator() , nth - 1 , snake_case__ ) )
if __name__ == "__main__":
print(f"{solution() = }")
| 30
| 0
|
"""simple docstring"""
def lowercase (snake_case__ : str ) -> int:
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 155
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 30
| 0
|
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def a__ ( UpperCAmelCase : Optional[int] ) -> Any:
for param in module.parameters():
UpperCAmelCase : Dict = False
def a__ ( ) -> Tuple:
UpperCAmelCase : Dict = '''cuda''' if torch.cuda.is_available() else '''cpu'''
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
UpperCAmelCase : Tuple = '''mps'''
if device == "mps":
print(
'''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
''' with generations.''' )
return device
def a__ ( UpperCAmelCase : int ) -> Tuple:
UpperCAmelCase : int = plt.imshow(snake_case__ )
fig.axes.get_xaxis().set_visible(snake_case__ )
fig.axes.get_yaxis().set_visible(snake_case__ )
plt.show()
def a__ ( ) -> Union[str, Any]:
UpperCAmelCase : List[Any] = datetime.now()
UpperCAmelCase : Union[str, Any] = current_time.strftime('''%H:%M:%S''' )
return timestamp
| 336
|
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
__a = logging.get_logger(__name__)
__a = {
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
'constant': get_constant_schedule,
'constant_w_warmup': get_constant_schedule_with_warmup,
}
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[str]=None , SCREAMING_SNAKE_CASE_ : Optional[Any]=None , *SCREAMING_SNAKE_CASE_ : str , **SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> List[str]:
super().__init__(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if config is None:
assert isinstance(self.model , SCREAMING_SNAKE_CASE_ ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
f''' {self.model.__class__}'''
)
lowercase_ = self.model.config
else:
lowercase_ = config
lowercase_ = data_args
lowercase_ = self.config.tgt_vocab_size if isinstance(self.config , SCREAMING_SNAKE_CASE_ ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
f'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
''' padding..''' )
if self.args.label_smoothing == 0:
lowercase_ = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
lowercase_ = label_smoothed_nll_loss
def _lowercase ( self : int , SCREAMING_SNAKE_CASE_ : int ) -> Optional[Any]:
if self.optimizer is None:
lowercase_ = ['''bias''', '''LayerNorm.weight''']
lowercase_ = [
{
'''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
'''weight_decay''': self.args.weight_decay,
},
{
'''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
lowercase_ = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
lowercase_ = Adafactor
lowercase_ = {'''scale_parameter''': False, '''relative_step''': False}
else:
lowercase_ = AdamW
lowercase_ = {
'''betas''': (self.args.adam_betaa, self.args.adam_betaa),
'''eps''': self.args.adam_epsilon,
}
lowercase_ = self.args.learning_rate
if self.sharded_ddp:
lowercase_ = OSS(
params=SCREAMING_SNAKE_CASE_ , optim=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
else:
lowercase_ = optimizer_cls(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if self.lr_scheduler is None:
lowercase_ = self._get_lr_scheduler(SCREAMING_SNAKE_CASE_ )
else: # ignoring --lr_scheduler
logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.''' )
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : Dict ) -> Dict:
lowercase_ = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
lowercase_ = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
lowercase_ = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
lowercase_ = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=SCREAMING_SNAKE_CASE_ )
return scheduler
def _lowercase ( self : Tuple ) -> Optional[torch.utils.data.Sampler]:
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : List[Any] ) -> Any:
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
lowercase_ = model(**SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )[0]
lowercase_ = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
lowercase_ , lowercase_ = model(**SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )[:2]
else:
# compute label smoothed loss
lowercase_ = model(**SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )[0]
lowercase_ = torch.nn.functional.log_softmax(SCREAMING_SNAKE_CASE_ , dim=-1 )
lowercase_ , lowercase_ = self.loss_fn(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def _lowercase ( self : str , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> List[Any]:
lowercase_ = inputs.pop('''labels''' )
lowercase_ , lowercase_ = self._compute_loss(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return loss
def _lowercase ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : nn.Module , SCREAMING_SNAKE_CASE_ : Dict[str, Union[torch.Tensor, Any]] , SCREAMING_SNAKE_CASE_ : bool , SCREAMING_SNAKE_CASE_ : Optional[List[str]] = None , ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
lowercase_ = self._prepare_inputs(SCREAMING_SNAKE_CASE_ )
lowercase_ = {
'''max_length''': self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
'''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
lowercase_ = self.model.generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , **SCREAMING_SNAKE_CASE_ , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
lowercase_ = self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_ , gen_kwargs['''max_length'''] )
lowercase_ = inputs.pop('''labels''' )
with torch.no_grad():
# compute loss on predict data
lowercase_ , lowercase_ = self._compute_loss(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowercase_ = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
lowercase_ = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
lowercase_ = self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_ , gen_kwargs['''max_length'''] )
return (loss, logits, labels)
def _lowercase ( self : str , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : int ) -> Tuple:
# If PAD token is not defined at least EOS token has to be defined
lowercase_ = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
'''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
f''' padded to `max_length`={max_length}''' )
lowercase_ = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
lowercase_ = tensor
return padded_tensor
| 30
| 0
|
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def count_of_possible_combinations(SCREAMING_SNAKE_CASE ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(snake_case__ )
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def count_of_possible_combinations_with_dp_array(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
__UpperCamelCase :Tuple = sum(
count_of_possible_combinations_with_dp_array(target - item , snake_case__ )
for item in array )
__UpperCamelCase :Optional[int] = answer
return answer
__UpperCamelCase :Optional[Any] = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(snake_case__ , snake_case__ )
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Dict = [0] * (target + 1)
__UpperCamelCase :Optional[Any] = 1
for i in range(1 , target + 1 ):
for j in range(snake_case__ ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
__lowercase = 3
__lowercase = 5
__lowercase = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 43
|
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : List[str] ) -> List[Any]:
lowercase_ = 1_0
def _lowercase ( self : int ) -> List[str]:
lowercase_ = [1, 2, 3, 4]
lowercase_ = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_ , self.block_size , 0 ) , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : int ) -> Optional[Any]:
lowercase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
lowercase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_ , self.block_size , 0 ) , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
lowercase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
lowercase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_ , self.block_size , 0 ) , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Any ) -> List[Any]:
lowercase_ = '''It was the year of Our Lord one thousand seven hundred and
seventy-five.\n\nSpiritual revelations were conceded to England at that
favoured period, as at this.'''
lowercase_ , lowercase_ = process_story(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , [] )
def _lowercase ( self : List[str] ) -> List[str]:
lowercase_ = ''''''
lowercase_ , lowercase_ = process_story(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , [] )
self.assertEqual(SCREAMING_SNAKE_CASE_ , [] )
def _lowercase ( self : Union[str, Any] ) -> Union[str, Any]:
lowercase_ = (
'''It was the year of Our Lord one thousand seven hundred and '''
'''seventy-five\n\nSpiritual revelations were conceded to England '''
'''at that favoured period, as at this.\n@highlight\n\nIt was the best of times'''
)
lowercase_ , lowercase_ = process_story(SCREAMING_SNAKE_CASE_ )
lowercase_ = [
'''It was the year of Our Lord one thousand seven hundred and seventy-five.''',
'''Spiritual revelations were conceded to England at that favoured period, as at this.''',
]
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowercase_ = ['''It was the best of times.''']
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Union[str, Any] ) -> Optional[Any]:
lowercase_ = torch.tensor([1, 2, 3, 4] )
lowercase_ = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_ , 0 ).numpy() , expected.numpy() )
def _lowercase ( self : List[Any] ) -> Tuple:
lowercase_ = torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3] )
lowercase_ = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_ , 2_3 ).numpy() , expected.numpy() )
def _lowercase ( self : int ) -> Dict:
lowercase_ = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
lowercase_ = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_ , 1 ).numpy() , expected.numpy() )
def _lowercase ( self : List[str] ) -> Tuple:
lowercase_ = 1_0_1
lowercase_ = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]] )
lowercase_ = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
lowercase_ = compute_token_type_ids(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
np.testing.assert_array_equal(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 30
| 0
|
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig
__snake_case = logging.get_logger(__name__)
# General docstring
__snake_case = '''PoolFormerConfig'''
# Base docstring
__snake_case = '''sail/poolformer_s12'''
__snake_case = [1, 5_1_2, 7, 7]
# Image classification docstring
__snake_case = '''sail/poolformer_s12'''
__snake_case = '''tabby, tabby cat'''
__snake_case = [
'''sail/poolformer_s12''',
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def _A ( _lowercase , _lowercase = 0.0 , _lowercase = False ) -> Optional[Any]:
"""simple docstring"""
if drop_prob == 0.0 or not training:
return input
__UpperCamelCase = 1 - drop_prob
__UpperCamelCase = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
__UpperCamelCase = keep_prob + torch.rand(snake_case__ , dtype=input.dtype , device=input.device )
random_tensor.floor_() # binarize
__UpperCamelCase = input.div(snake_case__ ) * random_tensor
return output
class __lowerCamelCase (nn.Module ):
def __init__( self: Dict,A_: Optional[float] = None ):
'''simple docstring'''
super().__init__()
__UpperCamelCase = drop_prob
def snake_case_ ( self: Tuple,A_: torch.Tensor ):
'''simple docstring'''
return drop_path(SCREAMING_SNAKE_CASE_,self.drop_prob,self.training )
def snake_case_ ( self: str ):
'''simple docstring'''
return "p={}".format(self.drop_prob )
class __lowerCamelCase (nn.Module ):
def __init__( self: Tuple,A_: Optional[int],A_: Optional[Any],A_: Any,A_: Dict,A_: Dict,A_: Any=None ):
'''simple docstring'''
super().__init__()
__UpperCamelCase = patch_size if isinstance(SCREAMING_SNAKE_CASE_,collections.abc.Iterable ) else (patch_size, patch_size)
__UpperCamelCase = stride if isinstance(SCREAMING_SNAKE_CASE_,collections.abc.Iterable ) else (stride, stride)
__UpperCamelCase = padding if isinstance(SCREAMING_SNAKE_CASE_,collections.abc.Iterable ) else (padding, padding)
__UpperCamelCase = nn.Convad(SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_,kernel_size=SCREAMING_SNAKE_CASE_,stride=SCREAMING_SNAKE_CASE_,padding=SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = norm_layer(SCREAMING_SNAKE_CASE_ ) if norm_layer else nn.Identity()
def snake_case_ ( self: str,A_: int ):
'''simple docstring'''
__UpperCamelCase = self.projection(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = self.norm(SCREAMING_SNAKE_CASE_ )
return embeddings
class __lowerCamelCase (nn.GroupNorm ):
def __init__( self: Union[str, Any],A_: Optional[Any],**A_: int ):
'''simple docstring'''
super().__init__(1,SCREAMING_SNAKE_CASE_,**SCREAMING_SNAKE_CASE_ )
class __lowerCamelCase (nn.Module ):
def __init__( self: Optional[Any],A_: int ):
'''simple docstring'''
super().__init__()
__UpperCamelCase = nn.AvgPoolad(SCREAMING_SNAKE_CASE_,stride=1,padding=pool_size // 2,count_include_pad=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self: Optional[Any],A_: Dict ):
'''simple docstring'''
return self.pool(SCREAMING_SNAKE_CASE_ ) - hidden_states
class __lowerCamelCase (nn.Module ):
def __init__( self: List[Any],A_: List[Any],A_: Dict,A_: Any,A_: Optional[int] ):
'''simple docstring'''
super().__init__()
__UpperCamelCase = nn.Convad(SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_,1 )
__UpperCamelCase = nn.Convad(SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_,1 )
__UpperCamelCase = PoolFormerDropPath(SCREAMING_SNAKE_CASE_ )
if isinstance(config.hidden_act,SCREAMING_SNAKE_CASE_ ):
__UpperCamelCase = ACTaFN[config.hidden_act]
else:
__UpperCamelCase = config.hidden_act
def snake_case_ ( self: List[str],A_: Tuple ):
'''simple docstring'''
__UpperCamelCase = self.conva(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = self.act_fn(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = self.drop(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = self.conva(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = self.drop(SCREAMING_SNAKE_CASE_ )
return hidden_states
class __lowerCamelCase (nn.Module ):
def __init__( self: Any,A_: str,A_: List[str],A_: List[Any],A_: List[Any],A_: List[str],A_: List[str] ):
'''simple docstring'''
super().__init__()
__UpperCamelCase = PoolFormerPooling(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = PoolFormerOutput(SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = PoolFormerGroupNorm(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = PoolFormerGroupNorm(SCREAMING_SNAKE_CASE_ )
# Useful for training neural nets
__UpperCamelCase = PoolFormerDropPath(SCREAMING_SNAKE_CASE_ ) if drop_path > 0.0 else nn.Identity()
__UpperCamelCase = config.use_layer_scale
if config.use_layer_scale:
__UpperCamelCase = nn.Parameter(
config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE_) ),requires_grad=SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = nn.Parameter(
config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE_) ),requires_grad=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self: List[str],A_: Union[str, Any] ):
'''simple docstring'''
if self.use_layer_scale:
__UpperCamelCase = self.pooling(self.before_norm(SCREAMING_SNAKE_CASE_ ) )
__UpperCamelCase = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
# First residual connection
__UpperCamelCase = hidden_states + self.drop_path(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = ()
__UpperCamelCase = self.output(self.after_norm(SCREAMING_SNAKE_CASE_ ) )
__UpperCamelCase = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
# Second residual connection
__UpperCamelCase = hidden_states + self.drop_path(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = (output,) + outputs
return outputs
else:
__UpperCamelCase = self.drop_path(self.pooling(self.before_norm(SCREAMING_SNAKE_CASE_ ) ) )
# First residual connection
__UpperCamelCase = pooling_output + hidden_states
__UpperCamelCase = ()
# Second residual connection inside the PoolFormerOutput block
__UpperCamelCase = self.drop_path(self.output(self.after_norm(SCREAMING_SNAKE_CASE_ ) ) )
__UpperCamelCase = hidden_states + layer_output
__UpperCamelCase = (output,) + outputs
return outputs
class __lowerCamelCase (nn.Module ):
def __init__( self: List[str],A_: Optional[int] ):
'''simple docstring'''
super().__init__()
__UpperCamelCase = config
# stochastic depth decay rule
__UpperCamelCase = [x.item() for x in torch.linspace(0,config.drop_path_rate,sum(config.depths ) )]
# patch embeddings
__UpperCamelCase = []
for i in range(config.num_encoder_blocks ):
embeddings.append(
PoolFormerEmbeddings(
patch_size=config.patch_sizes[i],stride=config.strides[i],padding=config.padding[i],num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1],hidden_size=config.hidden_sizes[i],) )
__UpperCamelCase = nn.ModuleList(SCREAMING_SNAKE_CASE_ )
# Transformer blocks
__UpperCamelCase = []
__UpperCamelCase = 0
for i in range(config.num_encoder_blocks ):
# each block consists of layers
__UpperCamelCase = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i] ):
layers.append(
PoolFormerLayer(
SCREAMING_SNAKE_CASE_,num_channels=config.hidden_sizes[i],pool_size=config.pool_size,hidden_size=config.hidden_sizes[i],intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ),drop_path=dpr[cur + j],) )
blocks.append(nn.ModuleList(SCREAMING_SNAKE_CASE_ ) )
__UpperCamelCase = nn.ModuleList(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self: Optional[int],A_: Optional[int],A_: Optional[Any]=False,A_: int=True ):
'''simple docstring'''
__UpperCamelCase = () if output_hidden_states else None
__UpperCamelCase = pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings,self.block ) ):
__UpperCamelCase, __UpperCamelCase = layers
# Get patch embeddings from hidden_states
__UpperCamelCase = embedding_layer(SCREAMING_SNAKE_CASE_ )
# Send the embeddings through the blocks
for _, blk in enumerate(SCREAMING_SNAKE_CASE_ ):
__UpperCamelCase = blk(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = layer_outputs[0]
if output_hidden_states:
__UpperCamelCase = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE_,hidden_states=SCREAMING_SNAKE_CASE_ )
class __lowerCamelCase (_a ):
_lowercase = PoolFormerConfig
_lowercase = 'poolformer'
_lowercase = 'pixel_values'
_lowercase = True
def snake_case_ ( self: List[str],A_: Any ):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE_,(nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0,std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(SCREAMING_SNAKE_CASE_,nn.LayerNorm ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
def snake_case_ ( self: Dict,A_: str,A_: List[str]=False ):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_ ):
__UpperCamelCase = value
__snake_case = r'''\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'''
__snake_case = r'''\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`PoolFormerImageProcessor.__call__`] for details.\n'''
@add_start_docstrings(
"""The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.""" , _a , )
class __lowerCamelCase (_a ):
def __init__( self: List[str],A_: int ):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = config
__UpperCamelCase = PoolFormerEncoder(SCREAMING_SNAKE_CASE_ )
# Initialize weights and apply final processing
self.post_init()
def snake_case_ ( self: Dict ):
'''simple docstring'''
return self.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,output_type=SCREAMING_SNAKE_CASE_,config_class=_CONFIG_FOR_DOC,modality='vision',expected_output=_EXPECTED_OUTPUT_SHAPE,)
def snake_case_ ( self: int,A_: Optional[torch.FloatTensor] = None,A_: Optional[bool] = None,A_: Optional[bool] = None,):
'''simple docstring'''
__UpperCamelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__UpperCamelCase = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
__UpperCamelCase = self.encoder(
SCREAMING_SNAKE_CASE_,output_hidden_states=SCREAMING_SNAKE_CASE_,return_dict=SCREAMING_SNAKE_CASE_,)
__UpperCamelCase = encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseModelOutputWithNoAttention(
last_hidden_state=SCREAMING_SNAKE_CASE_,hidden_states=encoder_outputs.hidden_states,)
class __lowerCamelCase (nn.Module ):
def __init__( self: List[str],A_: List[str] ):
'''simple docstring'''
super().__init__()
__UpperCamelCase = nn.Linear(config.hidden_size,config.hidden_size )
def snake_case_ ( self: str,A_: Union[str, Any] ):
'''simple docstring'''
__UpperCamelCase = self.dense(SCREAMING_SNAKE_CASE_ )
return output
@add_start_docstrings(
"""\n PoolFormer Model transformer with an image classification head on top\n """ , _a , )
class __lowerCamelCase (_a ):
def __init__( self: Dict,A_: str ):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = config.num_labels
__UpperCamelCase = PoolFormerModel(SCREAMING_SNAKE_CASE_ )
# Final norm
__UpperCamelCase = PoolFormerGroupNorm(config.hidden_sizes[-1] )
# Classifier head
__UpperCamelCase = (
nn.Linear(config.hidden_sizes[-1],config.num_labels ) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,output_type=SCREAMING_SNAKE_CASE_,config_class=_CONFIG_FOR_DOC,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,)
def snake_case_ ( self: List[Any],A_: Optional[torch.FloatTensor] = None,A_: Optional[torch.LongTensor] = None,A_: Optional[bool] = None,A_: Optional[bool] = None,):
'''simple docstring'''
__UpperCamelCase = return_dict if return_dict is not None else self.config.use_return_dict
__UpperCamelCase = self.poolformer(
SCREAMING_SNAKE_CASE_,output_hidden_states=SCREAMING_SNAKE_CASE_,return_dict=SCREAMING_SNAKE_CASE_,)
__UpperCamelCase = outputs[0]
__UpperCamelCase = self.classifier(self.norm(SCREAMING_SNAKE_CASE_ ).mean([-2, -1] ) )
__UpperCamelCase = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
__UpperCamelCase = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
__UpperCamelCase = 'single_label_classification'
else:
__UpperCamelCase = 'multi_label_classification'
if self.config.problem_type == "regression":
__UpperCamelCase = MSELoss()
if self.num_labels == 1:
__UpperCamelCase = loss_fct(logits.squeeze(),labels.squeeze() )
else:
__UpperCamelCase = loss_fct(SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_ )
elif self.config.problem_type == "single_label_classification":
__UpperCamelCase = CrossEntropyLoss()
__UpperCamelCase = loss_fct(logits.view(-1,self.num_labels ),labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
__UpperCamelCase = BCEWithLogitsLoss()
__UpperCamelCase = loss_fct(SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_ )
if not return_dict:
__UpperCamelCase = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=SCREAMING_SNAKE_CASE_,logits=SCREAMING_SNAKE_CASE_,hidden_states=outputs.hidden_states )
| 310
|
def a ( snake_case__: list ):
'''simple docstring'''
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
lowercase_ = []
def generate(snake_case__: int , snake_case__: list ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , snake_case__ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
lowercase_ , lowercase_ = arr[k - 1], arr[i]
else: # k is odd
lowercase_ , lowercase_ = arr[k - 1], arr[0]
generate(k - 1 , snake_case__ )
generate(len(snake_case__ ) , snake_case__ )
return res
if __name__ == "__main__":
__a = input('Enter numbers separated by a comma:\n').strip()
__a = [int(item) for item in user_input.split(',')]
print(heaps(arr))
| 30
| 0
|
"""simple docstring"""
def __lowerCAmelCase (_UpperCamelCase = 6008_5147_5143 ):
try:
__lowerCAmelCase : List[str] = int(snake_case__ )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise ValueError('Parameter n must be greater than or equal to one.' )
__lowerCAmelCase : List[Any] = 2
__lowerCAmelCase : List[str] = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
__lowerCAmelCase : Tuple = i
while n % i == 0:
__lowerCAmelCase : List[Any] = n // i
i += 1
return int(snake_case__ )
if __name__ == "__main__":
print(f'{solution() = }')
| 86
|
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def a ( ):
'''simple docstring'''
lowercase_ = argparse.ArgumentParser()
parser.add_argument(
'''-m''' , '''--pretrained_model_name_or_path''' , type=snake_case__ , default=snake_case__ , required=snake_case__ , help='''Path to pretrained model or model identifier from huggingface.co/models.''' , )
parser.add_argument(
'''-c''' , '''--caption''' , type=snake_case__ , default='''robotic cat with wings''' , help='''Text used to generate images.''' , )
parser.add_argument(
'''-n''' , '''--images_num''' , type=snake_case__ , default=4 , help='''How much images to generate.''' , )
parser.add_argument(
'''-s''' , '''--seed''' , type=snake_case__ , default=42 , help='''Seed for random process.''' , )
parser.add_argument(
'''-ci''' , '''--cuda_id''' , type=snake_case__ , default=0 , help='''cuda_id.''' , )
lowercase_ = parser.parse_args()
return args
def a ( snake_case__: Optional[Any] , snake_case__: Tuple , snake_case__: Union[str, Any] ):
'''simple docstring'''
if not len(snake_case__ ) == rows * cols:
raise ValueError('''The specified number of rows and columns are not correct.''' )
lowercase_ , lowercase_ = imgs[0].size
lowercase_ = Image.new('''RGB''' , size=(cols * w, rows * h) )
lowercase_ , lowercase_ = grid.size
for i, img in enumerate(snake_case__ ):
grid.paste(snake_case__ , box=(i % cols * w, i // cols * h) )
return grid
def a ( snake_case__: Tuple , snake_case__: Union[str, Any]="robotic cat with wings" , snake_case__: Union[str, Any]=7.5 , snake_case__: List[str]=50 , snake_case__: List[Any]=1 , snake_case__: Optional[int]=42 , ):
'''simple docstring'''
lowercase_ = torch.Generator(pipeline.device ).manual_seed(snake_case__ )
lowercase_ = pipeline(
snake_case__ , guidance_scale=snake_case__ , num_inference_steps=snake_case__ , generator=snake_case__ , num_images_per_prompt=snake_case__ , ).images
lowercase_ = int(math.sqrt(snake_case__ ) )
lowercase_ = image_grid(snake_case__ , rows=_rows , cols=num_images_per_prompt // _rows )
return grid, images
__a = parse_args()
# Load models and create wrapper for stable diffusion
__a = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
__a = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
__a = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
__a = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')
__a = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__a = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
__a = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, 'unet', unet)
else:
__a = unet.to(torch.device('cuda', args.cuda_id))
__a = pipeline.to(unet.device)
__a , __a = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '{}.png'.format('_'.join(args.caption.split()))))
__a = os.path.join(args.pretrained_model_name_or_path, '_'.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, '{}.png'.format(idx + 1)))
| 30
| 0
|
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Tuple ={
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AutoformerConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Any =[
"""AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AutoformerForPrediction""",
"""AutoformerModel""",
"""AutoformerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_autoformer import (
AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_autoformer import (
AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoformerForPrediction,
AutoformerModel,
AutoformerPreTrainedModel,
)
else:
import sys
_UpperCAmelCase : List[str] =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 262
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['RemBertTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['RemBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RemBertForCausalLM',
'RemBertForMaskedLM',
'RemBertForMultipleChoice',
'RemBertForQuestionAnswering',
'RemBertForSequenceClassification',
'RemBertForTokenClassification',
'RemBertLayer',
'RemBertModel',
'RemBertPreTrainedModel',
'load_tf_weights_in_rembert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRemBertForCausalLM',
'TFRemBertForMaskedLM',
'TFRemBertForMultipleChoice',
'TFRemBertForQuestionAnswering',
'TFRemBertForSequenceClassification',
'TFRemBertForTokenClassification',
'TFRemBertLayer',
'TFRemBertModel',
'TFRemBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 30
| 0
|
"""simple docstring"""
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __lowerCAmelCase :
'''simple docstring'''
def __UpperCAmelCase ( self , _a ):
raise NotImplementedError()
def __UpperCAmelCase ( self ):
raise NotImplementedError()
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , _a , _a = False , **_a ):
__a = tokenizer
__a = skip_prompt
__a = decode_kwargs
# variables used in the streaming process
__a = []
__a = 0
__a = True
def __UpperCAmelCase ( self , _a ):
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError('''TextStreamer only supports batch size 1''' )
elif len(value.shape ) > 1:
__a = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
__a = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
__a = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith('''\n''' ):
__a = text[self.print_len :]
__a = []
__a = 0
# If the last token is a CJK character, we print the characters.
elif len(SCREAMING_SNAKE_CASE_ ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
__a = text[self.print_len :]
self.print_len += len(SCREAMING_SNAKE_CASE_ )
# 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:
__a = text[self.print_len : text.rfind(''' ''' ) + 1]
self.print_len += len(SCREAMING_SNAKE_CASE_ )
self.on_finalized_text(SCREAMING_SNAKE_CASE_ )
def __UpperCAmelCase ( self ):
# Flush the cache, if it exists
if len(self.token_cache ) > 0:
__a = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
__a = text[self.print_len :]
__a = []
__a = 0
else:
__a = ''''''
__a = True
self.on_finalized_text(SCREAMING_SNAKE_CASE_ , stream_end=SCREAMING_SNAKE_CASE_ )
def __UpperCAmelCase ( self , _a , _a = False ):
print(SCREAMING_SNAKE_CASE_ , flush=SCREAMING_SNAKE_CASE_ , end='''''' if not stream_end else None )
def __UpperCAmelCase ( self , _a ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
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 __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , _a , _a = False , _a = None , **_a ):
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
__a = Queue()
__a = None
__a = timeout
def __UpperCAmelCase ( self , _a , _a = False ):
self.text_queue.put(SCREAMING_SNAKE_CASE_ , timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal , timeout=self.timeout )
def __iter__( self ):
return self
def __UpperCAmelCase ( self ):
__a = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 45
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['DeiTFeatureExtractor']
__a = ['DeiTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DeiTForImageClassification',
'DeiTForImageClassificationWithTeacher',
'DeiTForMaskedImageModeling',
'DeiTModel',
'DeiTPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDeiTForImageClassification',
'TFDeiTForImageClassificationWithTeacher',
'TFDeiTForMaskedImageModeling',
'TFDeiTModel',
'TFDeiTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 30
| 0
|
import unittest
import numpy as np
from transformers import AlbertConfig, 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.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=512 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=4 , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = seq_length
_lowerCAmelCase = is_training
_lowerCAmelCase = use_attention_mask
_lowerCAmelCase = use_token_type_ids
_lowerCAmelCase = use_labels
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = type_vocab_size
_lowerCAmelCase = type_sequence_label_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = num_choices
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase = None
if self.use_attention_mask:
_lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCAmelCase = None
if self.use_token_type_ids:
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_lowerCAmelCase = AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=SCREAMING_SNAKE_CASE_ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = config_and_inputs
_lowerCAmelCase = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = FlaxAlbertModelTester(self )
@slow
def snake_case ( self ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
_lowerCAmelCase = model_class_name.from_pretrained("""albert-base-v2""" )
_lowerCAmelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = FlaxAlbertModel.from_pretrained("""albert-base-v2""" )
_lowerCAmelCase = np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
_lowerCAmelCase = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
_lowerCAmelCase = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )[0]
_lowerCAmelCase = (1, 11, 768)
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 82
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__a = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'processing_trocr': ['TrOCRProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
'TrOCRForCausalLM',
'TrOCRPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 30
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_A = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ConditionalDetrConfig',
'ConditionalDetrOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ['ConditionalDetrFeatureExtractor']
_A = ['ConditionalDetrImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConditionalDetrForObjectDetection',
'ConditionalDetrForSegmentation',
'ConditionalDetrModel',
'ConditionalDetrPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62
|
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
__a = logging.get_logger(__name__)
# General docstring
__a = 'RegNetConfig'
# Base docstring
__a = 'facebook/regnet-y-040'
__a = [1, 1_0_8_8, 7, 7]
# Image classification docstring
__a = 'facebook/regnet-y-040'
__a = 'tabby, tabby cat'
__a = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 3 , SCREAMING_SNAKE_CASE_ : int = 1 , SCREAMING_SNAKE_CASE_ : int = 1 , SCREAMING_SNAKE_CASE_ : Optional[str] = "relu" , **SCREAMING_SNAKE_CASE_ : Any , ) -> List[str]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
lowercase_ = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
lowercase_ = tf.keras.layers.ConvaD(
filters=SCREAMING_SNAKE_CASE_ , kernel_size=SCREAMING_SNAKE_CASE_ , strides=SCREAMING_SNAKE_CASE_ , padding='''VALID''' , groups=SCREAMING_SNAKE_CASE_ , use_bias=SCREAMING_SNAKE_CASE_ , name='''convolution''' , )
lowercase_ = tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name='''normalization''' )
lowercase_ = ACTaFN[activation] if activation is not None else tf.identity
def _lowercase ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Any:
lowercase_ = self.convolution(self.padding(SCREAMING_SNAKE_CASE_ ) )
lowercase_ = self.normalization(SCREAMING_SNAKE_CASE_ )
lowercase_ = self.activation(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : str , SCREAMING_SNAKE_CASE_ : RegNetConfig , **SCREAMING_SNAKE_CASE_ : str ) -> Any:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = config.num_channels
lowercase_ = TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name='''embedder''' , )
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : str ) -> Optional[int]:
lowercase_ = shape_list(SCREAMING_SNAKE_CASE_ )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
'''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''' )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
lowercase_ = tf.transpose(SCREAMING_SNAKE_CASE_ , perm=(0, 2, 3, 1) )
lowercase_ = self.embedder(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 2 , **SCREAMING_SNAKE_CASE_ : List[str] ) -> Union[str, Any]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = tf.keras.layers.ConvaD(
filters=SCREAMING_SNAKE_CASE_ , kernel_size=1 , strides=SCREAMING_SNAKE_CASE_ , use_bias=SCREAMING_SNAKE_CASE_ , name='''convolution''' )
lowercase_ = tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name='''normalization''' )
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : tf.Tensor , SCREAMING_SNAKE_CASE_ : bool = False ) -> tf.Tensor:
return self.normalization(self.convolution(SCREAMING_SNAKE_CASE_ ) , training=SCREAMING_SNAKE_CASE_ )
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , **SCREAMING_SNAKE_CASE_ : int ) -> Optional[Any]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = tf.keras.layers.GlobalAveragePoolingaD(keepdims=SCREAMING_SNAKE_CASE_ , name='''pooler''' )
lowercase_ = [
tf.keras.layers.ConvaD(filters=SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation='''relu''' , name='''attention.0''' ),
tf.keras.layers.ConvaD(filters=SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation='''sigmoid''' , name='''attention.2''' ),
]
def _lowercase ( self : str , SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> Dict:
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
lowercase_ = self.pooler(SCREAMING_SNAKE_CASE_ )
for layer_module in self.attention:
lowercase_ = layer_module(SCREAMING_SNAKE_CASE_ )
lowercase_ = hidden_state * pooled
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : str , SCREAMING_SNAKE_CASE_ : RegNetConfig , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 1 , **SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> List[str]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = in_channels != out_channels or stride != 1
lowercase_ = max(1 , out_channels // config.groups_width )
lowercase_ = (
TFRegNetShortCut(SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , name='''shortcut''' )
if should_apply_shortcut
else tf.keras.layers.Activation('''linear''' , name='''shortcut''' )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
lowercase_ = [
TFRegNetConvLayer(SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation=config.hidden_act , name='''layer.0''' ),
TFRegNetConvLayer(
SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , groups=SCREAMING_SNAKE_CASE_ , activation=config.hidden_act , name='''layer.1''' ),
TFRegNetConvLayer(SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation=SCREAMING_SNAKE_CASE_ , name='''layer.2''' ),
]
lowercase_ = ACTaFN[config.hidden_act]
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Any:
lowercase_ = hidden_state
for layer_module in self.layers:
lowercase_ = layer_module(SCREAMING_SNAKE_CASE_ )
lowercase_ = self.shortcut(SCREAMING_SNAKE_CASE_ )
hidden_state += residual
lowercase_ = self.activation(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Any , SCREAMING_SNAKE_CASE_ : RegNetConfig , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 1 , **SCREAMING_SNAKE_CASE_ : List[str] ) -> int:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = in_channels != out_channels or stride != 1
lowercase_ = max(1 , out_channels // config.groups_width )
lowercase_ = (
TFRegNetShortCut(SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , name='''shortcut''' )
if should_apply_shortcut
else tf.keras.layers.Activation('''linear''' , name='''shortcut''' )
)
lowercase_ = [
TFRegNetConvLayer(SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation=config.hidden_act , name='''layer.0''' ),
TFRegNetConvLayer(
SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , groups=SCREAMING_SNAKE_CASE_ , activation=config.hidden_act , name='''layer.1''' ),
TFRegNetSELayer(SCREAMING_SNAKE_CASE_ , reduced_channels=int(round(in_channels / 4 ) ) , name='''layer.2''' ),
TFRegNetConvLayer(SCREAMING_SNAKE_CASE_ , kernel_size=1 , activation=SCREAMING_SNAKE_CASE_ , name='''layer.3''' ),
]
lowercase_ = ACTaFN[config.hidden_act]
def _lowercase ( self : int , SCREAMING_SNAKE_CASE_ : Dict ) -> Optional[Any]:
lowercase_ = hidden_state
for layer_module in self.layers:
lowercase_ = layer_module(SCREAMING_SNAKE_CASE_ )
lowercase_ = self.shortcut(SCREAMING_SNAKE_CASE_ )
hidden_state += residual
lowercase_ = self.activation(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : str , SCREAMING_SNAKE_CASE_ : RegNetConfig , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 2 , SCREAMING_SNAKE_CASE_ : int = 2 , **SCREAMING_SNAKE_CASE_ : List[Any] ) -> List[str]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = TFRegNetXLayer if config.layer_type == '''x''' else TFRegNetYLayer
lowercase_ = [
# downsampling is done in the first layer with stride of 2
layer(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , name='''layers.0''' ),
*[layer(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , name=f'''layers.{i+1}''' ) for i in range(depth - 1 )],
]
def _lowercase ( self : Tuple , SCREAMING_SNAKE_CASE_ : int ) -> int:
for layer_module in self.layers:
lowercase_ = layer_module(SCREAMING_SNAKE_CASE_ )
return hidden_state
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : RegNetConfig , **SCREAMING_SNAKE_CASE_ : Dict ) -> Tuple:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
SCREAMING_SNAKE_CASE_ , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name='''stages.0''' , ) )
lowercase_ = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(SCREAMING_SNAKE_CASE_ , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , depth=SCREAMING_SNAKE_CASE_ , name=f'''stages.{i+1}''' ) )
def _lowercase ( self : Dict , SCREAMING_SNAKE_CASE_ : tf.Tensor , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : bool = True ) -> TFBaseModelOutputWithNoAttention:
lowercase_ = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowercase_ = hidden_states + (hidden_state,)
lowercase_ = stage_module(SCREAMING_SNAKE_CASE_ )
if output_hidden_states:
lowercase_ = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE_ , hidden_states=SCREAMING_SNAKE_CASE_ )
@keras_serializable
class lowercase__( tf.keras.layers.Layer ):
"""simple docstring"""
a :str = RegNetConfig
def __init__( self : str , SCREAMING_SNAKE_CASE_ : Dict , **SCREAMING_SNAKE_CASE_ : List[str] ) -> Any:
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowercase_ = config
lowercase_ = TFRegNetEmbeddings(SCREAMING_SNAKE_CASE_ , name='''embedder''' )
lowercase_ = TFRegNetEncoder(SCREAMING_SNAKE_CASE_ , name='''encoder''' )
lowercase_ = tf.keras.layers.GlobalAveragePoolingaD(keepdims=SCREAMING_SNAKE_CASE_ , name='''pooler''' )
@unpack_inputs
def _lowercase ( self : Dict , SCREAMING_SNAKE_CASE_ : tf.Tensor , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : bool = False , ) -> TFBaseModelOutputWithPoolingAndNoAttention:
lowercase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ = self.embedder(SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
lowercase_ = self.encoder(
SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
lowercase_ = encoder_outputs[0]
lowercase_ = self.pooler(SCREAMING_SNAKE_CASE_ )
# Change to NCHW output format have uniformity in the modules
lowercase_ = tf.transpose(SCREAMING_SNAKE_CASE_ , perm=(0, 3, 1, 2) )
lowercase_ = tf.transpose(SCREAMING_SNAKE_CASE_ , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
lowercase_ = tuple([tf.transpose(SCREAMING_SNAKE_CASE_ , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=SCREAMING_SNAKE_CASE_ , pooler_output=SCREAMING_SNAKE_CASE_ , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Tuple = RegNetConfig
a :Any = 'regnet'
a :List[str] = 'pixel_values'
@property
def _lowercase ( self : List[str] ) -> str:
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4) , dtype=tf.floataa )}
__a = r'\n Parameters:\n This model is a Tensorflow\n [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n behavior.\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'
__a = r'\n Args:\n pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConveNextImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
'The bare RegNet model outputting raw features without any specific head on top.' , UpperCAmelCase , )
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : RegNetConfig , *SCREAMING_SNAKE_CASE_ : Any , **SCREAMING_SNAKE_CASE_ : str ) -> List[str]:
super().__init__(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowercase_ = TFRegNetMainLayer(SCREAMING_SNAKE_CASE_ , name='''regnet''' )
@unpack_inputs
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=SCREAMING_SNAKE_CASE_ , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : tf.Tensor , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : Optional[Any]=False , ) -> Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
lowercase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ = self.regnet(
pixel_values=SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
'\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , UpperCAmelCase , )
class lowercase__( UpperCAmelCase , UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : RegNetConfig , *SCREAMING_SNAKE_CASE_ : str , **SCREAMING_SNAKE_CASE_ : int ) -> Union[str, Any]:
super().__init__(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowercase_ = config.num_labels
lowercase_ = TFRegNetMainLayer(SCREAMING_SNAKE_CASE_ , name='''regnet''' )
# classification head
lowercase_ = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name='''classifier.1''' ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=SCREAMING_SNAKE_CASE_ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : tf.Tensor = None , SCREAMING_SNAKE_CASE_ : tf.Tensor = None , SCREAMING_SNAKE_CASE_ : bool = None , SCREAMING_SNAKE_CASE_ : bool = None , SCREAMING_SNAKE_CASE_ : Dict=False , ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
lowercase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ = self.regnet(
SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
lowercase_ = outputs.pooler_output if return_dict else outputs[1]
lowercase_ = self.classifier[0](SCREAMING_SNAKE_CASE_ )
lowercase_ = self.classifier[1](SCREAMING_SNAKE_CASE_ )
lowercase_ = None if labels is None else self.hf_compute_loss(labels=SCREAMING_SNAKE_CASE_ , logits=SCREAMING_SNAKE_CASE_ )
if not return_dict:
lowercase_ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=SCREAMING_SNAKE_CASE_ , logits=SCREAMING_SNAKE_CASE_ , hidden_states=outputs.hidden_states )
| 30
| 0
|
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
__lowerCamelCase : int = logging.get_logger(__name__) # pylint: disable=invalid-name
__lowerCamelCase : Dict = """\n Examples:\n ```py\n >>> from PIL import Image\n >>> import torch\n >>> from diffusers import DiffusionPipeline\n >>> from diffusers.utils import export_to_gif, load_image\n\n >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n >>> repo = \"openai/shap-e-img2img\"\n >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n >>> pipe = pipe.to(device)\n\n >>> guidance_scale = 3.0\n >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n >>> image = load_image(image_url).convert(\"RGB\")\n\n >>> images = pipe(\n ... image,\n ... guidance_scale=guidance_scale,\n ... num_inference_steps=64,\n ... frame_size=256,\n ... ).images\n\n >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n ```\n"""
@dataclass
class A__ ( __snake_case ):
_UpperCAmelCase :Union[PIL.Image.Image, np.ndarray]
class A__ ( __snake_case ):
def __init__( self , A_ , A_ , A_ , A_ , A_ , ):
'''simple docstring'''
super().__init__()
self.register_modules(
prior=SCREAMING_SNAKE_CASE_ , image_encoder=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ , renderer=SCREAMING_SNAKE_CASE_ , )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
if latents is None:
UpperCamelCase : Optional[Any] = randn_tensor(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ )
else:
if latents.shape != shape:
raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
UpperCamelCase : Dict = latents.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = latents * scheduler.init_noise_sigma
return latents
def __UpperCamelCase( self , A_=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
UpperCamelCase : Union[str, Any] = torch.device(F"""cuda:{gpu_id}""" )
UpperCamelCase : str = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@property
def __UpperCamelCase( self ):
'''simple docstring'''
if self.device != torch.device("meta" ) or not hasattr(self.image_encoder , "_hf_hook" ):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(SCREAMING_SNAKE_CASE_ , "_hf_hook" )
and hasattr(module._hf_hook , "execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , ):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and isinstance(image[0] , torch.Tensor ):
UpperCamelCase : Dict = torch.cat(SCREAMING_SNAKE_CASE_ , axis=0 ) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE_ , axis=0 )
if not isinstance(SCREAMING_SNAKE_CASE_ , torch.Tensor ):
UpperCamelCase : Tuple = self.image_processor(SCREAMING_SNAKE_CASE_ , return_tensors="pt" ).pixel_values[0].unsqueeze(0 )
UpperCamelCase : List[str] = image.to(dtype=self.image_encoder.dtype , device=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = self.image_encoder(SCREAMING_SNAKE_CASE_ )["last_hidden_state"]
UpperCamelCase : Any = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
UpperCamelCase : Optional[Any] = image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_ , dim=0 )
if do_classifier_free_guidance:
UpperCamelCase : List[str] = torch.zeros_like(SCREAMING_SNAKE_CASE_ )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCamelCase : Optional[Any] = torch.cat([negative_image_embeds, image_embeds] )
return image_embeds
@torch.no_grad()
@replace_example_docstring(SCREAMING_SNAKE_CASE_ )
def __call__( self , A_ , A_ = 1 , A_ = 25 , A_ = None , A_ = None , A_ = 4.0 , A_ = 64 , A_ = "pil" , A_ = True , ):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE_ , PIL.Image.Image ):
UpperCamelCase : Tuple = 1
elif isinstance(SCREAMING_SNAKE_CASE_ , torch.Tensor ):
UpperCamelCase : str = image.shape[0]
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image) ):
UpperCamelCase : str = len(SCREAMING_SNAKE_CASE_ )
else:
raise ValueError(
F"""`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(SCREAMING_SNAKE_CASE_ )}""" )
UpperCamelCase : int = self._execution_device
UpperCamelCase : Optional[Any] = batch_size * num_images_per_prompt
UpperCamelCase : int = guidance_scale > 1.0
UpperCamelCase : Optional[Any] = self._encode_image(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# prior
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ , device=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = self.scheduler.timesteps
UpperCamelCase : Optional[int] = self.prior.config.num_embeddings
UpperCamelCase : int = self.prior.config.embedding_dim
UpperCamelCase : Dict = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.scheduler , )
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
UpperCamelCase : Union[str, Any] = latents.reshape(latents.shape[0] , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_ ) ):
# expand the latents if we are doing classifier free guidance
UpperCamelCase : Tuple = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCamelCase : Optional[int] = self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = self.prior(
SCREAMING_SNAKE_CASE_ , timestep=SCREAMING_SNAKE_CASE_ , proj_embedding=SCREAMING_SNAKE_CASE_ , ).predicted_image_embedding
# remove the variance
UpperCamelCase , UpperCamelCase : Optional[int] = noise_pred.split(
scaled_model_input.shape[2] , dim=2 ) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
UpperCamelCase , UpperCamelCase : int = noise_pred.chunk(2 )
UpperCamelCase : List[str] = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
UpperCamelCase : str = self.scheduler.step(
SCREAMING_SNAKE_CASE_ , timestep=SCREAMING_SNAKE_CASE_ , sample=SCREAMING_SNAKE_CASE_ , ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = []
for i, latent in enumerate(SCREAMING_SNAKE_CASE_ ):
print()
UpperCamelCase : Dict = self.renderer.decode(
latent[None, :] , SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , ray_batch_size=4096 , n_coarse_samples=64 , n_fine_samples=128 , )
images.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = torch.stack(SCREAMING_SNAKE_CASE_ )
if output_type not in ["np", "pil"]:
raise ValueError(F"""Only the output types `pil` and `np` are supported not output_type={output_type}""" )
UpperCamelCase : List[str] = images.cpu().numpy()
if output_type == "pil":
UpperCamelCase : List[str] = [self.numpy_to_pil(SCREAMING_SNAKE_CASE_ ) for image in images]
# Offload last model to CPU
if hasattr(self , "final_offload_hook" ) and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=SCREAMING_SNAKE_CASE_ )
| 52
|
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
from ..pytorch_utils import ConvaD
if is_accelerate_available():
from accelerate import init_empty_weights
from accelerate.utils import find_tied_parameters
__a = logging.get_logger(__name__)
def a ( snake_case__: Optional[int] , snake_case__: Dict , snake_case__: int , snake_case__: List[str]=None , snake_case__: List[Any]=None ):
'''simple docstring'''
# Recurse if needed
if "." in tensor_name:
lowercase_ = tensor_name.split('''.''' )
for split in splits[:-1]:
lowercase_ = getattr(snake_case__ , snake_case__ )
if new_module is None:
raise ValueError(F'''{module} has no attribute {split}.''' )
lowercase_ = new_module
lowercase_ = splits[-1]
if tensor_name not in module._parameters and tensor_name not in module._buffers:
raise ValueError(F'''{module} does not have a parameter or a buffer named {tensor_name}.''' )
lowercase_ = tensor_name in module._buffers
lowercase_ = getattr(snake_case__ , snake_case__ )
if old_value.device == torch.device('''meta''' ) and device not in ["meta", torch.device('''meta''' )] and value is None:
raise ValueError(F'''{tensor_name} is on the meta device, we need a `value` to put in on {device}.''' )
lowercase_ = False
lowercase_ = False
if is_buffer or not is_bitsandbytes_available():
lowercase_ = False
lowercase_ = False
else:
lowercase_ = hasattr(bnb.nn , '''Params4bit''' ) and isinstance(module._parameters[tensor_name] , bnb.nn.Paramsabit )
lowercase_ = isinstance(module._parameters[tensor_name] , bnb.nn.IntaParams )
if is_abit or is_abit:
lowercase_ = module._parameters[tensor_name]
if param.device.type != "cuda":
if value is None:
lowercase_ = old_value.to(snake_case__ )
elif isinstance(snake_case__ , torch.Tensor ):
lowercase_ = value.to('''cpu''' )
if value.dtype == torch.inta:
lowercase_ = version.parse(importlib.metadata.version('''bitsandbytes''' ) ) > version.parse(
'''0.37.2''' )
if not is_abit_serializable:
raise ValueError(
'''Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. '''
'''Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.''' )
else:
lowercase_ = torch.tensor(snake_case__ , device='''cpu''' )
# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
# Since weights are saved in the correct "orientation", we skip transposing when loading.
if issubclass(module.source_cls , snake_case__ ) and fpaa_statistics is None:
lowercase_ = new_value.T
lowercase_ = old_value.__dict__
if is_abit:
lowercase_ = bnb.nn.IntaParams(snake_case__ , requires_grad=snake_case__ , **snake_case__ ).to(snake_case__ )
elif is_abit:
lowercase_ = bnb.nn.Paramsabit(snake_case__ , requires_grad=snake_case__ , **snake_case__ ).to(snake_case__ )
lowercase_ = new_value
if fpaa_statistics is not None:
setattr(module.weight , '''SCB''' , fpaa_statistics.to(snake_case__ ) )
else:
if value is None:
lowercase_ = old_value.to(snake_case__ )
elif isinstance(snake_case__ , torch.Tensor ):
lowercase_ = value.to(snake_case__ )
else:
lowercase_ = torch.tensor(snake_case__ , device=snake_case__ )
if is_buffer:
lowercase_ = new_value
else:
lowercase_ = nn.Parameter(snake_case__ , requires_grad=old_value.requires_grad )
lowercase_ = new_value
def a ( snake_case__: str , snake_case__: Union[str, Any]=None , snake_case__: Any=None , snake_case__: List[str]=None , snake_case__: Optional[Any]=False ):
'''simple docstring'''
for name, module in model.named_children():
if current_key_name is None:
lowercase_ = []
current_key_name.append(snake_case__ )
if (isinstance(snake_case__ , nn.Linear ) or isinstance(snake_case__ , snake_case__ )) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
if not any(key in '''.'''.join(snake_case__ ) for key in modules_to_not_convert ):
with init_empty_weights():
if isinstance(snake_case__ , snake_case__ ):
lowercase_ , lowercase_ = module.weight.shape
else:
lowercase_ = module.in_features
lowercase_ = module.out_features
if quantization_config.quantization_method() == "llm_int8":
lowercase_ = bnb.nn.LinearabitLt(
snake_case__ , snake_case__ , module.bias is not None , has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight , threshold=quantization_config.llm_inta_threshold , )
lowercase_ = True
else:
if (
quantization_config.llm_inta_skip_modules is not None
and name in quantization_config.llm_inta_skip_modules
):
pass
else:
lowercase_ = bnb.nn.Linearabit(
snake_case__ , snake_case__ , module.bias is not None , quantization_config.bnb_abit_compute_dtype , compress_statistics=quantization_config.bnb_abit_use_double_quant , quant_type=quantization_config.bnb_abit_quant_type , )
lowercase_ = True
# Store the module class in case we need to transpose the weight later
lowercase_ = type(snake_case__ )
# Force requires grad to False to avoid unexpected errors
model._modules[name].requires_grad_(snake_case__ )
if len(list(module.children() ) ) > 0:
lowercase_ , lowercase_ = _replace_with_bnb_linear(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , has_been_replaced=snake_case__ , )
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def a ( snake_case__: Any , snake_case__: Any=None , snake_case__: Union[str, Any]=None , snake_case__: str=None ):
'''simple docstring'''
lowercase_ = ['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
lowercase_ , lowercase_ = _replace_with_bnb_linear(
snake_case__ , snake_case__ , snake_case__ , snake_case__ )
if not has_been_replaced:
logger.warning(
'''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
''' Please double check your model architecture, or submit an issue on github if you think this is'''
''' a bug.''' )
return model
def a ( *snake_case__: str , **snake_case__: Dict ):
'''simple docstring'''
warnings.warn(
'''`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead''' , snake_case__ , )
return replace_with_bnb_linear(*snake_case__ , **snake_case__ )
def a ( *snake_case__: Any , **snake_case__: List[Any] ):
'''simple docstring'''
warnings.warn(
'''`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead''' , snake_case__ , )
return set_module_quantized_tensor_to_device(*snake_case__ , **snake_case__ )
def a ( snake_case__: Optional[Any] ):
'''simple docstring'''
lowercase_ = deepcopy(snake_case__ ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
tied_model.tie_weights()
lowercase_ = find_tied_parameters(snake_case__ )
# For compatibility with Accelerate < 0.18
if isinstance(snake_case__ , snake_case__ ):
lowercase_ = sum(list(tied_params.values() ) , [] ) + list(tied_params.keys() )
else:
lowercase_ = sum(snake_case__ , [] )
lowercase_ = len(snake_case__ ) > 0
# Check if it is a base model
lowercase_ = not hasattr(snake_case__ , model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
lowercase_ = list(model.named_children() )
lowercase_ = [list_modules[-1][0]]
# add last module together with tied weights
lowercase_ = set(snake_case__ ) - set(snake_case__ )
lowercase_ = list(set(snake_case__ ) ) + list(snake_case__ )
# remove ".weight" from the keys
lowercase_ = ['''.weight''', '''.bias''']
lowercase_ = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
lowercase_ = name.replace(snake_case__ , '''''' )
filtered_module_names.append(snake_case__ )
return filtered_module_names
| 30
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|
"""simple docstring"""
from collections.abc import Callable
def A_ ( _lowerCAmelCase : Callable[[float], float], _lowerCAmelCase : float, _lowerCAmelCase : float ):
"""simple docstring"""
_a = a
_a = b
if function(snake_case__ ) == 0: # one of the a or b is a root for the function
return a
elif function(snake_case__ ) == 0:
return b
elif (
function(snake_case__ ) * function(snake_case__ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError('''could not find root in given interval.''' )
else:
_a = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(snake_case__ ) == 0:
return mid
elif function(snake_case__ ) * function(snake_case__ ) < 0:
_a = mid
else:
_a = mid
_a = start + (end - start) / 2.0
return mid
def A_ ( _lowerCAmelCase : float ):
"""simple docstring"""
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1000))
import doctest
doctest.testmod()
| 320
|
import argparse
import os
import re
__a = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__a = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
__a = re.compile(r'\s*\(\s*"(\S[^"]+)"')
def a ( snake_case__: str , snake_case__: bool = False ):
'''simple docstring'''
with open(snake_case__ , '''r''' , encoding='''utf-8''' ) as f:
lowercase_ = f.read()
lowercase_ = content.split('''\n''' )
lowercase_ = []
lowercase_ = 0
while line_idx < len(snake_case__ ):
if _re_intro_mapping.search(lines[line_idx] ) is not None:
lowercase_ = len(re.search(r'''^(\s*)\S''' , lines[line_idx] ).groups()[0] ) + 8
# Start of a new mapping!
while not lines[line_idx].startswith(''' ''' * indent + '''(''' ):
new_lines.append(lines[line_idx] )
line_idx += 1
lowercase_ = []
while lines[line_idx].strip() != "]":
# Blocks either fit in one line or not
if lines[line_idx].strip() == "(":
lowercase_ = line_idx
while not lines[line_idx].startswith(''' ''' * indent + ''')''' ):
line_idx += 1
blocks.append('''\n'''.join(lines[start_idx : line_idx + 1] ) )
else:
blocks.append(lines[line_idx] )
line_idx += 1
# Sort blocks by their identifiers
lowercase_ = sorted(snake_case__ , key=lambda snake_case__ : _re_identifier.search(snake_case__ ).groups()[0] )
new_lines += blocks
else:
new_lines.append(lines[line_idx] )
line_idx += 1
if overwrite:
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as f:
f.write('''\n'''.join(snake_case__ ) )
elif "\n".join(snake_case__ ) != content:
return True
def a ( snake_case__: bool = False ):
'''simple docstring'''
lowercase_ = [os.path.join(snake_case__ , snake_case__ ) for f in os.listdir(snake_case__ ) if f.endswith('''.py''' )]
lowercase_ = [sort_auto_mapping(snake_case__ , overwrite=snake_case__ ) for fname in fnames]
if not overwrite and any(snake_case__ ):
lowercase_ = [f for f, d in zip(snake_case__ , snake_case__ ) if d]
raise ValueError(
F'''The following files have auto mappings that need sorting: {', '.join(snake_case__ )}. Run `make style` to fix'''
''' this.''' )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
__a = parser.parse_args()
sort_all_auto_mappings(not args.check_only)
| 30
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|
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def lowercase (*snake_case__ : Optional[Any] ) -> List[Any]:
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
lowerCAmelCase = list(snake_case__ )
for i in range(len(snake_case__ ) ):
lowerCAmelCase = None
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
return objects
def lowercase (snake_case__ : Exception ) -> List[str]:
'''simple docstring'''
lowerCAmelCase = [
"""CUDA out of memory.""", # CUDA OOM
"""cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""", # CUDNN SNAFU
"""DefaultCPUAllocator: can\'t allocate memory""", # CPU OOM
]
if isinstance(snake_case__ , snake_case__ ) and len(exception.args ) == 1:
return any(err in exception.args[0] for err in _statements )
return False
def lowercase (snake_case__ : callable = None , snake_case__ : int = 128 ) -> Dict:
'''simple docstring'''
if function is None:
return functools.partial(snake_case__ , starting_batch_size=snake_case__ )
lowerCAmelCase = starting_batch_size
def decorator(*snake_case__ : List[str] , **snake_case__ : List[str] ):
nonlocal batch_size
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
lowerCAmelCase = list(inspect.signature(snake_case__ ).parameters.keys() )
# Guard against user error
if len(snake_case__ ) < (len(snake_case__ ) + 1):
lowerCAmelCase = """, """.join([f'''{arg}={value}''' for arg, value in zip(params[1:] , args[1:] )] )
raise TypeError(
f'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
f'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`''' )
while True:
if batch_size == 0:
raise RuntimeError("""No executable batch size found, reached zero.""" )
try:
return function(snake_case__ , *snake_case__ , **snake_case__ )
except Exception as e:
if should_reduce_batch_size(snake_case__ ):
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
batch_size //= 2
else:
raise
return decorator
| 155
|
def a ( snake_case__: list , snake_case__: list , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if index == number_of_items:
return 0
lowercase_ = 0
lowercase_ = 0
lowercase_ = knapsack(snake_case__ , snake_case__ , snake_case__ , snake_case__ , index + 1 )
if weights[index] <= max_weight:
lowercase_ = values[index] + knapsack(
snake_case__ , snake_case__ , snake_case__ , max_weight - weights[index] , index + 1 )
return max(snake_case__ , snake_case__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 30
| 0
|
from ..utils import DummyObject, requires_backends
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ['flax']
def __init__( self : int, *__A : Tuple, **__A : List[str] ):
requires_backends(self, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : List[Any], *__A : Optional[int], **__A : Dict ):
requires_backends(cls, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : Optional[int], *__A : str, **__A : List[str] ):
requires_backends(cls, ['''flax'''] )
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ['flax']
def __init__( self : Union[str, Any], *__A : List[str], **__A : str ):
requires_backends(self, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : Union[str, Any], *__A : Any, **__A : List[str] ):
requires_backends(cls, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : List[Any], *__A : Optional[int], **__A : List[str] ):
requires_backends(cls, ['''flax'''] )
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ['flax']
def __init__( self : List[str], *__A : Any, **__A : int ):
requires_backends(self, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : int, *__A : Any, **__A : Optional[int] ):
requires_backends(cls, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : Optional[int], *__A : List[str], **__A : Any ):
requires_backends(cls, ['''flax'''] )
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ['flax']
def __init__( self : Tuple, *__A : int, **__A : Tuple ):
requires_backends(self, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : Tuple, *__A : Any, **__A : List[Any] ):
requires_backends(cls, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : str, *__A : Union[str, Any], **__A : List[Any] ):
requires_backends(cls, ['''flax'''] )
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ['flax']
def __init__( self : Optional[Any], *__A : Union[str, Any], **__A : List[str] ):
requires_backends(self, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : List[Any], *__A : List[Any], **__A : Optional[int] ):
requires_backends(cls, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : Tuple, *__A : List[str], **__A : Optional[Any] ):
requires_backends(cls, ['''flax'''] )
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ['flax']
def __init__( self : Optional[int], *__A : Tuple, **__A : Any ):
requires_backends(self, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : List[str], *__A : Any, **__A : Dict ):
requires_backends(cls, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : Tuple, *__A : int, **__A : str ):
requires_backends(cls, ['''flax'''] )
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ['flax']
def __init__( self : List[Any], *__A : List[str], **__A : Union[str, Any] ):
requires_backends(self, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : List[Any], *__A : Optional[int], **__A : List[str] ):
requires_backends(cls, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : Union[str, Any], *__A : Optional[Any], **__A : int ):
requires_backends(cls, ['''flax'''] )
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ['flax']
def __init__( self : List[str], *__A : Dict, **__A : Optional[int] ):
requires_backends(self, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : Optional[Any], *__A : List[Any], **__A : Union[str, Any] ):
requires_backends(cls, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : Union[str, Any], *__A : List[Any], **__A : Optional[Any] ):
requires_backends(cls, ['''flax'''] )
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ['flax']
def __init__( self : Dict, *__A : Any, **__A : Dict ):
requires_backends(self, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : Dict, *__A : str, **__A : List[str] ):
requires_backends(cls, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : int, *__A : Tuple, **__A : Any ):
requires_backends(cls, ['''flax'''] )
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ['flax']
def __init__( self : Dict, *__A : Optional[int], **__A : Dict ):
requires_backends(self, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : Optional[int], *__A : List[str], **__A : int ):
requires_backends(cls, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : Optional[Any], *__A : Any, **__A : List[str] ):
requires_backends(cls, ['''flax'''] )
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ['flax']
def __init__( self : Union[str, Any], *__A : Any, **__A : Dict ):
requires_backends(self, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : int, *__A : List[Any], **__A : Optional[Any] ):
requires_backends(cls, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : List[str], *__A : Optional[Any], **__A : Tuple ):
requires_backends(cls, ['''flax'''] )
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ['flax']
def __init__( self : Optional[Any], *__A : str, **__A : Union[str, Any] ):
requires_backends(self, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : int, *__A : Dict, **__A : Dict ):
requires_backends(cls, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : Optional[Any], *__A : List[str], **__A : Union[str, Any] ):
requires_backends(cls, ['''flax'''] )
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ['flax']
def __init__( self : Any, *__A : Optional[Any], **__A : Dict ):
requires_backends(self, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : Union[str, Any], *__A : Dict, **__A : Optional[Any] ):
requires_backends(cls, ['''flax'''] )
@classmethod
def __magic_name__ ( cls : Optional[Any], *__A : Optional[int], **__A : Optional[Any] ):
requires_backends(cls, ['''flax'''] )
| 336
|
import argparse
from collections import defaultdict
import yaml
__a = 'docs/source/en/_toctree.yml'
def a ( snake_case__: Dict ):
'''simple docstring'''
lowercase_ = defaultdict(snake_case__ )
for doc in model_doc:
counts[doc["local"]] += 1
lowercase_ = [key for key, value in counts.items() if value > 1]
lowercase_ = []
for duplicate_key in duplicates:
lowercase_ = list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key} )
if len(snake_case__ ) > 1:
raise ValueError(
F'''{duplicate_key} is present several times in the documentation table of content at '''
'''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
'''others.''' )
# Only add this once
new_doc.append({'''local''': duplicate_key, '''title''': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1] )
# Sort
return sorted(snake_case__ , key=lambda snake_case__ : s["title"].lower() )
def a ( snake_case__: List[Any]=False ):
'''simple docstring'''
with open(snake_case__ , encoding='''utf-8''' ) as f:
lowercase_ = yaml.safe_load(f.read() )
# Get to the API doc
lowercase_ = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase_ = content[api_idx]['''sections''']
# Then to the model doc
lowercase_ = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
lowercase_ = api_doc[model_idx]['''sections''']
lowercase_ = [(idx, section) for idx, section in enumerate(snake_case__ ) if '''sections''' in section]
lowercase_ = False
for idx, modality_doc in modalities_docs:
lowercase_ = modality_doc['''sections''']
lowercase_ = clean_model_doc_toc(snake_case__ )
if old_modality_doc != new_modality_doc:
lowercase_ = True
if overwrite:
lowercase_ = new_modality_doc
if diff:
if overwrite:
lowercase_ = model_doc
lowercase_ = api_doc
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(snake_case__ , allow_unicode=snake_case__ ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
__a = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 30
| 0
|
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''BAAI/AltCLIP''': '''https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json''',
# See all AltCLIP models at https://huggingface.co/models?filter=altclip
}
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : str = 'altclip_text_model'
def __init__( self , __lowercase=250_002 , __lowercase=1_024 , __lowercase=24 , __lowercase=16 , __lowercase=4_096 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=514 , __lowercase=1 , __lowercase=0.02 , __lowercase=0.02 , __lowercase=1E-0_5 , __lowercase=1 , __lowercase=0 , __lowercase=2 , __lowercase="absolute" , __lowercase=True , __lowercase=768 , **__lowercase , ) -> Any:
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Tuple = vocab_size
__UpperCamelCase :Union[str, Any] = hidden_size
__UpperCamelCase :List[Any] = num_hidden_layers
__UpperCamelCase :Union[str, Any] = num_attention_heads
__UpperCamelCase :Any = hidden_act
__UpperCamelCase :Any = intermediate_size
__UpperCamelCase :str = hidden_dropout_prob
__UpperCamelCase :int = attention_probs_dropout_prob
__UpperCamelCase :Optional[Any] = max_position_embeddings
__UpperCamelCase :int = type_vocab_size
__UpperCamelCase :Optional[int] = initializer_range
__UpperCamelCase :int = initializer_factor
__UpperCamelCase :Optional[Any] = layer_norm_eps
__UpperCamelCase :Tuple = position_embedding_type
__UpperCamelCase :Union[str, Any] = use_cache
__UpperCamelCase :List[str] = project_dim
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : Tuple = 'altclip_vision_model'
def __init__( self , __lowercase=768 , __lowercase=3_072 , __lowercase=512 , __lowercase=12 , __lowercase=12 , __lowercase=3 , __lowercase=224 , __lowercase=32 , __lowercase="quick_gelu" , __lowercase=1E-5 , __lowercase=0.0 , __lowercase=0.02 , __lowercase=1.0 , **__lowercase , ) -> str:
super().__init__(**SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Any = hidden_size
__UpperCamelCase :Optional[Any] = intermediate_size
__UpperCamelCase :Optional[int] = projection_dim
__UpperCamelCase :Dict = num_hidden_layers
__UpperCamelCase :Tuple = num_attention_heads
__UpperCamelCase :Optional[int] = num_channels
__UpperCamelCase :Union[str, Any] = patch_size
__UpperCamelCase :int = image_size
__UpperCamelCase :Dict = initializer_range
__UpperCamelCase :Union[str, Any] = initializer_factor
__UpperCamelCase :str = attention_dropout
__UpperCamelCase :List[str] = layer_norm_eps
__UpperCamelCase :Optional[Any] = hidden_act
@classmethod
def UpperCamelCase__ ( cls , __lowercase , **__lowercase) -> "PretrainedConfig":
cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE_)
__UpperCamelCase , __UpperCamelCase :List[str] = cls.get_config_dict(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_)
# get the vision config dict if we are loading from AltCLIPConfig
if config_dict.get('''model_type''') == "altclip":
__UpperCamelCase :Optional[Any] = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''') and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")
return cls.from_dict(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_)
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : Any = 'altclip'
a__ : List[str] = True
def __init__( self , __lowercase=None , __lowercase=None , __lowercase=768 , __lowercase=2.65_92 , **__lowercase) -> List[str]:
# If `_config_dict` exist, we use them for the backward compatibility.
# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
# of confusion!).
__UpperCamelCase :Union[str, Any] = kwargs.pop('''text_config_dict''' , SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[str] = kwargs.pop('''vision_config_dict''' , SCREAMING_SNAKE_CASE_)
super().__init__(**SCREAMING_SNAKE_CASE_)
# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
if text_config_dict is not None:
if text_config is None:
__UpperCamelCase :Union[str, Any] = {}
# This is the complete result when using `text_config_dict`.
__UpperCamelCase :str = AltCLIPTextConfig(**SCREAMING_SNAKE_CASE_).to_dict()
# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
for key, value in _text_config_dict.items():
if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
# If specified in `text_config_dict`
if key in text_config_dict:
__UpperCamelCase :List[str] = (
f"""`{key}` is found in both `text_config_dict` and `text_config` but with different values. """
f"""The value `text_config_dict[\"{key}\"]` will be used instead."""
)
# If inferred from default argument values (just to be super careful)
else:
__UpperCamelCase :Any = (
f"""`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The """
f"""value `text_config[\"{key}\"]` will be overriden."""
)
logger.warning(SCREAMING_SNAKE_CASE_)
# Update all values in `text_config` with the ones in `_text_config_dict`.
text_config.update(_text_config_dict)
if vision_config_dict is not None:
if vision_config is None:
__UpperCamelCase :Optional[int] = {}
# This is the complete result when using `vision_config_dict`.
__UpperCamelCase :Union[str, Any] = AltCLIPVisionConfig(**SCREAMING_SNAKE_CASE_).to_dict()
# convert keys to string instead of integer
if "id2label" in _vision_config_dict:
__UpperCamelCase :Dict = {
str(SCREAMING_SNAKE_CASE_): value for key, value in _vision_config_dict['''id2label'''].items()
}
# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
for key, value in _vision_config_dict.items():
if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
# If specified in `vision_config_dict`
if key in vision_config_dict:
__UpperCamelCase :str = (
f"""`{key}` is found in both `vision_config_dict` and `vision_config` but with different """
f"""values. The value `vision_config_dict[\"{key}\"]` will be used instead."""
)
# If inferred from default argument values (just to be super careful)
else:
__UpperCamelCase :Optional[Any] = (
f"""`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. """
f"""The value `vision_config[\"{key}\"]` will be overriden."""
)
logger.warning(SCREAMING_SNAKE_CASE_)
# Update all values in `vision_config` with the ones in `_vision_config_dict`.
vision_config.update(_vision_config_dict)
if text_config is None:
__UpperCamelCase :Optional[Any] = {}
logger.info('''`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.''')
if vision_config is None:
__UpperCamelCase :Any = {}
logger.info('''`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.''')
__UpperCamelCase :int = AltCLIPTextConfig(**SCREAMING_SNAKE_CASE_)
__UpperCamelCase :Any = AltCLIPVisionConfig(**SCREAMING_SNAKE_CASE_)
__UpperCamelCase :List[Any] = projection_dim
__UpperCamelCase :str = logit_scale_init_value
__UpperCamelCase :List[Any] = 1.0
@classmethod
def UpperCamelCase__ ( cls , __lowercase , __lowercase , **__lowercase) -> List[Any]:
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **SCREAMING_SNAKE_CASE_)
def UpperCamelCase__ ( self) -> Dict:
__UpperCamelCase :Optional[Any] = copy.deepcopy(self.__dict__)
__UpperCamelCase :List[str] = self.text_config.to_dict()
__UpperCamelCase :Any = self.vision_config.to_dict()
__UpperCamelCase :Tuple = self.__class__.model_type
return output
| 43
|
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Union[str, Any] = 'upernet'
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : Dict=None , SCREAMING_SNAKE_CASE_ : str=5_1_2 , SCREAMING_SNAKE_CASE_ : Tuple=0.02 , SCREAMING_SNAKE_CASE_ : Optional[Any]=[1, 2, 3, 6] , SCREAMING_SNAKE_CASE_ : Optional[int]=True , SCREAMING_SNAKE_CASE_ : Tuple=0.4 , SCREAMING_SNAKE_CASE_ : Optional[int]=3_8_4 , SCREAMING_SNAKE_CASE_ : str=2_5_6 , SCREAMING_SNAKE_CASE_ : Dict=1 , SCREAMING_SNAKE_CASE_ : Tuple=False , SCREAMING_SNAKE_CASE_ : str=2_5_5 , **SCREAMING_SNAKE_CASE_ : str , ) -> int:
super().__init__(**SCREAMING_SNAKE_CASE_ )
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowercase_ = CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase_ = backbone_config.get('''model_type''' )
lowercase_ = CONFIG_MAPPING[backbone_model_type]
lowercase_ = config_class.from_dict(SCREAMING_SNAKE_CASE_ )
lowercase_ = backbone_config
lowercase_ = hidden_size
lowercase_ = initializer_range
lowercase_ = pool_scales
lowercase_ = use_auxiliary_head
lowercase_ = auxiliary_loss_weight
lowercase_ = auxiliary_in_channels
lowercase_ = auxiliary_channels
lowercase_ = auxiliary_num_convs
lowercase_ = auxiliary_concat_input
lowercase_ = loss_ignore_index
def _lowercase ( self : List[str] ) -> List[str]:
lowercase_ = copy.deepcopy(self.__dict__ )
lowercase_ = self.backbone_config.to_dict()
lowercase_ = self.__class__.model_type
return output
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def _A ( _lowercase ) -> Optional[int]:
"""simple docstring"""
__UpperCamelCase = [0] * len(snake_case__ )
for i in range(1 , len(snake_case__ ) ):
# use last results for better performance - dynamic programming
__UpperCamelCase = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
__UpperCamelCase = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
__UpperCamelCase = j
return prefix_result
def _A ( _lowercase ) -> Optional[int]:
"""simple docstring"""
return max(prefix_function(snake_case__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 310
|
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
__a = 'sshleifer/bart-tiny-random'
__a = 'patrickvonplaten/t5-tiny-random'
@require_torch
class lowercase__( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _lowercase ( self : Any ) -> Tuple:
return AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : int ) -> List[Any]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.num_hidden_layers , 1 )
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : List[str] ) -> List[Any]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=SCREAMING_SNAKE_CASE_ )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , self.teacher_config.encoder_layers )
def _lowercase ( self : List[Any] ) -> List[Any]:
lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , 1 )
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=SCREAMING_SNAKE_CASE_ , d=SCREAMING_SNAKE_CASE_ )
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"""simple docstring"""
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
lowerCamelCase__ = logging.get_logger(__name__)
logging.set_verbosity_info()
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase ):
if "xprophetnet" in prophetnet_checkpoint_path:
__lowerCAmelCase : int = XLMProphetNetForConditionalGenerationOld.from_pretrained(snake_case__ )
__lowerCAmelCase , __lowerCAmelCase : Optional[Any] = XLMProphetNetForConditionalGeneration.from_pretrained(
snake_case__ , output_loading_info=snake_case__ )
else:
__lowerCAmelCase : str = ProphetNetForConditionalGenerationOld.from_pretrained(snake_case__ )
__lowerCAmelCase , __lowerCAmelCase : int = ProphetNetForConditionalGeneration.from_pretrained(
snake_case__ , output_loading_info=snake_case__ )
__lowerCAmelCase : Optional[Any] = ['key_proj', 'value_proj', 'query_proj']
__lowerCAmelCase : str = {
'self_attn': 'ngram_self_attn',
'cross_attn': 'encoder_attn',
'cross_attn_layer_norm': 'encoder_attn_layer_norm',
'feed_forward_layer_norm': 'final_layer_norm',
'feed_forward': '',
'intermediate': 'fc1',
'output': 'fc2',
'key_proj': 'k_proj',
'query_proj': 'q_proj',
'value_proj': 'v_proj',
'word_embeddings': 'embed_tokens',
'embeddings_layer_norm': 'emb_layer_norm',
'relative_pos_embeddings': 'relative_linear',
'ngram_embeddings': 'ngram_input_embed',
'position_embeddings': 'embed_positions',
}
for key in loading_info["missing_keys"]:
__lowerCAmelCase : Any = key.split('.' )
if attributes[0] == "lm_head":
__lowerCAmelCase : Optional[int] = prophet
__lowerCAmelCase : Union[str, Any] = prophet_old
else:
__lowerCAmelCase : Any = prophet.prophetnet
__lowerCAmelCase : Optional[Any] = prophet_old.model
__lowerCAmelCase : List[str] = False
for attribute in attributes:
if attribute in mapping:
__lowerCAmelCase : str = mapping[attribute]
if not hasattr(snake_case__ , snake_case__ ) and len(snake_case__ ) > 0:
__lowerCAmelCase : Any = attribute
elif hasattr(snake_case__ , snake_case__ ):
__lowerCAmelCase : Union[str, Any] = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
__lowerCAmelCase : Union[str, Any] = old_model.weight
logger.info(F"{attribute} is initialized." )
__lowerCAmelCase : Dict = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
__lowerCAmelCase : int = old_model.bias
logger.info(F"{attribute} is initialized" )
__lowerCAmelCase : int = True
break
elif attribute in special_keys and hasattr(snake_case__ , 'in_proj_weight' ):
__lowerCAmelCase : List[str] = old_model.in_proj_weight.shape[0] // 3
__lowerCAmelCase : Optional[int] = getattr(snake_case__ , snake_case__ )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
__lowerCAmelCase : Optional[Any] = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
__lowerCAmelCase : Any = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
__lowerCAmelCase : Union[str, Any] = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
__lowerCAmelCase : Union[str, Any] = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
__lowerCAmelCase : Optional[int] = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
__lowerCAmelCase : Tuple = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
__lowerCAmelCase : Union[str, Any] = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
__lowerCAmelCase : int = nn.Parameter(old_model.embed_positions.weight[:512, :] )
__lowerCAmelCase : Optional[Any] = True
break
if attribute.isdigit():
__lowerCAmelCase : Tuple = model[int(snake_case__ )]
__lowerCAmelCase : Any = old_model[int(snake_case__ )]
else:
__lowerCAmelCase : Any = getattr(snake_case__ , snake_case__ )
if old_attribute == "":
__lowerCAmelCase : Tuple = old_model
else:
if not hasattr(snake_case__ , snake_case__ ):
raise ValueError(F"{old_model} does not have {old_attribute}" )
__lowerCAmelCase : Tuple = getattr(snake_case__ , snake_case__ )
if not is_key_init:
raise ValueError(F"{key} was not correctly initialized!" )
print(F"Saving model to {pytorch_dump_folder_path}" )
prophet.save_pretrained(snake_case__ )
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--prophetnet_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
lowerCamelCase__ = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 86
|
def a ( snake_case__: int = 100 ):
'''simple docstring'''
lowercase_ = (n * (n + 1) // 2) ** 2
lowercase_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"{solution() = }")
| 30
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|
import argparse
import os
# New Code #
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
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_UpperCAmelCase : Any =16
_UpperCAmelCase : int =32
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ = 16 )-> List[Any]:
lowerCAmelCase_ : Dict = AutoTokenizer.from_pretrained('''bert-base-cased''' )
lowerCAmelCase_ : List[str] = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(lowerCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase_ : Union[str, Any] = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=snake_case__ , max_length=snake_case__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowerCAmelCase_ : str = datasets.map(
snake_case__ , batched=snake_case__ , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowerCAmelCase_ : str = 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.
lowerCAmelCase_ : Union[str, Any] = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowerCAmelCase_ : Any = 16
elif accelerator.mixed_precision != "no":
lowerCAmelCase_ : Optional[int] = 8
else:
lowerCAmelCase_ : List[str] = None
return tokenizer.pad(
snake_case__ , padding='''longest''' , max_length=snake_case__ , pad_to_multiple_of=snake_case__ , return_tensors='''pt''' , )
# Instantiate dataloaders.
lowerCAmelCase_ : List[Any] = DataLoader(
tokenized_datasets['''train'''] , shuffle=snake_case__ , collate_fn=snake_case__ , batch_size=snake_case__ )
lowerCAmelCase_ : Optional[int] = DataLoader(
tokenized_datasets['''validation'''] , shuffle=snake_case__ , collate_fn=snake_case__ , batch_size=snake_case__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_UpperCAmelCase : Tuple =mocked_dataloaders # noqa: F811
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Union[str, Any]:
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , snake_case__ ) == "1":
lowerCAmelCase_ : int = 2
# Initialize accelerator
lowerCAmelCase_ : Dict = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCAmelCase_ : Union[str, Any] = config['''lr''']
lowerCAmelCase_ : Any = int(config['''num_epochs'''] )
lowerCAmelCase_ : List[str] = int(config['''seed'''] )
lowerCAmelCase_ : List[str] = int(config['''batch_size'''] )
lowerCAmelCase_ : Dict = evaluate.load('''glue''' , '''mrpc''' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=snake_case__ )
def inner_training_loop(lowerCAmelCase_ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(snake_case__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCAmelCase_ : Tuple = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=snake_case__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowerCAmelCase_ : Union[str, Any] = model.to(accelerator.device )
# Instantiate optimizer
lowerCAmelCase_ : Optional[int] = AdamW(params=model.parameters() , lr=snake_case__ )
lowerCAmelCase_ , lowerCAmelCase_ : Dict = get_dataloaders(snake_case__ , snake_case__ )
# Instantiate scheduler
lowerCAmelCase_ : Any = get_linear_schedule_with_warmup(
optimizer=snake_case__ , num_warmup_steps=100 , num_training_steps=(len(snake_case__ ) * num_epochs) , )
# 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.
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = accelerator.prepare(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
# Now we train the model
for epoch in range(snake_case__ ):
model.train()
for step, batch in enumerate(snake_case__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowerCAmelCase_ : Tuple = model(**snake_case__ )
lowerCAmelCase_ : Any = outputs.loss
accelerator.backward(snake_case__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(snake_case__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCAmelCase_ : Any = model(**snake_case__ )
lowerCAmelCase_ : Optional[int] = outputs.logits.argmax(dim=-1 )
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=snake_case__ , references=snake_case__ , )
lowerCAmelCase_ : Tuple = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , snake_case__ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def lowerCAmelCase ( )-> int:
lowerCAmelCase_ : int = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=snake_case__ , default=snake_case__ , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
lowerCAmelCase_ : Any = parser.parse_args()
lowerCAmelCase_ : Optional[Any] = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(snake_case__ , snake_case__ )
if __name__ == "__main__":
main()
| 262
|
import logging
from transformers.configuration_utils import PretrainedConfig
__a = logging.getLogger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Optional[int] = 'masked_bert'
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : List[Any]=3_0_5_2_2 , SCREAMING_SNAKE_CASE_ : List[str]=7_6_8 , SCREAMING_SNAKE_CASE_ : Optional[int]=1_2 , SCREAMING_SNAKE_CASE_ : Any=1_2 , SCREAMING_SNAKE_CASE_ : str=3_0_7_2 , SCREAMING_SNAKE_CASE_ : Union[str, Any]="gelu" , SCREAMING_SNAKE_CASE_ : Optional[int]=0.1 , SCREAMING_SNAKE_CASE_ : List[str]=0.1 , SCREAMING_SNAKE_CASE_ : Tuple=5_1_2 , SCREAMING_SNAKE_CASE_ : str=2 , SCREAMING_SNAKE_CASE_ : Dict=0.02 , SCREAMING_SNAKE_CASE_ : Any=1e-12 , SCREAMING_SNAKE_CASE_ : Any=0 , SCREAMING_SNAKE_CASE_ : Optional[int]="topK" , SCREAMING_SNAKE_CASE_ : Dict="constant" , SCREAMING_SNAKE_CASE_ : Optional[Any]=0.0 , **SCREAMING_SNAKE_CASE_ : Optional[int] , ) -> Optional[Any]:
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
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_ = type_vocab_size
lowercase_ = initializer_range
lowercase_ = layer_norm_eps
lowercase_ = pruning_method
lowercase_ = mask_init
lowercase_ = mask_scale
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|
"""simple docstring"""
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewTokensCriteria,
MaxTimeCriteria,
StoppingCriteriaList,
validate_stopping_criteria,
)
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self , _a ):
__a = 3
__a = 250
__a = ids_tensor((batch_size, length) , SCREAMING_SNAKE_CASE_ )
__a = torch.ones((batch_size, length) , device=SCREAMING_SNAKE_CASE_ , dtype=torch.float ) / length
return input_ids, scores
def __UpperCAmelCase ( self ):
__a , __a = self._get_tensors(5 )
__a = StoppingCriteriaList(
[
MaxLengthCriteria(max_length=10 ),
MaxTimeCriteria(max_time=0.1 ),
] )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
__a , __a = self._get_tensors(9 )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
__a , __a = self._get_tensors(10 )
self.assertTrue(criteria(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def __UpperCAmelCase ( self ):
__a = MaxLengthCriteria(max_length=10 )
__a , __a = self._get_tensors(5 )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
__a , __a = self._get_tensors(9 )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
__a , __a = self._get_tensors(10 )
self.assertTrue(criteria(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def __UpperCAmelCase ( self ):
__a = MaxNewTokensCriteria(start_length=5 , max_new_tokens=5 )
__a , __a = self._get_tensors(5 )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
__a , __a = self._get_tensors(9 )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
__a , __a = self._get_tensors(10 )
self.assertTrue(criteria(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
__a = StoppingCriteriaList([criteria] )
self.assertEqual(criteria_list.max_length , 10 )
def __UpperCAmelCase ( self ):
__a , __a = self._get_tensors(5 )
__a = MaxTimeCriteria(max_time=0.1 )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
__a = MaxTimeCriteria(max_time=0.1 , initial_timestamp=time.time() - 0.2 )
self.assertTrue(criteria(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def __UpperCAmelCase ( self ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) , 10 )
with self.assertWarns(SCREAMING_SNAKE_CASE_ ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) , 11 )
__a = validate_stopping_criteria(StoppingCriteriaList() , 11 )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 1 )
| 45
|
import os
def a ( ):
'''simple docstring'''
lowercase_ = os.path.join(os.path.dirname(snake_case__ ) , '''num.txt''' )
with open(snake_case__ ) as file_hand:
return str(sum(int(snake_case__ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 30
| 0
|
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
smartaaa_timesteps,
smartaaa_timesteps,
superaa_timesteps,
superaa_timesteps,
superaaa_timesteps,
)
@dataclass
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_if import IFPipeline
from .pipeline_if_imgaimg import IFImgaImgPipeline
from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
from .pipeline_if_inpainting import IFInpaintingPipeline
from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
from .pipeline_if_superresolution import IFSuperResolutionPipeline
from .safety_checker import IFSafetyChecker
from .watermark import IFWatermarker
| 82
|
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
'''compression_format, is_archive''' , [
('''7z''', True),
('''bz2''', False),
('''gzip''', False),
('''lz4''', False),
('''tar''', True),
('''xz''', False),
('''zip''', True),
('''zstd''', False),
] , )
def a ( snake_case__: int , snake_case__: Tuple , snake_case__: Dict , snake_case__: Dict , snake_case__: List[Any] , snake_case__: int , snake_case__: List[Any] , snake_case__: Optional[int] , snake_case__: str , snake_case__: Union[str, Any] , snake_case__: List[str] , snake_case__: int , ):
'''simple docstring'''
lowercase_ = {
'''7z''': (seven_zip_file, SevenZipExtractor),
'''bz2''': (bza_file, BzipaExtractor),
'''gzip''': (gz_file, GzipExtractor),
'''lz4''': (lza_file, LzaExtractor),
'''tar''': (tar_file, TarExtractor),
'''xz''': (xz_file, XzExtractor),
'''zip''': (zip_file, ZipExtractor),
'''zstd''': (zstd_file, ZstdExtractor),
}
lowercase_ , lowercase_ = input_paths_and_base_extractors[compression_format]
if input_path is None:
lowercase_ = F'''for \'{compression_format}\' compression_format, '''
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(snake_case__ )
assert base_extractor.is_extractable(snake_case__ )
lowercase_ = tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
base_extractor.extract(snake_case__ , snake_case__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
lowercase_ = file_path.read_text(encoding='''utf-8''' )
else:
lowercase_ = output_path.read_text(encoding='''utf-8''' )
lowercase_ = text_file.read_text(encoding='''utf-8''' )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
'''compression_format, is_archive''' , [
('''7z''', True),
('''bz2''', False),
('''gzip''', False),
('''lz4''', False),
('''tar''', True),
('''xz''', False),
('''zip''', True),
('''zstd''', False),
] , )
def a ( snake_case__: List[Any] , snake_case__: int , snake_case__: Optional[int] , snake_case__: Union[str, Any] , snake_case__: List[Any] , snake_case__: Tuple , snake_case__: Optional[int] , snake_case__: List[str] , snake_case__: Union[str, Any] , snake_case__: Tuple , snake_case__: int , snake_case__: Optional[int] , ):
'''simple docstring'''
lowercase_ = {
'''7z''': seven_zip_file,
'''bz2''': bza_file,
'''gzip''': gz_file,
'''lz4''': lza_file,
'''tar''': tar_file,
'''xz''': xz_file,
'''zip''': zip_file,
'''zstd''': zstd_file,
}
lowercase_ = input_paths[compression_format]
if input_path is None:
lowercase_ = F'''for \'{compression_format}\' compression_format, '''
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(snake_case__ )
lowercase_ = Extractor.infer_extractor_format(snake_case__ )
assert extractor_format is not None
lowercase_ = tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
Extractor.extract(snake_case__ , snake_case__ , snake_case__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
lowercase_ = file_path.read_text(encoding='''utf-8''' )
else:
lowercase_ = output_path.read_text(encoding='''utf-8''' )
lowercase_ = text_file.read_text(encoding='''utf-8''' )
assert extracted_file_content == expected_file_content
@pytest.fixture
def a ( snake_case__: Union[str, Any] , snake_case__: List[Any] ):
'''simple docstring'''
import tarfile
lowercase_ = tmp_path / '''data_dot_dot'''
directory.mkdir()
lowercase_ = directory / '''tar_file_with_dot_dot.tar'''
with tarfile.TarFile(snake_case__ , '''w''' ) as f:
f.add(snake_case__ , arcname=os.path.join('''..''' , text_file.name ) )
return path
@pytest.fixture
def a ( snake_case__: int ):
'''simple docstring'''
import tarfile
lowercase_ = tmp_path / '''data_sym_link'''
directory.mkdir()
lowercase_ = directory / '''tar_file_with_sym_link.tar'''
os.symlink('''..''' , directory / '''subdir''' , target_is_directory=snake_case__ )
with tarfile.TarFile(snake_case__ , '''w''' ) as f:
f.add(str(directory / '''subdir''' ) , arcname='''subdir''' ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
'''insecure_tar_file, error_log''' , [('''tar_file_with_dot_dot''', '''illegal path'''), ('''tar_file_with_sym_link''', '''Symlink''')] , )
def a ( snake_case__: List[Any] , snake_case__: Optional[int] , snake_case__: List[str] , snake_case__: List[str] , snake_case__: int , snake_case__: Optional[Any] ):
'''simple docstring'''
lowercase_ = {
'''tar_file_with_dot_dot''': tar_file_with_dot_dot,
'''tar_file_with_sym_link''': tar_file_with_sym_link,
}
lowercase_ = insecure_tar_files[insecure_tar_file]
lowercase_ = tmp_path / '''extracted'''
TarExtractor.extract(snake_case__ , snake_case__ )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def a ( snake_case__: Optional[int] ):
'''simple docstring'''
# We should have less false positives than zipfile.is_zipfile
# We do that by checking only the magic number
lowercase_ = tmpdir / '''not_a_zip_file'''
# From: https://github.com/python/cpython/pull/5053
lowercase_ = (
B'''\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'''
B'''\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'''
B'''DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'''
B'''\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'''
)
with not_a_zip_file.open('''wb''' ) as f:
f.write(snake_case__ )
assert zipfile.is_zipfile(str(snake_case__ ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(snake_case__ ) # but we're right
| 30
| 0
|
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int = 4_00_00_00 ):
__UpperCamelCase =[]
__UpperCamelCase , __UpperCamelCase =0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(snake_case__ )
__UpperCamelCase , __UpperCamelCase =b, a + b
return sum(snake_case__ )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 62
|
from __future__ import annotations
def a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''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 a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if length > 1:
lowercase_ = int(length / 2 )
for i in range(snake_case__ , low + middle ):
comp_and_swap(snake_case__ , snake_case__ , i + middle , snake_case__ )
bitonic_merge(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
bitonic_merge(snake_case__ , low + middle , snake_case__ , snake_case__ )
def a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if length > 1:
lowercase_ = int(length / 2 )
bitonic_sort(snake_case__ , snake_case__ , snake_case__ , 1 )
bitonic_sort(snake_case__ , low + middle , snake_case__ , 0 )
bitonic_merge(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
if __name__ == "__main__":
__a = input('Enter numbers separated by a comma:\n').strip()
__a = [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=', ')
| 30
| 0
|
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
__lowerCamelCase : Optional[int] = logging.get_logger(__name__)
__lowerCamelCase : int = {
"""linear""": get_linear_schedule_with_warmup,
"""cosine""": get_cosine_schedule_with_warmup,
"""cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
"""polynomial""": get_polynomial_decay_schedule_with_warmup,
"""constant""": get_constant_schedule,
"""constant_w_warmup""": get_constant_schedule_with_warmup,
}
class A__ ( __snake_case ):
def __init__( self , A_=None , A_=None , *A_ , **A_ ):
'''simple docstring'''
super().__init__(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if config is None:
assert isinstance(self.model , SCREAMING_SNAKE_CASE_ ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
F""" {self.model.__class__}"""
)
UpperCamelCase : Tuple = self.model.config
else:
UpperCamelCase : str = config
UpperCamelCase : List[str] = data_args
UpperCamelCase : Any = self.config.tgt_vocab_size if isinstance(self.config , SCREAMING_SNAKE_CASE_ ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
" padding.." )
if self.args.label_smoothing == 0:
UpperCamelCase : List[str] = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
UpperCamelCase : Optional[int] = label_smoothed_nll_loss
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
if self.optimizer is None:
UpperCamelCase : Optional[int] = ["bias", "LayerNorm.weight"]
UpperCamelCase : List[str] = [
{
"params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
"weight_decay": self.args.weight_decay,
},
{
"params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
"weight_decay": 0.0,
},
]
UpperCamelCase : Any = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
UpperCamelCase : Any = Adafactor
UpperCamelCase : Union[str, Any] = {"scale_parameter": False, "relative_step": False}
else:
UpperCamelCase : str = AdamW
UpperCamelCase : Any = {
"betas": (self.args.adam_betaa, self.args.adam_betaa),
"eps": self.args.adam_epsilon,
}
UpperCamelCase : int = self.args.learning_rate
if self.sharded_ddp:
UpperCamelCase : List[str] = OSS(
params=SCREAMING_SNAKE_CASE_ , optim=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
else:
UpperCamelCase : Any = optimizer_cls(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if self.lr_scheduler is None:
UpperCamelCase : List[str] = self._get_lr_scheduler(SCREAMING_SNAKE_CASE_ )
else: # ignoring --lr_scheduler
logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored." )
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
UpperCamelCase : str = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
UpperCamelCase : List[str] = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
UpperCamelCase : Any = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=SCREAMING_SNAKE_CASE_ )
return scheduler
def __UpperCamelCase( self ):
'''simple docstring'''
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def __UpperCamelCase( self , A_ , A_ , A_ ):
'''simple docstring'''
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
UpperCamelCase : Tuple = model(**SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[int] = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
UpperCamelCase , UpperCamelCase : List[Any] = model(**SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )[:2]
else:
# compute label smoothed loss
UpperCamelCase : int = model(**SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[Any] = torch.nn.functional.log_softmax(SCREAMING_SNAKE_CASE_ , dim=-1 )
UpperCamelCase , UpperCamelCase : Any = self.loss_fn(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def __UpperCamelCase( self , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : Any = inputs.pop("labels" )
UpperCamelCase , UpperCamelCase : int = self._compute_loss(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return loss
def __UpperCamelCase( self , A_ , A_ , A_ , A_ = None , ):
'''simple docstring'''
UpperCamelCase : Dict = self._prepare_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = {
"max_length": self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
"num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
UpperCamelCase : Optional[int] = self.model.generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , **SCREAMING_SNAKE_CASE_ , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
UpperCamelCase : Any = self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_ , gen_kwargs["max_length"] )
UpperCamelCase : List[str] = inputs.pop("labels" )
with torch.no_grad():
# compute loss on predict data
UpperCamelCase , UpperCamelCase : Union[str, Any] = self._compute_loss(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
UpperCamelCase : Optional[Any] = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
UpperCamelCase : Tuple = self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_ , gen_kwargs["max_length"] )
return (loss, logits, labels)
def __UpperCamelCase( self , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
"Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
F""" padded to `max_length`={max_length}""" )
UpperCamelCase : Optional[int] = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
UpperCamelCase : List[Any] = tensor
return padded_tensor
| 52
|
from __future__ import annotations
from collections.abc import MutableSequence
class lowercase__:
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : MutableSequence[float] ) -> None:
if len(SCREAMING_SNAKE_CASE_ ) != degree + 1:
raise ValueError(
'''The number of coefficients should be equal to the degree + 1.''' )
lowercase_ = list(SCREAMING_SNAKE_CASE_ )
lowercase_ = degree
def __add__( self : Any , SCREAMING_SNAKE_CASE_ : Polynomial ) -> Polynomial:
if self.degree > polynomial_a.degree:
lowercase_ = self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree , SCREAMING_SNAKE_CASE_ )
else:
lowercase_ = polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree , SCREAMING_SNAKE_CASE_ )
def __sub__( self : str , SCREAMING_SNAKE_CASE_ : Polynomial ) -> Polynomial:
return self + polynomial_a * Polynomial(0 , [-1] )
def __neg__( self : int ) -> Polynomial:
return Polynomial(self.degree , [-c for c in self.coefficients] )
def __mul__( self : Any , SCREAMING_SNAKE_CASE_ : Polynomial ) -> Polynomial:
lowercase_ = [0] * (self.degree + polynomial_a.degree + 1)
for i in range(self.degree + 1 ):
for j in range(polynomial_a.degree + 1 ):
coefficients[i + j] += (
self.coefficients[i] * polynomial_a.coefficients[j]
)
return Polynomial(self.degree + polynomial_a.degree , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Dict , SCREAMING_SNAKE_CASE_ : int | float ) -> int | float:
lowercase_ = 0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self : Tuple ) -> str:
lowercase_ = ''''''
for i in range(self.degree , -1 , -1 ):
if self.coefficients[i] == 0:
continue
elif self.coefficients[i] > 0:
if polynomial:
polynomial += " + "
else:
polynomial += " - "
if i == 0:
polynomial += str(abs(self.coefficients[i] ) )
elif i == 1:
polynomial += str(abs(self.coefficients[i] ) ) + "x"
else:
polynomial += str(abs(self.coefficients[i] ) ) + "x^" + str(SCREAMING_SNAKE_CASE_ )
return polynomial
def __repr__( self : Optional[Any] ) -> str:
return self.__str__()
def _lowercase ( self : int ) -> Polynomial:
lowercase_ = [0] * self.degree
for i in range(self.degree ):
lowercase_ = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : int | float = 0 ) -> Polynomial:
lowercase_ = [0] * (self.degree + 2)
lowercase_ = constant
for i in range(self.degree + 1 ):
lowercase_ = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 , SCREAMING_SNAKE_CASE_ )
def __eq__( self : str , SCREAMING_SNAKE_CASE_ : object ) -> bool:
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return False
if self.degree != polynomial_a.degree:
return False
for i in range(self.degree + 1 ):
if self.coefficients[i] != polynomial_a.coefficients[i]:
return False
return True
def __ne__( self : List[str] , SCREAMING_SNAKE_CASE_ : object ) -> bool:
return not self.__eq__(SCREAMING_SNAKE_CASE_ )
| 30
| 0
|
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__snake_case = logging.getLogger(__name__)
@dataclass
@add_start_docstrings(TrainingArguments.__doc__ )
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : bool = field(default=a__ , metadata={'help': 'Whether to use SortishSampler or not.'} )
A_ : bool = field(
default=a__ , metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'} )
A_ : Optional[int] = field(
default=a__ , metadata={
'help': (
'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
'to the `max_length` value of the model configuration.'
)
} , )
A_ : Optional[int] = field(
default=a__ , metadata={
'help': (
'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
'to the `num_beams` value of the model configuration.'
)
} , )
A_ : Optional[Union[str, Path, GenerationConfig]] = field(
default=a__ , metadata={
'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
} , )
def _UpperCAmelCase ( self ) -> List[Any]:
_a = super().to_dict()
for k, v in d.items():
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_a = v.to_dict()
return d
| 320
|
import itertools
import math
def a ( snake_case__: int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(snake_case__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def a ( ):
'''simple docstring'''
lowercase_ = 2
while True:
if is_prime(snake_case__ ):
yield num
num += 1
def a ( snake_case__: int = 10_001 ):
'''simple docstring'''
return next(itertools.islice(prime_generator() , nth - 1 , snake_case__ ) )
if __name__ == "__main__":
print(f"{solution() = }")
| 30
| 0
|
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