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def lowerCAmelCase_ ( UpperCamelCase_ = 50 ) -> int:
UpperCamelCase_ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 365 |
from functools import lru_cache
def lowerCAmelCase_ ( UpperCamelCase_ ) -> set:
UpperCamelCase_ = 2
UpperCamelCase_ = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(UpperCamelCase_ )
if n > 1:
factors.add(UpperCamelCase_ )
return factors
@lru_cache
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
return len(unique_prime_factors(UpperCamelCase_ ) )
def lowerCAmelCase_ ( UpperCamelCase_ ) -> bool:
return len(set(UpperCamelCase_ ) ) in (0, 1)
def lowerCAmelCase_ ( UpperCamelCase_ ) -> list:
UpperCamelCase_ = 2
while True:
# Increment each value of a generated range
UpperCamelCase_ = [base + i for i in range(UpperCamelCase_ )]
# Run elements through out unique_prime_factors function
# Append our target number to the end.
UpperCamelCase_ = [upf_len(UpperCamelCase_ ) for x in group]
checker.append(UpperCamelCase_ )
# If all numbers in the list are equal, return the group variable.
if equality(UpperCamelCase_ ):
return group
# Increment our base variable by 1
base += 1
def lowerCAmelCase_ ( UpperCamelCase_ = 4 ) -> int:
UpperCamelCase_ = run(UpperCamelCase_ )
return results[0] if len(UpperCamelCase_ ) else None
if __name__ == "__main__":
print(solution())
| 328 | 0 |
# Copyright (c) 2021-, NVIDIA CORPORATION. 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.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=0 ) -> Optional[Any]:
# Format the message.
if name is None:
UpperCamelCase_ = None
else:
UpperCamelCase_ = "." * max(0 , spaces - 2 ) + "# {:" + str(50 - spaces ) + "s}"
UpperCamelCase_ = fmt.format(UpperCamelCase_ )
# Print and recurse (if needed).
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
if msg is not None:
print(UpperCamelCase_ )
for k in val.keys():
recursive_print(UpperCamelCase_ , val[k] , spaces + 2 )
elif isinstance(UpperCamelCase_ , torch.Tensor ):
print(UpperCamelCase_ , ":" , val.size() )
else:
print(UpperCamelCase_ , ":" , UpperCamelCase_ )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Tuple:
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
UpperCamelCase_ = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
UpperCamelCase_ = (num_heads, hidden_size, num_splits) + input_shape[1:]
UpperCamelCase_ = param.view(*UpperCamelCase_ )
UpperCamelCase_ = param.transpose(0 , 2 )
UpperCamelCase_ = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
UpperCamelCase_ = (num_heads, num_splits, hidden_size) + input_shape[1:]
UpperCamelCase_ = param.view(*UpperCamelCase_ )
UpperCamelCase_ = param.transpose(0 , 1 ).contiguous()
UpperCamelCase_ = param.view(*UpperCamelCase_ )
return param
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Optional[int]:
# The converted output model.
UpperCamelCase_ = {}
# old versions did not store training args
UpperCamelCase_ = input_state_dict.get("args" , UpperCamelCase_ )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
UpperCamelCase_ = ds_args.padded_vocab_size
UpperCamelCase_ = ds_args.max_position_embeddings
UpperCamelCase_ = ds_args.hidden_size
UpperCamelCase_ = ds_args.num_layers
UpperCamelCase_ = ds_args.num_attention_heads
UpperCamelCase_ = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
UpperCamelCase_ = config.n_head
# The hidden_size per head.
UpperCamelCase_ = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
UpperCamelCase_ = input_state_dict["checkpoint_version"]
else:
UpperCamelCase_ = 0.0
# The model.
UpperCamelCase_ = input_state_dict["model"]
# The language model.
UpperCamelCase_ = model["language_model"]
# The embeddings.
UpperCamelCase_ = lm["embedding"]
# The word embeddings.
UpperCamelCase_ = embeddings["word_embeddings"]["weight"]
# Truncate the embedding table to vocab_size rows.
UpperCamelCase_ = word_embeddings[: config.vocab_size, :]
UpperCamelCase_ = word_embeddings
# The position embeddings.
UpperCamelCase_ = embeddings["position_embeddings"]["weight"]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
UpperCamelCase_ = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' )
# Store the position embeddings.
UpperCamelCase_ = pos_embeddings
# The transformer.
UpperCamelCase_ = lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]
# The regex to extract layer names.
UpperCamelCase_ = re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)" )
# The simple map of names for "automated" rules.
UpperCamelCase_ = {
"attention.dense": ".attn.c_proj.",
"self_attention.dense": ".attn.c_proj.",
"mlp.dense_h_to_4h": ".mlp.c_fc.",
"mlp.dense_4h_to_h": ".mlp.c_proj.",
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
UpperCamelCase_ = layer_re.match(UpperCamelCase_ )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
UpperCamelCase_ = int(m.group(1 ) )
# The name of the operation.
UpperCamelCase_ = m.group(2 )
# Is it a weight or a bias?
UpperCamelCase_ = m.group(3 )
# The name of the layer.
UpperCamelCase_ = F'''transformer.h.{layer_idx}'''
# For layernorm(s), simply store the layer norm.
if op_name.endswith("layernorm" ):
UpperCamelCase_ = "ln_1" if op_name.startswith("input" ) else "ln_2"
UpperCamelCase_ = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
UpperCamelCase_ = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , UpperCamelCase_ , UpperCamelCase_ )
UpperCamelCase_ = causal_mask
# Insert a "dummy" tensor for masked_bias.
UpperCamelCase_ = torch.tensor(-1e4 , dtype=torch.floataa )
UpperCamelCase_ = masked_bias
UpperCamelCase_ = fix_query_key_value_ordering(UpperCamelCase_ , UpperCamelCase_ , 3 , UpperCamelCase_ , UpperCamelCase_ )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
UpperCamelCase_ = out_val.transpose(0 , 1 ).contiguous()
# Store.
UpperCamelCase_ = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
UpperCamelCase_ = fix_query_key_value_ordering(UpperCamelCase_ , UpperCamelCase_ , 3 , UpperCamelCase_ , UpperCamelCase_ )
# Store. No change of shape.
UpperCamelCase_ = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
UpperCamelCase_ = megatron_to_transformers[op_name]
UpperCamelCase_ = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
UpperCamelCase_ = megatron_to_transformers[op_name]
UpperCamelCase_ = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
UpperCamelCase_ = transformer["final_layernorm.weight"]
UpperCamelCase_ = transformer["final_layernorm.bias"]
# For LM head, transformers' wants the matrix to weight embeddings.
UpperCamelCase_ = word_embeddings
# It should be done!
return output_state_dict
def lowerCAmelCase_ ( ) -> int:
# Create the argument parser.
UpperCamelCase_ = argparse.ArgumentParser()
parser.add_argument("--print-checkpoint-structure" , action="store_true" )
parser.add_argument(
"path_to_checkpoint" , type=UpperCamelCase_ , help="Path to the checkpoint file (.zip archive or direct .pt file)" , )
parser.add_argument(
"--config_file" , default="" , type=UpperCamelCase_ , help="An optional config json file describing the pre-trained model." , )
UpperCamelCase_ = parser.parse_args()
# Extract the basename.
UpperCamelCase_ = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' )
if args.path_to_checkpoint.endswith(".zip" ):
with zipfile.ZipFile(args.path_to_checkpoint , "r" ) as checkpoint:
with checkpoint.open("release/mp_rank_00/model_optim_rng.pt" ) as pytorch_dict:
UpperCamelCase_ = torch.load(UpperCamelCase_ , map_location="cpu" )
else:
UpperCamelCase_ = torch.load(args.path_to_checkpoint , map_location="cpu" )
UpperCamelCase_ = input_state_dict.get("args" , UpperCamelCase_ )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
UpperCamelCase_ = "gelu_fast"
elif ds_args.openai_gelu:
UpperCamelCase_ = "gelu_new"
else:
UpperCamelCase_ = "gelu"
else:
# in the very early days this used to be "gelu_new"
UpperCamelCase_ = "gelu_new"
# Spell out all parameters in case the defaults change.
UpperCamelCase_ = GPTaConfig(
vocab_size=50257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=UpperCamelCase_ , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1e-5 , initializer_range=0.02 , summary_type="cls_index" , summary_use_proj=UpperCamelCase_ , summary_activation=UpperCamelCase_ , summary_proj_to_labels=UpperCamelCase_ , summary_first_dropout=0.1 , scale_attn_weights=UpperCamelCase_ , use_cache=UpperCamelCase_ , bos_token_id=50256 , eos_token_id=50256 , )
else:
UpperCamelCase_ = GPTaConfig.from_json_file(args.config_file )
UpperCamelCase_ = ["GPT2LMHeadModel"]
# Convert.
print("Converting" )
UpperCamelCase_ = convert_megatron_checkpoint(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(UpperCamelCase_ , UpperCamelCase_ )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
UpperCamelCase_ = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
UpperCamelCase_ = "gpt2"
elif tokenizer_type == "PretrainedFromHF":
UpperCamelCase_ = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' )
else:
UpperCamelCase_ = "gpt2"
UpperCamelCase_ = AutoTokenizer.from_pretrained(UpperCamelCase_ )
UpperCamelCase_ = type(UpperCamelCase_ ).__name__
UpperCamelCase_ = tokenizer_class
# Store the config to file.
print("Saving config" )
config.save_pretrained(UpperCamelCase_ )
# Save tokenizer based on args
print(F'''Adding {tokenizer_class} tokenizer files''' )
tokenizer.save_pretrained(UpperCamelCase_ )
# Store the state_dict to file.
UpperCamelCase_ = os.path.join(UpperCamelCase_ , "pytorch_model.bin" )
print(F'''Saving checkpoint to "{output_checkpoint_file}"''' )
torch.save(UpperCamelCase_ , UpperCamelCase_ )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 366 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
UpperCamelCase_ = len(UpperCamelCase_ )
UpperCamelCase_ = len(matrix[0] )
UpperCamelCase_ = min(UpperCamelCase_ , UpperCamelCase_ )
for row in range(UpperCamelCase_ ):
# Check if diagonal element is not zero
if matrix[row][row] != 0:
# Eliminate all the elements below the diagonal
for col in range(row + 1 , UpperCamelCase_ ):
UpperCamelCase_ = matrix[col][row] / matrix[row][row]
for i in range(UpperCamelCase_ , UpperCamelCase_ ):
matrix[col][i] -= multiplier * matrix[row][i]
else:
# Find a non-zero diagonal element to swap rows
UpperCamelCase_ = True
for i in range(row + 1 , UpperCamelCase_ ):
if matrix[i][row] != 0:
UpperCamelCase_ , UpperCamelCase_ = matrix[i], matrix[row]
UpperCamelCase_ = False
break
if reduce:
rank -= 1
for i in range(UpperCamelCase_ ):
UpperCamelCase_ = matrix[i][rank]
# Reduce the row pointer by one to stay on the same row
row -= 1
return rank
if __name__ == "__main__":
import doctest
doctest.testmod()
| 328 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : Union[str, Any] = '''roformer'''
def __init__( self: int , _SCREAMING_SNAKE_CASE: List[str]=50000 , _SCREAMING_SNAKE_CASE: Any=None , _SCREAMING_SNAKE_CASE: Optional[Any]=768 , _SCREAMING_SNAKE_CASE: int=12 , _SCREAMING_SNAKE_CASE: Dict=12 , _SCREAMING_SNAKE_CASE: Optional[Any]=3072 , _SCREAMING_SNAKE_CASE: Dict="gelu" , _SCREAMING_SNAKE_CASE: Any=0.1 , _SCREAMING_SNAKE_CASE: Dict=0.1 , _SCREAMING_SNAKE_CASE: List[str]=1536 , _SCREAMING_SNAKE_CASE: str=2 , _SCREAMING_SNAKE_CASE: Union[str, Any]=0.02 , _SCREAMING_SNAKE_CASE: str=1e-12 , _SCREAMING_SNAKE_CASE: Any=0 , _SCREAMING_SNAKE_CASE: str=False , _SCREAMING_SNAKE_CASE: Optional[int]=True , **_SCREAMING_SNAKE_CASE: Optional[Any] , ) -> str:
"""simple docstring"""
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = vocab_size
UpperCamelCase_ = hidden_size if embedding_size is None else embedding_size
UpperCamelCase_ = hidden_size
UpperCamelCase_ = num_hidden_layers
UpperCamelCase_ = num_attention_heads
UpperCamelCase_ = hidden_act
UpperCamelCase_ = intermediate_size
UpperCamelCase_ = hidden_dropout_prob
UpperCamelCase_ = attention_probs_dropout_prob
UpperCamelCase_ = max_position_embeddings
UpperCamelCase_ = type_vocab_size
UpperCamelCase_ = initializer_range
UpperCamelCase_ = layer_norm_eps
UpperCamelCase_ = rotary_value
UpperCamelCase_ = use_cache
class _UpperCamelCase ( lowerCAmelCase_ ):
@property
def lowercase ( self: Tuple ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
UpperCamelCase_ = {0: "batch", 1: "choice", 2: "sequence"}
else:
UpperCamelCase_ = {0: "batch", 1: "sequence"}
UpperCamelCase_ = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 367 |
import math
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> List[str]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(UpperCamelCase_ )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:
return 1 # any number raised to 0 is 1
raise AssertionError("This should never happen" )
if __name__ == "__main__": # Main function
# Read two numbers from input and typecast them to int using map function.
# Here x is the base and y is the power.
_UpperCAmelCase = 'Enter the base and the power separated by a comma: '
_UpperCAmelCase , _UpperCAmelCase = map(int, input(prompt).split(','))
_UpperCAmelCase , _UpperCAmelCase = map(int, input(prompt).split(','))
# We find the log of each number, using the function res(), which takes two
# arguments.
_UpperCAmelCase = res(xa, ya)
_UpperCAmelCase = res(xa, ya)
# We check for the largest number
if resa > resa:
print('Largest number is', xa, '^', ya)
elif resa > resa:
print('Largest number is', xa, '^', ya)
else:
print('Both are equal')
| 328 | 0 |
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> int:
if index == number_of_items:
return 0
UpperCamelCase_ = 0
UpperCamelCase_ = 0
UpperCamelCase_ = knapsack(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , index + 1 )
if weights[index] <= max_weight:
UpperCamelCase_ = values[index] + knapsack(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , max_weight - weights[index] , index + 1 )
return max(UpperCamelCase_ , UpperCamelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 368 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_UpperCAmelCase = transforms.Compose(
[
transforms.Resize((2_5_6, 2_5_6)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def lowerCAmelCase_ ( UpperCamelCase_ ) -> List[Any]:
if isinstance(UpperCamelCase_ , torch.Tensor ):
return image
elif isinstance(UpperCamelCase_ , PIL.Image.Image ):
UpperCamelCase_ = [image]
UpperCamelCase_ = [trans(img.convert("RGB" ) ) for img in image]
UpperCamelCase_ = torch.stack(UpperCamelCase_ )
return image
class _UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self: List[Any] , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: Dict ) -> str:
"""simple docstring"""
super().__init__()
# make sure scheduler can always be converted to DDIM
UpperCamelCase_ = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE )
def lowercase ( self: List[str] , _SCREAMING_SNAKE_CASE: Dict ) -> Optional[Any]:
"""simple docstring"""
if strength < 0 or strength > 1:
raise ValueError(f'''The value of strength should in [0.0, 1.0] but is {strength}''' )
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: List[str] ) -> int:
"""simple docstring"""
UpperCamelCase_ = min(int(num_inference_steps * strength ) , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = max(num_inference_steps - init_timestep , 0 )
UpperCamelCase_ = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def lowercase ( self: Tuple , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: Optional[int]=None ) -> List[Any]:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , (torch.Tensor, PIL.Image.Image, list) ):
raise ValueError(
f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_SCREAMING_SNAKE_CASE )}''' )
UpperCamelCase_ = image.to(device=_SCREAMING_SNAKE_CASE , dtype=_SCREAMING_SNAKE_CASE )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and len(_SCREAMING_SNAKE_CASE ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(_SCREAMING_SNAKE_CASE )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
UpperCamelCase_ = init_latents.shape
UpperCamelCase_ = randn_tensor(_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , device=_SCREAMING_SNAKE_CASE , dtype=_SCREAMING_SNAKE_CASE )
# get latents
print("add noise to latents at timestep" , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.scheduler.add_noise(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = init_latents
return latents
@torch.no_grad()
def __call__( self: Dict , _SCREAMING_SNAKE_CASE: Union[torch.FloatTensor, PIL.Image.Image] = None , _SCREAMING_SNAKE_CASE: float = 0.8 , _SCREAMING_SNAKE_CASE: int = 1 , _SCREAMING_SNAKE_CASE: Optional[Union[torch.Generator, List[torch.Generator]]] = None , _SCREAMING_SNAKE_CASE: float = 0.0 , _SCREAMING_SNAKE_CASE: int = 50 , _SCREAMING_SNAKE_CASE: Optional[bool] = None , _SCREAMING_SNAKE_CASE: Optional[str] = "pil" , _SCREAMING_SNAKE_CASE: bool = True , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
self.check_inputs(_SCREAMING_SNAKE_CASE )
# 2. Preprocess image
UpperCamelCase_ = preprocess(_SCREAMING_SNAKE_CASE )
# 3. set timesteps
self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE , device=self.device )
UpperCamelCase_ , UpperCamelCase_ = self.get_timesteps(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , self.device )
UpperCamelCase_ = timesteps[:1].repeat(_SCREAMING_SNAKE_CASE )
# 4. Prepare latent variables
UpperCamelCase_ = self.prepare_latents(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , self.unet.dtype , self.device , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = latents
# 5. Denoising loop
for t in self.progress_bar(_SCREAMING_SNAKE_CASE ):
# 1. predict noise model_output
UpperCamelCase_ = self.unet(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
UpperCamelCase_ = self.scheduler.step(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , eta=_SCREAMING_SNAKE_CASE , use_clipped_model_output=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , ).prev_sample
UpperCamelCase_ = (image / 2 + 0.5).clamp(0 , 1 )
UpperCamelCase_ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCamelCase_ = self.numpy_to_pil(_SCREAMING_SNAKE_CASE )
if not return_dict:
return (image, latent_timestep.item())
return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE )
| 328 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class _UpperCamelCase :
_UpperCamelCase : int
_UpperCamelCase : int
class _UpperCamelCase :
def __init__( self: Dict , _SCREAMING_SNAKE_CASE: int ) -> Dict:
"""simple docstring"""
UpperCamelCase_ = [[] for _ in range(_SCREAMING_SNAKE_CASE )]
UpperCamelCase_ = size
def __getitem__( self: Dict , _SCREAMING_SNAKE_CASE: int ) -> Iterator[Edge]:
"""simple docstring"""
return iter(self._graph[vertex] )
@property
def lowercase ( self: Tuple ) -> str:
"""simple docstring"""
return self._size
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: int ) -> Dict:
"""simple docstring"""
if weight not in (0, 1):
raise ValueError("Edge weight must be either 0 or 1." )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("Vertex indexes must be in [0; size)." )
self._graph[from_vertex].append(Edge(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: int ) -> int | None:
"""simple docstring"""
UpperCamelCase_ = deque([start_vertex] )
UpperCamelCase_ = [None] * self.size
UpperCamelCase_ = 0
while queue:
UpperCamelCase_ = queue.popleft()
UpperCamelCase_ = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
UpperCamelCase_ = current_distance + edge.weight
UpperCamelCase_ = distances[edge.destination_vertex]
if (
isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
and new_distance >= dest_vertex_distance
):
continue
UpperCamelCase_ = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("No path from start_vertex to finish_vertex." )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 369 |
import re
from filelock import FileLock
try:
import nltk
_UpperCAmelCase = True
except (ImportError, ModuleNotFoundError):
_UpperCAmelCase = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def lowerCAmelCase_ ( UpperCamelCase_ ) -> str:
re.sub("<n>" , "" , UpperCamelCase_ ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(UpperCamelCase_ ) )
| 328 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase_ ( UpperCamelCase_ ):
for param in module.parameters():
UpperCamelCase_ = False
def lowerCAmelCase_ ( ):
UpperCamelCase_ = "cuda" if torch.cuda.is_available() else "cpu"
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
UpperCamelCase_ = "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 lowerCAmelCase_ ( UpperCamelCase_ ):
UpperCamelCase_ = plt.imshow(UpperCamelCase_ )
fig.axes.get_xaxis().set_visible(UpperCamelCase_ )
fig.axes.get_yaxis().set_visible(UpperCamelCase_ )
plt.show()
def lowerCAmelCase_ ( ):
UpperCamelCase_ = datetime.now()
UpperCamelCase_ = current_time.strftime("%H:%M:%S" )
return timestamp
| 370 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
_UpperCamelCase : Optional[Any] = DiTPipeline
_UpperCamelCase : Any = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
_UpperCamelCase : Dict = PipelineTesterMixin.required_optional_params - {
'''latents''',
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
_UpperCamelCase : Optional[int] = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
_UpperCamelCase : Dict = False
def lowercase ( self: str ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase_ = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=_SCREAMING_SNAKE_CASE , activation_fn="gelu-approximate" , num_embeds_ada_norm=1000 , norm_type="ada_norm_zero" , norm_elementwise_affine=_SCREAMING_SNAKE_CASE , )
UpperCamelCase_ = AutoencoderKL()
UpperCamelCase_ = DDIMScheduler()
UpperCamelCase_ = {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
return components
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: Dict , _SCREAMING_SNAKE_CASE: List[str]=0 ) -> Dict:
"""simple docstring"""
if str(_SCREAMING_SNAKE_CASE ).startswith("mps" ):
UpperCamelCase_ = torch.manual_seed(_SCREAMING_SNAKE_CASE )
else:
UpperCamelCase_ = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = {
"class_labels": [1],
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def lowercase ( self: Any ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ = "cpu"
UpperCamelCase_ = self.get_dummy_components()
UpperCamelCase_ = self.pipeline_class(**_SCREAMING_SNAKE_CASE )
pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = pipe(**_SCREAMING_SNAKE_CASE ).images
UpperCamelCase_ = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
UpperCamelCase_ = np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57] )
UpperCamelCase_ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_SCREAMING_SNAKE_CASE , 1e-3 )
def lowercase ( self: Optional[int] ) -> Any:
"""simple docstring"""
self._test_inference_batch_single_identical(relax_max_difference=_SCREAMING_SNAKE_CASE , expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def lowercase ( self: Optional[Any] ) -> Optional[int]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class _UpperCamelCase ( unittest.TestCase ):
def lowercase ( self: Optional[int] ) -> List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase ( self: Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = torch.manual_seed(0 )
UpperCamelCase_ = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256" )
pipe.to("cuda" )
UpperCamelCase_ = ["vase", "umbrella", "white shark", "white wolf"]
UpperCamelCase_ = pipe.get_label_ids(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = pipe(_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , num_inference_steps=40 , output_type="np" ).images
for word, image in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase_ = load_numpy(
f'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy''' )
assert np.abs((expected_image - image).max() ) < 1e-2
def lowercase ( self: int ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ = DiTPipeline.from_pretrained("facebook/DiT-XL-2-512" )
UpperCamelCase_ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("cuda" )
UpperCamelCase_ = ["vase", "umbrella"]
UpperCamelCase_ = pipe.get_label_ids(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = torch.manual_seed(0 )
UpperCamelCase_ = pipe(_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , num_inference_steps=25 , output_type="np" ).images
for word, image in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
f'''/dit/{word}_512.npy''' )
assert np.abs((expected_image - image).max() ) < 1e-1
| 328 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
'TapasForMaskedLM',
'TapasForQuestionAnswering',
'TapasForSequenceClassification',
'TapasModel',
'TapasPreTrainedModel',
'load_tf_weights_in_tapas',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFTapasForMaskedLM',
'TFTapasForQuestionAnswering',
'TFTapasForSequenceClassification',
'TFTapasModel',
'TFTapasPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
from .tokenization_tapas import TapasTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_tapas import (
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTapasForMaskedLM,
TFTapasForQuestionAnswering,
TFTapasForSequenceClassification,
TFTapasModel,
TFTapasPreTrainedModel,
)
else:
import sys
_UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 371 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _UpperCamelCase :
def __init__( self: str ) -> Any:
"""simple docstring"""
UpperCamelCase_ = ""
UpperCamelCase_ = ""
UpperCamelCase_ = []
UpperCamelCase_ = 0
UpperCamelCase_ = 256
UpperCamelCase_ = 0
UpperCamelCase_ = 0
UpperCamelCase_ = 0
UpperCamelCase_ = 0
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: Dict ) -> str:
"""simple docstring"""
UpperCamelCase_ = cva.imread(_SCREAMING_SNAKE_CASE , 0 )
UpperCamelCase_ = copy.deepcopy(self.img )
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = plt.hist(self.img.ravel() , 256 , [0, 256] , label="x" )
UpperCamelCase_ = np.sum(_SCREAMING_SNAKE_CASE )
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
UpperCamelCase_ = x[i] / self.k
self.sk += prk
UpperCamelCase_ = (self.L - 1) * self.sk
if self.rem != 0:
UpperCamelCase_ = int(last % last )
UpperCamelCase_ = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = int(np.ma.count(self.img ) / self.img[1].size )
UpperCamelCase_ = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
UpperCamelCase_ = self.img[j][i]
if num != self.last_list[num]:
UpperCamelCase_ = self.last_list[num]
cva.imwrite("output_data/output.jpg" , self.img )
def lowercase ( self: Any ) -> Optional[Any]:
"""simple docstring"""
plt.hist(self.img.ravel() , 256 , [0, 256] )
def lowercase ( self: Tuple ) -> Union[str, Any]:
"""simple docstring"""
cva.imshow("Output-Image" , self.img )
cva.imshow("Input-Image" , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
_UpperCAmelCase = os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
_UpperCAmelCase = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 328 | 0 |
from functools import reduce
_UpperCAmelCase = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def lowerCAmelCase_ ( UpperCamelCase_ = N ) -> int:
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda UpperCamelCase_ , UpperCamelCase_ : str(int(UpperCamelCase_ ) * int(UpperCamelCase_ ) ) , n[i : i + 13] ) )
for i in range(len(UpperCamelCase_ ) - 12 ) )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 350 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_UpperCAmelCase = '\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n journal={arXiv preprint arXiv:1905.00537},\n year={2019}\n}\n'
_UpperCAmelCase = '\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n'
_UpperCAmelCase = '\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n predictions: list of predictions to score. Depending on the SuperGlUE subset:\n - for \'record\': list of question-answer dictionaries with the following keys:\n - \'idx\': index of the question as specified by the dataset\n - \'prediction_text\': the predicted answer text\n - for \'multirc\': list of question-answer dictionaries with the following keys:\n - \'idx\': index of the question-answer pair as specified by the dataset\n - \'prediction\': the predicted answer label\n - otherwise: list of predicted labels\n references: list of reference labels. Depending on the SuperGLUE subset:\n - for \'record\': list of question-answers dictionaries with the following keys:\n - \'idx\': index of the question as specified by the dataset\n - \'answers\': list of possible answers\n - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n - for \'record\':\n - \'exact_match\': Exact match between answer and gold answer\n - \'f1\': F1 score\n - for \'multirc\':\n - \'exact_match\': Exact match between answer and gold answer\n - \'f1_m\': Per-question macro-F1 score\n - \'f1_a\': Average F1 score over all answers\n - for \'axb\':\n \'matthews_correlation\': Matthew Correlation\n - for \'cb\':\n - \'accuracy\': Accuracy\n - \'f1\': F1 score\n - for all others:\n - \'accuracy\': Accuracy\nExamples:\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\') # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0, \'f1\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')\n >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]\n >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 1.0, \'f1\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')\n >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'matthews_correlation\': 1.0}\n'
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> Optional[Any]:
return float((preds == labels).mean() )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_="binary" ) -> Tuple:
UpperCamelCase_ = simple_accuracy(UpperCamelCase_ , UpperCamelCase_ )
UpperCamelCase_ = float(fa_score(y_true=UpperCamelCase_ , y_pred=UpperCamelCase_ , average=UpperCamelCase_ ) )
return {
"accuracy": acc,
"f1": fa,
}
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> List[Any]:
UpperCamelCase_ = {}
for id_pred, label in zip(UpperCamelCase_ , UpperCamelCase_ ):
UpperCamelCase_ = F'''{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'''
UpperCamelCase_ = id_pred["prediction"]
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
UpperCamelCase_ = [(pred, label)]
UpperCamelCase_ , UpperCamelCase_ = [], []
for question, preds_labels in question_map.items():
UpperCamelCase_ , UpperCamelCase_ = zip(*UpperCamelCase_ )
UpperCamelCase_ = fa_score(y_true=UpperCamelCase_ , y_pred=UpperCamelCase_ , average="macro" )
fas.append(UpperCamelCase_ )
UpperCamelCase_ = int(sum(pred == label for pred, label in preds_labels ) == len(UpperCamelCase_ ) )
ems.append(UpperCamelCase_ )
UpperCamelCase_ = float(sum(UpperCamelCase_ ) / len(UpperCamelCase_ ) )
UpperCamelCase_ = sum(UpperCamelCase_ ) / len(UpperCamelCase_ )
UpperCamelCase_ = float(fa_score(y_true=UpperCamelCase_ , y_pred=[id_pred["prediction"] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCamelCase ( datasets.Metric ):
def lowercase ( self: Optional[int] ) -> Optional[int]:
"""simple docstring"""
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
"You should supply a configuration name selected in "
"[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None , )
def lowercase ( self: List[Any] ) -> int:
"""simple docstring"""
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value("int64" ),
"query": datasets.Value("int64" ),
},
"prediction_text": datasets.Value("string" ),
},
"references": {
"idx": {
"passage": datasets.Value("int64" ),
"query": datasets.Value("int64" ),
},
"answers": datasets.Sequence(datasets.Value("string" ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value("int64" ),
"paragraph": datasets.Value("int64" ),
"question": datasets.Value("int64" ),
},
"prediction": datasets.Value("int64" ),
},
"references": datasets.Value("int64" ),
}
else:
return {
"predictions": datasets.Value("int64" ),
"references": datasets.Value("int64" ),
}
def lowercase ( self: Tuple , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: List[str] ) -> Dict:
"""simple docstring"""
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )}
elif self.config_name == "cb":
return acc_and_fa(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , fa_avg="macro" )
elif self.config_name == "record":
UpperCamelCase_ = [
{
"qas": [
{"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
for ref in references
]
}
]
UpperCamelCase_ = {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
return evaluate_record(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )[0]
elif self.config_name == "multirc":
return evaluate_multirc(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )}
else:
raise KeyError(
"You should supply a configuration name selected in "
"[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]" )
| 328 | 0 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def lowerCAmelCase_ ( UpperCamelCase_ = 8 ) -> str:
UpperCamelCase_ = ascii_letters + digits + punctuation
return "".join(secrets.choice(UpperCamelCase_ ) for _ in range(UpperCamelCase_ ) )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> str:
# Password Generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i -= len(UpperCamelCase_ )
UpperCamelCase_ = i // 3
UpperCamelCase_ = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
UpperCamelCase_ = (
chars_incl
+ random(UpperCamelCase_ , quotient + remainder )
+ random(UpperCamelCase_ , UpperCamelCase_ )
+ random(UpperCamelCase_ , UpperCamelCase_ )
)
UpperCamelCase_ = list(UpperCamelCase_ )
shuffle(UpperCamelCase_ )
return "".join(UpperCamelCase_ )
# random is a generalised function for letters, characters and numbers
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> str:
return "".join(secrets.choice(UpperCamelCase_ ) for _ in range(UpperCamelCase_ ) )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> Optional[Any]:
pass # Put your code here...
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> int:
pass # Put your code here...
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> Any:
pass # Put your code here...
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ = 8 ) -> bool:
if len(UpperCamelCase_ ) < min_length:
# Your Password must be at least 8 characters long
return False
UpperCamelCase_ = any(char in ascii_uppercase for char in password )
UpperCamelCase_ = any(char in ascii_lowercase for char in password )
UpperCamelCase_ = any(char in digits for char in password )
UpperCamelCase_ = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def lowerCAmelCase_ ( ) -> Optional[int]:
UpperCamelCase_ = int(input("Please indicate the max length of your password: " ).strip() )
UpperCamelCase_ = input(
"Please indicate the characters that must be in your password: " ).strip()
print("Password generated:" , password_generator(UpperCamelCase_ ) )
print(
"Alternative Password generated:" , alternative_password_generator(UpperCamelCase_ , UpperCamelCase_ ) , )
print("[If you are thinking of using this passsword, You better save it.]" )
if __name__ == "__main__":
main()
| 351 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : str = '''mgp-str'''
def __init__( self: Optional[Any] , _SCREAMING_SNAKE_CASE: Optional[int]=[32, 128] , _SCREAMING_SNAKE_CASE: Tuple=4 , _SCREAMING_SNAKE_CASE: Optional[Any]=3 , _SCREAMING_SNAKE_CASE: Optional[int]=27 , _SCREAMING_SNAKE_CASE: Tuple=38 , _SCREAMING_SNAKE_CASE: Tuple=50257 , _SCREAMING_SNAKE_CASE: List[Any]=30522 , _SCREAMING_SNAKE_CASE: Optional[Any]=768 , _SCREAMING_SNAKE_CASE: Dict=12 , _SCREAMING_SNAKE_CASE: List[str]=12 , _SCREAMING_SNAKE_CASE: Dict=4.0 , _SCREAMING_SNAKE_CASE: int=True , _SCREAMING_SNAKE_CASE: Tuple=False , _SCREAMING_SNAKE_CASE: Tuple=1e-5 , _SCREAMING_SNAKE_CASE: Optional[Any]=0.0 , _SCREAMING_SNAKE_CASE: Tuple=0.0 , _SCREAMING_SNAKE_CASE: List[Any]=0.0 , _SCREAMING_SNAKE_CASE: List[str]=False , _SCREAMING_SNAKE_CASE: int=0.02 , **_SCREAMING_SNAKE_CASE: Any , ) -> str:
"""simple docstring"""
super().__init__(**_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = image_size
UpperCamelCase_ = patch_size
UpperCamelCase_ = num_channels
UpperCamelCase_ = max_token_length
UpperCamelCase_ = num_character_labels
UpperCamelCase_ = num_bpe_labels
UpperCamelCase_ = num_wordpiece_labels
UpperCamelCase_ = hidden_size
UpperCamelCase_ = num_hidden_layers
UpperCamelCase_ = num_attention_heads
UpperCamelCase_ = mlp_ratio
UpperCamelCase_ = distilled
UpperCamelCase_ = layer_norm_eps
UpperCamelCase_ = drop_rate
UpperCamelCase_ = qkv_bias
UpperCamelCase_ = attn_drop_rate
UpperCamelCase_ = drop_path_rate
UpperCamelCase_ = output_aa_attentions
UpperCamelCase_ = initializer_range
| 328 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ConditionalDetrConfig',
'ConditionalDetrOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = ['ConditionalDetrFeatureExtractor']
_UpperCAmelCase = ['ConditionalDetrImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
'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
_UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 352 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
_UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class _UpperCamelCase :
_UpperCamelCase : str = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_UpperCamelCase : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCamelCase : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCamelCase : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
_UpperCamelCase : bool = field(default=lowerCAmelCase_ , metadata={'''help''': '''Whether tp freeze the encoder.'''} )
_UpperCamelCase : bool = field(default=lowerCAmelCase_ , metadata={'''help''': '''Whether to freeze the embeddings.'''} )
@dataclass
class _UpperCamelCase :
_UpperCamelCase : str = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
_UpperCamelCase : Optional[str] = field(
default='''summarization''' , metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''} , )
_UpperCamelCase : Optional[int] = field(
default=1_0_2_4 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_UpperCamelCase : Optional[int] = field(
default=1_2_8 , metadata={
'''help''': (
'''The maximum total sequence length for target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_UpperCamelCase : Optional[int] = field(
default=1_4_2 , metadata={
'''help''': (
'''The maximum total sequence length for validation target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded. '''
'''This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '''
'''during ``evaluate`` and ``predict``.'''
)
} , )
_UpperCamelCase : Optional[int] = field(
default=1_4_2 , metadata={
'''help''': (
'''The maximum total sequence length for test target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_UpperCamelCase : Optional[int] = field(default=-1 , metadata={'''help''': '''# training examples. -1 means use all.'''} )
_UpperCamelCase : Optional[int] = field(default=-1 , metadata={'''help''': '''# validation examples. -1 means use all.'''} )
_UpperCamelCase : Optional[int] = field(default=-1 , metadata={'''help''': '''# test examples. -1 means use all.'''} )
_UpperCamelCase : Optional[str] = field(default=lowerCAmelCase_ , metadata={'''help''': '''Source language id for translation.'''} )
_UpperCamelCase : Optional[str] = field(default=lowerCAmelCase_ , metadata={'''help''': '''Target language id for translation.'''} )
_UpperCamelCase : Optional[int] = field(default=lowerCAmelCase_ , metadata={'''help''': '''# num_beams to use for evaluation.'''} )
_UpperCamelCase : bool = field(
default=lowerCAmelCase_ , metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''} , )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Optional[int]:
logger.info(F'''***** {split} metrics *****''' )
for key in sorted(metrics.keys() ):
logger.info(F''' {key} = {metrics[key]}''' )
save_json(UpperCamelCase_ , os.path.join(UpperCamelCase_ , F'''{split}_results.json''' ) )
def lowerCAmelCase_ ( ) -> Optional[int]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCamelCase_ = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = parser.parse_args_into_dataclasses()
check_output_dir(UpperCamelCase_ )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("Training/evaluation parameters %s" , UpperCamelCase_ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase_ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
UpperCamelCase_ = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
assert hasattr(UpperCamelCase_ , UpperCamelCase_ ), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
setattr(UpperCamelCase_ , UpperCamelCase_ , getattr(UpperCamelCase_ , UpperCamelCase_ ) )
UpperCamelCase_ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
UpperCamelCase_ = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=".ckpt" in model_args.model_name_or_path , config=UpperCamelCase_ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(UpperCamelCase_ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
UpperCamelCase_ = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(UpperCamelCase_ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
UpperCamelCase_ = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
UpperCamelCase_ = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(UpperCamelCase_ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
UpperCamelCase_ = SeqaSeqDataset
# Get datasets
UpperCamelCase_ = (
dataset_class(
UpperCamelCase_ , type_path="train" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_train
else None
)
UpperCamelCase_ = (
dataset_class(
UpperCamelCase_ , type_path="val" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
UpperCamelCase_ = (
dataset_class(
UpperCamelCase_ , type_path="test" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
UpperCamelCase_ = (
build_compute_metrics_fn(data_args.task , UpperCamelCase_ ) if training_args.predict_with_generate else None
)
UpperCamelCase_ = SeqaSeqTrainer(
model=UpperCamelCase_ , args=UpperCamelCase_ , data_args=UpperCamelCase_ , train_dataset=UpperCamelCase_ , eval_dataset=UpperCamelCase_ , data_collator=SeqaSeqDataCollator(
UpperCamelCase_ , UpperCamelCase_ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=UpperCamelCase_ , tokenizer=UpperCamelCase_ , )
UpperCamelCase_ = {}
# Training
if training_args.do_train:
logger.info("*** Train ***" )
UpperCamelCase_ = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
UpperCamelCase_ = train_result.metrics
UpperCamelCase_ = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("train" , UpperCamelCase_ , training_args.output_dir )
all_metrics.update(UpperCamelCase_ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , "trainer_state.json" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
UpperCamelCase_ = trainer.evaluate(metric_key_prefix="val" )
UpperCamelCase_ = data_args.n_val
UpperCamelCase_ = round(metrics["val_loss"] , 4 )
if trainer.is_world_process_zero():
handle_metrics("val" , UpperCamelCase_ , training_args.output_dir )
all_metrics.update(UpperCamelCase_ )
if training_args.do_predict:
logger.info("*** Predict ***" )
UpperCamelCase_ = trainer.predict(test_dataset=UpperCamelCase_ , metric_key_prefix="test" )
UpperCamelCase_ = test_output.metrics
UpperCamelCase_ = data_args.n_test
if trainer.is_world_process_zero():
UpperCamelCase_ = round(metrics["test_loss"] , 4 )
handle_metrics("test" , UpperCamelCase_ , training_args.output_dir )
all_metrics.update(UpperCamelCase_ )
if training_args.predict_with_generate:
UpperCamelCase_ = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=UpperCamelCase_ , clean_up_tokenization_spaces=UpperCamelCase_ )
UpperCamelCase_ = lmap(str.strip , UpperCamelCase_ )
write_txt_file(UpperCamelCase_ , os.path.join(training_args.output_dir , "test_generations.txt" ) )
if trainer.is_world_process_zero():
save_json(UpperCamelCase_ , os.path.join(training_args.output_dir , "all_results.json" ) )
return all_metrics
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Optional[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 328 | 0 |
import os
import sys
import unittest
_UpperCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
_UpperCAmelCase = os.path.join(git_repo_path, 'src', 'diffusers')
class _UpperCamelCase ( unittest.TestCase ):
def lowercase ( self: Tuple ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ = find_backend(" if not is_torch_available():" )
self.assertEqual(_SCREAMING_SNAKE_CASE , "torch" )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
UpperCamelCase_ = find_backend(" if not (is_torch_available() and is_transformers_available()):" )
self.assertEqual(_SCREAMING_SNAKE_CASE , "torch_and_transformers" )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
UpperCamelCase_ = find_backend(
" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):" )
self.assertEqual(_SCREAMING_SNAKE_CASE , "torch_and_transformers_and_onnx" )
def lowercase ( self: Any ) -> str:
"""simple docstring"""
UpperCamelCase_ = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("torch" , _SCREAMING_SNAKE_CASE )
self.assertIn("torch_and_transformers" , _SCREAMING_SNAKE_CASE )
self.assertIn("flax_and_transformers" , _SCREAMING_SNAKE_CASE )
self.assertIn("torch_and_transformers_and_onnx" , _SCREAMING_SNAKE_CASE )
# Likewise, we can't assert on the exact content of a key
self.assertIn("UNet2DModel" , objects["torch"] )
self.assertIn("FlaxUNet2DConditionModel" , objects["flax"] )
self.assertIn("StableDiffusionPipeline" , objects["torch_and_transformers"] )
self.assertIn("FlaxStableDiffusionPipeline" , objects["flax_and_transformers"] )
self.assertIn("LMSDiscreteScheduler" , objects["torch_and_scipy"] )
self.assertIn("OnnxStableDiffusionPipeline" , objects["torch_and_transformers_and_onnx"] )
def lowercase ( self: Union[str, Any] ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ = create_dummy_object("CONSTANT" , "'torch'" )
self.assertEqual(_SCREAMING_SNAKE_CASE , "\nCONSTANT = None\n" )
UpperCamelCase_ = create_dummy_object("function" , "'torch'" )
self.assertEqual(
_SCREAMING_SNAKE_CASE , "\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n" )
UpperCamelCase_ = "\nclass FakeClass(metaclass=DummyObject):\n _backends = 'torch'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, 'torch')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n"
UpperCamelCase_ = create_dummy_object("FakeClass" , "'torch'" )
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def lowercase ( self: Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n"
UpperCamelCase_ = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]} )
self.assertEqual(dummy_files["torch"] , _SCREAMING_SNAKE_CASE )
| 353 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> list:
UpperCamelCase_ = int(UpperCamelCase_ )
if n_element < 1:
UpperCamelCase_ = ValueError("a should be a positive number" )
raise my_error
UpperCamelCase_ = [1]
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = (0, 0, 0)
UpperCamelCase_ = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
_UpperCAmelCase = input('Enter the last number (nth term) of the Hamming Number Series: ')
print('Formula of Hamming Number Series => 2^i * 3^j * 5^k')
_UpperCAmelCase = hamming(int(n))
print('-----------------------------------------------------')
print(f'''The list with nth numbers is: {hamming_numbers}''')
print('-----------------------------------------------------')
| 328 | 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.
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
_UpperCAmelCase = 'Create a default config file for Accelerate with only a few flags set.'
def lowerCAmelCase_ ( UpperCamelCase_="no" , UpperCamelCase_ = default_json_config_file , UpperCamelCase_ = False ) -> Union[str, Any]:
UpperCamelCase_ = Path(UpperCamelCase_ )
path.parent.mkdir(parents=UpperCamelCase_ , exist_ok=UpperCamelCase_ )
if path.exists():
print(
F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.''' )
return False
UpperCamelCase_ = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}''' )
UpperCamelCase_ = {
"compute_environment": "LOCAL_MACHINE",
"mixed_precision": mixed_precision,
}
if torch.cuda.is_available():
UpperCamelCase_ = torch.cuda.device_count()
UpperCamelCase_ = num_gpus
UpperCamelCase_ = False
if num_gpus > 1:
UpperCamelCase_ = "MULTI_GPU"
else:
UpperCamelCase_ = "NO"
elif is_xpu_available() and use_xpu:
UpperCamelCase_ = torch.xpu.device_count()
UpperCamelCase_ = num_xpus
UpperCamelCase_ = False
if num_xpus > 1:
UpperCamelCase_ = "MULTI_XPU"
else:
UpperCamelCase_ = "NO"
elif is_npu_available():
UpperCamelCase_ = torch.npu.device_count()
UpperCamelCase_ = num_npus
UpperCamelCase_ = False
if num_npus > 1:
UpperCamelCase_ = "MULTI_NPU"
else:
UpperCamelCase_ = "NO"
else:
UpperCamelCase_ = 0
UpperCamelCase_ = True
UpperCamelCase_ = 1
UpperCamelCase_ = "NO"
UpperCamelCase_ = ClusterConfig(**UpperCamelCase_ )
config.to_json_file(UpperCamelCase_ )
return path
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> Optional[Any]:
UpperCamelCase_ = parser.add_parser("default" , parents=UpperCamelCase_ , help=UpperCamelCase_ , formatter_class=UpperCamelCase_ )
parser.add_argument(
"--config_file" , default=UpperCamelCase_ , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , dest="save_location" , )
parser.add_argument(
"--mixed_precision" , choices=["no", "fp16", "bf16"] , type=UpperCamelCase_ , help="Whether or not to use mixed precision training. "
"Choose between FP16 and BF16 (bfloat16) training. "
"BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later." , default="no" , )
parser.set_defaults(func=UpperCamelCase_ )
return parser
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Tuple:
UpperCamelCase_ = write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(F'''accelerate configuration saved at {config_file}''' )
| 354 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
_UpperCamelCase : List[Any] = IFImgaImgSuperResolutionPipeline
_UpperCamelCase : Any = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
_UpperCamelCase : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''original_image'''} )
_UpperCamelCase : List[Any] = PipelineTesterMixin.required_optional_params - {'''latents'''}
def lowercase ( self: List[str] ) -> Any:
"""simple docstring"""
return self._get_superresolution_dummy_components()
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: Optional[int]=0 ) -> List[Any]:
"""simple docstring"""
if str(_SCREAMING_SNAKE_CASE ).startswith("mps" ):
UpperCamelCase_ = torch.manual_seed(_SCREAMING_SNAKE_CASE )
else:
UpperCamelCase_ = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = floats_tensor((1, 3, 16, 16) , rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"original_image": original_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def lowercase ( self: Any ) -> Union[str, Any]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def lowercase ( self: int ) -> Tuple:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda" , reason="float16 requires CUDA" )
def lowercase ( self: Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1e-1 )
def lowercase ( self: List[Any] ) -> Union[str, Any]:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def lowercase ( self: Dict ) -> Any:
"""simple docstring"""
self._test_save_load_local()
def lowercase ( self: Any ) -> Dict:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 328 | 0 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class _UpperCamelCase ( tf.keras.layers.Layer ):
def __init__( self: List[str] , _SCREAMING_SNAKE_CASE: Dict[str, int] , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: int = None , _SCREAMING_SNAKE_CASE: int = None ) -> str:
"""simple docstring"""
super().__init__()
UpperCamelCase_ = pad_token_id
UpperCamelCase_ = max_length
UpperCamelCase_ = vocab
UpperCamelCase_ = merges
UpperCamelCase_ = BytePairTokenizer(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , sequence_length=_SCREAMING_SNAKE_CASE )
@classmethod
def lowercase ( cls: Any , _SCREAMING_SNAKE_CASE: GPTaTokenizer , *_SCREAMING_SNAKE_CASE: int , **_SCREAMING_SNAKE_CASE: Dict ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ = [" ".join(_SCREAMING_SNAKE_CASE ) for m in tokenizer.bpe_ranks.keys()]
UpperCamelCase_ = tokenizer.get_vocab()
return cls(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@classmethod
def lowercase ( cls: int , _SCREAMING_SNAKE_CASE: Union[str, os.PathLike] , *_SCREAMING_SNAKE_CASE: List[Any] , **_SCREAMING_SNAKE_CASE: Optional[int] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = GPTaTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
return cls.from_tokenizer(_SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@classmethod
def lowercase ( cls: int , _SCREAMING_SNAKE_CASE: Any ) -> List[str]:
"""simple docstring"""
return cls(**_SCREAMING_SNAKE_CASE )
def lowercase ( self: Dict ) -> Dict:
"""simple docstring"""
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def lowercase ( self: List[str] , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: int = None ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = self.tf_tokenizer(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = tf.ones_like(_SCREAMING_SNAKE_CASE )
if self.pad_token_id is not None:
# pad the tokens up to max length
UpperCamelCase_ = max_length if max_length is not None else self.max_length
if max_length is not None:
UpperCamelCase_ , UpperCamelCase_ = pad_model_inputs(
_SCREAMING_SNAKE_CASE , max_seq_length=_SCREAMING_SNAKE_CASE , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 355 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_UpperCAmelCase = {'UserAgent': UserAgent().random}
def lowerCAmelCase_ ( UpperCamelCase_ ) -> dict:
UpperCamelCase_ = script.contents[0]
UpperCamelCase_ = json.loads(data[data.find("{\"config\"" ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class _UpperCamelCase :
def __init__( self: Optional[Any] , _SCREAMING_SNAKE_CASE: str ) -> str:
"""simple docstring"""
UpperCamelCase_ = f'''https://www.instagram.com/{username}/'''
UpperCamelCase_ = self.get_json()
def lowercase ( self: Union[str, Any] ) -> dict:
"""simple docstring"""
UpperCamelCase_ = requests.get(self.url , headers=_SCREAMING_SNAKE_CASE ).text
UpperCamelCase_ = BeautifulSoup(_SCREAMING_SNAKE_CASE , "html.parser" ).find_all("script" )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self: Tuple ) -> str:
"""simple docstring"""
return f'''{self.__class__.__name__}(\'{self.username}\')'''
def __str__( self: List[Any] ) -> str:
"""simple docstring"""
return f'''{self.fullname} ({self.username}) is {self.biography}'''
@property
def lowercase ( self: List[str] ) -> str:
"""simple docstring"""
return self.user_data["username"]
@property
def lowercase ( self: int ) -> str:
"""simple docstring"""
return self.user_data["full_name"]
@property
def lowercase ( self: List[Any] ) -> str:
"""simple docstring"""
return self.user_data["biography"]
@property
def lowercase ( self: List[Any] ) -> str:
"""simple docstring"""
return self.user_data["business_email"]
@property
def lowercase ( self: List[Any] ) -> str:
"""simple docstring"""
return self.user_data["external_url"]
@property
def lowercase ( self: List[Any] ) -> int:
"""simple docstring"""
return self.user_data["edge_followed_by"]["count"]
@property
def lowercase ( self: List[str] ) -> int:
"""simple docstring"""
return self.user_data["edge_follow"]["count"]
@property
def lowercase ( self: List[str] ) -> int:
"""simple docstring"""
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def lowercase ( self: List[str] ) -> str:
"""simple docstring"""
return self.user_data["profile_pic_url_hd"]
@property
def lowercase ( self: Optional[int] ) -> bool:
"""simple docstring"""
return self.user_data["is_verified"]
@property
def lowercase ( self: List[str] ) -> bool:
"""simple docstring"""
return self.user_data["is_private"]
def lowerCAmelCase_ ( UpperCamelCase_ = "github" ) -> None:
import os
if os.environ.get("CI" ):
return # test failing on GitHub Actions
UpperCamelCase_ = InstagramUser(UpperCamelCase_ )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , UpperCamelCase_ )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 150
assert instagram_user.number_of_followers > 120000
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "[email protected]"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith("https://instagram." )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
_UpperCAmelCase = InstagramUser('github')
print(instagram_user)
print(f'''{instagram_user.number_of_posts = }''')
print(f'''{instagram_user.number_of_followers = }''')
print(f'''{instagram_user.number_of_followings = }''')
print(f'''{instagram_user.email = }''')
print(f'''{instagram_user.website = }''')
print(f'''{instagram_user.profile_picture_url = }''')
print(f'''{instagram_user.is_verified = }''')
print(f'''{instagram_user.is_private = }''')
| 328 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {'vocab_file': 'spiece.model'}
_UpperCAmelCase = {
'vocab_file': {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
}
}
_UpperCAmelCase = {
'albert-base-v1': 5_1_2,
'albert-large-v1': 5_1_2,
'albert-xlarge-v1': 5_1_2,
'albert-xxlarge-v1': 5_1_2,
'albert-base-v2': 5_1_2,
'albert-large-v2': 5_1_2,
'albert-xlarge-v2': 5_1_2,
'albert-xxlarge-v2': 5_1_2,
}
_UpperCAmelCase = '▁'
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : Optional[int] = VOCAB_FILES_NAMES
_UpperCamelCase : int = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self: str , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Union[str, Any]=True , _SCREAMING_SNAKE_CASE: Union[str, Any]=True , _SCREAMING_SNAKE_CASE: Any=False , _SCREAMING_SNAKE_CASE: List[Any]="[CLS]" , _SCREAMING_SNAKE_CASE: Tuple="[SEP]" , _SCREAMING_SNAKE_CASE: List[Any]="<unk>" , _SCREAMING_SNAKE_CASE: str="[SEP]" , _SCREAMING_SNAKE_CASE: Any="<pad>" , _SCREAMING_SNAKE_CASE: Dict="[CLS]" , _SCREAMING_SNAKE_CASE: Any="[MASK]" , _SCREAMING_SNAKE_CASE: Optional[Dict[str, Any]] = None , **_SCREAMING_SNAKE_CASE: List[str] , ) -> None:
"""simple docstring"""
UpperCamelCase_ = (
AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE , normalized=_SCREAMING_SNAKE_CASE )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else mask_token
)
UpperCamelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_SCREAMING_SNAKE_CASE , remove_space=_SCREAMING_SNAKE_CASE , keep_accents=_SCREAMING_SNAKE_CASE , bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **_SCREAMING_SNAKE_CASE , )
UpperCamelCase_ = do_lower_case
UpperCamelCase_ = remove_space
UpperCamelCase_ = keep_accents
UpperCamelCase_ = vocab_file
UpperCamelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_SCREAMING_SNAKE_CASE )
@property
def lowercase ( self: int ) -> List[str]:
"""simple docstring"""
return len(self.sp_model )
def lowercase ( self: Dict ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self: Any ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = self.__dict__.copy()
UpperCamelCase_ = None
return state
def __setstate__( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: Optional[Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
UpperCamelCase_ = {}
UpperCamelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowercase ( self: int , _SCREAMING_SNAKE_CASE: Union[str, Any] ) -> str:
"""simple docstring"""
if self.remove_space:
UpperCamelCase_ = " ".join(inputs.strip().split() )
else:
UpperCamelCase_ = inputs
UpperCamelCase_ = outputs.replace("``" , "\"" ).replace("''" , "\"" )
if not self.keep_accents:
UpperCamelCase_ = unicodedata.normalize("NFKD" , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = "".join([c for c in outputs if not unicodedata.combining(_SCREAMING_SNAKE_CASE )] )
if self.do_lower_case:
UpperCamelCase_ = outputs.lower()
return outputs
def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: str ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ = self.preprocess_text(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.sp_model.encode(_SCREAMING_SNAKE_CASE , out_type=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = []
for piece in pieces:
if len(_SCREAMING_SNAKE_CASE ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
UpperCamelCase_ = self.sp_model.EncodeAsPieces(piece[:-1].replace(_SCREAMING_SNAKE_CASE , "" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
UpperCamelCase_ = cur_pieces[1:]
else:
UpperCamelCase_ = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_SCREAMING_SNAKE_CASE )
else:
new_pieces.append(_SCREAMING_SNAKE_CASE )
return new_pieces
def lowercase ( self: List[str] , _SCREAMING_SNAKE_CASE: List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE )
def lowercase ( self: Optional[int] , _SCREAMING_SNAKE_CASE: List[Any] ) -> List[Any]:
"""simple docstring"""
return self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE )
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: Dict ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ = []
UpperCamelCase_ = ""
UpperCamelCase_ = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE ) + token
UpperCamelCase_ = True
UpperCamelCase_ = []
else:
current_sub_tokens.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = False
out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE )
return out_string.strip()
def lowercase ( self: List[str] , _SCREAMING_SNAKE_CASE: List[int] , _SCREAMING_SNAKE_CASE: Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
UpperCamelCase_ = [self.sep_token_id]
UpperCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: List[int] , _SCREAMING_SNAKE_CASE: Optional[List[int]] = None , _SCREAMING_SNAKE_CASE: bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_SCREAMING_SNAKE_CASE , token_ids_a=_SCREAMING_SNAKE_CASE , already_has_special_tokens=_SCREAMING_SNAKE_CASE )
if token_ids_a is not None:
return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
def lowercase ( self: List[Any] , _SCREAMING_SNAKE_CASE: List[int] , _SCREAMING_SNAKE_CASE: Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
UpperCamelCase_ = [self.sep_token_id]
UpperCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase ( self: List[Any] , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCamelCase_ = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(_SCREAMING_SNAKE_CASE , "wb" ) as fi:
UpperCamelCase_ = self.sp_model.serialized_model_proto()
fi.write(_SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 356 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
get_image_size,
infer_channel_dimension_format,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends
if is_vision_available():
import textwrap
from PIL import Image, ImageDraw, ImageFont
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
_UpperCAmelCase = False
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = 'ybelkada/fonts'
def lowerCAmelCase_ ( ) -> Dict:
if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
raise ImportError(
F'''You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '''
"Pix2StructImageProcessor. Please upgrade torch." )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> List[Any]:
requires_backends(UpperCamelCase_ , ["torch"] )
_check_torch_version()
UpperCamelCase_ = image_tensor.unsqueeze(0 )
UpperCamelCase_ = torch.nn.functional.unfold(UpperCamelCase_ , (patch_height, patch_width) , stride=(patch_height, patch_width) )
UpperCamelCase_ = patches.reshape(image_tensor.size(0 ) , image_tensor.size(1 ) , UpperCamelCase_ , UpperCamelCase_ , -1 )
UpperCamelCase_ = patches.permute(0 , 4 , 2 , 3 , 1 ).reshape(
image_tensor.size(2 ) // patch_height , image_tensor.size(3 ) // patch_width , image_tensor.size(1 ) * patch_height * patch_width , )
return patches.unsqueeze(0 )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ = 36 , UpperCamelCase_ = "black" , UpperCamelCase_ = "white" , UpperCamelCase_ = 5 , UpperCamelCase_ = 5 , UpperCamelCase_ = 5 , UpperCamelCase_ = 5 , UpperCamelCase_ = None , UpperCamelCase_ = None , ) -> Image.Image:
requires_backends(UpperCamelCase_ , "vision" )
# Add new lines so that each line is no more than 80 characters.
UpperCamelCase_ = textwrap.TextWrapper(width=80 )
UpperCamelCase_ = wrapper.wrap(text=UpperCamelCase_ )
UpperCamelCase_ = "\n".join(UpperCamelCase_ )
if font_bytes is not None and font_path is None:
UpperCamelCase_ = io.BytesIO(UpperCamelCase_ )
elif font_path is not None:
UpperCamelCase_ = font_path
else:
UpperCamelCase_ = hf_hub_download(UpperCamelCase_ , "Arial.TTF" )
UpperCamelCase_ = ImageFont.truetype(UpperCamelCase_ , encoding="UTF-8" , size=UpperCamelCase_ )
# Use a temporary canvas to determine the width and height in pixels when
# rendering the text.
UpperCamelCase_ = ImageDraw.Draw(Image.new("RGB" , (1, 1) , UpperCamelCase_ ) )
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = temp_draw.textbbox((0, 0) , UpperCamelCase_ , UpperCamelCase_ )
# Create the actual image with a bit of padding around the text.
UpperCamelCase_ = text_width + left_padding + right_padding
UpperCamelCase_ = text_height + top_padding + bottom_padding
UpperCamelCase_ = Image.new("RGB" , (image_width, image_height) , UpperCamelCase_ )
UpperCamelCase_ = ImageDraw.Draw(UpperCamelCase_ )
draw.text(xy=(left_padding, top_padding) , text=UpperCamelCase_ , fill=UpperCamelCase_ , font=UpperCamelCase_ )
return image
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , **UpperCamelCase_ ) -> Union[str, Any]:
requires_backends(UpperCamelCase_ , "vision" )
# Convert to PIL image if necessary
UpperCamelCase_ = to_pil_image(UpperCamelCase_ )
UpperCamelCase_ = render_text(UpperCamelCase_ , **UpperCamelCase_ )
UpperCamelCase_ = max(header_image.width , image.width )
UpperCamelCase_ = int(image.height * (new_width / image.width) )
UpperCamelCase_ = int(header_image.height * (new_width / header_image.width) )
UpperCamelCase_ = Image.new("RGB" , (new_width, new_height + new_header_height) , "white" )
new_image.paste(header_image.resize((new_width, new_header_height) ) , (0, 0) )
new_image.paste(image.resize((new_width, new_height) ) , (0, new_header_height) )
# Convert back to the original framework if necessary
UpperCamelCase_ = to_numpy_array(UpperCamelCase_ )
if infer_channel_dimension_format(UpperCamelCase_ ) == ChannelDimension.LAST:
UpperCamelCase_ = to_channel_dimension_format(UpperCamelCase_ , ChannelDimension.LAST )
return new_image
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : str = ['''flattened_patches''']
def __init__( self: List[Any] , _SCREAMING_SNAKE_CASE: bool = True , _SCREAMING_SNAKE_CASE: bool = True , _SCREAMING_SNAKE_CASE: Dict[str, int] = None , _SCREAMING_SNAKE_CASE: int = 2048 , _SCREAMING_SNAKE_CASE: bool = False , **_SCREAMING_SNAKE_CASE: Optional[Any] , ) -> None:
"""simple docstring"""
super().__init__(**_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = patch_size if patch_size is not None else {"height": 16, "width": 16}
UpperCamelCase_ = do_normalize
UpperCamelCase_ = do_convert_rgb
UpperCamelCase_ = max_patches
UpperCamelCase_ = is_vqa
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: np.ndarray , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: dict , **_SCREAMING_SNAKE_CASE: Union[str, Any] ) -> np.ndarray:
"""simple docstring"""
requires_backends(self.extract_flattened_patches , "torch" )
_check_torch_version()
# convert to torch
UpperCamelCase_ = to_channel_dimension_format(_SCREAMING_SNAKE_CASE , ChannelDimension.FIRST )
UpperCamelCase_ = torch.from_numpy(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ , UpperCamelCase_ = patch_size["height"], patch_size["width"]
UpperCamelCase_ , UpperCamelCase_ = get_image_size(_SCREAMING_SNAKE_CASE )
# maximize scale s.t.
UpperCamelCase_ = math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
UpperCamelCase_ = max(min(math.floor(scale * image_height / patch_height ) , _SCREAMING_SNAKE_CASE ) , 1 )
UpperCamelCase_ = max(min(math.floor(scale * image_width / patch_width ) , _SCREAMING_SNAKE_CASE ) , 1 )
UpperCamelCase_ = max(num_feasible_rows * patch_height , 1 )
UpperCamelCase_ = max(num_feasible_cols * patch_width , 1 )
UpperCamelCase_ = torch.nn.functional.interpolate(
image.unsqueeze(0 ) , size=(resized_height, resized_width) , mode="bilinear" , align_corners=_SCREAMING_SNAKE_CASE , antialias=_SCREAMING_SNAKE_CASE , ).squeeze(0 )
# [1, rows, columns, patch_height * patch_width * image_channels]
UpperCamelCase_ = torch_extract_patches(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = patches.shape
UpperCamelCase_ = patches_shape[1]
UpperCamelCase_ = patches_shape[2]
UpperCamelCase_ = patches_shape[3]
# [rows * columns, patch_height * patch_width * image_channels]
UpperCamelCase_ = patches.reshape([rows * columns, depth] )
# [rows * columns, 1]
UpperCamelCase_ = torch.arange(_SCREAMING_SNAKE_CASE ).reshape([rows, 1] ).repeat(1 , _SCREAMING_SNAKE_CASE ).reshape([rows * columns, 1] )
UpperCamelCase_ = torch.arange(_SCREAMING_SNAKE_CASE ).reshape([1, columns] ).repeat(_SCREAMING_SNAKE_CASE , 1 ).reshape([rows * columns, 1] )
# Offset by 1 so the ids do not contain zeros, which represent padding.
row_ids += 1
col_ids += 1
# Prepare additional patch features.
# [rows * columns, 1]
UpperCamelCase_ = row_ids.to(torch.floataa )
UpperCamelCase_ = col_ids.to(torch.floataa )
# [rows * columns, 2 + patch_height * patch_width * image_channels]
UpperCamelCase_ = torch.cat([row_ids, col_ids, patches] , -1 )
# [max_patches, 2 + patch_height * patch_width * image_channels]
UpperCamelCase_ = torch.nn.functional.pad(_SCREAMING_SNAKE_CASE , [0, 0, 0, max_patches - (rows * columns)] ).float()
UpperCamelCase_ = to_numpy_array(_SCREAMING_SNAKE_CASE )
return result
def lowercase ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: np.ndarray , _SCREAMING_SNAKE_CASE: Optional[Union[str, ChannelDimension]] = None , **_SCREAMING_SNAKE_CASE: List[str] ) -> np.ndarray:
"""simple docstring"""
if image.dtype == np.uinta:
UpperCamelCase_ = image.astype(np.floataa )
# take mean across the whole `image`
UpperCamelCase_ = np.mean(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = np.std(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = max(_SCREAMING_SNAKE_CASE , 1.0 / math.sqrt(np.prod(image.shape ) ) )
return normalize(_SCREAMING_SNAKE_CASE , mean=_SCREAMING_SNAKE_CASE , std=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def lowercase ( self: Optional[int] , _SCREAMING_SNAKE_CASE: ImageInput , _SCREAMING_SNAKE_CASE: Optional[str] = None , _SCREAMING_SNAKE_CASE: bool = None , _SCREAMING_SNAKE_CASE: Optional[bool] = None , _SCREAMING_SNAKE_CASE: Optional[int] = None , _SCREAMING_SNAKE_CASE: Optional[Dict[str, int]] = None , _SCREAMING_SNAKE_CASE: Optional[Union[str, TensorType]] = None , _SCREAMING_SNAKE_CASE: ChannelDimension = ChannelDimension.FIRST , **_SCREAMING_SNAKE_CASE: List[Any] , ) -> ImageInput:
"""simple docstring"""
UpperCamelCase_ = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase_ = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
UpperCamelCase_ = patch_size if patch_size is not None else self.patch_size
UpperCamelCase_ = max_patches if max_patches is not None else self.max_patches
UpperCamelCase_ = self.is_vqa
if kwargs.get("data_format" , _SCREAMING_SNAKE_CASE ) is not None:
raise ValueError("data_format is not an accepted input as the outputs are " )
UpperCamelCase_ = 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." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
UpperCamelCase_ = [convert_to_rgb(_SCREAMING_SNAKE_CASE ) for image in images]
# All transformations expect numpy arrays.
UpperCamelCase_ = [to_numpy_array(_SCREAMING_SNAKE_CASE ) for image in images]
if is_vqa:
if header_text is None:
raise ValueError("A header text must be provided for VQA models." )
UpperCamelCase_ = kwargs.pop("font_bytes" , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = kwargs.pop("font_path" , _SCREAMING_SNAKE_CASE )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase_ = [header_text] * len(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = [
render_header(_SCREAMING_SNAKE_CASE , header_text[i] , font_bytes=_SCREAMING_SNAKE_CASE , font_path=_SCREAMING_SNAKE_CASE )
for i, image in enumerate(_SCREAMING_SNAKE_CASE )
]
if do_normalize:
UpperCamelCase_ = [self.normalize(image=_SCREAMING_SNAKE_CASE ) for image in images]
# convert to torch tensor and permute
UpperCamelCase_ = [
self.extract_flattened_patches(image=_SCREAMING_SNAKE_CASE , max_patches=_SCREAMING_SNAKE_CASE , patch_size=_SCREAMING_SNAKE_CASE )
for image in images
]
# create attention mask in numpy
UpperCamelCase_ = [(image.sum(axis=-1 ) != 0).astype(np.floataa ) for image in images]
UpperCamelCase_ = BatchFeature(
data={"flattened_patches": images, "attention_mask": attention_masks} , tensor_type=_SCREAMING_SNAKE_CASE )
return encoded_outputs
| 328 | 0 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : Optional[int] = ['''image_processor''']
_UpperCamelCase : str = '''SamImageProcessor'''
def __init__( self: Optional[int] , _SCREAMING_SNAKE_CASE: str ) -> int:
"""simple docstring"""
super().__init__(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.image_processor
UpperCamelCase_ = -10
UpperCamelCase_ = self.image_processor.size["longest_edge"]
def __call__( self: str , _SCREAMING_SNAKE_CASE: Union[str, Any]=None , _SCREAMING_SNAKE_CASE: List[Any]=None , _SCREAMING_SNAKE_CASE: List[str]=None , _SCREAMING_SNAKE_CASE: Dict=None , _SCREAMING_SNAKE_CASE: Optional[Union[str, TensorType]] = None , **_SCREAMING_SNAKE_CASE: Optional[Any] , ) -> BatchEncoding:
"""simple docstring"""
UpperCamelCase_ = self.image_processor(
_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
# pop arguments that are not used in the foward but used nevertheless
UpperCamelCase_ = encoding_image_processor["original_sizes"]
if hasattr(_SCREAMING_SNAKE_CASE , "numpy" ): # Checks if Torch or TF tensor
UpperCamelCase_ = original_sizes.numpy()
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = self._check_and_preprocess_points(
input_points=_SCREAMING_SNAKE_CASE , input_labels=_SCREAMING_SNAKE_CASE , input_boxes=_SCREAMING_SNAKE_CASE , )
UpperCamelCase_ = self._normalize_and_convert(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , input_points=_SCREAMING_SNAKE_CASE , input_labels=_SCREAMING_SNAKE_CASE , input_boxes=_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , )
return encoding_image_processor
def lowercase ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: str=None , _SCREAMING_SNAKE_CASE: Tuple=None , _SCREAMING_SNAKE_CASE: List[Any]=None , _SCREAMING_SNAKE_CASE: Tuple="pt" , ) -> str:
"""simple docstring"""
if input_points is not None:
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
UpperCamelCase_ = [
self._normalize_coordinates(self.target_size , _SCREAMING_SNAKE_CASE , original_sizes[0] ) for point in input_points
]
else:
UpperCamelCase_ = [
self._normalize_coordinates(self.target_size , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for point, original_size in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
UpperCamelCase_ , UpperCamelCase_ = self._pad_points_and_labels(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = np.array(_SCREAMING_SNAKE_CASE )
if input_labels is not None:
UpperCamelCase_ = np.array(_SCREAMING_SNAKE_CASE )
if input_boxes is not None:
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
UpperCamelCase_ = [
self._normalize_coordinates(self.target_size , _SCREAMING_SNAKE_CASE , original_sizes[0] , is_bounding_box=_SCREAMING_SNAKE_CASE )
for box in input_boxes
]
else:
UpperCamelCase_ = [
self._normalize_coordinates(self.target_size , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , is_bounding_box=_SCREAMING_SNAKE_CASE )
for box, original_size in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
]
UpperCamelCase_ = np.array(_SCREAMING_SNAKE_CASE )
if input_boxes is not None:
if return_tensors == "pt":
UpperCamelCase_ = torch.from_numpy(_SCREAMING_SNAKE_CASE )
# boxes batch size of 1 by default
UpperCamelCase_ = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
UpperCamelCase_ = tf.convert_to_tensor(_SCREAMING_SNAKE_CASE )
# boxes batch size of 1 by default
UpperCamelCase_ = tf.expand_dims(_SCREAMING_SNAKE_CASE , 1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({"input_boxes": input_boxes} )
if input_points is not None:
if return_tensors == "pt":
UpperCamelCase_ = torch.from_numpy(_SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
UpperCamelCase_ = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
UpperCamelCase_ = tf.convert_to_tensor(_SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
UpperCamelCase_ = tf.expand_dims(_SCREAMING_SNAKE_CASE , 1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({"input_points": input_points} )
if input_labels is not None:
if return_tensors == "pt":
UpperCamelCase_ = torch.from_numpy(_SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
UpperCamelCase_ = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
UpperCamelCase_ = tf.convert_to_tensor(_SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
UpperCamelCase_ = tf.expand_dims(_SCREAMING_SNAKE_CASE , 1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({"input_labels": input_labels} )
return encoding_image_processor
def lowercase ( self: int , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: Dict ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ = max([point.shape[0] for point in input_points] )
UpperCamelCase_ = []
for i, point in enumerate(_SCREAMING_SNAKE_CASE ):
if point.shape[0] != expected_nb_points:
UpperCamelCase_ = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value] , axis=0 )
UpperCamelCase_ = np.append(input_labels[i] , [self.point_pad_value] )
processed_input_points.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = processed_input_points
return input_points, input_labels
def lowercase ( self: List[str] , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: np.ndarray , _SCREAMING_SNAKE_CASE: Union[str, Any] , _SCREAMING_SNAKE_CASE: Tuple=False ) -> np.ndarray:
"""simple docstring"""
UpperCamelCase_ , UpperCamelCase_ = original_size
UpperCamelCase_ , UpperCamelCase_ = self.image_processor._get_preprocess_shape(_SCREAMING_SNAKE_CASE , longest_edge=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = deepcopy(_SCREAMING_SNAKE_CASE ).astype(_SCREAMING_SNAKE_CASE )
if is_bounding_box:
UpperCamelCase_ = coords.reshape(-1 , 2 , 2 )
UpperCamelCase_ = coords[..., 0] * (new_w / old_w)
UpperCamelCase_ = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
UpperCamelCase_ = coords.reshape(-1 , 4 )
return coords
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: Optional[Any]=None , _SCREAMING_SNAKE_CASE: Union[str, Any]=None , _SCREAMING_SNAKE_CASE: Any=None , ) -> Any:
"""simple docstring"""
if input_points is not None:
if hasattr(_SCREAMING_SNAKE_CASE , "numpy" ): # Checks for TF or Torch tensor
UpperCamelCase_ = input_points.numpy().tolist()
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or not isinstance(input_points[0] , _SCREAMING_SNAKE_CASE ):
raise ValueError("Input points must be a list of list of floating points." )
UpperCamelCase_ = [np.array(_SCREAMING_SNAKE_CASE ) for input_point in input_points]
else:
UpperCamelCase_ = None
if input_labels is not None:
if hasattr(_SCREAMING_SNAKE_CASE , "numpy" ):
UpperCamelCase_ = input_labels.numpy().tolist()
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or not isinstance(input_labels[0] , _SCREAMING_SNAKE_CASE ):
raise ValueError("Input labels must be a list of list integers." )
UpperCamelCase_ = [np.array(_SCREAMING_SNAKE_CASE ) for label in input_labels]
else:
UpperCamelCase_ = None
if input_boxes is not None:
if hasattr(_SCREAMING_SNAKE_CASE , "numpy" ):
UpperCamelCase_ = input_boxes.numpy().tolist()
if (
not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
or not isinstance(input_boxes[0] , _SCREAMING_SNAKE_CASE )
or not isinstance(input_boxes[0][0] , _SCREAMING_SNAKE_CASE )
):
raise ValueError("Input boxes must be a list of list of list of floating points." )
UpperCamelCase_ = [np.array(_SCREAMING_SNAKE_CASE ).astype(np.floataa ) for box in input_boxes]
else:
UpperCamelCase_ = None
return input_points, input_labels, input_boxes
@property
def lowercase ( self: List[str] ) -> Dict:
"""simple docstring"""
UpperCamelCase_ = self.image_processor.model_input_names
return list(dict.fromkeys(_SCREAMING_SNAKE_CASE ) )
def lowercase ( self: Union[str, Any] , *_SCREAMING_SNAKE_CASE: Optional[Any] , **_SCREAMING_SNAKE_CASE: Union[str, Any] ) -> str:
"""simple docstring"""
return self.image_processor.post_process_masks(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
| 357 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __init__( self: Any , _SCREAMING_SNAKE_CASE: int = 768 , ) -> Tuple:
"""simple docstring"""
super().__init__()
UpperCamelCase_ = nn.Parameter(torch.zeros(1 , _SCREAMING_SNAKE_CASE ) )
UpperCamelCase_ = nn.Parameter(torch.ones(1 , _SCREAMING_SNAKE_CASE ) )
def lowercase ( self: List[Any] , _SCREAMING_SNAKE_CASE: Optional[Union[str, torch.device]] = None , _SCREAMING_SNAKE_CASE: Optional[torch.dtype] = None , ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ = nn.Parameter(self.mean.to(_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase_ = nn.Parameter(self.std.to(_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE ) )
return self
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: Dict ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = (embeds - self.mean) * 1.0 / self.std
return embeds
def lowercase ( self: List[Any] , _SCREAMING_SNAKE_CASE: Union[str, Any] ) -> Dict:
"""simple docstring"""
UpperCamelCase_ = (embeds * self.std) + self.mean
return embeds
| 328 | 0 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
_UpperCAmelCase = ''
_UpperCAmelCase = ''
_UpperCAmelCase = ''
_UpperCAmelCase = 1 # (0 is vertical, 1 is horizontal)
def lowerCAmelCase_ ( ) -> None:
UpperCamelCase_ , UpperCamelCase_ = get_dataset(UpperCamelCase_ , UpperCamelCase_ )
print("Processing..." )
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = update_image_and_anno(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
for index, image in enumerate(UpperCamelCase_ ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
UpperCamelCase_ = random_chars(32 )
UpperCamelCase_ = paths[index].split(os.sep )[-1].rsplit("." , 1 )[0]
UpperCamelCase_ = F'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
cva.imwrite(F'''/{file_root}.jpg''' , UpperCamelCase_ , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F'''Success {index+1}/{len(UpperCamelCase_ )} with {file_name}''' )
UpperCamelCase_ = []
for anno in new_annos[index]:
UpperCamelCase_ = F'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
annos_list.append(UpperCamelCase_ )
with open(F'''/{file_root}.txt''' , "w" ) as outfile:
outfile.write("\n".join(line for line in annos_list ) )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> tuple[list, list]:
UpperCamelCase_ = []
UpperCamelCase_ = []
for label_file in glob.glob(os.path.join(UpperCamelCase_ , "*.txt" ) ):
UpperCamelCase_ = label_file.split(os.sep )[-1].rsplit("." , 1 )[0]
with open(UpperCamelCase_ ) as in_file:
UpperCamelCase_ = in_file.readlines()
UpperCamelCase_ = os.path.join(UpperCamelCase_ , F'''{label_name}.jpg''' )
UpperCamelCase_ = []
for obj_list in obj_lists:
UpperCamelCase_ = obj_list.rstrip("\n" ).split(" " )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(UpperCamelCase_ )
labels.append(UpperCamelCase_ )
return img_paths, labels
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = 1 ) -> tuple[list, list, list]:
UpperCamelCase_ = []
UpperCamelCase_ = []
UpperCamelCase_ = []
for idx in range(len(UpperCamelCase_ ) ):
UpperCamelCase_ = []
UpperCamelCase_ = img_list[idx]
path_list.append(UpperCamelCase_ )
UpperCamelCase_ = anno_list[idx]
UpperCamelCase_ = cva.imread(UpperCamelCase_ )
if flip_type == 1:
UpperCamelCase_ = cva.flip(UpperCamelCase_ , UpperCamelCase_ )
for bbox in img_annos:
UpperCamelCase_ = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
UpperCamelCase_ = cva.flip(UpperCamelCase_ , UpperCamelCase_ )
for bbox in img_annos:
UpperCamelCase_ = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(UpperCamelCase_ )
new_imgs_list.append(UpperCamelCase_ )
return new_imgs_list, new_annos_lists, path_list
def lowerCAmelCase_ ( UpperCamelCase_ = 32 ) -> str:
assert number_char > 1, "The number of character should greater than 1"
UpperCamelCase_ = ascii_lowercase + digits
return "".join(random.choice(UpperCamelCase_ ) for _ in range(UpperCamelCase_ ) )
if __name__ == "__main__":
main()
print('DONE ✅')
| 358 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_UpperCAmelCase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'language-modeling',
'summarization',
'token-classification',
'question-answering',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
_UpperCAmelCase = logging.getLogger()
def lowerCAmelCase_ ( ) -> Optional[int]:
UpperCamelCase_ = argparse.ArgumentParser()
parser.add_argument("-f" )
UpperCamelCase_ = parser.parse_args()
return args.f
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_="eval" ) -> Any:
UpperCamelCase_ = os.path.join(UpperCamelCase_ , F'''{split}_results.json''' )
if os.path.exists(UpperCamelCase_ ):
with open(UpperCamelCase_ , "r" ) as f:
return json.load(UpperCamelCase_ )
raise ValueError(F'''can\'t find {path}''' )
_UpperCAmelCase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _UpperCamelCase ( lowerCAmelCase_ ):
def lowercase ( self: Optional[Any] ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--eval_steps=2
--warmup_steps=2
--seed=42
--max_seq_length=128
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_flax_glue.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE )
self.assertGreaterEqual(result["eval_accuracy"] , 0.75 )
@slow
def lowercase ( self: int ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_clm_flax.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--block_size 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_clm_flax.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE )
self.assertLess(result["eval_perplexity"] , 100 )
@slow
def lowercase ( self: Any ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_summarization.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--test_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=8
--do_train
--do_eval
--do_predict
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_summarization_flax.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE , split="test" )
self.assertGreaterEqual(result["test_rouge1"] , 10 )
self.assertGreaterEqual(result["test_rouge2"] , 2 )
self.assertGreaterEqual(result["test_rougeL"] , 7 )
self.assertGreaterEqual(result["test_rougeLsum"] , 7 )
@slow
def lowercase ( self: str ) -> int:
"""simple docstring"""
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_mlm.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--overwrite_output_dir
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--logging_steps 2 --eval_steps 2
--do_train
--do_eval
--num_train_epochs=1
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_mlm_flax.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE )
self.assertLess(result["eval_perplexity"] , 42 )
@slow
def lowercase ( self: Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_t5_mlm_flax.py
--model_name_or_path t5-small
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_ta_mlm_flax.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE )
self.assertGreaterEqual(result["eval_accuracy"] , 0.42 )
@slow
def lowercase ( self: str ) -> int:
"""simple docstring"""
UpperCamelCase_ = 7 if get_gpu_count() > 1 else 2
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_flax_ner.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--do_train
--do_eval
--warmup_steps=2
--learning_rate=2e-4
--logging_steps 2 --eval_steps 2
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_flax_ner.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE )
self.assertGreaterEqual(result["eval_accuracy"] , 0.75 )
self.assertGreaterEqual(result["eval_f1"] , 0.3 )
@slow
def lowercase ( self: Union[str, Any] ) -> Any:
"""simple docstring"""
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_qa.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=2
--do_train
--do_eval
--logging_steps 2 --eval_steps 2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_qa.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE )
self.assertGreaterEqual(result["eval_f1"] , 30 )
self.assertGreaterEqual(result["eval_exact"] , 30 )
| 328 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
_UpperCAmelCase = None
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = '▁'
_UpperCAmelCase = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
_UpperCAmelCase = {
'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
'tokenizer_file': {
'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
},
}
_UpperCAmelCase = {
'google/pegasus-xsum': 5_1_2,
}
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : List[Any] = VOCAB_FILES_NAMES
_UpperCamelCase : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase : Union[str, Any] = PegasusTokenizer
_UpperCamelCase : Any = ['''input_ids''', '''attention_mask''']
def __init__( self: Any , _SCREAMING_SNAKE_CASE: Union[str, Any]=None , _SCREAMING_SNAKE_CASE: Dict=None , _SCREAMING_SNAKE_CASE: str="<pad>" , _SCREAMING_SNAKE_CASE: List[str]="</s>" , _SCREAMING_SNAKE_CASE: int="<unk>" , _SCREAMING_SNAKE_CASE: str="<mask_2>" , _SCREAMING_SNAKE_CASE: Dict="<mask_1>" , _SCREAMING_SNAKE_CASE: List[Any]=None , _SCREAMING_SNAKE_CASE: Optional[int]=103 , **_SCREAMING_SNAKE_CASE: Optional[Any] , ) -> int:
"""simple docstring"""
UpperCamelCase_ = offset
if additional_special_tokens is not None:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError(
f'''additional_special_tokens should be of type {type(_SCREAMING_SNAKE_CASE )}, but is'''
f''' {type(_SCREAMING_SNAKE_CASE )}''' )
UpperCamelCase_ = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'''<unk_{i}>''' for i in range(len(_SCREAMING_SNAKE_CASE ) , self.offset - 1 )
]
if len(set(_SCREAMING_SNAKE_CASE ) ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
UpperCamelCase_ = additional_special_tokens_extended
else:
UpperCamelCase_ = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'''<unk_{i}>''' for i in range(2 , self.offset )]
super().__init__(
_SCREAMING_SNAKE_CASE , tokenizer_file=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , mask_token_sent=_SCREAMING_SNAKE_CASE , offset=_SCREAMING_SNAKE_CASE , additional_special_tokens=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
UpperCamelCase_ = vocab_file
UpperCamelCase_ = False if not self.vocab_file else True
def lowercase ( self: Tuple , _SCREAMING_SNAKE_CASE: Any ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
"There should be 3 special tokens: mask_token, pad_token, and eos_token +"
f''' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}''' )
return [1 if x in all_special_ids else 0 for x in seq]
def lowercase ( self: int , _SCREAMING_SNAKE_CASE: List , _SCREAMING_SNAKE_CASE: Optional[List] = None , _SCREAMING_SNAKE_CASE: bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return self._special_token_mask(_SCREAMING_SNAKE_CASE )
elif token_ids_a is None:
return self._special_token_mask(_SCREAMING_SNAKE_CASE ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: Union[str, Any]=None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCamelCase_ = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ):
copyfile(self.vocab_file , _SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 359 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
for param in module.parameters():
UpperCamelCase_ = False
def lowerCAmelCase_ ( ) -> Dict:
UpperCamelCase_ = "cuda" if torch.cuda.is_available() else "cpu"
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
UpperCamelCase_ = "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 lowerCAmelCase_ ( UpperCamelCase_ ) -> Union[str, Any]:
UpperCamelCase_ = plt.imshow(UpperCamelCase_ )
fig.axes.get_xaxis().set_visible(UpperCamelCase_ )
fig.axes.get_yaxis().set_visible(UpperCamelCase_ )
plt.show()
def lowerCAmelCase_ ( ) -> List[str]:
UpperCamelCase_ = datetime.now()
UpperCamelCase_ = current_time.strftime("%H:%M:%S" )
return timestamp
| 328 | 0 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> Any:
# Load checkpoint
UpperCamelCase_ = torch.load(UpperCamelCase_ , map_location="cpu" )
UpperCamelCase_ = chkpt["model"]
# We have the base model one level deeper than the original XLM repository
UpperCamelCase_ = {}
for k, v in state_dict.items():
if "pred_layer" in k:
UpperCamelCase_ = v
else:
UpperCamelCase_ = v
UpperCamelCase_ = chkpt["params"]
UpperCamelCase_ = {n: v for n, v in config.items() if not isinstance(UpperCamelCase_ , (torch.FloatTensor, numpy.ndarray) )}
UpperCamelCase_ = chkpt["dico_word2id"]
UpperCamelCase_ = {s + "</w>" if s.find("@@" ) == -1 and i > 13 else s.replace("@@" , "" ): i for s, i in vocab.items()}
# Save pytorch-model
UpperCamelCase_ = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
UpperCamelCase_ = pytorch_dump_folder_path + "/" + CONFIG_NAME
UpperCamelCase_ = pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]
print(F'''Save PyTorch model to {pytorch_weights_dump_path}''' )
torch.save(UpperCamelCase_ , UpperCamelCase_ )
print(F'''Save configuration file to {pytorch_config_dump_path}''' )
with open(UpperCamelCase_ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(UpperCamelCase_ , indent=2 ) + "\n" )
print(F'''Save vocab file to {pytorch_config_dump_path}''' )
with open(UpperCamelCase_ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(UpperCamelCase_ , indent=2 ) + "\n" )
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--xlm_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.'
)
_UpperCAmelCase = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 360 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = '▁'
_UpperCAmelCase = {'vocab_file': 'spiece.model'}
_UpperCAmelCase = {
'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}
_UpperCAmelCase = {
'google/pegasus-xsum': 5_1_2,
}
_UpperCAmelCase = logging.get_logger(__name__)
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : Optional[Any] = VOCAB_FILES_NAMES
_UpperCamelCase : List[Any] = VOCAB_FILES_NAMES
_UpperCamelCase : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase : Optional[int] = ['''input_ids''', '''attention_mask''']
def __init__( self: str , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: str="<pad>" , _SCREAMING_SNAKE_CASE: Optional[Any]="</s>" , _SCREAMING_SNAKE_CASE: Any="<unk>" , _SCREAMING_SNAKE_CASE: int="<mask_2>" , _SCREAMING_SNAKE_CASE: List[Any]="<mask_1>" , _SCREAMING_SNAKE_CASE: Union[str, Any]=None , _SCREAMING_SNAKE_CASE: Optional[int]=103 , _SCREAMING_SNAKE_CASE: Optional[Dict[str, Any]] = None , **_SCREAMING_SNAKE_CASE: Dict , ) -> None:
"""simple docstring"""
UpperCamelCase_ = offset
if additional_special_tokens is not None:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError(
f'''additional_special_tokens should be of type {type(_SCREAMING_SNAKE_CASE )}, but is'''
f''' {type(_SCREAMING_SNAKE_CASE )}''' )
UpperCamelCase_ = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'''<unk_{i}>''' for i in range(len(_SCREAMING_SNAKE_CASE ) , self.offset - 1 )
]
if len(set(_SCREAMING_SNAKE_CASE ) ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
UpperCamelCase_ = additional_special_tokens_extended
else:
UpperCamelCase_ = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'''<unk_{i}>''' for i in range(2 , self.offset )]
UpperCamelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , mask_token_sent=_SCREAMING_SNAKE_CASE , offset=_SCREAMING_SNAKE_CASE , additional_special_tokens=_SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **_SCREAMING_SNAKE_CASE , )
UpperCamelCase_ = mask_token_sent
UpperCamelCase_ = vocab_file
UpperCamelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_SCREAMING_SNAKE_CASE )
# add special tokens to encoder dict
UpperCamelCase_ = {
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
} )
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1 )} )
UpperCamelCase_ = {v: k for k, v in self.encoder.items()}
@property
def lowercase ( self: Dict ) -> int:
"""simple docstring"""
return len(self.sp_model ) + self.offset
def lowercase ( self: int ) -> Dict[str, int]:
"""simple docstring"""
UpperCamelCase_ = {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self: Optional[int] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = self.__dict__.copy()
UpperCamelCase_ = None
return state
def __setstate__( self: List[Any] , _SCREAMING_SNAKE_CASE: List[Any] ) -> Any:
"""simple docstring"""
UpperCamelCase_ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
UpperCamelCase_ = {}
UpperCamelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowercase ( self: Optional[int] , _SCREAMING_SNAKE_CASE: str ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(_SCREAMING_SNAKE_CASE , out_type=_SCREAMING_SNAKE_CASE )
def lowercase ( self: Tuple , _SCREAMING_SNAKE_CASE: str ) -> int:
"""simple docstring"""
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
UpperCamelCase_ = self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE )
return sp_id + self.offset
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: int ) -> str:
"""simple docstring"""
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
UpperCamelCase_ = self.sp_model.IdToPiece(index - self.offset )
return token
def lowercase ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: Tuple ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = []
UpperCamelCase_ = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE ) + token
UpperCamelCase_ = []
else:
current_sub_tokens.append(_SCREAMING_SNAKE_CASE )
out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE )
return out_string.strip()
def lowercase ( self: List[str] , _SCREAMING_SNAKE_CASE: Optional[int]=False ) -> Union[str, Any]:
"""simple docstring"""
return 1
def lowercase ( self: int , _SCREAMING_SNAKE_CASE: str ) -> str:
"""simple docstring"""
UpperCamelCase_ = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: List , _SCREAMING_SNAKE_CASE: Optional[List] = None , _SCREAMING_SNAKE_CASE: bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return self._special_token_mask(_SCREAMING_SNAKE_CASE )
elif token_ids_a is None:
return self._special_token_mask(_SCREAMING_SNAKE_CASE ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: List[Any]=None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCamelCase_ = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(_SCREAMING_SNAKE_CASE , "wb" ) as fi:
UpperCamelCase_ = self.sp_model.serialized_model_proto()
fi.write(_SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 328 | 0 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
_UpperCAmelCase = namedtuple('covid_data', 'cases deaths recovered')
def lowerCAmelCase_ ( UpperCamelCase_ = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
UpperCamelCase_ = "//div[@class = \"maincounter-number\"]/span/text()"
return covid_data(*html.fromstring(requests.get(UpperCamelCase_ ).content ).xpath(UpperCamelCase_ ) )
_UpperCAmelCase = 'Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}'
print(fmt.format(*covid_stats()))
| 361 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
'TapasForMaskedLM',
'TapasForQuestionAnswering',
'TapasForSequenceClassification',
'TapasModel',
'TapasPreTrainedModel',
'load_tf_weights_in_tapas',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFTapasForMaskedLM',
'TFTapasForQuestionAnswering',
'TFTapasForSequenceClassification',
'TFTapasModel',
'TFTapasPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
from .tokenization_tapas import TapasTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_tapas import (
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTapasForMaskedLM,
TFTapasForQuestionAnswering,
TFTapasForSequenceClassification,
TFTapasModel,
TFTapasPreTrainedModel,
)
else:
import sys
_UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 328 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
),
'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
'cl-tohoku/bert-base-japanese-whole-word-masking': (
'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
),
'cl-tohoku/bert-base-japanese-char': (
'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
),
'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
),
'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
# See all BERT models at https://huggingface.co/models?filter=bert
}
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : Union[str, Any] = '''bert'''
def __init__( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: Optional[Any]=30522 , _SCREAMING_SNAKE_CASE: Union[str, Any]=768 , _SCREAMING_SNAKE_CASE: Tuple=12 , _SCREAMING_SNAKE_CASE: Dict=12 , _SCREAMING_SNAKE_CASE: List[str]=3072 , _SCREAMING_SNAKE_CASE: Tuple="gelu" , _SCREAMING_SNAKE_CASE: Optional[int]=0.1 , _SCREAMING_SNAKE_CASE: Optional[Any]=0.1 , _SCREAMING_SNAKE_CASE: Optional[int]=512 , _SCREAMING_SNAKE_CASE: Optional[Any]=2 , _SCREAMING_SNAKE_CASE: List[Any]=0.02 , _SCREAMING_SNAKE_CASE: str=1e-12 , _SCREAMING_SNAKE_CASE: List[Any]=0 , _SCREAMING_SNAKE_CASE: str="absolute" , _SCREAMING_SNAKE_CASE: Optional[int]=True , _SCREAMING_SNAKE_CASE: str=None , **_SCREAMING_SNAKE_CASE: int , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = vocab_size
UpperCamelCase_ = hidden_size
UpperCamelCase_ = num_hidden_layers
UpperCamelCase_ = num_attention_heads
UpperCamelCase_ = hidden_act
UpperCamelCase_ = intermediate_size
UpperCamelCase_ = hidden_dropout_prob
UpperCamelCase_ = attention_probs_dropout_prob
UpperCamelCase_ = max_position_embeddings
UpperCamelCase_ = type_vocab_size
UpperCamelCase_ = initializer_range
UpperCamelCase_ = layer_norm_eps
UpperCamelCase_ = position_embedding_type
UpperCamelCase_ = use_cache
UpperCamelCase_ = classifier_dropout
class _UpperCamelCase ( lowerCAmelCase_ ):
@property
def lowercase ( self: Dict ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
UpperCamelCase_ = {0: "batch", 1: "choice", 2: "sequence"}
else:
UpperCamelCase_ = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 362 |
import argparse
import json
from tqdm import tqdm
def lowerCAmelCase_ ( ) -> Tuple:
UpperCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=UpperCamelCase_ , default="biencoder-nq-dev.json" , help="Path to raw DPR training data" , )
parser.add_argument(
"--evaluation_set" , type=UpperCamelCase_ , help="where to store parsed evaluation_set file" , )
parser.add_argument(
"--gold_data_path" , type=UpperCamelCase_ , help="where to store parsed gold_data_path file" , )
UpperCamelCase_ = parser.parse_args()
with open(args.src_path , "r" ) as src_file, open(args.evaluation_set , "w" ) as eval_file, open(
args.gold_data_path , "w" ) as gold_file:
UpperCamelCase_ = json.load(UpperCamelCase_ )
for dpr_record in tqdm(UpperCamelCase_ ):
UpperCamelCase_ = dpr_record["question"]
UpperCamelCase_ = [context["title"] for context in dpr_record["positive_ctxs"]]
eval_file.write(question + "\n" )
gold_file.write("\t".join(UpperCamelCase_ ) + "\n" )
if __name__ == "__main__":
main()
| 328 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = ['MobileViTFeatureExtractor']
_UpperCAmelCase = ['MobileViTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MobileViTForImageClassification',
'MobileViTForSemanticSegmentation',
'MobileViTModel',
'MobileViTPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFMobileViTForImageClassification',
'TFMobileViTForSemanticSegmentation',
'TFMobileViTModel',
'TFMobileViTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilevit import MobileViTFeatureExtractor
from .image_processing_mobilevit import MobileViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilevit import (
MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTModel,
MobileViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilevit import (
TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileViTForImageClassification,
TFMobileViTForSemanticSegmentation,
TFMobileViTModel,
TFMobileViTPreTrainedModel,
)
else:
import sys
_UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 363 |
import requests
from bsa import BeautifulSoup
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> str:
UpperCamelCase_ = BeautifulSoup(requests.get(UpperCamelCase_ , params=UpperCamelCase_ ).content , "html.parser" )
UpperCamelCase_ = soup.find("div" , attrs={"class": "gs_ri"} )
UpperCamelCase_ = div.find("div" , attrs={"class": "gs_fl"} ).find_all("a" )
return anchors[2].get_text()
if __name__ == "__main__":
_UpperCAmelCase = {
'title': (
'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
'capacitance, volumetric capacitance, and energy density'
),
'journal': 'Chem. Mater.',
'volume': 3_0,
'pages': '3979-3990',
'year': 2_0_1_8,
'hl': 'en',
}
print(get_citation('https://scholar.google.com/scholar_lookup', params=params))
| 328 | 0 |
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 _UpperCamelCase :
def __init__( self: Optional[int] , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: Optional[int]=13 , _SCREAMING_SNAKE_CASE: Optional[int]=30 , _SCREAMING_SNAKE_CASE: Union[str, Any]=2 , _SCREAMING_SNAKE_CASE: Union[str, Any]=3 , _SCREAMING_SNAKE_CASE: Tuple=True , _SCREAMING_SNAKE_CASE: Optional[Any]=True , _SCREAMING_SNAKE_CASE: Optional[int]=32 , _SCREAMING_SNAKE_CASE: int=5 , _SCREAMING_SNAKE_CASE: List[Any]=4 , _SCREAMING_SNAKE_CASE: Optional[Any]=37 , _SCREAMING_SNAKE_CASE: Dict="gelu" , _SCREAMING_SNAKE_CASE: Tuple=0.1 , _SCREAMING_SNAKE_CASE: int=0.1 , _SCREAMING_SNAKE_CASE: Any=10 , _SCREAMING_SNAKE_CASE: List[Any]=0.02 , _SCREAMING_SNAKE_CASE: int=3 , _SCREAMING_SNAKE_CASE: str=0.6 , _SCREAMING_SNAKE_CASE: Optional[Any]=None , ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ = parent
UpperCamelCase_ = batch_size
UpperCamelCase_ = image_size
UpperCamelCase_ = patch_size
UpperCamelCase_ = num_channels
UpperCamelCase_ = is_training
UpperCamelCase_ = use_labels
UpperCamelCase_ = hidden_size
UpperCamelCase_ = num_hidden_layers
UpperCamelCase_ = num_attention_heads
UpperCamelCase_ = intermediate_size
UpperCamelCase_ = hidden_act
UpperCamelCase_ = hidden_dropout_prob
UpperCamelCase_ = attention_probs_dropout_prob
UpperCamelCase_ = type_sequence_label_size
UpperCamelCase_ = initializer_range
UpperCamelCase_ = mask_ratio
UpperCamelCase_ = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCamelCase_ = (image_size // patch_size) ** 2
UpperCamelCase_ = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def lowercase ( self: str ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase_ = None
if self.use_labels:
UpperCamelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase_ = self.get_config()
return config, pixel_values, labels
def lowercase ( self: Optional[Any] ) -> Tuple:
"""simple docstring"""
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=_SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: Dict , _SCREAMING_SNAKE_CASE: List[Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = ViTMAEModel(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase_ = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase ( self: List[str] , _SCREAMING_SNAKE_CASE: Union[str, Any] , _SCREAMING_SNAKE_CASE: Union[str, Any] , _SCREAMING_SNAKE_CASE: Any ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = ViTMAEForPreTraining(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase_ = model(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = (self.image_size // self.patch_size) ** 2
UpperCamelCase_ = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCamelCase_ = 1
UpperCamelCase_ = ViTMAEForPreTraining(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCamelCase_ = model(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def lowercase ( self: Dict ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = self.prepare_config_and_inputs()
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = config_and_inputs
UpperCamelCase_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
_UpperCamelCase : str = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
_UpperCamelCase : Union[str, Any] = {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}
_UpperCamelCase : Union[str, Any] = False
_UpperCamelCase : Any = False
_UpperCamelCase : Union[str, Any] = False
_UpperCamelCase : Optional[int] = False
def lowercase ( self: Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ = ViTMAEModelTester(self )
UpperCamelCase_ = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , has_text_modality=_SCREAMING_SNAKE_CASE , hidden_size=37 )
def lowercase ( self: int ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="ViTMAE does not use inputs_embeds" )
def lowercase ( self: Optional[int] ) -> Any:
"""simple docstring"""
pass
def lowercase ( self: Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ , UpperCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase_ = model_class(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCamelCase_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE , nn.Linear ) )
def lowercase ( self: int ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ , UpperCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase_ = model_class(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase_ = [*signature.parameters.keys()]
UpperCamelCase_ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , _SCREAMING_SNAKE_CASE )
def lowercase ( self: Optional[int] ) -> str:
"""simple docstring"""
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def lowercase ( self: int ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_SCREAMING_SNAKE_CASE )
def lowercase ( self: List[Any] , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: Union[str, Any] , _SCREAMING_SNAKE_CASE: Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
np.random.seed(2 )
UpperCamelCase_ = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
UpperCamelCase_ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCamelCase_ = torch.from_numpy(_SCREAMING_SNAKE_CASE )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCamelCase_ = pt_noise
super().check_pt_tf_models(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def lowercase ( self: str ) -> str:
"""simple docstring"""
UpperCamelCase_ , UpperCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase_ = model_class(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCamelCase_ = model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
UpperCamelCase_ = outputs[0].cpu().numpy()
UpperCamelCase_ = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = model_class.from_pretrained(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCamelCase_ = model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
# Make sure we don't have nans
UpperCamelCase_ = after_outputs[0].cpu().numpy()
UpperCamelCase_ = 0
UpperCamelCase_ = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_SCREAMING_SNAKE_CASE , 1e-5 )
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowercase ( self: int ) -> Dict:
"""simple docstring"""
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowercase ( self: str ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowercase ( self: Optional[Any] ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load" )
def lowercase ( self: List[Any] ) -> int:
"""simple docstring"""
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def lowercase ( self: List[Any] ) -> Union[str, Any]:
"""simple docstring"""
pass
@slow
def lowercase ( self: Optional[int] ) -> List[Any]:
"""simple docstring"""
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase_ = ViTMAEModel.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
def lowerCAmelCase_ ( ) -> Tuple:
UpperCamelCase_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _UpperCamelCase ( unittest.TestCase ):
@cached_property
def lowercase ( self: str ) -> Optional[int]:
"""simple docstring"""
return ViTImageProcessor.from_pretrained("facebook/vit-mae-base" ) if is_vision_available() else None
@slow
def lowercase ( self: List[Any] ) -> str:
"""simple docstring"""
np.random.seed(2 )
UpperCamelCase_ = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base" ).to(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.default_image_processor
UpperCamelCase_ = prepare_img()
UpperCamelCase_ = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors="pt" ).to(_SCREAMING_SNAKE_CASE )
# 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)
UpperCamelCase_ = ViTMAEConfig()
UpperCamelCase_ = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCamelCase_ = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
UpperCamelCase_ = model(**_SCREAMING_SNAKE_CASE , noise=torch.from_numpy(_SCREAMING_SNAKE_CASE ).to(device=_SCREAMING_SNAKE_CASE ) )
# verify the logits
UpperCamelCase_ = torch.Size((1, 196, 768) )
self.assertEqual(outputs.logits.shape , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = torch.tensor(
[[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(_SCREAMING_SNAKE_CASE ) , atol=1e-4 ) )
| 364 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __init__( self: List[str] , *,
_SCREAMING_SNAKE_CASE: int = 4 , _SCREAMING_SNAKE_CASE: int = 768 , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: str , ) -> Tuple:
"""simple docstring"""
super().__init__()
UpperCamelCase_ = nn.Parameter(torch.zeros(_SCREAMING_SNAKE_CASE ) )
# parameters for additional clip time embeddings
UpperCamelCase_ = nn.Linear(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = nn.Linear(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# parameters for encoder hidden states
UpperCamelCase_ = clip_extra_context_tokens
UpperCamelCase_ = nn.Linear(
_SCREAMING_SNAKE_CASE , self.clip_extra_context_tokens * cross_attention_dim )
UpperCamelCase_ = nn.Linear(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = nn.LayerNorm(_SCREAMING_SNAKE_CASE )
def lowercase ( self: Optional[int] , *, _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Tuple ) -> str:
"""simple docstring"""
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
UpperCamelCase_ = image_embeddings.shape[0]
UpperCamelCase_ = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
UpperCamelCase_ = classifier_free_guidance_embeddings.expand(
_SCREAMING_SNAKE_CASE , -1 )
UpperCamelCase_ = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
UpperCamelCase_ = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
UpperCamelCase_ = self.embedding_proj(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.clip_image_embeddings_project_to_time_embeddings(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
UpperCamelCase_ = self.clip_extra_context_tokens_proj(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = clip_extra_context_tokens.reshape(_SCREAMING_SNAKE_CASE , -1 , self.clip_extra_context_tokens )
UpperCamelCase_ = clip_extra_context_tokens.permute(0 , 2 , 1 )
UpperCamelCase_ = self.encoder_hidden_states_proj(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.text_encoder_hidden_states_norm(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 328 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class _UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
_UpperCamelCase : List[Any] = ShapEImgaImgPipeline
_UpperCamelCase : Any = ['''image''']
_UpperCamelCase : Dict = ['''image''']
_UpperCamelCase : Dict = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
_UpperCamelCase : Optional[Any] = False
@property
def lowercase ( self: Union[str, Any] ) -> Any:
"""simple docstring"""
return 32
@property
def lowercase ( self: Any ) -> Tuple:
"""simple docstring"""
return 32
@property
def lowercase ( self: Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
return self.time_input_dim * 4
@property
def lowercase ( self: List[str] ) -> Any:
"""simple docstring"""
return 8
@property
def lowercase ( self: Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase_ = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
UpperCamelCase_ = CLIPVisionModel(_SCREAMING_SNAKE_CASE )
return model
@property
def lowercase ( self: str ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ = CLIPImageProcessor(
crop_size=224 , do_center_crop=_SCREAMING_SNAKE_CASE , do_normalize=_SCREAMING_SNAKE_CASE , do_resize=_SCREAMING_SNAKE_CASE , image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73] , image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11] , resample=3 , size=224 , )
return image_processor
@property
def lowercase ( self: Optional[int] ) -> str:
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase_ = {
"num_attention_heads": 2,
"attention_head_dim": 16,
"embedding_dim": self.time_input_dim,
"num_embeddings": 32,
"embedding_proj_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"num_layers": 1,
"clip_embed_dim": self.time_input_dim * 2,
"additional_embeddings": 0,
"time_embed_act_fn": "gelu",
"norm_in_type": "layer",
"embedding_proj_norm_type": "layer",
"encoder_hid_proj_type": None,
"added_emb_type": None,
}
UpperCamelCase_ = PriorTransformer(**_SCREAMING_SNAKE_CASE )
return model
@property
def lowercase ( self: Union[str, Any] ) -> Any:
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase_ = {
"param_shapes": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"d_latent": self.time_input_dim,
"d_hidden": self.renderer_dim,
"n_output": 12,
"background": (
0.1,
0.1,
0.1,
),
}
UpperCamelCase_ = ShapERenderer(**_SCREAMING_SNAKE_CASE )
return model
def lowercase ( self: Dict ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = self.dummy_prior
UpperCamelCase_ = self.dummy_image_encoder
UpperCamelCase_ = self.dummy_image_processor
UpperCamelCase_ = self.dummy_renderer
UpperCamelCase_ = HeunDiscreteScheduler(
beta_schedule="exp" , num_train_timesteps=1024 , prediction_type="sample" , use_karras_sigmas=_SCREAMING_SNAKE_CASE , clip_sample=_SCREAMING_SNAKE_CASE , clip_sample_range=1.0 , )
UpperCamelCase_ = {
"prior": prior,
"image_encoder": image_encoder,
"image_processor": image_processor,
"renderer": renderer,
"scheduler": scheduler,
}
return components
def lowercase ( self: int , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: List[Any]=0 ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
if str(_SCREAMING_SNAKE_CASE ).startswith("mps" ):
UpperCamelCase_ = torch.manual_seed(_SCREAMING_SNAKE_CASE )
else:
UpperCamelCase_ = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = {
"image": input_image,
"generator": generator,
"num_inference_steps": 1,
"frame_size": 32,
"output_type": "np",
}
return inputs
def lowercase ( self: List[Any] ) -> int:
"""simple docstring"""
UpperCamelCase_ = "cpu"
UpperCamelCase_ = self.get_dummy_components()
UpperCamelCase_ = self.pipeline_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase_ = output.images[0]
UpperCamelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
UpperCamelCase_ = np.array(
[
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase ( self: List[Any] ) -> Optional[Any]:
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowercase ( self: Any ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = torch_device == "cpu"
UpperCamelCase_ = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_SCREAMING_SNAKE_CASE , relax_max_difference=_SCREAMING_SNAKE_CASE , )
def lowercase ( self: int ) -> str:
"""simple docstring"""
UpperCamelCase_ = self.get_dummy_components()
UpperCamelCase_ = self.pipeline_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = 1
UpperCamelCase_ = 2
UpperCamelCase_ = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
for key in inputs.keys():
if key in self.batch_params:
UpperCamelCase_ = batch_size * [inputs[key]]
UpperCamelCase_ = pipe(**_SCREAMING_SNAKE_CASE , num_images_per_prompt=_SCREAMING_SNAKE_CASE )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
def lowercase ( self: List[Any] ) -> Tuple:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase ( self: Union[str, Any] ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png" )
UpperCamelCase_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/shap_e/test_shap_e_img2img_out.npy" )
UpperCamelCase_ = ShapEImgaImgPipeline.from_pretrained("openai/shap-e-img2img" )
UpperCamelCase_ = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(0 )
UpperCamelCase_ = pipe(
_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type="np" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
| 365 |
from functools import lru_cache
def lowerCAmelCase_ ( UpperCamelCase_ ) -> set:
UpperCamelCase_ = 2
UpperCamelCase_ = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(UpperCamelCase_ )
if n > 1:
factors.add(UpperCamelCase_ )
return factors
@lru_cache
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
return len(unique_prime_factors(UpperCamelCase_ ) )
def lowerCAmelCase_ ( UpperCamelCase_ ) -> bool:
return len(set(UpperCamelCase_ ) ) in (0, 1)
def lowerCAmelCase_ ( UpperCamelCase_ ) -> list:
UpperCamelCase_ = 2
while True:
# Increment each value of a generated range
UpperCamelCase_ = [base + i for i in range(UpperCamelCase_ )]
# Run elements through out unique_prime_factors function
# Append our target number to the end.
UpperCamelCase_ = [upf_len(UpperCamelCase_ ) for x in group]
checker.append(UpperCamelCase_ )
# If all numbers in the list are equal, return the group variable.
if equality(UpperCamelCase_ ):
return group
# Increment our base variable by 1
base += 1
def lowerCAmelCase_ ( UpperCamelCase_ = 4 ) -> int:
UpperCamelCase_ = run(UpperCamelCase_ )
return results[0] if len(UpperCamelCase_ ) else None
if __name__ == "__main__":
print(solution())
| 328 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'facebook/convnextv2-tiny-1k-224': 'https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json',
}
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
_UpperCamelCase : str = '''convnextv2'''
def __init__( self: List[str] , _SCREAMING_SNAKE_CASE: str=3 , _SCREAMING_SNAKE_CASE: Optional[Any]=4 , _SCREAMING_SNAKE_CASE: Union[str, Any]=4 , _SCREAMING_SNAKE_CASE: Union[str, Any]=None , _SCREAMING_SNAKE_CASE: Optional[int]=None , _SCREAMING_SNAKE_CASE: List[str]="gelu" , _SCREAMING_SNAKE_CASE: Dict=0.02 , _SCREAMING_SNAKE_CASE: List[Any]=1e-12 , _SCREAMING_SNAKE_CASE: List[str]=0.0 , _SCREAMING_SNAKE_CASE: Union[str, Any]=224 , _SCREAMING_SNAKE_CASE: Any=None , _SCREAMING_SNAKE_CASE: str=None , **_SCREAMING_SNAKE_CASE: Tuple , ) -> Dict:
"""simple docstring"""
super().__init__(**_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = num_channels
UpperCamelCase_ = patch_size
UpperCamelCase_ = num_stages
UpperCamelCase_ = [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
UpperCamelCase_ = [3, 3, 9, 3] if depths is None else depths
UpperCamelCase_ = hidden_act
UpperCamelCase_ = initializer_range
UpperCamelCase_ = layer_norm_eps
UpperCamelCase_ = drop_path_rate
UpperCamelCase_ = image_size
UpperCamelCase_ = ["stem"] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
UpperCamelCase_ , UpperCamelCase_ = get_aligned_output_features_output_indices(
out_features=_SCREAMING_SNAKE_CASE , out_indices=_SCREAMING_SNAKE_CASE , stage_names=self.stage_names )
| 366 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
UpperCamelCase_ = len(UpperCamelCase_ )
UpperCamelCase_ = len(matrix[0] )
UpperCamelCase_ = min(UpperCamelCase_ , UpperCamelCase_ )
for row in range(UpperCamelCase_ ):
# Check if diagonal element is not zero
if matrix[row][row] != 0:
# Eliminate all the elements below the diagonal
for col in range(row + 1 , UpperCamelCase_ ):
UpperCamelCase_ = matrix[col][row] / matrix[row][row]
for i in range(UpperCamelCase_ , UpperCamelCase_ ):
matrix[col][i] -= multiplier * matrix[row][i]
else:
# Find a non-zero diagonal element to swap rows
UpperCamelCase_ = True
for i in range(row + 1 , UpperCamelCase_ ):
if matrix[i][row] != 0:
UpperCamelCase_ , UpperCamelCase_ = matrix[i], matrix[row]
UpperCamelCase_ = False
break
if reduce:
rank -= 1
for i in range(UpperCamelCase_ ):
UpperCamelCase_ = matrix[i][rank]
# Reduce the row pointer by one to stay on the same row
row -= 1
return rank
if __name__ == "__main__":
import doctest
doctest.testmod()
| 328 | 0 |
_UpperCAmelCase = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0',
'huggingface-hub': 'huggingface-hub>=0.13.2',
'requests-mock': 'requests-mock==1.10.0',
'importlib_metadata': 'importlib_metadata',
'invisible-watermark': 'invisible-watermark',
'isort': 'isort>=5.5.4',
'jax': 'jax>=0.2.8,!=0.3.2',
'jaxlib': 'jaxlib>=0.1.65',
'Jinja2': 'Jinja2',
'k-diffusion': 'k-diffusion>=0.0.12',
'torchsde': 'torchsde',
'note_seq': 'note_seq',
'librosa': 'librosa',
'numpy': 'numpy',
'omegaconf': 'omegaconf',
'parameterized': 'parameterized',
'protobuf': 'protobuf>=3.20.3,<4',
'pytest': 'pytest',
'pytest-timeout': 'pytest-timeout',
'pytest-xdist': 'pytest-xdist',
'ruff': 'ruff>=0.0.241',
'safetensors': 'safetensors',
'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
'scipy': 'scipy',
'onnx': 'onnx',
'regex': 'regex!=2019.12.17',
'requests': 'requests',
'tensorboard': 'tensorboard',
'torch': 'torch>=1.4',
'torchvision': 'torchvision',
'transformers': 'transformers>=4.25.1',
'urllib3': 'urllib3<=2.0.0',
}
| 367 |
import math
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> List[str]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(UpperCamelCase_ )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:
return 1 # any number raised to 0 is 1
raise AssertionError("This should never happen" )
if __name__ == "__main__": # Main function
# Read two numbers from input and typecast them to int using map function.
# Here x is the base and y is the power.
_UpperCAmelCase = 'Enter the base and the power separated by a comma: '
_UpperCAmelCase , _UpperCAmelCase = map(int, input(prompt).split(','))
_UpperCAmelCase , _UpperCAmelCase = map(int, input(prompt).split(','))
# We find the log of each number, using the function res(), which takes two
# arguments.
_UpperCAmelCase = res(xa, ya)
_UpperCAmelCase = res(xa, ya)
# We check for the largest number
if resa > resa:
print('Largest number is', xa, '^', ya)
elif resa > resa:
print('Largest number is', xa, '^', ya)
else:
print('Both are equal')
| 328 | 0 |
import string
def lowerCAmelCase_ ( UpperCamelCase_ ) -> None:
for key in range(len(string.ascii_uppercase ) ):
UpperCamelCase_ = ""
for symbol in message:
if symbol in string.ascii_uppercase:
UpperCamelCase_ = string.ascii_uppercase.find(UpperCamelCase_ )
UpperCamelCase_ = num - key
if num < 0:
UpperCamelCase_ = num + len(string.ascii_uppercase )
UpperCamelCase_ = translated + string.ascii_uppercase[num]
else:
UpperCamelCase_ = translated + symbol
print(F'''Decryption using Key #{key}: {translated}''' )
def lowerCAmelCase_ ( ) -> None:
UpperCamelCase_ = input("Encrypted message: " )
UpperCamelCase_ = message.upper()
decrypt(UpperCamelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 368 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_UpperCAmelCase = transforms.Compose(
[
transforms.Resize((2_5_6, 2_5_6)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def lowerCAmelCase_ ( UpperCamelCase_ ) -> List[Any]:
if isinstance(UpperCamelCase_ , torch.Tensor ):
return image
elif isinstance(UpperCamelCase_ , PIL.Image.Image ):
UpperCamelCase_ = [image]
UpperCamelCase_ = [trans(img.convert("RGB" ) ) for img in image]
UpperCamelCase_ = torch.stack(UpperCamelCase_ )
return image
class _UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self: List[Any] , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: Dict ) -> str:
"""simple docstring"""
super().__init__()
# make sure scheduler can always be converted to DDIM
UpperCamelCase_ = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE )
def lowercase ( self: List[str] , _SCREAMING_SNAKE_CASE: Dict ) -> Optional[Any]:
"""simple docstring"""
if strength < 0 or strength > 1:
raise ValueError(f'''The value of strength should in [0.0, 1.0] but is {strength}''' )
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: List[str] ) -> int:
"""simple docstring"""
UpperCamelCase_ = min(int(num_inference_steps * strength ) , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = max(num_inference_steps - init_timestep , 0 )
UpperCamelCase_ = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def lowercase ( self: Tuple , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: Optional[int]=None ) -> List[Any]:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , (torch.Tensor, PIL.Image.Image, list) ):
raise ValueError(
f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_SCREAMING_SNAKE_CASE )}''' )
UpperCamelCase_ = image.to(device=_SCREAMING_SNAKE_CASE , dtype=_SCREAMING_SNAKE_CASE )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and len(_SCREAMING_SNAKE_CASE ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(_SCREAMING_SNAKE_CASE )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
UpperCamelCase_ = init_latents.shape
UpperCamelCase_ = randn_tensor(_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , device=_SCREAMING_SNAKE_CASE , dtype=_SCREAMING_SNAKE_CASE )
# get latents
print("add noise to latents at timestep" , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.scheduler.add_noise(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = init_latents
return latents
@torch.no_grad()
def __call__( self: Dict , _SCREAMING_SNAKE_CASE: Union[torch.FloatTensor, PIL.Image.Image] = None , _SCREAMING_SNAKE_CASE: float = 0.8 , _SCREAMING_SNAKE_CASE: int = 1 , _SCREAMING_SNAKE_CASE: Optional[Union[torch.Generator, List[torch.Generator]]] = None , _SCREAMING_SNAKE_CASE: float = 0.0 , _SCREAMING_SNAKE_CASE: int = 50 , _SCREAMING_SNAKE_CASE: Optional[bool] = None , _SCREAMING_SNAKE_CASE: Optional[str] = "pil" , _SCREAMING_SNAKE_CASE: bool = True , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
self.check_inputs(_SCREAMING_SNAKE_CASE )
# 2. Preprocess image
UpperCamelCase_ = preprocess(_SCREAMING_SNAKE_CASE )
# 3. set timesteps
self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE , device=self.device )
UpperCamelCase_ , UpperCamelCase_ = self.get_timesteps(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , self.device )
UpperCamelCase_ = timesteps[:1].repeat(_SCREAMING_SNAKE_CASE )
# 4. Prepare latent variables
UpperCamelCase_ = self.prepare_latents(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , self.unet.dtype , self.device , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = latents
# 5. Denoising loop
for t in self.progress_bar(_SCREAMING_SNAKE_CASE ):
# 1. predict noise model_output
UpperCamelCase_ = self.unet(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
UpperCamelCase_ = self.scheduler.step(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , eta=_SCREAMING_SNAKE_CASE , use_clipped_model_output=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , ).prev_sample
UpperCamelCase_ = (image / 2 + 0.5).clamp(0 , 1 )
UpperCamelCase_ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCamelCase_ = self.numpy_to_pil(_SCREAMING_SNAKE_CASE )
if not return_dict:
return (image, latent_timestep.item())
return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE )
| 328 | 0 |
"""simple docstring"""
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('3.8'):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
_UpperCAmelCase = ''
if version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):
class _UpperCamelCase ( tr.AbstractTransform ):
def __init__( self: Optional[Any] , _SCREAMING_SNAKE_CASE: str = " " ) -> str:
"""simple docstring"""
UpperCamelCase_ = sentence_delimiter
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: str ) -> Any:
"""simple docstring"""
return list(_SCREAMING_SNAKE_CASE )
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: List[str] ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ = []
for sent_idx, sentence in enumerate(_SCREAMING_SNAKE_CASE ):
chars.extend(self.process_string(_SCREAMING_SNAKE_CASE ) )
if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(_SCREAMING_SNAKE_CASE ) - 1:
chars.append(self.sentence_delimiter )
return chars
_UpperCAmelCase = tr.Compose(
[tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
)
else:
_UpperCAmelCase = tr.Compose(
[
tr.RemoveMultipleSpaces(),
tr.Strip(),
tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
tr.ReduceToListOfListOfChars(),
]
)
_UpperCAmelCase = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'
_UpperCAmelCase = '\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n'
_UpperCAmelCase = '\nComputes CER score of transcribed segments against references.\nArgs:\n references: list of references for each speech input.\n predictions: list of transcribtions to score.\n concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n (float): the character error rate\n\nExamples:\n\n >>> predictions = ["this is the prediction", "there is an other sample"]\n >>> references = ["this is the reference", "there is another one"]\n >>> cer = datasets.load_metric("cer")\n >>> cer_score = cer.compute(predictions=predictions, references=references)\n >>> print(cer_score)\n 0.34146341463414637\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCamelCase ( datasets.Metric ):
def lowercase ( self: Optional[Any] ) -> int:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , codebase_urls=["https://github.com/jitsi/jiwer/"] , reference_urls=[
"https://en.wikipedia.org/wiki/Word_error_rate",
"https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates",
] , )
def lowercase ( self: int , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Optional[int]=False ) -> Dict:
"""simple docstring"""
if concatenate_texts:
return jiwer.compute_measures(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , truth_transform=_SCREAMING_SNAKE_CASE , hypothesis_transform=_SCREAMING_SNAKE_CASE , )["wer"]
UpperCamelCase_ = 0
UpperCamelCase_ = 0
for prediction, reference in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase_ = jiwer.compute_measures(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , truth_transform=_SCREAMING_SNAKE_CASE , hypothesis_transform=_SCREAMING_SNAKE_CASE , )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 369 |
import re
from filelock import FileLock
try:
import nltk
_UpperCAmelCase = True
except (ImportError, ModuleNotFoundError):
_UpperCAmelCase = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def lowerCAmelCase_ ( UpperCamelCase_ ) -> str:
re.sub("<n>" , "" , UpperCamelCase_ ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(UpperCamelCase_ ) )
| 328 | 0 |
import unittest
import numpy as np
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = None , ):
UpperCamelCase_ = np.shape(UpperCamelCase_ )
UpperCamelCase_ = np.shape(UpperCamelCase_ )
UpperCamelCase_ = np.shape(UpperCamelCase_ )
if shape_a[0] != shape_b[0]:
UpperCamelCase_ = (
"Expected the same number of rows for A and B. "
F'''Instead found A of size {shape_a} and B of size {shape_b}'''
)
raise ValueError(UpperCamelCase_ )
if shape_b[1] != shape_c[1]:
UpperCamelCase_ = (
"Expected the same number of columns for B and C. "
F'''Instead found B of size {shape_b} and C of size {shape_c}'''
)
raise ValueError(UpperCamelCase_ )
UpperCamelCase_ = pseudo_inv
if a_inv is None:
try:
UpperCamelCase_ = np.linalg.inv(UpperCamelCase_ )
except np.linalg.LinAlgError:
raise ValueError(
"Input matrix A is not invertible. Cannot compute Schur complement." )
return mat_c - mat_b.T @ a_inv @ mat_b
class _UpperCamelCase ( unittest.TestCase ):
def lowercase ( self: Union[str, Any] ) -> None:
"""simple docstring"""
UpperCamelCase_ = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
UpperCamelCase_ = np.array([[0, 3], [3, 0], [2, 3]] )
UpperCamelCase_ = np.array([[2, 1], [6, 3]] )
UpperCamelCase_ = schur_complement(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = np.block([[a, b], [b.T, c]] )
UpperCamelCase_ = np.linalg.det(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = np.linalg.det(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = np.linalg.det(_SCREAMING_SNAKE_CASE )
self.assertAlmostEqual(_SCREAMING_SNAKE_CASE , det_a * det_s )
def lowercase ( self: Optional[int] ) -> None:
"""simple docstring"""
UpperCamelCase_ = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
UpperCamelCase_ = np.array([[0, 3], [3, 0], [2, 3]] )
UpperCamelCase_ = np.array([[2, 1], [6, 3]] )
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
schur_complement(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def lowercase ( self: Optional[int] ) -> None:
"""simple docstring"""
UpperCamelCase_ = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
UpperCamelCase_ = np.array([[0, 3], [3, 0], [2, 3]] )
UpperCamelCase_ = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
schur_complement(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main()
| 370 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
_UpperCamelCase : Optional[Any] = DiTPipeline
_UpperCamelCase : Any = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
_UpperCamelCase : Dict = PipelineTesterMixin.required_optional_params - {
'''latents''',
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
_UpperCamelCase : Optional[int] = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
_UpperCamelCase : Dict = False
def lowercase ( self: str ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase_ = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=_SCREAMING_SNAKE_CASE , activation_fn="gelu-approximate" , num_embeds_ada_norm=1000 , norm_type="ada_norm_zero" , norm_elementwise_affine=_SCREAMING_SNAKE_CASE , )
UpperCamelCase_ = AutoencoderKL()
UpperCamelCase_ = DDIMScheduler()
UpperCamelCase_ = {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
return components
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: Dict , _SCREAMING_SNAKE_CASE: List[str]=0 ) -> Dict:
"""simple docstring"""
if str(_SCREAMING_SNAKE_CASE ).startswith("mps" ):
UpperCamelCase_ = torch.manual_seed(_SCREAMING_SNAKE_CASE )
else:
UpperCamelCase_ = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = {
"class_labels": [1],
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def lowercase ( self: Any ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ = "cpu"
UpperCamelCase_ = self.get_dummy_components()
UpperCamelCase_ = self.pipeline_class(**_SCREAMING_SNAKE_CASE )
pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = pipe(**_SCREAMING_SNAKE_CASE ).images
UpperCamelCase_ = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
UpperCamelCase_ = np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57] )
UpperCamelCase_ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_SCREAMING_SNAKE_CASE , 1e-3 )
def lowercase ( self: Optional[int] ) -> Any:
"""simple docstring"""
self._test_inference_batch_single_identical(relax_max_difference=_SCREAMING_SNAKE_CASE , expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def lowercase ( self: Optional[Any] ) -> Optional[int]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class _UpperCamelCase ( unittest.TestCase ):
def lowercase ( self: Optional[int] ) -> List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase ( self: Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = torch.manual_seed(0 )
UpperCamelCase_ = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256" )
pipe.to("cuda" )
UpperCamelCase_ = ["vase", "umbrella", "white shark", "white wolf"]
UpperCamelCase_ = pipe.get_label_ids(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = pipe(_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , num_inference_steps=40 , output_type="np" ).images
for word, image in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase_ = load_numpy(
f'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy''' )
assert np.abs((expected_image - image).max() ) < 1e-2
def lowercase ( self: int ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ = DiTPipeline.from_pretrained("facebook/DiT-XL-2-512" )
UpperCamelCase_ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("cuda" )
UpperCamelCase_ = ["vase", "umbrella"]
UpperCamelCase_ = pipe.get_label_ids(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = torch.manual_seed(0 )
UpperCamelCase_ = pipe(_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , num_inference_steps=25 , output_type="np" ).images
for word, image in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
f'''/dit/{word}_512.npy''' )
assert np.abs((expected_image - image).max() ) < 1e-1
| 328 | 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, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class _UpperCamelCase ( unittest.TestCase ):
def __init__( self: Optional[int] , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: Optional[int]=7 , _SCREAMING_SNAKE_CASE: str=3 , _SCREAMING_SNAKE_CASE: Optional[int]=18 , _SCREAMING_SNAKE_CASE: Optional[int]=30 , _SCREAMING_SNAKE_CASE: Dict=400 , _SCREAMING_SNAKE_CASE: Optional[Any]=True , _SCREAMING_SNAKE_CASE: str=None , _SCREAMING_SNAKE_CASE: Optional[int]=True , _SCREAMING_SNAKE_CASE: Optional[int]=None , _SCREAMING_SNAKE_CASE: Dict=True , _SCREAMING_SNAKE_CASE: Tuple=[0.5, 0.5, 0.5] , _SCREAMING_SNAKE_CASE: Optional[Any]=[0.5, 0.5, 0.5] , ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ = size if size is not None else {"shortest_edge": 18}
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
def lowercase ( self: Dict ) -> Optional[int]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class _UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
_UpperCamelCase : str = LevitImageProcessor if is_vision_available() else None
def lowercase ( self: Any ) -> str:
"""simple docstring"""
UpperCamelCase_ = LevitImageProcessingTester(self )
@property
def lowercase ( self: List[str] ) -> Optional[int]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase ( self: Dict ) -> Dict:
"""simple docstring"""
UpperCamelCase_ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , "image_mean" ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , "image_std" ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , "do_normalize" ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , "do_resize" ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , "do_center_crop" ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , "size" ) )
def lowercase ( self: Optional[Any] ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
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 lowercase ( self: Tuple ) -> Tuple:
"""simple docstring"""
pass
def lowercase ( self: Union[str, Any] ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , Image.Image )
# Test not batched input
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 lowercase ( self: Any ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=_SCREAMING_SNAKE_CASE , numpify=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , np.ndarray )
# 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 lowercase ( self: Optional[int] ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=_SCREAMING_SNAKE_CASE , torchify=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , torch.Tensor )
# Test not batched input
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"],
) , )
| 371 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _UpperCamelCase :
def __init__( self: str ) -> Any:
"""simple docstring"""
UpperCamelCase_ = ""
UpperCamelCase_ = ""
UpperCamelCase_ = []
UpperCamelCase_ = 0
UpperCamelCase_ = 256
UpperCamelCase_ = 0
UpperCamelCase_ = 0
UpperCamelCase_ = 0
UpperCamelCase_ = 0
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: Dict ) -> str:
"""simple docstring"""
UpperCamelCase_ = cva.imread(_SCREAMING_SNAKE_CASE , 0 )
UpperCamelCase_ = copy.deepcopy(self.img )
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = plt.hist(self.img.ravel() , 256 , [0, 256] , label="x" )
UpperCamelCase_ = np.sum(_SCREAMING_SNAKE_CASE )
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
UpperCamelCase_ = x[i] / self.k
self.sk += prk
UpperCamelCase_ = (self.L - 1) * self.sk
if self.rem != 0:
UpperCamelCase_ = int(last % last )
UpperCamelCase_ = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = int(np.ma.count(self.img ) / self.img[1].size )
UpperCamelCase_ = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
UpperCamelCase_ = self.img[j][i]
if num != self.last_list[num]:
UpperCamelCase_ = self.last_list[num]
cva.imwrite("output_data/output.jpg" , self.img )
def lowercase ( self: Any ) -> Optional[Any]:
"""simple docstring"""
plt.hist(self.img.ravel() , 256 , [0, 256] )
def lowercase ( self: Tuple ) -> Union[str, Any]:
"""simple docstring"""
cva.imshow("Output-Image" , self.img )
cva.imshow("Input-Image" , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
_UpperCAmelCase = os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
_UpperCAmelCase = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 328 | 0 |
import os
import pytest
from attr import dataclass
_UpperCAmelCase = 'us-east-1' # defaults region
@dataclass
class _UpperCamelCase :
_UpperCamelCase : str
_UpperCamelCase : Any = '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
_UpperCamelCase : List[str] = {
'''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 : Tuple = {**hyperparameters, '''max_steps''': 1_0_0_0}
@property
def lowercase ( self: Tuple ) -> str:
"""simple docstring"""
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 lowercase ( self: List[Any] ) -> str:
"""simple docstring"""
return f'''{self.framework}-transfromers-test'''
@property
def lowercase ( self: int ) -> str:
"""simple docstring"""
return f'''./tests/sagemaker/scripts/{self.framework}'''
@property
def lowercase ( self: Dict ) -> str:
"""simple docstring"""
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 lowerCAmelCase_ ( UpperCamelCase_ ) -> Dict:
UpperCamelCase_ = SageMakerTestEnvironment(framework=request.cls.framework )
| 350 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_UpperCAmelCase = '\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n journal={arXiv preprint arXiv:1905.00537},\n year={2019}\n}\n'
_UpperCAmelCase = '\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n'
_UpperCAmelCase = '\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n predictions: list of predictions to score. Depending on the SuperGlUE subset:\n - for \'record\': list of question-answer dictionaries with the following keys:\n - \'idx\': index of the question as specified by the dataset\n - \'prediction_text\': the predicted answer text\n - for \'multirc\': list of question-answer dictionaries with the following keys:\n - \'idx\': index of the question-answer pair as specified by the dataset\n - \'prediction\': the predicted answer label\n - otherwise: list of predicted labels\n references: list of reference labels. Depending on the SuperGLUE subset:\n - for \'record\': list of question-answers dictionaries with the following keys:\n - \'idx\': index of the question as specified by the dataset\n - \'answers\': list of possible answers\n - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n - for \'record\':\n - \'exact_match\': Exact match between answer and gold answer\n - \'f1\': F1 score\n - for \'multirc\':\n - \'exact_match\': Exact match between answer and gold answer\n - \'f1_m\': Per-question macro-F1 score\n - \'f1_a\': Average F1 score over all answers\n - for \'axb\':\n \'matthews_correlation\': Matthew Correlation\n - for \'cb\':\n - \'accuracy\': Accuracy\n - \'f1\': F1 score\n - for all others:\n - \'accuracy\': Accuracy\nExamples:\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\') # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0, \'f1\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')\n >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]\n >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 1.0, \'f1\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')\n >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'matthews_correlation\': 1.0}\n'
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> Optional[Any]:
return float((preds == labels).mean() )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_="binary" ) -> Tuple:
UpperCamelCase_ = simple_accuracy(UpperCamelCase_ , UpperCamelCase_ )
UpperCamelCase_ = float(fa_score(y_true=UpperCamelCase_ , y_pred=UpperCamelCase_ , average=UpperCamelCase_ ) )
return {
"accuracy": acc,
"f1": fa,
}
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> List[Any]:
UpperCamelCase_ = {}
for id_pred, label in zip(UpperCamelCase_ , UpperCamelCase_ ):
UpperCamelCase_ = F'''{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'''
UpperCamelCase_ = id_pred["prediction"]
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
UpperCamelCase_ = [(pred, label)]
UpperCamelCase_ , UpperCamelCase_ = [], []
for question, preds_labels in question_map.items():
UpperCamelCase_ , UpperCamelCase_ = zip(*UpperCamelCase_ )
UpperCamelCase_ = fa_score(y_true=UpperCamelCase_ , y_pred=UpperCamelCase_ , average="macro" )
fas.append(UpperCamelCase_ )
UpperCamelCase_ = int(sum(pred == label for pred, label in preds_labels ) == len(UpperCamelCase_ ) )
ems.append(UpperCamelCase_ )
UpperCamelCase_ = float(sum(UpperCamelCase_ ) / len(UpperCamelCase_ ) )
UpperCamelCase_ = sum(UpperCamelCase_ ) / len(UpperCamelCase_ )
UpperCamelCase_ = float(fa_score(y_true=UpperCamelCase_ , y_pred=[id_pred["prediction"] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCamelCase ( datasets.Metric ):
def lowercase ( self: Optional[int] ) -> Optional[int]:
"""simple docstring"""
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
"You should supply a configuration name selected in "
"[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None , )
def lowercase ( self: List[Any] ) -> int:
"""simple docstring"""
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value("int64" ),
"query": datasets.Value("int64" ),
},
"prediction_text": datasets.Value("string" ),
},
"references": {
"idx": {
"passage": datasets.Value("int64" ),
"query": datasets.Value("int64" ),
},
"answers": datasets.Sequence(datasets.Value("string" ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value("int64" ),
"paragraph": datasets.Value("int64" ),
"question": datasets.Value("int64" ),
},
"prediction": datasets.Value("int64" ),
},
"references": datasets.Value("int64" ),
}
else:
return {
"predictions": datasets.Value("int64" ),
"references": datasets.Value("int64" ),
}
def lowercase ( self: Tuple , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: List[str] ) -> Dict:
"""simple docstring"""
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )}
elif self.config_name == "cb":
return acc_and_fa(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , fa_avg="macro" )
elif self.config_name == "record":
UpperCamelCase_ = [
{
"qas": [
{"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
for ref in references
]
}
]
UpperCamelCase_ = {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
return evaluate_record(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )[0]
elif self.config_name == "multirc":
return evaluate_multirc(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )}
else:
raise KeyError(
"You should supply a configuration name selected in "
"[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]" )
| 328 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase = {'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = ['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
_UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 351 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : str = '''mgp-str'''
def __init__( self: Optional[Any] , _SCREAMING_SNAKE_CASE: Optional[int]=[32, 128] , _SCREAMING_SNAKE_CASE: Tuple=4 , _SCREAMING_SNAKE_CASE: Optional[Any]=3 , _SCREAMING_SNAKE_CASE: Optional[int]=27 , _SCREAMING_SNAKE_CASE: Tuple=38 , _SCREAMING_SNAKE_CASE: Tuple=50257 , _SCREAMING_SNAKE_CASE: List[Any]=30522 , _SCREAMING_SNAKE_CASE: Optional[Any]=768 , _SCREAMING_SNAKE_CASE: Dict=12 , _SCREAMING_SNAKE_CASE: List[str]=12 , _SCREAMING_SNAKE_CASE: Dict=4.0 , _SCREAMING_SNAKE_CASE: int=True , _SCREAMING_SNAKE_CASE: Tuple=False , _SCREAMING_SNAKE_CASE: Tuple=1e-5 , _SCREAMING_SNAKE_CASE: Optional[Any]=0.0 , _SCREAMING_SNAKE_CASE: Tuple=0.0 , _SCREAMING_SNAKE_CASE: List[Any]=0.0 , _SCREAMING_SNAKE_CASE: List[str]=False , _SCREAMING_SNAKE_CASE: int=0.02 , **_SCREAMING_SNAKE_CASE: Any , ) -> str:
"""simple docstring"""
super().__init__(**_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = image_size
UpperCamelCase_ = patch_size
UpperCamelCase_ = num_channels
UpperCamelCase_ = max_token_length
UpperCamelCase_ = num_character_labels
UpperCamelCase_ = num_bpe_labels
UpperCamelCase_ = num_wordpiece_labels
UpperCamelCase_ = hidden_size
UpperCamelCase_ = num_hidden_layers
UpperCamelCase_ = num_attention_heads
UpperCamelCase_ = mlp_ratio
UpperCamelCase_ = distilled
UpperCamelCase_ = layer_norm_eps
UpperCamelCase_ = drop_rate
UpperCamelCase_ = qkv_bias
UpperCamelCase_ = attn_drop_rate
UpperCamelCase_ = drop_path_rate
UpperCamelCase_ = output_aa_attentions
UpperCamelCase_ = initializer_range
| 328 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
'LiltForQuestionAnswering',
'LiltForSequenceClassification',
'LiltForTokenClassification',
'LiltModel',
'LiltPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lilt import (
LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
LiltPreTrainedModel,
)
else:
import sys
_UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 352 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
_UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class _UpperCamelCase :
_UpperCamelCase : str = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_UpperCamelCase : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCamelCase : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCamelCase : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
_UpperCamelCase : bool = field(default=lowerCAmelCase_ , metadata={'''help''': '''Whether tp freeze the encoder.'''} )
_UpperCamelCase : bool = field(default=lowerCAmelCase_ , metadata={'''help''': '''Whether to freeze the embeddings.'''} )
@dataclass
class _UpperCamelCase :
_UpperCamelCase : str = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
_UpperCamelCase : Optional[str] = field(
default='''summarization''' , metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''} , )
_UpperCamelCase : Optional[int] = field(
default=1_0_2_4 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_UpperCamelCase : Optional[int] = field(
default=1_2_8 , metadata={
'''help''': (
'''The maximum total sequence length for target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_UpperCamelCase : Optional[int] = field(
default=1_4_2 , metadata={
'''help''': (
'''The maximum total sequence length for validation target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded. '''
'''This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '''
'''during ``evaluate`` and ``predict``.'''
)
} , )
_UpperCamelCase : Optional[int] = field(
default=1_4_2 , metadata={
'''help''': (
'''The maximum total sequence length for test target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_UpperCamelCase : Optional[int] = field(default=-1 , metadata={'''help''': '''# training examples. -1 means use all.'''} )
_UpperCamelCase : Optional[int] = field(default=-1 , metadata={'''help''': '''# validation examples. -1 means use all.'''} )
_UpperCamelCase : Optional[int] = field(default=-1 , metadata={'''help''': '''# test examples. -1 means use all.'''} )
_UpperCamelCase : Optional[str] = field(default=lowerCAmelCase_ , metadata={'''help''': '''Source language id for translation.'''} )
_UpperCamelCase : Optional[str] = field(default=lowerCAmelCase_ , metadata={'''help''': '''Target language id for translation.'''} )
_UpperCamelCase : Optional[int] = field(default=lowerCAmelCase_ , metadata={'''help''': '''# num_beams to use for evaluation.'''} )
_UpperCamelCase : bool = field(
default=lowerCAmelCase_ , metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''} , )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Optional[int]:
logger.info(F'''***** {split} metrics *****''' )
for key in sorted(metrics.keys() ):
logger.info(F''' {key} = {metrics[key]}''' )
save_json(UpperCamelCase_ , os.path.join(UpperCamelCase_ , F'''{split}_results.json''' ) )
def lowerCAmelCase_ ( ) -> Optional[int]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCamelCase_ = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = parser.parse_args_into_dataclasses()
check_output_dir(UpperCamelCase_ )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("Training/evaluation parameters %s" , UpperCamelCase_ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase_ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
UpperCamelCase_ = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
assert hasattr(UpperCamelCase_ , UpperCamelCase_ ), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
setattr(UpperCamelCase_ , UpperCamelCase_ , getattr(UpperCamelCase_ , UpperCamelCase_ ) )
UpperCamelCase_ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
UpperCamelCase_ = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=".ckpt" in model_args.model_name_or_path , config=UpperCamelCase_ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(UpperCamelCase_ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
UpperCamelCase_ = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(UpperCamelCase_ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
UpperCamelCase_ = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
UpperCamelCase_ = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(UpperCamelCase_ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
UpperCamelCase_ = SeqaSeqDataset
# Get datasets
UpperCamelCase_ = (
dataset_class(
UpperCamelCase_ , type_path="train" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_train
else None
)
UpperCamelCase_ = (
dataset_class(
UpperCamelCase_ , type_path="val" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
UpperCamelCase_ = (
dataset_class(
UpperCamelCase_ , type_path="test" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
UpperCamelCase_ = (
build_compute_metrics_fn(data_args.task , UpperCamelCase_ ) if training_args.predict_with_generate else None
)
UpperCamelCase_ = SeqaSeqTrainer(
model=UpperCamelCase_ , args=UpperCamelCase_ , data_args=UpperCamelCase_ , train_dataset=UpperCamelCase_ , eval_dataset=UpperCamelCase_ , data_collator=SeqaSeqDataCollator(
UpperCamelCase_ , UpperCamelCase_ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=UpperCamelCase_ , tokenizer=UpperCamelCase_ , )
UpperCamelCase_ = {}
# Training
if training_args.do_train:
logger.info("*** Train ***" )
UpperCamelCase_ = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
UpperCamelCase_ = train_result.metrics
UpperCamelCase_ = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("train" , UpperCamelCase_ , training_args.output_dir )
all_metrics.update(UpperCamelCase_ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , "trainer_state.json" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
UpperCamelCase_ = trainer.evaluate(metric_key_prefix="val" )
UpperCamelCase_ = data_args.n_val
UpperCamelCase_ = round(metrics["val_loss"] , 4 )
if trainer.is_world_process_zero():
handle_metrics("val" , UpperCamelCase_ , training_args.output_dir )
all_metrics.update(UpperCamelCase_ )
if training_args.do_predict:
logger.info("*** Predict ***" )
UpperCamelCase_ = trainer.predict(test_dataset=UpperCamelCase_ , metric_key_prefix="test" )
UpperCamelCase_ = test_output.metrics
UpperCamelCase_ = data_args.n_test
if trainer.is_world_process_zero():
UpperCamelCase_ = round(metrics["test_loss"] , 4 )
handle_metrics("test" , UpperCamelCase_ , training_args.output_dir )
all_metrics.update(UpperCamelCase_ )
if training_args.predict_with_generate:
UpperCamelCase_ = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=UpperCamelCase_ , clean_up_tokenization_spaces=UpperCamelCase_ )
UpperCamelCase_ = lmap(str.strip , UpperCamelCase_ )
write_txt_file(UpperCamelCase_ , os.path.join(training_args.output_dir , "test_generations.txt" ) )
if trainer.is_world_process_zero():
save_json(UpperCamelCase_ , os.path.join(training_args.output_dir , "all_results.json" ) )
return all_metrics
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Optional[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 328 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _UpperCamelCase ( datasets.BeamBasedBuilder ):
def lowercase ( self: List[str] ) -> Union[str, Any]:
"""simple docstring"""
return datasets.DatasetInfo(
features=datasets.Features({"content": datasets.Value("string" )} ) , supervised_keys=_SCREAMING_SNAKE_CASE , )
def lowercase ( self: List[str] , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: Tuple ) -> Dict:
"""simple docstring"""
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"examples": get_test_dummy_examples()} )]
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: Dict ) -> int:
"""simple docstring"""
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(_SCREAMING_SNAKE_CASE )
class _UpperCamelCase ( datasets.BeamBasedBuilder ):
def lowercase ( self: Union[str, Any] ) -> Dict:
"""simple docstring"""
return datasets.DatasetInfo(
features=datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string" )} )} ) , supervised_keys=_SCREAMING_SNAKE_CASE , )
def lowercase ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: Tuple ) -> Any:
"""simple docstring"""
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"examples": get_test_nested_examples()} )
]
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: List[str] ) -> Union[str, Any]:
"""simple docstring"""
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(_SCREAMING_SNAKE_CASE )
def lowerCAmelCase_ ( ) -> str:
return [(i, {"content": content}) for i, content in enumerate(["foo", "bar", "foobar"] )]
def lowerCAmelCase_ ( ) -> Optional[Any]:
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["foo", "bar", "foobar"] )]
class _UpperCamelCase ( lowerCAmelCase_ ):
@require_beam
def lowercase ( self: str ) -> int:
"""simple docstring"""
UpperCamelCase_ = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCamelCase_ = DummyBeamDataset(cache_dir=_SCREAMING_SNAKE_CASE , beam_runner="DirectRunner" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(_SCREAMING_SNAKE_CASE , builder.name , "default" , "0.0.0" , f'''{builder.name}-train.arrow''' ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"content": datasets.Value("string" )} ) )
UpperCamelCase_ = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , _SCREAMING_SNAKE_CASE )
self.assertEqual(dset["train"].info.splits["train"].num_examples , _SCREAMING_SNAKE_CASE )
self.assertDictEqual(dset["train"][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset["train"][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
@require_beam
def lowercase ( self: Dict ) -> Union[str, Any]:
"""simple docstring"""
import apache_beam as beam
UpperCamelCase_ = beam.io.parquetio.WriteToParquet
UpperCamelCase_ = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCamelCase_ = DummyBeamDataset(cache_dir=_SCREAMING_SNAKE_CASE , beam_runner="DirectRunner" )
with patch("apache_beam.io.parquetio.WriteToParquet" ) as write_parquet_mock:
UpperCamelCase_ = partial(_SCREAMING_SNAKE_CASE , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
_SCREAMING_SNAKE_CASE , builder.name , "default" , "0.0.0" , f'''{builder.name}-train-00000-of-00002.arrow''' ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
_SCREAMING_SNAKE_CASE , builder.name , "default" , "0.0.0" , f'''{builder.name}-train-00000-of-00002.arrow''' ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"content": datasets.Value("string" )} ) )
UpperCamelCase_ = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , _SCREAMING_SNAKE_CASE )
self.assertEqual(dset["train"].info.splits["train"].num_examples , _SCREAMING_SNAKE_CASE )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset["train"]["content"] ) , sorted(["foo", "bar", "foobar"] ) )
self.assertTrue(
os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
@require_beam
def lowercase ( self: Any ) -> Union[str, Any]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCamelCase_ = DummyBeamDataset(cache_dir=_SCREAMING_SNAKE_CASE )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def lowercase ( self: int ) -> Dict:
"""simple docstring"""
UpperCamelCase_ = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCamelCase_ = NestedBeamDataset(cache_dir=_SCREAMING_SNAKE_CASE , beam_runner="DirectRunner" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(_SCREAMING_SNAKE_CASE , builder.name , "default" , "0.0.0" , f'''{builder.name}-train.arrow''' ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string" )} )} ) )
UpperCamelCase_ = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , _SCREAMING_SNAKE_CASE )
self.assertEqual(dset["train"].info.splits["train"].num_examples , _SCREAMING_SNAKE_CASE )
self.assertDictEqual(dset["train"][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset["train"][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
| 353 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> list:
UpperCamelCase_ = int(UpperCamelCase_ )
if n_element < 1:
UpperCamelCase_ = ValueError("a should be a positive number" )
raise my_error
UpperCamelCase_ = [1]
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = (0, 0, 0)
UpperCamelCase_ = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
_UpperCAmelCase = input('Enter the last number (nth term) of the Hamming Number Series: ')
print('Formula of Hamming Number Series => 2^i * 3^j * 5^k')
_UpperCAmelCase = hamming(int(n))
print('-----------------------------------------------------')
print(f'''The list with nth numbers is: {hamming_numbers}''')
print('-----------------------------------------------------')
| 328 | 0 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_UpperCAmelCase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'language-modeling',
'summarization',
'token-classification',
'question-answering',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
_UpperCAmelCase = logging.getLogger()
def lowerCAmelCase_ ( ) -> Optional[int]:
UpperCamelCase_ = argparse.ArgumentParser()
parser.add_argument("-f" )
UpperCamelCase_ = parser.parse_args()
return args.f
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_="eval" ) -> Any:
UpperCamelCase_ = os.path.join(UpperCamelCase_ , F'''{split}_results.json''' )
if os.path.exists(UpperCamelCase_ ):
with open(UpperCamelCase_ , "r" ) as f:
return json.load(UpperCamelCase_ )
raise ValueError(F'''can\'t find {path}''' )
_UpperCAmelCase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _UpperCamelCase ( lowerCAmelCase_ ):
def lowercase ( self: Optional[Any] ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--eval_steps=2
--warmup_steps=2
--seed=42
--max_seq_length=128
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_flax_glue.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE )
self.assertGreaterEqual(result["eval_accuracy"] , 0.75 )
@slow
def lowercase ( self: int ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_clm_flax.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--block_size 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_clm_flax.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE )
self.assertLess(result["eval_perplexity"] , 100 )
@slow
def lowercase ( self: Any ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_summarization.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--test_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=8
--do_train
--do_eval
--do_predict
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_summarization_flax.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE , split="test" )
self.assertGreaterEqual(result["test_rouge1"] , 10 )
self.assertGreaterEqual(result["test_rouge2"] , 2 )
self.assertGreaterEqual(result["test_rougeL"] , 7 )
self.assertGreaterEqual(result["test_rougeLsum"] , 7 )
@slow
def lowercase ( self: str ) -> int:
"""simple docstring"""
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_mlm.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--overwrite_output_dir
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--logging_steps 2 --eval_steps 2
--do_train
--do_eval
--num_train_epochs=1
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_mlm_flax.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE )
self.assertLess(result["eval_perplexity"] , 42 )
@slow
def lowercase ( self: Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_t5_mlm_flax.py
--model_name_or_path t5-small
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_ta_mlm_flax.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE )
self.assertGreaterEqual(result["eval_accuracy"] , 0.42 )
@slow
def lowercase ( self: str ) -> int:
"""simple docstring"""
UpperCamelCase_ = 7 if get_gpu_count() > 1 else 2
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_flax_ner.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--do_train
--do_eval
--warmup_steps=2
--learning_rate=2e-4
--logging_steps 2 --eval_steps 2
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_flax_ner.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE )
self.assertGreaterEqual(result["eval_accuracy"] , 0.75 )
self.assertGreaterEqual(result["eval_f1"] , 0.3 )
@slow
def lowercase ( self: Union[str, Any] ) -> Any:
"""simple docstring"""
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_qa.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=2
--do_train
--do_eval
--logging_steps 2 --eval_steps 2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_qa.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE )
self.assertGreaterEqual(result["eval_f1"] , 30 )
self.assertGreaterEqual(result["eval_exact"] , 30 )
| 354 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
_UpperCamelCase : List[Any] = IFImgaImgSuperResolutionPipeline
_UpperCamelCase : Any = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
_UpperCamelCase : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''original_image'''} )
_UpperCamelCase : List[Any] = PipelineTesterMixin.required_optional_params - {'''latents'''}
def lowercase ( self: List[str] ) -> Any:
"""simple docstring"""
return self._get_superresolution_dummy_components()
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: Optional[int]=0 ) -> List[Any]:
"""simple docstring"""
if str(_SCREAMING_SNAKE_CASE ).startswith("mps" ):
UpperCamelCase_ = torch.manual_seed(_SCREAMING_SNAKE_CASE )
else:
UpperCamelCase_ = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = floats_tensor((1, 3, 16, 16) , rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"original_image": original_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def lowercase ( self: Any ) -> Union[str, Any]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def lowercase ( self: int ) -> Tuple:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda" , reason="float16 requires CUDA" )
def lowercase ( self: Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1e-1 )
def lowercase ( self: List[Any] ) -> Union[str, Any]:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def lowercase ( self: Dict ) -> Any:
"""simple docstring"""
self._test_save_load_local()
def lowercase ( self: Any ) -> Dict:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 328 | 0 |
"""simple docstring"""
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __init__( self: List[str] , *,
_SCREAMING_SNAKE_CASE: int = 4 , _SCREAMING_SNAKE_CASE: int = 768 , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: str , ) -> Tuple:
"""simple docstring"""
super().__init__()
UpperCamelCase_ = nn.Parameter(torch.zeros(_SCREAMING_SNAKE_CASE ) )
# parameters for additional clip time embeddings
UpperCamelCase_ = nn.Linear(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = nn.Linear(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# parameters for encoder hidden states
UpperCamelCase_ = clip_extra_context_tokens
UpperCamelCase_ = nn.Linear(
_SCREAMING_SNAKE_CASE , self.clip_extra_context_tokens * cross_attention_dim )
UpperCamelCase_ = nn.Linear(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = nn.LayerNorm(_SCREAMING_SNAKE_CASE )
def lowercase ( self: Optional[int] , *, _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Tuple ) -> str:
"""simple docstring"""
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
UpperCamelCase_ = image_embeddings.shape[0]
UpperCamelCase_ = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
UpperCamelCase_ = classifier_free_guidance_embeddings.expand(
_SCREAMING_SNAKE_CASE , -1 )
UpperCamelCase_ = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
UpperCamelCase_ = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
UpperCamelCase_ = self.embedding_proj(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.clip_image_embeddings_project_to_time_embeddings(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
UpperCamelCase_ = self.clip_extra_context_tokens_proj(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = clip_extra_context_tokens.reshape(_SCREAMING_SNAKE_CASE , -1 , self.clip_extra_context_tokens )
UpperCamelCase_ = clip_extra_context_tokens.permute(0 , 2 , 1 )
UpperCamelCase_ = self.encoder_hidden_states_proj(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.text_encoder_hidden_states_norm(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 355 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_UpperCAmelCase = {'UserAgent': UserAgent().random}
def lowerCAmelCase_ ( UpperCamelCase_ ) -> dict:
UpperCamelCase_ = script.contents[0]
UpperCamelCase_ = json.loads(data[data.find("{\"config\"" ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class _UpperCamelCase :
def __init__( self: Optional[Any] , _SCREAMING_SNAKE_CASE: str ) -> str:
"""simple docstring"""
UpperCamelCase_ = f'''https://www.instagram.com/{username}/'''
UpperCamelCase_ = self.get_json()
def lowercase ( self: Union[str, Any] ) -> dict:
"""simple docstring"""
UpperCamelCase_ = requests.get(self.url , headers=_SCREAMING_SNAKE_CASE ).text
UpperCamelCase_ = BeautifulSoup(_SCREAMING_SNAKE_CASE , "html.parser" ).find_all("script" )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self: Tuple ) -> str:
"""simple docstring"""
return f'''{self.__class__.__name__}(\'{self.username}\')'''
def __str__( self: List[Any] ) -> str:
"""simple docstring"""
return f'''{self.fullname} ({self.username}) is {self.biography}'''
@property
def lowercase ( self: List[str] ) -> str:
"""simple docstring"""
return self.user_data["username"]
@property
def lowercase ( self: int ) -> str:
"""simple docstring"""
return self.user_data["full_name"]
@property
def lowercase ( self: List[Any] ) -> str:
"""simple docstring"""
return self.user_data["biography"]
@property
def lowercase ( self: List[Any] ) -> str:
"""simple docstring"""
return self.user_data["business_email"]
@property
def lowercase ( self: List[Any] ) -> str:
"""simple docstring"""
return self.user_data["external_url"]
@property
def lowercase ( self: List[Any] ) -> int:
"""simple docstring"""
return self.user_data["edge_followed_by"]["count"]
@property
def lowercase ( self: List[str] ) -> int:
"""simple docstring"""
return self.user_data["edge_follow"]["count"]
@property
def lowercase ( self: List[str] ) -> int:
"""simple docstring"""
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def lowercase ( self: List[str] ) -> str:
"""simple docstring"""
return self.user_data["profile_pic_url_hd"]
@property
def lowercase ( self: Optional[int] ) -> bool:
"""simple docstring"""
return self.user_data["is_verified"]
@property
def lowercase ( self: List[str] ) -> bool:
"""simple docstring"""
return self.user_data["is_private"]
def lowerCAmelCase_ ( UpperCamelCase_ = "github" ) -> None:
import os
if os.environ.get("CI" ):
return # test failing on GitHub Actions
UpperCamelCase_ = InstagramUser(UpperCamelCase_ )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , UpperCamelCase_ )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 150
assert instagram_user.number_of_followers > 120000
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "[email protected]"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith("https://instagram." )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
_UpperCAmelCase = InstagramUser('github')
print(instagram_user)
print(f'''{instagram_user.number_of_posts = }''')
print(f'''{instagram_user.number_of_followers = }''')
print(f'''{instagram_user.number_of_followings = }''')
print(f'''{instagram_user.email = }''')
print(f'''{instagram_user.website = }''')
print(f'''{instagram_user.profile_picture_url = }''')
print(f'''{instagram_user.is_verified = }''')
print(f'''{instagram_user.is_private = }''')
| 328 | 0 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _UpperCamelCase :
def __init__( self: str ) -> Any:
"""simple docstring"""
UpperCamelCase_ = ""
UpperCamelCase_ = ""
UpperCamelCase_ = []
UpperCamelCase_ = 0
UpperCamelCase_ = 256
UpperCamelCase_ = 0
UpperCamelCase_ = 0
UpperCamelCase_ = 0
UpperCamelCase_ = 0
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: Dict ) -> str:
"""simple docstring"""
UpperCamelCase_ = cva.imread(_SCREAMING_SNAKE_CASE , 0 )
UpperCamelCase_ = copy.deepcopy(self.img )
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = plt.hist(self.img.ravel() , 256 , [0, 256] , label="x" )
UpperCamelCase_ = np.sum(_SCREAMING_SNAKE_CASE )
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
UpperCamelCase_ = x[i] / self.k
self.sk += prk
UpperCamelCase_ = (self.L - 1) * self.sk
if self.rem != 0:
UpperCamelCase_ = int(last % last )
UpperCamelCase_ = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = int(np.ma.count(self.img ) / self.img[1].size )
UpperCamelCase_ = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
UpperCamelCase_ = self.img[j][i]
if num != self.last_list[num]:
UpperCamelCase_ = self.last_list[num]
cva.imwrite("output_data/output.jpg" , self.img )
def lowercase ( self: Any ) -> Optional[Any]:
"""simple docstring"""
plt.hist(self.img.ravel() , 256 , [0, 256] )
def lowercase ( self: Tuple ) -> Union[str, Any]:
"""simple docstring"""
cva.imshow("Output-Image" , self.img )
cva.imshow("Input-Image" , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
_UpperCAmelCase = os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
_UpperCAmelCase = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 356 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
get_image_size,
infer_channel_dimension_format,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends
if is_vision_available():
import textwrap
from PIL import Image, ImageDraw, ImageFont
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
_UpperCAmelCase = False
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = 'ybelkada/fonts'
def lowerCAmelCase_ ( ) -> Dict:
if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
raise ImportError(
F'''You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '''
"Pix2StructImageProcessor. Please upgrade torch." )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> List[Any]:
requires_backends(UpperCamelCase_ , ["torch"] )
_check_torch_version()
UpperCamelCase_ = image_tensor.unsqueeze(0 )
UpperCamelCase_ = torch.nn.functional.unfold(UpperCamelCase_ , (patch_height, patch_width) , stride=(patch_height, patch_width) )
UpperCamelCase_ = patches.reshape(image_tensor.size(0 ) , image_tensor.size(1 ) , UpperCamelCase_ , UpperCamelCase_ , -1 )
UpperCamelCase_ = patches.permute(0 , 4 , 2 , 3 , 1 ).reshape(
image_tensor.size(2 ) // patch_height , image_tensor.size(3 ) // patch_width , image_tensor.size(1 ) * patch_height * patch_width , )
return patches.unsqueeze(0 )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ = 36 , UpperCamelCase_ = "black" , UpperCamelCase_ = "white" , UpperCamelCase_ = 5 , UpperCamelCase_ = 5 , UpperCamelCase_ = 5 , UpperCamelCase_ = 5 , UpperCamelCase_ = None , UpperCamelCase_ = None , ) -> Image.Image:
requires_backends(UpperCamelCase_ , "vision" )
# Add new lines so that each line is no more than 80 characters.
UpperCamelCase_ = textwrap.TextWrapper(width=80 )
UpperCamelCase_ = wrapper.wrap(text=UpperCamelCase_ )
UpperCamelCase_ = "\n".join(UpperCamelCase_ )
if font_bytes is not None and font_path is None:
UpperCamelCase_ = io.BytesIO(UpperCamelCase_ )
elif font_path is not None:
UpperCamelCase_ = font_path
else:
UpperCamelCase_ = hf_hub_download(UpperCamelCase_ , "Arial.TTF" )
UpperCamelCase_ = ImageFont.truetype(UpperCamelCase_ , encoding="UTF-8" , size=UpperCamelCase_ )
# Use a temporary canvas to determine the width and height in pixels when
# rendering the text.
UpperCamelCase_ = ImageDraw.Draw(Image.new("RGB" , (1, 1) , UpperCamelCase_ ) )
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = temp_draw.textbbox((0, 0) , UpperCamelCase_ , UpperCamelCase_ )
# Create the actual image with a bit of padding around the text.
UpperCamelCase_ = text_width + left_padding + right_padding
UpperCamelCase_ = text_height + top_padding + bottom_padding
UpperCamelCase_ = Image.new("RGB" , (image_width, image_height) , UpperCamelCase_ )
UpperCamelCase_ = ImageDraw.Draw(UpperCamelCase_ )
draw.text(xy=(left_padding, top_padding) , text=UpperCamelCase_ , fill=UpperCamelCase_ , font=UpperCamelCase_ )
return image
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , **UpperCamelCase_ ) -> Union[str, Any]:
requires_backends(UpperCamelCase_ , "vision" )
# Convert to PIL image if necessary
UpperCamelCase_ = to_pil_image(UpperCamelCase_ )
UpperCamelCase_ = render_text(UpperCamelCase_ , **UpperCamelCase_ )
UpperCamelCase_ = max(header_image.width , image.width )
UpperCamelCase_ = int(image.height * (new_width / image.width) )
UpperCamelCase_ = int(header_image.height * (new_width / header_image.width) )
UpperCamelCase_ = Image.new("RGB" , (new_width, new_height + new_header_height) , "white" )
new_image.paste(header_image.resize((new_width, new_header_height) ) , (0, 0) )
new_image.paste(image.resize((new_width, new_height) ) , (0, new_header_height) )
# Convert back to the original framework if necessary
UpperCamelCase_ = to_numpy_array(UpperCamelCase_ )
if infer_channel_dimension_format(UpperCamelCase_ ) == ChannelDimension.LAST:
UpperCamelCase_ = to_channel_dimension_format(UpperCamelCase_ , ChannelDimension.LAST )
return new_image
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : str = ['''flattened_patches''']
def __init__( self: List[Any] , _SCREAMING_SNAKE_CASE: bool = True , _SCREAMING_SNAKE_CASE: bool = True , _SCREAMING_SNAKE_CASE: Dict[str, int] = None , _SCREAMING_SNAKE_CASE: int = 2048 , _SCREAMING_SNAKE_CASE: bool = False , **_SCREAMING_SNAKE_CASE: Optional[Any] , ) -> None:
"""simple docstring"""
super().__init__(**_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = patch_size if patch_size is not None else {"height": 16, "width": 16}
UpperCamelCase_ = do_normalize
UpperCamelCase_ = do_convert_rgb
UpperCamelCase_ = max_patches
UpperCamelCase_ = is_vqa
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: np.ndarray , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: dict , **_SCREAMING_SNAKE_CASE: Union[str, Any] ) -> np.ndarray:
"""simple docstring"""
requires_backends(self.extract_flattened_patches , "torch" )
_check_torch_version()
# convert to torch
UpperCamelCase_ = to_channel_dimension_format(_SCREAMING_SNAKE_CASE , ChannelDimension.FIRST )
UpperCamelCase_ = torch.from_numpy(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ , UpperCamelCase_ = patch_size["height"], patch_size["width"]
UpperCamelCase_ , UpperCamelCase_ = get_image_size(_SCREAMING_SNAKE_CASE )
# maximize scale s.t.
UpperCamelCase_ = math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
UpperCamelCase_ = max(min(math.floor(scale * image_height / patch_height ) , _SCREAMING_SNAKE_CASE ) , 1 )
UpperCamelCase_ = max(min(math.floor(scale * image_width / patch_width ) , _SCREAMING_SNAKE_CASE ) , 1 )
UpperCamelCase_ = max(num_feasible_rows * patch_height , 1 )
UpperCamelCase_ = max(num_feasible_cols * patch_width , 1 )
UpperCamelCase_ = torch.nn.functional.interpolate(
image.unsqueeze(0 ) , size=(resized_height, resized_width) , mode="bilinear" , align_corners=_SCREAMING_SNAKE_CASE , antialias=_SCREAMING_SNAKE_CASE , ).squeeze(0 )
# [1, rows, columns, patch_height * patch_width * image_channels]
UpperCamelCase_ = torch_extract_patches(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = patches.shape
UpperCamelCase_ = patches_shape[1]
UpperCamelCase_ = patches_shape[2]
UpperCamelCase_ = patches_shape[3]
# [rows * columns, patch_height * patch_width * image_channels]
UpperCamelCase_ = patches.reshape([rows * columns, depth] )
# [rows * columns, 1]
UpperCamelCase_ = torch.arange(_SCREAMING_SNAKE_CASE ).reshape([rows, 1] ).repeat(1 , _SCREAMING_SNAKE_CASE ).reshape([rows * columns, 1] )
UpperCamelCase_ = torch.arange(_SCREAMING_SNAKE_CASE ).reshape([1, columns] ).repeat(_SCREAMING_SNAKE_CASE , 1 ).reshape([rows * columns, 1] )
# Offset by 1 so the ids do not contain zeros, which represent padding.
row_ids += 1
col_ids += 1
# Prepare additional patch features.
# [rows * columns, 1]
UpperCamelCase_ = row_ids.to(torch.floataa )
UpperCamelCase_ = col_ids.to(torch.floataa )
# [rows * columns, 2 + patch_height * patch_width * image_channels]
UpperCamelCase_ = torch.cat([row_ids, col_ids, patches] , -1 )
# [max_patches, 2 + patch_height * patch_width * image_channels]
UpperCamelCase_ = torch.nn.functional.pad(_SCREAMING_SNAKE_CASE , [0, 0, 0, max_patches - (rows * columns)] ).float()
UpperCamelCase_ = to_numpy_array(_SCREAMING_SNAKE_CASE )
return result
def lowercase ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: np.ndarray , _SCREAMING_SNAKE_CASE: Optional[Union[str, ChannelDimension]] = None , **_SCREAMING_SNAKE_CASE: List[str] ) -> np.ndarray:
"""simple docstring"""
if image.dtype == np.uinta:
UpperCamelCase_ = image.astype(np.floataa )
# take mean across the whole `image`
UpperCamelCase_ = np.mean(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = np.std(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = max(_SCREAMING_SNAKE_CASE , 1.0 / math.sqrt(np.prod(image.shape ) ) )
return normalize(_SCREAMING_SNAKE_CASE , mean=_SCREAMING_SNAKE_CASE , std=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def lowercase ( self: Optional[int] , _SCREAMING_SNAKE_CASE: ImageInput , _SCREAMING_SNAKE_CASE: Optional[str] = None , _SCREAMING_SNAKE_CASE: bool = None , _SCREAMING_SNAKE_CASE: Optional[bool] = None , _SCREAMING_SNAKE_CASE: Optional[int] = None , _SCREAMING_SNAKE_CASE: Optional[Dict[str, int]] = None , _SCREAMING_SNAKE_CASE: Optional[Union[str, TensorType]] = None , _SCREAMING_SNAKE_CASE: ChannelDimension = ChannelDimension.FIRST , **_SCREAMING_SNAKE_CASE: List[Any] , ) -> ImageInput:
"""simple docstring"""
UpperCamelCase_ = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase_ = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
UpperCamelCase_ = patch_size if patch_size is not None else self.patch_size
UpperCamelCase_ = max_patches if max_patches is not None else self.max_patches
UpperCamelCase_ = self.is_vqa
if kwargs.get("data_format" , _SCREAMING_SNAKE_CASE ) is not None:
raise ValueError("data_format is not an accepted input as the outputs are " )
UpperCamelCase_ = 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." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
UpperCamelCase_ = [convert_to_rgb(_SCREAMING_SNAKE_CASE ) for image in images]
# All transformations expect numpy arrays.
UpperCamelCase_ = [to_numpy_array(_SCREAMING_SNAKE_CASE ) for image in images]
if is_vqa:
if header_text is None:
raise ValueError("A header text must be provided for VQA models." )
UpperCamelCase_ = kwargs.pop("font_bytes" , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = kwargs.pop("font_path" , _SCREAMING_SNAKE_CASE )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase_ = [header_text] * len(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = [
render_header(_SCREAMING_SNAKE_CASE , header_text[i] , font_bytes=_SCREAMING_SNAKE_CASE , font_path=_SCREAMING_SNAKE_CASE )
for i, image in enumerate(_SCREAMING_SNAKE_CASE )
]
if do_normalize:
UpperCamelCase_ = [self.normalize(image=_SCREAMING_SNAKE_CASE ) for image in images]
# convert to torch tensor and permute
UpperCamelCase_ = [
self.extract_flattened_patches(image=_SCREAMING_SNAKE_CASE , max_patches=_SCREAMING_SNAKE_CASE , patch_size=_SCREAMING_SNAKE_CASE )
for image in images
]
# create attention mask in numpy
UpperCamelCase_ = [(image.sum(axis=-1 ) != 0).astype(np.floataa ) for image in images]
UpperCamelCase_ = BatchFeature(
data={"flattened_patches": images, "attention_mask": attention_masks} , tensor_type=_SCREAMING_SNAKE_CASE )
return encoded_outputs
| 328 | 0 |
_UpperCAmelCase = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCAmelCase = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCAmelCase = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',
5: 'Friday',
6: 'Saturday',
}
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> str:
assert len(str(UpperCamelCase_ ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
UpperCamelCase_ = year // 100
UpperCamelCase_ = (5 * (century % 4) + 2) % 7
UpperCamelCase_ = year % 100
UpperCamelCase_ = centurian % 12
UpperCamelCase_ = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
UpperCamelCase_ = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
else DOOMSDAY_LEAP[month - 1]
)
UpperCamelCase_ = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 357 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __init__( self: Any , _SCREAMING_SNAKE_CASE: int = 768 , ) -> Tuple:
"""simple docstring"""
super().__init__()
UpperCamelCase_ = nn.Parameter(torch.zeros(1 , _SCREAMING_SNAKE_CASE ) )
UpperCamelCase_ = nn.Parameter(torch.ones(1 , _SCREAMING_SNAKE_CASE ) )
def lowercase ( self: List[Any] , _SCREAMING_SNAKE_CASE: Optional[Union[str, torch.device]] = None , _SCREAMING_SNAKE_CASE: Optional[torch.dtype] = None , ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ = nn.Parameter(self.mean.to(_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase_ = nn.Parameter(self.std.to(_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE ) )
return self
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: Dict ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = (embeds - self.mean) * 1.0 / self.std
return embeds
def lowercase ( self: List[Any] , _SCREAMING_SNAKE_CASE: Union[str, Any] ) -> Dict:
"""simple docstring"""
UpperCamelCase_ = (embeds * self.std) + self.mean
return embeds
| 328 | 0 |
"""simple docstring"""
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def lowerCAmelCase_ ( UpperCamelCase_ ) -> str:
UpperCamelCase_ = int(UpperCamelCase_ )
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = t // 3600, (t // 60) % 60, t % 60
return F'''{h}:{m:02d}:{s:02d}''' if h != 0 else F'''{m:02d}:{s:02d}'''
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=300 ) -> str:
# docstyle-ignore
return F'''
<div>
{prefix}
<progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>
{label}
</div>
'''
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Any:
UpperCamelCase_ = "<table border=\"1\" class=\"dataframe\">\n"
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += F''' <th>{i}</th>\n'''
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
UpperCamelCase_ = F'''{elt:.6f}''' if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else str(UpperCamelCase_ )
html_code += F''' <td>{elt}</td>\n'''
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class _UpperCamelCase :
_UpperCamelCase : int = 5
_UpperCamelCase : int = 0.2
def __init__( self: List[Any] , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: Optional[str] = None , _SCREAMING_SNAKE_CASE: bool = True , _SCREAMING_SNAKE_CASE: Optional["NotebookTrainingTracker"] = None , _SCREAMING_SNAKE_CASE: int = 300 , ) -> int:
"""simple docstring"""
UpperCamelCase_ = total
UpperCamelCase_ = "" if prefix is None else prefix
UpperCamelCase_ = leave
UpperCamelCase_ = parent
UpperCamelCase_ = width
UpperCamelCase_ = None
UpperCamelCase_ = None
UpperCamelCase_ = None
def lowercase ( self: List[str] , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: bool = False , _SCREAMING_SNAKE_CASE: str = None ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ = value
if comment is not None:
UpperCamelCase_ = comment
if self.last_value is None:
UpperCamelCase_ = UpperCamelCase_ = time.time()
UpperCamelCase_ = UpperCamelCase_ = value
UpperCamelCase_ = UpperCamelCase_ = None
UpperCamelCase_ = self.warmup
UpperCamelCase_ = 1
self.update_bar(_SCREAMING_SNAKE_CASE )
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for , self.total ):
if self.first_calls > 0:
self.first_calls -= 1
UpperCamelCase_ = time.time()
UpperCamelCase_ = current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
UpperCamelCase_ = self.elapsed_time / (value - self.start_value)
else:
UpperCamelCase_ = None
if value >= self.total:
UpperCamelCase_ = self.total
UpperCamelCase_ = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
UpperCamelCase_ = self.average_time_per_item * (self.total - value)
self.update_bar(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = value
UpperCamelCase_ = current_time
if self.average_time_per_item is None:
UpperCamelCase_ = 1
else:
UpperCamelCase_ = max(int(self.update_every / self.average_time_per_item ) , 1 )
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: Dict , _SCREAMING_SNAKE_CASE: Optional[int]=None ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ = " " * (len(str(self.total ) ) - len(str(_SCREAMING_SNAKE_CASE ) )) + str(_SCREAMING_SNAKE_CASE )
if self.elapsed_time is None:
UpperCamelCase_ = f'''[{spaced_value}/{self.total} : < :'''
elif self.predicted_remaining is None:
UpperCamelCase_ = f'''[{spaced_value}/{self.total} {format_time(self.elapsed_time )}'''
else:
UpperCamelCase_ = (
f'''[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <'''
f''' {format_time(self.predicted_remaining )}'''
)
self.label += f''', {1/self.average_time_per_item:.2f} it/s'''
self.label += "]" if self.comment is None or len(self.comment ) == 0 else f''', {self.comment}]'''
self.display()
def lowercase ( self: Dict ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
UpperCamelCase_ = disp.display(disp.HTML(self.html_code ) , display_id=_SCREAMING_SNAKE_CASE )
else:
self.output.update(disp.HTML(self.html_code ) )
def lowercase ( self: List[Any] ) -> Optional[int]:
"""simple docstring"""
if self.parent is None and self.output is not None:
self.output.update(disp.HTML("" ) )
class _UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self: Optional[Any] , _SCREAMING_SNAKE_CASE: Union[str, Any] , _SCREAMING_SNAKE_CASE: Union[str, Any]=None ) -> Tuple:
"""simple docstring"""
super().__init__(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = None if column_names is None else [column_names]
UpperCamelCase_ = None
def lowercase ( self: List[str] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table )
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
UpperCamelCase_ = disp.display(disp.HTML(self.html_code ) , display_id=_SCREAMING_SNAKE_CASE )
else:
self.output.update(disp.HTML(self.html_code ) )
def lowercase ( self: Tuple , _SCREAMING_SNAKE_CASE: int ) -> List[str]:
"""simple docstring"""
if self.inner_table is None:
UpperCamelCase_ = [list(values.keys() ), list(values.values() )]
else:
UpperCamelCase_ = self.inner_table[0]
if len(self.inner_table ) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = columns
self.inner_table.append([values[c] for c in columns] )
def lowercase ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: Union[str, Any] , _SCREAMING_SNAKE_CASE: Tuple=None , _SCREAMING_SNAKE_CASE: List[Any]=300 ) -> Any:
"""simple docstring"""
UpperCamelCase_ = NotebookProgressBar(_SCREAMING_SNAKE_CASE , prefix=_SCREAMING_SNAKE_CASE , parent=self , width=_SCREAMING_SNAKE_CASE )
return self.child_bar
def lowercase ( self: str ) -> int:
"""simple docstring"""
UpperCamelCase_ = None
self.display()
class _UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self: Union[str, Any] ) -> Dict:
"""simple docstring"""
UpperCamelCase_ = None
UpperCamelCase_ = None
UpperCamelCase_ = False
def lowercase ( self: Tuple , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: Union[str, Any] , **_SCREAMING_SNAKE_CASE: Dict ) -> Dict:
"""simple docstring"""
UpperCamelCase_ = "Epoch" if args.evaluation_strategy == IntervalStrategy.EPOCH else "Step"
UpperCamelCase_ = 0
UpperCamelCase_ = 0
UpperCamelCase_ = [self.first_column] + ["Training Loss"]
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append("Validation Loss" )
UpperCamelCase_ = NotebookTrainingTracker(state.max_steps , _SCREAMING_SNAKE_CASE )
def lowercase ( self: int , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: Dict , _SCREAMING_SNAKE_CASE: int , **_SCREAMING_SNAKE_CASE: List[Any] ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ = int(state.epoch ) if int(state.epoch ) == state.epoch else f'''{state.epoch:.2f}'''
self.training_tracker.update(
state.global_step + 1 , comment=f'''Epoch {epoch}/{state.num_train_epochs}''' , force_update=self._force_next_update , )
UpperCamelCase_ = False
def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: Optional[int]=None , **_SCREAMING_SNAKE_CASE: Union[str, Any] ) -> List[Any]:
"""simple docstring"""
if not has_length(_SCREAMING_SNAKE_CASE ):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
UpperCamelCase_ = self.training_tracker.add_child(len(_SCREAMING_SNAKE_CASE ) )
else:
UpperCamelCase_ = NotebookProgressBar(len(_SCREAMING_SNAKE_CASE ) )
self.prediction_bar.update(1 )
else:
self.prediction_bar.update(self.prediction_bar.value + 1 )
def lowercase ( self: Optional[int] , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: Any , **_SCREAMING_SNAKE_CASE: Tuple ) -> Tuple:
"""simple docstring"""
if self.prediction_bar is not None:
self.prediction_bar.close()
UpperCamelCase_ = None
def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: List[str]=None , **_SCREAMING_SNAKE_CASE: List[str] ) -> List[Any]:
"""simple docstring"""
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
UpperCamelCase_ = {"Training Loss": logs["loss"]}
# First column is necessarily Step sine we're not in epoch eval strategy
UpperCamelCase_ = state.global_step
self.training_tracker.write_line(_SCREAMING_SNAKE_CASE )
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: Optional[Any]=None , **_SCREAMING_SNAKE_CASE: List[Any] ) -> str:
"""simple docstring"""
if self.training_tracker is not None:
UpperCamelCase_ = {"Training Loss": "No log", "Validation Loss": "No log"}
for log in reversed(state.log_history ):
if "loss" in log:
UpperCamelCase_ = log["loss"]
break
if self.first_column == "Epoch":
UpperCamelCase_ = int(state.epoch )
else:
UpperCamelCase_ = state.global_step
UpperCamelCase_ = "eval"
for k in metrics:
if k.endswith("_loss" ):
UpperCamelCase_ = re.sub(R"\_loss$" , "" , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = metrics.pop("total_flos" , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = metrics.pop("epoch" , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = metrics.pop(f'''{metric_key_prefix}_runtime''' , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = metrics.pop(f'''{metric_key_prefix}_samples_per_second''' , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = metrics.pop(f'''{metric_key_prefix}_steps_per_second''' , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = metrics.pop(f'''{metric_key_prefix}_jit_compilation_time''' , _SCREAMING_SNAKE_CASE )
for k, v in metrics.items():
if k == f'''{metric_key_prefix}_loss''':
UpperCamelCase_ = v
else:
UpperCamelCase_ = k.split("_" )
UpperCamelCase_ = " ".join([part.capitalize() for part in splits[1:]] )
UpperCamelCase_ = v
self.training_tracker.write_line(_SCREAMING_SNAKE_CASE )
self.training_tracker.remove_child()
UpperCamelCase_ = None
# Evaluation takes a long time so we should force the next update.
UpperCamelCase_ = True
def lowercase ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: Dict , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: str , **_SCREAMING_SNAKE_CASE: List[str] ) -> List[Any]:
"""simple docstring"""
self.training_tracker.update(
state.global_step , comment=f'''Epoch {int(state.epoch )}/{state.num_train_epochs}''' , force_update=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = None
| 358 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_UpperCAmelCase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'language-modeling',
'summarization',
'token-classification',
'question-answering',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
_UpperCAmelCase = logging.getLogger()
def lowerCAmelCase_ ( ) -> Optional[int]:
UpperCamelCase_ = argparse.ArgumentParser()
parser.add_argument("-f" )
UpperCamelCase_ = parser.parse_args()
return args.f
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_="eval" ) -> Any:
UpperCamelCase_ = os.path.join(UpperCamelCase_ , F'''{split}_results.json''' )
if os.path.exists(UpperCamelCase_ ):
with open(UpperCamelCase_ , "r" ) as f:
return json.load(UpperCamelCase_ )
raise ValueError(F'''can\'t find {path}''' )
_UpperCAmelCase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _UpperCamelCase ( lowerCAmelCase_ ):
def lowercase ( self: Optional[Any] ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--eval_steps=2
--warmup_steps=2
--seed=42
--max_seq_length=128
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_flax_glue.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE )
self.assertGreaterEqual(result["eval_accuracy"] , 0.75 )
@slow
def lowercase ( self: int ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_clm_flax.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--block_size 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_clm_flax.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE )
self.assertLess(result["eval_perplexity"] , 100 )
@slow
def lowercase ( self: Any ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_summarization.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--test_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=8
--do_train
--do_eval
--do_predict
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_summarization_flax.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE , split="test" )
self.assertGreaterEqual(result["test_rouge1"] , 10 )
self.assertGreaterEqual(result["test_rouge2"] , 2 )
self.assertGreaterEqual(result["test_rougeL"] , 7 )
self.assertGreaterEqual(result["test_rougeLsum"] , 7 )
@slow
def lowercase ( self: str ) -> int:
"""simple docstring"""
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_mlm.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--overwrite_output_dir
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--logging_steps 2 --eval_steps 2
--do_train
--do_eval
--num_train_epochs=1
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_mlm_flax.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE )
self.assertLess(result["eval_perplexity"] , 42 )
@slow
def lowercase ( self: Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_t5_mlm_flax.py
--model_name_or_path t5-small
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_ta_mlm_flax.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE )
self.assertGreaterEqual(result["eval_accuracy"] , 0.42 )
@slow
def lowercase ( self: str ) -> int:
"""simple docstring"""
UpperCamelCase_ = 7 if get_gpu_count() > 1 else 2
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_flax_ner.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--do_train
--do_eval
--warmup_steps=2
--learning_rate=2e-4
--logging_steps 2 --eval_steps 2
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_flax_ner.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE )
self.assertGreaterEqual(result["eval_accuracy"] , 0.75 )
self.assertGreaterEqual(result["eval_f1"] , 0.3 )
@slow
def lowercase ( self: Union[str, Any] ) -> Any:
"""simple docstring"""
UpperCamelCase_ = self.get_auto_remove_tmp_dir()
UpperCamelCase_ = f'''
run_qa.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=2
--do_train
--do_eval
--logging_steps 2 --eval_steps 2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
'''.split()
with patch.object(_SCREAMING_SNAKE_CASE , "argv" , _SCREAMING_SNAKE_CASE ):
run_qa.main()
UpperCamelCase_ = get_results(_SCREAMING_SNAKE_CASE )
self.assertGreaterEqual(result["eval_f1"] , 30 )
self.assertGreaterEqual(result["eval_exact"] , 30 )
| 328 | 0 |
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
_UpperCAmelCase = pytest.mark.integration
_UpperCAmelCase = {'comet'}
_UpperCAmelCase = importlib.util.find_spec('fairseq') is not None
_UpperCAmelCase = {'code_eval'}
_UpperCAmelCase = os.name == 'nt'
_UpperCAmelCase = {'bertscore', 'frugalscore', 'perplexity'}
_UpperCAmelCase = importlib.util.find_spec('transformers') is not None
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Union[str, Any]:
@wraps(UpperCamelCase_ )
def wrapper(self , UpperCamelCase_ ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest("\"test requires Fairseq\"" )
else:
test_case(self , UpperCamelCase_ )
return wrapper
def lowerCAmelCase_ ( UpperCamelCase_ ) -> List[Any]:
@wraps(UpperCamelCase_ )
def wrapper(self , UpperCamelCase_ ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest("\"test requires transformers\"" )
else:
test_case(self , UpperCamelCase_ )
return wrapper
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
@wraps(UpperCamelCase_ )
def wrapper(self , UpperCamelCase_ ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest("\"test not supported on Windows\"" )
else:
test_case(self , UpperCamelCase_ )
return wrapper
def lowerCAmelCase_ ( ) -> Optional[Any]:
UpperCamelCase_ = [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob("./metrics/*/" )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
@local
class _UpperCamelCase ( parameterized.TestCase ):
_UpperCamelCase : Any = {}
_UpperCamelCase : Dict = None
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning" )
@pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning" )
def lowercase ( self: List[Any] , _SCREAMING_SNAKE_CASE: int ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ = "[...]"
UpperCamelCase_ = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("metrics" , _SCREAMING_SNAKE_CASE ) ).module_path )
UpperCamelCase_ = datasets.load.import_main_class(metric_module.__name__ , dataset=_SCREAMING_SNAKE_CASE )
# check parameters
UpperCamelCase_ = inspect.signature(metric._compute ).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values() ) ) # no **kwargs
# run doctest
with self.patch_intensive_calls(_SCREAMING_SNAKE_CASE , metric_module.__name__ ):
with self.use_local_metrics():
try:
UpperCamelCase_ = doctest.testmod(_SCREAMING_SNAKE_CASE , verbose=_SCREAMING_SNAKE_CASE , raise_on_error=_SCREAMING_SNAKE_CASE )
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@slow
def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: List[str] ) -> int:
"""simple docstring"""
UpperCamelCase_ = "[...]"
UpperCamelCase_ = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("metrics" , _SCREAMING_SNAKE_CASE ) ).module_path )
# run doctest
with self.use_local_metrics():
UpperCamelCase_ = doctest.testmod(_SCREAMING_SNAKE_CASE , verbose=_SCREAMING_SNAKE_CASE , raise_on_error=_SCREAMING_SNAKE_CASE )
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@contextmanager
def lowercase ( self: int , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Optional[Any] ) -> Optional[int]:
"""simple docstring"""
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](_SCREAMING_SNAKE_CASE ):
yield
else:
yield
@contextmanager
def lowercase ( self: Union[str, Any] ) -> Any:
"""simple docstring"""
def load_local_metric(_SCREAMING_SNAKE_CASE: Dict , *_SCREAMING_SNAKE_CASE: List[str] , **_SCREAMING_SNAKE_CASE: List[Any] ):
return load_metric(os.path.join("metrics" , _SCREAMING_SNAKE_CASE ) , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
with patch("datasets.load_metric" ) as mock_load_metric:
UpperCamelCase_ = load_local_metric
yield
@classmethod
def lowercase ( cls: Dict , _SCREAMING_SNAKE_CASE: int ) -> Union[str, Any]:
"""simple docstring"""
def wrapper(_SCREAMING_SNAKE_CASE: List[Any] ):
UpperCamelCase_ = contextmanager(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher("bleurt" )
def lowerCAmelCase_ ( UpperCamelCase_ ) -> List[str]:
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string("sv" , "" , "" ) # handle pytest cli flags
class _UpperCamelCase ( lowerCAmelCase_ ):
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: Any ) -> Optional[int]:
"""simple docstring"""
assert len(input_dict["input_ids"] ) == 2
return np.array([1.03, 1.04] )
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch("bleurt.score._create_predictor" ) as mock_create_predictor:
UpperCamelCase_ = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher("bertscore" )
def lowerCAmelCase_ ( UpperCamelCase_ ) -> List[str]:
import torch
def bert_cos_score_idf(UpperCamelCase_ , UpperCamelCase_ , *UpperCamelCase_ , **UpperCamelCase_ ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(UpperCamelCase_ ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch("bert_score.scorer.get_model" ), patch(
"bert_score.scorer.bert_cos_score_idf" ) as mock_bert_cos_score_idf:
UpperCamelCase_ = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher("comet" )
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Any:
def load_from_checkpoint(UpperCamelCase_ ):
class _UpperCamelCase :
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: List[str] , *_SCREAMING_SNAKE_CASE: str , **_SCREAMING_SNAKE_CASE: Union[str, Any] ) -> List[str]:
"""simple docstring"""
assert len(_SCREAMING_SNAKE_CASE ) == 2
UpperCamelCase_ = [0.19, 0.92]
return scores, sum(_SCREAMING_SNAKE_CASE ) / len(_SCREAMING_SNAKE_CASE )
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch("comet.download_model" ) as mock_download_model:
UpperCamelCase_ = None
with patch("comet.load_from_checkpoint" ) as mock_load_from_checkpoint:
UpperCamelCase_ = load_from_checkpoint
yield
def lowerCAmelCase_ ( ) -> Union[str, Any]:
UpperCamelCase_ = load_metric(os.path.join("metrics" , "seqeval" ) )
UpperCamelCase_ = "ERROR"
UpperCamelCase_ = F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
with pytest.raises(UpperCamelCase_ , match=re.escape(UpperCamelCase_ ) ):
metric.compute(predictions=[] , references=[] , scheme=UpperCamelCase_ )
| 359 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
for param in module.parameters():
UpperCamelCase_ = False
def lowerCAmelCase_ ( ) -> Dict:
UpperCamelCase_ = "cuda" if torch.cuda.is_available() else "cpu"
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
UpperCamelCase_ = "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 lowerCAmelCase_ ( UpperCamelCase_ ) -> Union[str, Any]:
UpperCamelCase_ = plt.imshow(UpperCamelCase_ )
fig.axes.get_xaxis().set_visible(UpperCamelCase_ )
fig.axes.get_yaxis().set_visible(UpperCamelCase_ )
plt.show()
def lowerCAmelCase_ ( ) -> List[str]:
UpperCamelCase_ = datetime.now()
UpperCamelCase_ = current_time.strftime("%H:%M:%S" )
return timestamp
| 328 | 0 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 360 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = '▁'
_UpperCAmelCase = {'vocab_file': 'spiece.model'}
_UpperCAmelCase = {
'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}
_UpperCAmelCase = {
'google/pegasus-xsum': 5_1_2,
}
_UpperCAmelCase = logging.get_logger(__name__)
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : Optional[Any] = VOCAB_FILES_NAMES
_UpperCamelCase : List[Any] = VOCAB_FILES_NAMES
_UpperCamelCase : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase : Optional[int] = ['''input_ids''', '''attention_mask''']
def __init__( self: str , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: str="<pad>" , _SCREAMING_SNAKE_CASE: Optional[Any]="</s>" , _SCREAMING_SNAKE_CASE: Any="<unk>" , _SCREAMING_SNAKE_CASE: int="<mask_2>" , _SCREAMING_SNAKE_CASE: List[Any]="<mask_1>" , _SCREAMING_SNAKE_CASE: Union[str, Any]=None , _SCREAMING_SNAKE_CASE: Optional[int]=103 , _SCREAMING_SNAKE_CASE: Optional[Dict[str, Any]] = None , **_SCREAMING_SNAKE_CASE: Dict , ) -> None:
"""simple docstring"""
UpperCamelCase_ = offset
if additional_special_tokens is not None:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError(
f'''additional_special_tokens should be of type {type(_SCREAMING_SNAKE_CASE )}, but is'''
f''' {type(_SCREAMING_SNAKE_CASE )}''' )
UpperCamelCase_ = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'''<unk_{i}>''' for i in range(len(_SCREAMING_SNAKE_CASE ) , self.offset - 1 )
]
if len(set(_SCREAMING_SNAKE_CASE ) ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
UpperCamelCase_ = additional_special_tokens_extended
else:
UpperCamelCase_ = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'''<unk_{i}>''' for i in range(2 , self.offset )]
UpperCamelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , mask_token_sent=_SCREAMING_SNAKE_CASE , offset=_SCREAMING_SNAKE_CASE , additional_special_tokens=_SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **_SCREAMING_SNAKE_CASE , )
UpperCamelCase_ = mask_token_sent
UpperCamelCase_ = vocab_file
UpperCamelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_SCREAMING_SNAKE_CASE )
# add special tokens to encoder dict
UpperCamelCase_ = {
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
} )
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1 )} )
UpperCamelCase_ = {v: k for k, v in self.encoder.items()}
@property
def lowercase ( self: Dict ) -> int:
"""simple docstring"""
return len(self.sp_model ) + self.offset
def lowercase ( self: int ) -> Dict[str, int]:
"""simple docstring"""
UpperCamelCase_ = {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self: Optional[int] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = self.__dict__.copy()
UpperCamelCase_ = None
return state
def __setstate__( self: List[Any] , _SCREAMING_SNAKE_CASE: List[Any] ) -> Any:
"""simple docstring"""
UpperCamelCase_ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
UpperCamelCase_ = {}
UpperCamelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowercase ( self: Optional[int] , _SCREAMING_SNAKE_CASE: str ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(_SCREAMING_SNAKE_CASE , out_type=_SCREAMING_SNAKE_CASE )
def lowercase ( self: Tuple , _SCREAMING_SNAKE_CASE: str ) -> int:
"""simple docstring"""
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
UpperCamelCase_ = self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE )
return sp_id + self.offset
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: int ) -> str:
"""simple docstring"""
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
UpperCamelCase_ = self.sp_model.IdToPiece(index - self.offset )
return token
def lowercase ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: Tuple ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = []
UpperCamelCase_ = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE ) + token
UpperCamelCase_ = []
else:
current_sub_tokens.append(_SCREAMING_SNAKE_CASE )
out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE )
return out_string.strip()
def lowercase ( self: List[str] , _SCREAMING_SNAKE_CASE: Optional[int]=False ) -> Union[str, Any]:
"""simple docstring"""
return 1
def lowercase ( self: int , _SCREAMING_SNAKE_CASE: str ) -> str:
"""simple docstring"""
UpperCamelCase_ = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: List , _SCREAMING_SNAKE_CASE: Optional[List] = None , _SCREAMING_SNAKE_CASE: bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return self._special_token_mask(_SCREAMING_SNAKE_CASE )
elif token_ids_a is None:
return self._special_token_mask(_SCREAMING_SNAKE_CASE ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: List[Any]=None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCamelCase_ = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(_SCREAMING_SNAKE_CASE , "wb" ) as fi:
UpperCamelCase_ = self.sp_model.serialized_model_proto()
fi.write(_SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 328 | 0 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Tuple:
UpperCamelCase_ = []
UpperCamelCase_ = set({"(", "[", "{"} )
UpperCamelCase_ = set({")", "]", "}"} )
UpperCamelCase_ = {"{": "}", "[": "]", "(": ")"}
for i in range(len(UpperCamelCase_ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(UpperCamelCase_ ) == 0 or (len(UpperCamelCase_ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(UpperCamelCase_ ) == 0
def lowerCAmelCase_ ( ) -> Tuple:
UpperCamelCase_ = input("Enter sequence of brackets: " )
if is_balanced(UpperCamelCase_ ):
print(UpperCamelCase_ , "is balanced" )
else:
print(UpperCamelCase_ , "is not balanced" )
if __name__ == "__main__":
main()
| 361 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
'TapasForMaskedLM',
'TapasForQuestionAnswering',
'TapasForSequenceClassification',
'TapasModel',
'TapasPreTrainedModel',
'load_tf_weights_in_tapas',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFTapasForMaskedLM',
'TFTapasForQuestionAnswering',
'TFTapasForSequenceClassification',
'TFTapasModel',
'TFTapasPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
from .tokenization_tapas import TapasTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_tapas import (
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTapasForMaskedLM,
TFTapasForQuestionAnswering,
TFTapasForSequenceClassification,
TFTapasModel,
TFTapasPreTrainedModel,
)
else:
import sys
_UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 328 | 0 |
"""simple docstring"""
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_UpperCAmelCase = [
# tf -> hf
('/', '.'),
('layer_', 'layers.'),
('kernel', 'weight'),
('beta', 'bias'),
('gamma', 'weight'),
('pegasus', 'model'),
]
_UpperCAmelCase = [
('.output.dense', '.fc2'),
('intermediate.LayerNorm', 'final_layer_norm'),
('intermediate.dense', 'fc1'),
]
_UpperCAmelCase = (
INIT_COMMON
+ [
('attention.self.LayerNorm', 'self_attn_layer_norm'),
('attention.output.dense', 'self_attn.out_proj'),
('attention.self', 'self_attn'),
('attention.encdec.LayerNorm', 'encoder_attn_layer_norm'),
('attention.encdec_output.dense', 'encoder_attn.out_proj'),
('attention.encdec', 'encoder_attn'),
('key', 'k_proj'),
('value', 'v_proj'),
('query', 'q_proj'),
('decoder.LayerNorm', 'decoder.layernorm_embedding'),
]
+ END_COMMON
)
_UpperCAmelCase = (
INIT_COMMON
+ [
('embeddings.word_embeddings', 'shared.weight'),
('embeddings.position_embeddings', 'embed_positions.weight'),
('attention.self.LayerNorm', 'self_attn_layer_norm'),
('attention.output.dense', 'self_attn.output'),
('attention.self', 'self_attn.self'),
('encoder.LayerNorm', 'encoder.layernorm_embedding'),
]
+ END_COMMON
)
_UpperCAmelCase = [
'encdec/key/bias',
'encdec/query/bias',
'encdec/value/bias',
'self/key/bias',
'self/query/bias',
'self/value/bias',
'encdec_output/dense/bias',
'attention/output/dense/bias',
]
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> List[Any]:
for tf_name, hf_name in patterns:
UpperCamelCase_ = k.replace(UpperCamelCase_ , UpperCamelCase_ )
return k
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> BigBirdPegasusForConditionalGeneration:
UpperCamelCase_ = BigBirdPegasusConfig(**UpperCamelCase_ )
UpperCamelCase_ = BigBirdPegasusForConditionalGeneration(UpperCamelCase_ )
UpperCamelCase_ = torch_model.state_dict()
UpperCamelCase_ = {}
# separating decoder weights
UpperCamelCase_ = {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder" )}
UpperCamelCase_ = {k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder" )}
for k, v in tqdm(decoder_weights.items() , "tf -> hf conversion" ):
UpperCamelCase_ = [k.endswith(UpperCamelCase_ ) for ending in KEYS_TO_IGNORE]
if any(UpperCamelCase_ ):
continue
UpperCamelCase_ = DECODER_PATTERNS
UpperCamelCase_ = rename_state_dict_key(UpperCamelCase_ , UpperCamelCase_ )
if new_k not in state_dict:
raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )
if any(True if i in k else False for i in ["dense", "query", "key", "value"] ):
UpperCamelCase_ = v.T
UpperCamelCase_ = torch.from_numpy(UpperCamelCase_ )
assert v.shape == state_dict[new_k].shape, F'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''
for k, v in tqdm(remaining_weights.items() , "tf -> hf conversion" ):
UpperCamelCase_ = [k.endswith(UpperCamelCase_ ) for ending in KEYS_TO_IGNORE]
if any(UpperCamelCase_ ):
continue
UpperCamelCase_ = REMAINING_PATTERNS
UpperCamelCase_ = rename_state_dict_key(UpperCamelCase_ , UpperCamelCase_ )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )
if any(True if i in k else False for i in ["dense", "query", "key", "value"] ):
UpperCamelCase_ = v.T
UpperCamelCase_ = torch.from_numpy(UpperCamelCase_ )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, F'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''
UpperCamelCase_ = mapping["model.embed_positions.weight"]
UpperCamelCase_ = mapping.pop("model.embed_positions.weight" )
UpperCamelCase_ , UpperCamelCase_ = torch_model.load_state_dict(UpperCamelCase_ , strict=UpperCamelCase_ )
UpperCamelCase_ = [
k
for k in missing
if k
not in [
"final_logits_bias",
"model.encoder.embed_tokens.weight",
"model.decoder.embed_tokens.weight",
"lm_head.weight",
]
]
assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
assert extra == [], F'''no matches found for the following tf keys {extra}'''
return torch_model
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Dict:
UpperCamelCase_ = tf.train.list_variables(UpperCamelCase_ )
UpperCamelCase_ = {}
UpperCamelCase_ = ["global_step"]
for name, shape in tqdm(UpperCamelCase_ , desc="converting tf checkpoint to dict" ):
UpperCamelCase_ = any(pat in name for pat in ignore_name )
if skip_key:
continue
UpperCamelCase_ = tf.train.load_variable(UpperCamelCase_ , UpperCamelCase_ )
UpperCamelCase_ = array
return tf_weights
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Optional[int]:
UpperCamelCase_ = get_tf_weights_as_numpy(UpperCamelCase_ )
UpperCamelCase_ = convert_bigbird_pegasus(UpperCamelCase_ , UpperCamelCase_ )
torch_model.save_pretrained(UpperCamelCase_ )
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('--tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
parser.add_argument('--save_dir', default=None, type=str, help='Path to the output PyTorch model.')
_UpperCAmelCase = parser.parse_args()
_UpperCAmelCase = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 362 |
import argparse
import json
from tqdm import tqdm
def lowerCAmelCase_ ( ) -> Tuple:
UpperCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=UpperCamelCase_ , default="biencoder-nq-dev.json" , help="Path to raw DPR training data" , )
parser.add_argument(
"--evaluation_set" , type=UpperCamelCase_ , help="where to store parsed evaluation_set file" , )
parser.add_argument(
"--gold_data_path" , type=UpperCamelCase_ , help="where to store parsed gold_data_path file" , )
UpperCamelCase_ = parser.parse_args()
with open(args.src_path , "r" ) as src_file, open(args.evaluation_set , "w" ) as eval_file, open(
args.gold_data_path , "w" ) as gold_file:
UpperCamelCase_ = json.load(UpperCamelCase_ )
for dpr_record in tqdm(UpperCamelCase_ ):
UpperCamelCase_ = dpr_record["question"]
UpperCamelCase_ = [context["title"] for context in dpr_record["positive_ctxs"]]
eval_file.write(question + "\n" )
gold_file.write("\t".join(UpperCamelCase_ ) + "\n" )
if __name__ == "__main__":
main()
| 328 | 0 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class _UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self: int , _SCREAMING_SNAKE_CASE: str = "▁" , _SCREAMING_SNAKE_CASE: bool = True , _SCREAMING_SNAKE_CASE: Union[str, AddedToken] = "<unk>" , _SCREAMING_SNAKE_CASE: Union[str, AddedToken] = "</s>" , _SCREAMING_SNAKE_CASE: Union[str, AddedToken] = "<pad>" , ) -> int:
"""simple docstring"""
UpperCamelCase_ = {
"pad": {"id": 0, "token": pad_token},
"eos": {"id": 1, "token": eos_token},
"unk": {"id": 2, "token": unk_token},
}
UpperCamelCase_ = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
UpperCamelCase_ = token_dict["token"]
UpperCamelCase_ = Tokenizer(Unigram() )
UpperCamelCase_ = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(" {2,}" ) , " " ),
normalizers.Lowercase(),
] )
UpperCamelCase_ = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=_SCREAMING_SNAKE_CASE , add_prefix_space=_SCREAMING_SNAKE_CASE ),
pre_tokenizers.Digits(individual_digits=_SCREAMING_SNAKE_CASE ),
pre_tokenizers.Punctuation(),
] )
UpperCamelCase_ = decoders.Metaspace(replacement=_SCREAMING_SNAKE_CASE , add_prefix_space=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = TemplateProcessing(
single=f'''$A {self.special_tokens["eos"]["token"]}''' , special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])] , )
UpperCamelCase_ = {
"model": "SentencePieceUnigram",
"replacement": replacement,
"add_prefix_space": add_prefix_space,
}
super().__init__(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: Union[str, List[str]] , _SCREAMING_SNAKE_CASE: int = 8000 , _SCREAMING_SNAKE_CASE: bool = True , ) -> int:
"""simple docstring"""
UpperCamelCase_ = trainers.UnigramTrainer(
vocab_size=_SCREAMING_SNAKE_CASE , special_tokens=self.special_tokens_list , show_progress=_SCREAMING_SNAKE_CASE , )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase_ = [files]
self._tokenizer.train(_SCREAMING_SNAKE_CASE , trainer=_SCREAMING_SNAKE_CASE )
self.add_unk_id()
def lowercase ( self: int , _SCREAMING_SNAKE_CASE: Union[Iterator[str], Iterator[Iterator[str]]] , _SCREAMING_SNAKE_CASE: int = 8000 , _SCREAMING_SNAKE_CASE: bool = True , ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = trainers.UnigramTrainer(
vocab_size=_SCREAMING_SNAKE_CASE , special_tokens=self.special_tokens_list , show_progress=_SCREAMING_SNAKE_CASE , )
self._tokenizer.train_from_iterator(_SCREAMING_SNAKE_CASE , trainer=_SCREAMING_SNAKE_CASE )
self.add_unk_id()
def lowercase ( self: Union[str, Any] ) -> int:
"""simple docstring"""
UpperCamelCase_ = json.loads(self._tokenizer.to_str() )
UpperCamelCase_ = self.special_tokens["unk"]["id"]
UpperCamelCase_ = Tokenizer.from_str(json.dumps(_SCREAMING_SNAKE_CASE ) )
| 363 |
import requests
from bsa import BeautifulSoup
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> str:
UpperCamelCase_ = BeautifulSoup(requests.get(UpperCamelCase_ , params=UpperCamelCase_ ).content , "html.parser" )
UpperCamelCase_ = soup.find("div" , attrs={"class": "gs_ri"} )
UpperCamelCase_ = div.find("div" , attrs={"class": "gs_fl"} ).find_all("a" )
return anchors[2].get_text()
if __name__ == "__main__":
_UpperCAmelCase = {
'title': (
'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
'capacitance, volumetric capacitance, and energy density'
),
'journal': 'Chem. Mater.',
'volume': 3_0,
'pages': '3979-3990',
'year': 2_0_1_8,
'hl': 'en',
}
print(get_citation('https://scholar.google.com/scholar_lookup', params=params))
| 328 | 0 |
from __future__ import annotations
_UpperCAmelCase = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , ) -> tuple[list[list[int]], list[list[int]]]:
UpperCamelCase_ = [
[0 for col in range(len(grid[0] ) )] for row in range(len(UpperCamelCase_ ) )
] # the reference grid
UpperCamelCase_ = 1
UpperCamelCase_ = [
[0 for col in range(len(grid[0] ) )] for row in range(len(UpperCamelCase_ ) )
] # the action grid
UpperCamelCase_ = init[0]
UpperCamelCase_ = init[1]
UpperCamelCase_ = 0
UpperCamelCase_ = g + heuristic[x][y] # cost from starting cell to destination cell
UpperCamelCase_ = [[f, g, x, y]]
UpperCamelCase_ = False # flag that is set when search is complete
UpperCamelCase_ = False # flag set if we can't find expand
while not found and not resign:
if len(UpperCamelCase_ ) == 0:
raise ValueError("Algorithm is unable to find solution" )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
UpperCamelCase_ = cell.pop()
UpperCamelCase_ = next_cell[2]
UpperCamelCase_ = next_cell[3]
UpperCamelCase_ = next_cell[1]
if x == goal[0] and y == goal[1]:
UpperCamelCase_ = True
else:
for i in range(len(UpperCamelCase_ ) ): # to try out different valid actions
UpperCamelCase_ = x + DIRECTIONS[i][0]
UpperCamelCase_ = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(UpperCamelCase_ ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
UpperCamelCase_ = g + cost
UpperCamelCase_ = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
UpperCamelCase_ = 1
UpperCamelCase_ = i
UpperCamelCase_ = []
UpperCamelCase_ = goal[0]
UpperCamelCase_ = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
UpperCamelCase_ = x - DIRECTIONS[action[x][y]][0]
UpperCamelCase_ = y - DIRECTIONS[action[x][y]][1]
UpperCamelCase_ = xa
UpperCamelCase_ = ya
invpath.append([x, y] )
UpperCamelCase_ = []
for i in range(len(UpperCamelCase_ ) ):
path.append(invpath[len(UpperCamelCase_ ) - 1 - i] )
return path, action
if __name__ == "__main__":
_UpperCAmelCase = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
_UpperCAmelCase = [0, 0]
# all coordinates are given in format [y,x]
_UpperCAmelCase = [len(grid) - 1, len(grid[0]) - 1]
_UpperCAmelCase = 1
# the cost map which pushes the path closer to the goal
_UpperCAmelCase = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
_UpperCAmelCase = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
_UpperCAmelCase = 9_9
_UpperCAmelCase , _UpperCAmelCase = search(grid, init, goal, cost, heuristic)
print('ACTION MAP')
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 364 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __init__( self: List[str] , *,
_SCREAMING_SNAKE_CASE: int = 4 , _SCREAMING_SNAKE_CASE: int = 768 , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: str , ) -> Tuple:
"""simple docstring"""
super().__init__()
UpperCamelCase_ = nn.Parameter(torch.zeros(_SCREAMING_SNAKE_CASE ) )
# parameters for additional clip time embeddings
UpperCamelCase_ = nn.Linear(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = nn.Linear(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# parameters for encoder hidden states
UpperCamelCase_ = clip_extra_context_tokens
UpperCamelCase_ = nn.Linear(
_SCREAMING_SNAKE_CASE , self.clip_extra_context_tokens * cross_attention_dim )
UpperCamelCase_ = nn.Linear(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = nn.LayerNorm(_SCREAMING_SNAKE_CASE )
def lowercase ( self: Optional[int] , *, _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Tuple ) -> str:
"""simple docstring"""
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
UpperCamelCase_ = image_embeddings.shape[0]
UpperCamelCase_ = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
UpperCamelCase_ = classifier_free_guidance_embeddings.expand(
_SCREAMING_SNAKE_CASE , -1 )
UpperCamelCase_ = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
UpperCamelCase_ = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
UpperCamelCase_ = self.embedding_proj(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.clip_image_embeddings_project_to_time_embeddings(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
UpperCamelCase_ = self.clip_extra_context_tokens_proj(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = clip_extra_context_tokens.reshape(_SCREAMING_SNAKE_CASE , -1 , self.clip_extra_context_tokens )
UpperCamelCase_ = clip_extra_context_tokens.permute(0 , 2 , 1 )
UpperCamelCase_ = self.encoder_hidden_states_proj(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.text_encoder_hidden_states_norm(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 328 | 0 |
import requests
from bsa import BeautifulSoup
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> str:
UpperCamelCase_ = BeautifulSoup(requests.get(UpperCamelCase_ , params=UpperCamelCase_ ).content , "html.parser" )
UpperCamelCase_ = soup.find("div" , attrs={"class": "gs_ri"} )
UpperCamelCase_ = div.find("div" , attrs={"class": "gs_fl"} ).find_all("a" )
return anchors[2].get_text()
if __name__ == "__main__":
_UpperCAmelCase = {
'title': (
'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
'capacitance, volumetric capacitance, and energy density'
),
'journal': 'Chem. Mater.',
'volume': 3_0,
'pages': '3979-3990',
'year': 2_0_1_8,
'hl': 'en',
}
print(get_citation('https://scholar.google.com/scholar_lookup', params=params))
| 365 |
from functools import lru_cache
def lowerCAmelCase_ ( UpperCamelCase_ ) -> set:
UpperCamelCase_ = 2
UpperCamelCase_ = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(UpperCamelCase_ )
if n > 1:
factors.add(UpperCamelCase_ )
return factors
@lru_cache
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
return len(unique_prime_factors(UpperCamelCase_ ) )
def lowerCAmelCase_ ( UpperCamelCase_ ) -> bool:
return len(set(UpperCamelCase_ ) ) in (0, 1)
def lowerCAmelCase_ ( UpperCamelCase_ ) -> list:
UpperCamelCase_ = 2
while True:
# Increment each value of a generated range
UpperCamelCase_ = [base + i for i in range(UpperCamelCase_ )]
# Run elements through out unique_prime_factors function
# Append our target number to the end.
UpperCamelCase_ = [upf_len(UpperCamelCase_ ) for x in group]
checker.append(UpperCamelCase_ )
# If all numbers in the list are equal, return the group variable.
if equality(UpperCamelCase_ ):
return group
# Increment our base variable by 1
base += 1
def lowerCAmelCase_ ( UpperCamelCase_ = 4 ) -> int:
UpperCamelCase_ = run(UpperCamelCase_ )
return results[0] if len(UpperCamelCase_ ) else None
if __name__ == "__main__":
print(solution())
| 328 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class _UpperCamelCase :
_UpperCamelCase : str = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_UpperCamelCase : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCamelCase : Optional[str] = field(
default='''NER''' , metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''} )
_UpperCamelCase : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCamelCase : bool = field(default=lowerCAmelCase_ , metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_UpperCamelCase : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
@dataclass
class _UpperCamelCase :
_UpperCamelCase : str = field(
metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''} )
_UpperCamelCase : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''} , )
_UpperCamelCase : int = field(
default=1_2_8 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_UpperCamelCase : bool = field(
default=lowerCAmelCase_ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def lowerCAmelCase_ ( ) -> Dict:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCamelCase_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
" --overwrite_output_dir to overcome." )
UpperCamelCase_ = import_module("tasks" )
try:
UpperCamelCase_ = getattr(UpperCamelCase_ , model_args.task_type )
UpperCamelCase_ = token_classification_task_clazz()
except AttributeError:
raise ValueError(
F'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
F'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}''' )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s" , UpperCamelCase_ )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
UpperCamelCase_ = token_classification_task.get_labels(data_args.labels )
UpperCamelCase_ = dict(enumerate(UpperCamelCase_ ) )
UpperCamelCase_ = len(UpperCamelCase_ )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase_ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCamelCase_ , idalabel=UpperCamelCase_ , labelaid={label: i for i, label in enumerate(UpperCamelCase_ )} , cache_dir=model_args.cache_dir , )
UpperCamelCase_ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
UpperCamelCase_ = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=UpperCamelCase_ , cache_dir=model_args.cache_dir , )
# Get datasets
UpperCamelCase_ = (
TokenClassificationDataset(
token_classification_task=UpperCamelCase_ , data_dir=data_args.data_dir , tokenizer=UpperCamelCase_ , labels=UpperCamelCase_ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
UpperCamelCase_ = (
TokenClassificationDataset(
token_classification_task=UpperCamelCase_ , data_dir=data_args.data_dir , tokenizer=UpperCamelCase_ , labels=UpperCamelCase_ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(UpperCamelCase_ , UpperCamelCase_ ) -> Tuple[List[int], List[int]]:
UpperCamelCase_ = np.argmax(UpperCamelCase_ , axis=2 )
UpperCamelCase_ , UpperCamelCase_ = preds.shape
UpperCamelCase_ = [[] for _ in range(UpperCamelCase_ )]
UpperCamelCase_ = [[] for _ in range(UpperCamelCase_ )]
for i in range(UpperCamelCase_ ):
for j in range(UpperCamelCase_ ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(UpperCamelCase_ ) -> Dict:
UpperCamelCase_ , UpperCamelCase_ = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(UpperCamelCase_ , UpperCamelCase_ ),
"precision": precision_score(UpperCamelCase_ , UpperCamelCase_ ),
"recall": recall_score(UpperCamelCase_ , UpperCamelCase_ ),
"f1": fa_score(UpperCamelCase_ , UpperCamelCase_ ),
}
# Data collator
UpperCamelCase_ = DataCollatorWithPadding(UpperCamelCase_ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
UpperCamelCase_ = Trainer(
model=UpperCamelCase_ , args=UpperCamelCase_ , train_dataset=UpperCamelCase_ , eval_dataset=UpperCamelCase_ , compute_metrics=UpperCamelCase_ , data_collator=UpperCamelCase_ , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
UpperCamelCase_ = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
UpperCamelCase_ = trainer.evaluate()
UpperCamelCase_ = os.path.join(training_args.output_dir , "eval_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCamelCase_ , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in result.items():
logger.info(" %s = %s" , UpperCamelCase_ , UpperCamelCase_ )
writer.write("%s = %s\n" % (key, value) )
results.update(UpperCamelCase_ )
# Predict
if training_args.do_predict:
UpperCamelCase_ = TokenClassificationDataset(
token_classification_task=UpperCamelCase_ , data_dir=data_args.data_dir , tokenizer=UpperCamelCase_ , labels=UpperCamelCase_ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = trainer.predict(UpperCamelCase_ )
UpperCamelCase_ , UpperCamelCase_ = align_predictions(UpperCamelCase_ , UpperCamelCase_ )
UpperCamelCase_ = os.path.join(training_args.output_dir , "test_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCamelCase_ , "w" ) as writer:
for key, value in metrics.items():
logger.info(" %s = %s" , UpperCamelCase_ , UpperCamelCase_ )
writer.write("%s = %s\n" % (key, value) )
# Save predictions
UpperCamelCase_ = os.path.join(training_args.output_dir , "test_predictions.txt" )
if trainer.is_world_process_zero():
with open(UpperCamelCase_ , "w" ) as writer:
with open(os.path.join(data_args.data_dir , "test.txt" ) , "r" ) as f:
token_classification_task.write_predictions_to_file(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
return results
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Union[str, Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 366 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
UpperCamelCase_ = len(UpperCamelCase_ )
UpperCamelCase_ = len(matrix[0] )
UpperCamelCase_ = min(UpperCamelCase_ , UpperCamelCase_ )
for row in range(UpperCamelCase_ ):
# Check if diagonal element is not zero
if matrix[row][row] != 0:
# Eliminate all the elements below the diagonal
for col in range(row + 1 , UpperCamelCase_ ):
UpperCamelCase_ = matrix[col][row] / matrix[row][row]
for i in range(UpperCamelCase_ , UpperCamelCase_ ):
matrix[col][i] -= multiplier * matrix[row][i]
else:
# Find a non-zero diagonal element to swap rows
UpperCamelCase_ = True
for i in range(row + 1 , UpperCamelCase_ ):
if matrix[i][row] != 0:
UpperCamelCase_ , UpperCamelCase_ = matrix[i], matrix[row]
UpperCamelCase_ = False
break
if reduce:
rank -= 1
for i in range(UpperCamelCase_ ):
UpperCamelCase_ = matrix[i][rank]
# Reduce the row pointer by one to stay on the same row
row -= 1
return rank
if __name__ == "__main__":
import doctest
doctest.testmod()
| 328 | 0 |
from abc import ABC, abstractmethod
from typing import List, Optional
class _UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self: int ) -> Optional[Any]:
"""simple docstring"""
self.test()
def lowercase ( self: List[Any] ) -> Any:
"""simple docstring"""
UpperCamelCase_ = 0
UpperCamelCase_ = False
while not completed:
if counter == 1:
self.reset()
UpperCamelCase_ = self.advance()
if not self.does_advance(_SCREAMING_SNAKE_CASE ):
raise Exception(
"Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true." )
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = self.update(_SCREAMING_SNAKE_CASE )
counter += 1
if counter > 10000:
raise Exception("update() does not fulfill the constraint." )
if self.remaining() != 0:
raise Exception("Custom Constraint is not defined correctly." )
@abstractmethod
def lowercase ( self: Tuple ) -> Optional[Any]:
"""simple docstring"""
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def lowercase ( self: List[str] , _SCREAMING_SNAKE_CASE: int ) -> int:
"""simple docstring"""
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: int ) -> List[str]:
"""simple docstring"""
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def lowercase ( self: Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def lowercase ( self: Union[str, Any] ) -> str:
"""simple docstring"""
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def lowercase ( self: int , _SCREAMING_SNAKE_CASE: Tuple=False ) -> Union[str, Any]:
"""simple docstring"""
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class _UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self: Optional[int] , _SCREAMING_SNAKE_CASE: List[int] ) -> Union[str, Any]:
"""simple docstring"""
super(_SCREAMING_SNAKE_CASE , self ).__init__()
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or len(_SCREAMING_SNAKE_CASE ) == 0:
raise ValueError(f'''`token_ids` has to be a non-empty list, but is {token_ids}.''' )
if any((not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or token_id < 0) for token_id in token_ids ):
raise ValueError(f'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.''' )
UpperCamelCase_ = token_ids
UpperCamelCase_ = len(self.token_ids )
UpperCamelCase_ = -1 # the index of the currently fulfilled step
UpperCamelCase_ = False
def lowercase ( self: Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def lowercase ( self: Tuple , _SCREAMING_SNAKE_CASE: int ) -> int:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_SCREAMING_SNAKE_CASE )}''' )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: int ) -> List[Any]:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_SCREAMING_SNAKE_CASE )}''' )
UpperCamelCase_ = False
UpperCamelCase_ = False
UpperCamelCase_ = False
if self.does_advance(_SCREAMING_SNAKE_CASE ):
self.fulfilled_idx += 1
UpperCamelCase_ = True
if self.fulfilled_idx == (self.seqlen - 1):
UpperCamelCase_ = True
UpperCamelCase_ = completed
else:
# failed to make progress.
UpperCamelCase_ = True
self.reset()
return stepped, completed, reset
def lowercase ( self: Union[str, Any] ) -> str:
"""simple docstring"""
UpperCamelCase_ = False
UpperCamelCase_ = 0
def lowercase ( self: str ) -> List[str]:
"""simple docstring"""
return self.seqlen - (self.fulfilled_idx + 1)
def lowercase ( self: Tuple , _SCREAMING_SNAKE_CASE: int=False ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ = PhrasalConstraint(self.token_ids )
if stateful:
UpperCamelCase_ = self.seqlen
UpperCamelCase_ = self.fulfilled_idx
UpperCamelCase_ = self.completed
return new_constraint
class _UpperCamelCase :
def __init__( self: int , _SCREAMING_SNAKE_CASE: List[List[int]] , _SCREAMING_SNAKE_CASE: Tuple=True ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ = max([len(_SCREAMING_SNAKE_CASE ) for one in nested_token_ids] )
UpperCamelCase_ = {}
for token_ids in nested_token_ids:
UpperCamelCase_ = root
for tidx, token_id in enumerate(_SCREAMING_SNAKE_CASE ):
if token_id not in level:
UpperCamelCase_ = {}
UpperCamelCase_ = level[token_id]
if no_subsets and self.has_subsets(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError(
"Each list in `nested_token_ids` can't be a complete subset of another list, but is"
f''' {nested_token_ids}.''' )
UpperCamelCase_ = root
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: str ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ = self.trie
for current_token in current_seq:
UpperCamelCase_ = start[current_token]
UpperCamelCase_ = list(start.keys() )
return next_tokens
def lowercase ( self: Optional[int] , _SCREAMING_SNAKE_CASE: Optional[Any] ) -> int:
"""simple docstring"""
UpperCamelCase_ = self.next_tokens(_SCREAMING_SNAKE_CASE )
return len(_SCREAMING_SNAKE_CASE ) == 0
def lowercase ( self: Tuple , _SCREAMING_SNAKE_CASE: str ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ = list(root.values() )
if len(_SCREAMING_SNAKE_CASE ) == 0:
return 1
else:
return sum([self.count_leaves(_SCREAMING_SNAKE_CASE ) for nn in next_nodes] )
def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: Optional[Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = self.count_leaves(_SCREAMING_SNAKE_CASE )
return len(_SCREAMING_SNAKE_CASE ) != leaf_count
class _UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: List[List[int]] ) -> Dict:
"""simple docstring"""
super(_SCREAMING_SNAKE_CASE , self ).__init__()
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or len(_SCREAMING_SNAKE_CASE ) == 0:
raise ValueError(f'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''' )
if any(not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for token_ids in nested_token_ids ):
raise ValueError(f'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.''' )
if any(
any((not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
f'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.''' )
UpperCamelCase_ = DisjunctiveTrie(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = nested_token_ids
UpperCamelCase_ = self.trie.max_height
UpperCamelCase_ = []
UpperCamelCase_ = False
def lowercase ( self: Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = self.trie.next_tokens(self.current_seq )
if len(_SCREAMING_SNAKE_CASE ) == 0:
return None
else:
return token_list
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: int ) -> str:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_SCREAMING_SNAKE_CASE )}''' )
UpperCamelCase_ = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def lowercase ( self: int , _SCREAMING_SNAKE_CASE: int ) -> Optional[int]:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_SCREAMING_SNAKE_CASE )}''' )
UpperCamelCase_ = False
UpperCamelCase_ = False
UpperCamelCase_ = False
if self.does_advance(_SCREAMING_SNAKE_CASE ):
self.current_seq.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = True
else:
UpperCamelCase_ = True
self.reset()
UpperCamelCase_ = self.trie.reached_leaf(self.current_seq )
UpperCamelCase_ = completed
return stepped, completed, reset
def lowercase ( self: int ) -> int:
"""simple docstring"""
UpperCamelCase_ = False
UpperCamelCase_ = []
def lowercase ( self: Any ) -> Union[str, Any]:
"""simple docstring"""
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: Union[str, Any]=False ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ = DisjunctiveConstraint(self.token_ids )
if stateful:
UpperCamelCase_ = self.seqlen
UpperCamelCase_ = self.current_seq
UpperCamelCase_ = self.completed
return new_constraint
class _UpperCamelCase :
def __init__( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: List[Constraint] ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = constraints
# max # of steps required to fulfill a given constraint
UpperCamelCase_ = max([c.seqlen for c in constraints] )
UpperCamelCase_ = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = False
self.init_state()
def lowercase ( self: Any ) -> str:
"""simple docstring"""
UpperCamelCase_ = []
UpperCamelCase_ = None
UpperCamelCase_ = [constraint.copy(stateful=_SCREAMING_SNAKE_CASE ) for constraint in self.constraints]
def lowercase ( self: Union[str, Any] ) -> Dict:
"""simple docstring"""
UpperCamelCase_ = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def lowercase ( self: List[str] ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
UpperCamelCase_ = constraint.advance()
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
token_list.append(_SCREAMING_SNAKE_CASE )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
token_list.extend(_SCREAMING_SNAKE_CASE )
else:
UpperCamelCase_ = self.inprogress_constraint.advance()
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
token_list.append(_SCREAMING_SNAKE_CASE )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
token_list.extend(_SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) == 0:
return None
else:
return token_list
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: Optional[List[int]] ) -> Union[str, Any]:
"""simple docstring"""
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
UpperCamelCase_ , UpperCamelCase_ = self.add(_SCREAMING_SNAKE_CASE )
# the entire list of constraints are fulfilled
if self.completed:
break
def lowercase ( self: Optional[int] , _SCREAMING_SNAKE_CASE: int ) -> Optional[Any]:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError(f'''`token_id` should be an `int`, but is `{token_id}`.''' )
UpperCamelCase_ , UpperCamelCase_ = False, False
if self.completed:
UpperCamelCase_ = True
UpperCamelCase_ = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = self.inprogress_constraint.update(_SCREAMING_SNAKE_CASE )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_SCREAMING_SNAKE_CASE ) )
UpperCamelCase_ = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
UpperCamelCase_ = None
if len(self.pending_constraints ) == 0:
# we're done!
UpperCamelCase_ = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(_SCREAMING_SNAKE_CASE ):
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = pending_constraint.update(_SCREAMING_SNAKE_CASE )
if not stepped:
raise Exception(
"`constraint.update(token_id)` is not yielding incremental progress, "
"even though `constraint.does_advance(token_id)` is true." )
if complete:
self.complete_constraints.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = None
if not complete and stepped:
UpperCamelCase_ = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
UpperCamelCase_ = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
UpperCamelCase_ = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: Tuple=True ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
UpperCamelCase_ = [
constraint.copy(stateful=_SCREAMING_SNAKE_CASE ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
UpperCamelCase_ = self.inprogress_constraint.copy(stateful=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 367 |
import math
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> List[str]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(UpperCamelCase_ )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:
return 1 # any number raised to 0 is 1
raise AssertionError("This should never happen" )
if __name__ == "__main__": # Main function
# Read two numbers from input and typecast them to int using map function.
# Here x is the base and y is the power.
_UpperCAmelCase = 'Enter the base and the power separated by a comma: '
_UpperCAmelCase , _UpperCAmelCase = map(int, input(prompt).split(','))
_UpperCAmelCase , _UpperCAmelCase = map(int, input(prompt).split(','))
# We find the log of each number, using the function res(), which takes two
# arguments.
_UpperCAmelCase = res(xa, ya)
_UpperCAmelCase = res(xa, ya)
# We check for the largest number
if resa > resa:
print('Largest number is', xa, '^', ya)
elif resa > resa:
print('Largest number is', xa, '^', ya)
else:
print('Both are equal')
| 328 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
# See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : int = '''mctct'''
def __init__( self: int , _SCREAMING_SNAKE_CASE: Optional[Any]=8065 , _SCREAMING_SNAKE_CASE: List[str]=1536 , _SCREAMING_SNAKE_CASE: Optional[int]=36 , _SCREAMING_SNAKE_CASE: Optional[int]=6144 , _SCREAMING_SNAKE_CASE: Tuple=4 , _SCREAMING_SNAKE_CASE: str=384 , _SCREAMING_SNAKE_CASE: Union[str, Any]=920 , _SCREAMING_SNAKE_CASE: Tuple=1e-5 , _SCREAMING_SNAKE_CASE: Dict=0.3 , _SCREAMING_SNAKE_CASE: str="relu" , _SCREAMING_SNAKE_CASE: int=0.02 , _SCREAMING_SNAKE_CASE: Any=0.3 , _SCREAMING_SNAKE_CASE: Optional[Any]=0.3 , _SCREAMING_SNAKE_CASE: List[str]=1 , _SCREAMING_SNAKE_CASE: Optional[int]=0 , _SCREAMING_SNAKE_CASE: str=2 , _SCREAMING_SNAKE_CASE: List[str]=1 , _SCREAMING_SNAKE_CASE: Tuple=0.3 , _SCREAMING_SNAKE_CASE: str=1 , _SCREAMING_SNAKE_CASE: Optional[int]=(7,) , _SCREAMING_SNAKE_CASE: List[str]=(3,) , _SCREAMING_SNAKE_CASE: Tuple=80 , _SCREAMING_SNAKE_CASE: Any=1 , _SCREAMING_SNAKE_CASE: int=None , _SCREAMING_SNAKE_CASE: Any="sum" , _SCREAMING_SNAKE_CASE: Union[str, Any]=False , **_SCREAMING_SNAKE_CASE: List[str] , ) -> Tuple:
"""simple docstring"""
super().__init__(**_SCREAMING_SNAKE_CASE , pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = vocab_size
UpperCamelCase_ = hidden_size
UpperCamelCase_ = num_hidden_layers
UpperCamelCase_ = intermediate_size
UpperCamelCase_ = num_attention_heads
UpperCamelCase_ = attention_head_dim
UpperCamelCase_ = max_position_embeddings
UpperCamelCase_ = layer_norm_eps
UpperCamelCase_ = layerdrop
UpperCamelCase_ = hidden_act
UpperCamelCase_ = initializer_range
UpperCamelCase_ = hidden_dropout_prob
UpperCamelCase_ = attention_probs_dropout_prob
UpperCamelCase_ = pad_token_id
UpperCamelCase_ = bos_token_id
UpperCamelCase_ = eos_token_id
UpperCamelCase_ = conv_glu_dim
UpperCamelCase_ = conv_dropout
UpperCamelCase_ = num_conv_layers
UpperCamelCase_ = input_feat_per_channel
UpperCamelCase_ = input_channels
UpperCamelCase_ = conv_channels
UpperCamelCase_ = ctc_loss_reduction
UpperCamelCase_ = ctc_zero_infinity
# prevents config testing fail with exporting to json
UpperCamelCase_ = list(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = list(_SCREAMING_SNAKE_CASE )
if len(self.conv_kernel ) != self.num_conv_layers:
raise ValueError(
"Configuration for convolutional module is incorrect. "
"It is required that `len(config.conv_kernel)` == `config.num_conv_layers` "
f'''but is `len(config.conv_kernel) = {len(self.conv_kernel )}`, '''
f'''`config.num_conv_layers = {self.num_conv_layers}`.''' )
| 368 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_UpperCAmelCase = transforms.Compose(
[
transforms.Resize((2_5_6, 2_5_6)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def lowerCAmelCase_ ( UpperCamelCase_ ) -> List[Any]:
if isinstance(UpperCamelCase_ , torch.Tensor ):
return image
elif isinstance(UpperCamelCase_ , PIL.Image.Image ):
UpperCamelCase_ = [image]
UpperCamelCase_ = [trans(img.convert("RGB" ) ) for img in image]
UpperCamelCase_ = torch.stack(UpperCamelCase_ )
return image
class _UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self: List[Any] , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: Dict ) -> str:
"""simple docstring"""
super().__init__()
# make sure scheduler can always be converted to DDIM
UpperCamelCase_ = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE )
def lowercase ( self: List[str] , _SCREAMING_SNAKE_CASE: Dict ) -> Optional[Any]:
"""simple docstring"""
if strength < 0 or strength > 1:
raise ValueError(f'''The value of strength should in [0.0, 1.0] but is {strength}''' )
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: List[str] ) -> int:
"""simple docstring"""
UpperCamelCase_ = min(int(num_inference_steps * strength ) , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = max(num_inference_steps - init_timestep , 0 )
UpperCamelCase_ = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def lowercase ( self: Tuple , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: Optional[int]=None ) -> List[Any]:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , (torch.Tensor, PIL.Image.Image, list) ):
raise ValueError(
f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_SCREAMING_SNAKE_CASE )}''' )
UpperCamelCase_ = image.to(device=_SCREAMING_SNAKE_CASE , dtype=_SCREAMING_SNAKE_CASE )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and len(_SCREAMING_SNAKE_CASE ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(_SCREAMING_SNAKE_CASE )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
UpperCamelCase_ = init_latents.shape
UpperCamelCase_ = randn_tensor(_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , device=_SCREAMING_SNAKE_CASE , dtype=_SCREAMING_SNAKE_CASE )
# get latents
print("add noise to latents at timestep" , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.scheduler.add_noise(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = init_latents
return latents
@torch.no_grad()
def __call__( self: Dict , _SCREAMING_SNAKE_CASE: Union[torch.FloatTensor, PIL.Image.Image] = None , _SCREAMING_SNAKE_CASE: float = 0.8 , _SCREAMING_SNAKE_CASE: int = 1 , _SCREAMING_SNAKE_CASE: Optional[Union[torch.Generator, List[torch.Generator]]] = None , _SCREAMING_SNAKE_CASE: float = 0.0 , _SCREAMING_SNAKE_CASE: int = 50 , _SCREAMING_SNAKE_CASE: Optional[bool] = None , _SCREAMING_SNAKE_CASE: Optional[str] = "pil" , _SCREAMING_SNAKE_CASE: bool = True , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
self.check_inputs(_SCREAMING_SNAKE_CASE )
# 2. Preprocess image
UpperCamelCase_ = preprocess(_SCREAMING_SNAKE_CASE )
# 3. set timesteps
self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE , device=self.device )
UpperCamelCase_ , UpperCamelCase_ = self.get_timesteps(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , self.device )
UpperCamelCase_ = timesteps[:1].repeat(_SCREAMING_SNAKE_CASE )
# 4. Prepare latent variables
UpperCamelCase_ = self.prepare_latents(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , self.unet.dtype , self.device , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = latents
# 5. Denoising loop
for t in self.progress_bar(_SCREAMING_SNAKE_CASE ):
# 1. predict noise model_output
UpperCamelCase_ = self.unet(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
UpperCamelCase_ = self.scheduler.step(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , eta=_SCREAMING_SNAKE_CASE , use_clipped_model_output=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , ).prev_sample
UpperCamelCase_ = (image / 2 + 0.5).clamp(0 , 1 )
UpperCamelCase_ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCamelCase_ = self.numpy_to_pil(_SCREAMING_SNAKE_CASE )
if not return_dict:
return (image, latent_timestep.item())
return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE )
| 328 | 0 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_UpperCAmelCase = '\\n\n'
_UpperCAmelCase = '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'
_UpperCAmelCase = '\nArgs:\n model_id (str): model used for calculating Perplexity\n NOTE: Perplexity can only be calculated for causal language models.\n This includes models such as gpt2, causal variations of bert,\n causal versions of t5, and more (the full list can be found\n in the AutoModelForCausalLM documentation here:\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n input_texts (list of str): input text, each separate text snippet\n is one list entry.\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\n add_start_token (bool): whether to add the start token to the texts,\n so the perplexity can include the probability of the first word. Defaults to True.\n device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n perplexity: dictionary containing the perplexity scores for the texts\n in the input list, as well as the mean perplexity. If one of the input texts is\n longer than the max input length of the model, then it is truncated to the\n max length for the perplexity computation.\nExamples:\n Example 1:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... add_start_token=False,\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 78.22\n >>> print(round(results["perplexities"][0], 2))\n 11.11\n\n Example 2:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = datasets.load_dataset("wikitext",\n ... "wikitext-2-raw-v1",\n ... split="test")["text"][:50] # doctest:+ELLIPSIS\n [...]\n >>> input_texts = [s for s in input_texts if s!=\'\']\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 60.35\n >>> print(round(results["perplexities"][0], 2))\n 81.12\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCamelCase ( datasets.Metric ):
def lowercase ( self: List[str] ) -> Any:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"input_texts": datasets.Value("string" ),
} ) , reference_urls=["https://huggingface.co/docs/transformers/perplexity"] , )
def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: int = 16 , _SCREAMING_SNAKE_CASE: bool = True , _SCREAMING_SNAKE_CASE: Optional[Any]=None ) -> Optional[int]:
"""simple docstring"""
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
UpperCamelCase_ = "cuda"
else:
UpperCamelCase_ = "cuda" if torch.cuda.is_available() else "cpu"
UpperCamelCase_ = AutoModelForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = model.to(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
UpperCamelCase_ = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(_SCREAMING_SNAKE_CASE ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({"pad_token": existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
UpperCamelCase_ = model.config.max_length - 1
else:
UpperCamelCase_ = model.config.max_length
UpperCamelCase_ = tokenizer(
_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE , return_tensors="pt" , return_attention_mask=_SCREAMING_SNAKE_CASE , ).to(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = encodings["input_ids"]
UpperCamelCase_ = encodings["attention_mask"]
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
UpperCamelCase_ = []
UpperCamelCase_ = CrossEntropyLoss(reduction="none" )
for start_index in logging.tqdm(range(0 , len(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE ) ):
UpperCamelCase_ = min(start_index + batch_size , len(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase_ = encoded_texts[start_index:end_index]
UpperCamelCase_ = attn_masks[start_index:end_index]
if add_start_token:
UpperCamelCase_ = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
UpperCamelCase_ = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(_SCREAMING_SNAKE_CASE ), attn_mask] , dim=1 )
UpperCamelCase_ = encoded_batch
with torch.no_grad():
UpperCamelCase_ = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE ).logits
UpperCamelCase_ = out_logits[..., :-1, :].contiguous()
UpperCamelCase_ = labels[..., 1:].contiguous()
UpperCamelCase_ = attn_mask[..., 1:].contiguous()
UpperCamelCase_ = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2 ) , _SCREAMING_SNAKE_CASE ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(_SCREAMING_SNAKE_CASE )}
| 369 |
import re
from filelock import FileLock
try:
import nltk
_UpperCAmelCase = True
except (ImportError, ModuleNotFoundError):
_UpperCAmelCase = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def lowerCAmelCase_ ( UpperCamelCase_ ) -> str:
re.sub("<n>" , "" , UpperCamelCase_ ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(UpperCamelCase_ ) )
| 328 | 0 |
def lowerCAmelCase_ ( UpperCamelCase_ = 100 ):
UpperCamelCase_ = set()
UpperCamelCase_ = 0
UpperCamelCase_ = n + 1 # maximum limit
for a in range(2 , UpperCamelCase_ ):
for b in range(2 , UpperCamelCase_ ):
UpperCamelCase_ = a**b # calculates the current power
collect_powers.add(UpperCamelCase_ ) # adds the result to the set
return len(UpperCamelCase_ )
if __name__ == "__main__":
print('Number of terms ', solution(int(str(input()).strip())))
| 370 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
_UpperCamelCase : Optional[Any] = DiTPipeline
_UpperCamelCase : Any = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
_UpperCamelCase : Dict = PipelineTesterMixin.required_optional_params - {
'''latents''',
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
_UpperCamelCase : Optional[int] = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
_UpperCamelCase : Dict = False
def lowercase ( self: str ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase_ = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=_SCREAMING_SNAKE_CASE , activation_fn="gelu-approximate" , num_embeds_ada_norm=1000 , norm_type="ada_norm_zero" , norm_elementwise_affine=_SCREAMING_SNAKE_CASE , )
UpperCamelCase_ = AutoencoderKL()
UpperCamelCase_ = DDIMScheduler()
UpperCamelCase_ = {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
return components
def lowercase ( self: str , _SCREAMING_SNAKE_CASE: Dict , _SCREAMING_SNAKE_CASE: List[str]=0 ) -> Dict:
"""simple docstring"""
if str(_SCREAMING_SNAKE_CASE ).startswith("mps" ):
UpperCamelCase_ = torch.manual_seed(_SCREAMING_SNAKE_CASE )
else:
UpperCamelCase_ = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = {
"class_labels": [1],
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def lowercase ( self: Any ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ = "cpu"
UpperCamelCase_ = self.get_dummy_components()
UpperCamelCase_ = self.pipeline_class(**_SCREAMING_SNAKE_CASE )
pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = pipe(**_SCREAMING_SNAKE_CASE ).images
UpperCamelCase_ = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
UpperCamelCase_ = np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57] )
UpperCamelCase_ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_SCREAMING_SNAKE_CASE , 1e-3 )
def lowercase ( self: Optional[int] ) -> Any:
"""simple docstring"""
self._test_inference_batch_single_identical(relax_max_difference=_SCREAMING_SNAKE_CASE , expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def lowercase ( self: Optional[Any] ) -> Optional[int]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class _UpperCamelCase ( unittest.TestCase ):
def lowercase ( self: Optional[int] ) -> List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase ( self: Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = torch.manual_seed(0 )
UpperCamelCase_ = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256" )
pipe.to("cuda" )
UpperCamelCase_ = ["vase", "umbrella", "white shark", "white wolf"]
UpperCamelCase_ = pipe.get_label_ids(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = pipe(_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , num_inference_steps=40 , output_type="np" ).images
for word, image in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase_ = load_numpy(
f'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy''' )
assert np.abs((expected_image - image).max() ) < 1e-2
def lowercase ( self: int ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ = DiTPipeline.from_pretrained("facebook/DiT-XL-2-512" )
UpperCamelCase_ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("cuda" )
UpperCamelCase_ = ["vase", "umbrella"]
UpperCamelCase_ = pipe.get_label_ids(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = torch.manual_seed(0 )
UpperCamelCase_ = pipe(_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , num_inference_steps=25 , output_type="np" ).images
for word, image in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
f'''/dit/{word}_512.npy''' )
assert np.abs((expected_image - image).max() ) < 1e-1
| 328 | 0 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = False, False, False
@dataclass
class _UpperCamelCase :
_UpperCamelCase : Optional[int] = None
_UpperCamelCase : bool = True
_UpperCamelCase : bool = True
_UpperCamelCase : Optional[str] = None
# Automatically constructed
_UpperCamelCase : ClassVar[str] = "dict"
_UpperCamelCase : ClassVar[Any] = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()} )
_UpperCamelCase : str = field(default='''Audio''' , init=lowerCAmelCase_ , repr=lowerCAmelCase_ )
def __call__( self: int ) -> Union[str, Any]:
"""simple docstring"""
return self.pa_type
def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: Union[str, bytes, dict] ) -> dict:
"""simple docstring"""
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError("To support encoding audio data, please install 'soundfile'." ) from err
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return {"bytes": None, "path": value}
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
UpperCamelCase_ = BytesIO()
sf.write(_SCREAMING_SNAKE_CASE , value["array"] , value["sampling_rate"] , format="wav" )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get("path" ) is not None and os.path.isfile(value["path"] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith("pcm" ):
# "PCM" only has raw audio bytes
if value.get("sampling_rate" ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError("To use PCM files, please specify a 'sampling_rate' in Audio object" )
if value.get("bytes" ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
UpperCamelCase_ = np.frombuffer(value["bytes"] , dtype=np.intaa ).astype(np.floataa ) / 32767
else:
UpperCamelCase_ = np.memmap(value["path"] , dtype="h" , mode="r" ).astype(np.floataa ) / 32767
UpperCamelCase_ = BytesIO(bytes() )
sf.write(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , value["sampling_rate"] , format="wav" )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get("path" )}
elif value.get("bytes" ) is not None or value.get("path" ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get("bytes" ), "path": value.get("path" )}
else:
raise ValueError(
f'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: dict , _SCREAMING_SNAKE_CASE: Optional[Dict[str, Union[str, bool, None]]] = None ) -> dict:
"""simple docstring"""
if not self.decode:
raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead." )
UpperCamelCase_ , UpperCamelCase_ = (value["path"], BytesIO(value["bytes"] )) if value["bytes"] is not None else (value["path"], None)
if path is None and file is None:
raise ValueError(f'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError("To support decoding audio files, please install 'librosa' and 'soundfile'." ) from err
UpperCamelCase_ = xsplitext(_SCREAMING_SNAKE_CASE )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
"Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
"You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
"Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
"You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
if file is None:
UpperCamelCase_ = token_per_repo_id or {}
UpperCamelCase_ = path.split("::" )[-1]
try:
UpperCamelCase_ = string_to_dict(_SCREAMING_SNAKE_CASE , config.HUB_DATASETS_URL )["repo_id"]
UpperCamelCase_ = token_per_repo_id[repo_id]
except (ValueError, KeyError):
UpperCamelCase_ = None
with xopen(_SCREAMING_SNAKE_CASE , "rb" , use_auth_token=_SCREAMING_SNAKE_CASE ) as f:
UpperCamelCase_ , UpperCamelCase_ = sf.read(_SCREAMING_SNAKE_CASE )
else:
UpperCamelCase_ , UpperCamelCase_ = sf.read(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = array.T
if self.mono:
UpperCamelCase_ = librosa.to_mono(_SCREAMING_SNAKE_CASE )
if self.sampling_rate and self.sampling_rate != sampling_rate:
UpperCamelCase_ = librosa.resample(_SCREAMING_SNAKE_CASE , orig_sr=_SCREAMING_SNAKE_CASE , target_sr=self.sampling_rate )
UpperCamelCase_ = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def lowercase ( self: Optional[int] ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
"""simple docstring"""
from .features import Value
if self.decode:
raise ValueError("Cannot flatten a decoded Audio feature." )
return {
"bytes": Value("binary" ),
"path": Value("string" ),
}
def lowercase ( self: List[Any] , _SCREAMING_SNAKE_CASE: Union[pa.StringArray, pa.StructArray] ) -> pa.StructArray:
"""simple docstring"""
if pa.types.is_string(storage.type ):
UpperCamelCase_ = pa.array([None] * len(_SCREAMING_SNAKE_CASE ) , type=pa.binary() )
UpperCamelCase_ = pa.StructArray.from_arrays([bytes_array, storage] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
UpperCamelCase_ = pa.array([None] * len(_SCREAMING_SNAKE_CASE ) , type=pa.string() )
UpperCamelCase_ = pa.StructArray.from_arrays([storage, path_array] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices("array" ):
UpperCamelCase_ = pa.array([Audio().encode_example(_SCREAMING_SNAKE_CASE ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("bytes" ) >= 0:
UpperCamelCase_ = storage.field("bytes" )
else:
UpperCamelCase_ = pa.array([None] * len(_SCREAMING_SNAKE_CASE ) , type=pa.binary() )
if storage.type.get_field_index("path" ) >= 0:
UpperCamelCase_ = storage.field("path" )
else:
UpperCamelCase_ = pa.array([None] * len(_SCREAMING_SNAKE_CASE ) , type=pa.string() )
UpperCamelCase_ = pa.StructArray.from_arrays([bytes_array, path_array] , ["bytes", "path"] , mask=storage.is_null() )
return array_cast(_SCREAMING_SNAKE_CASE , self.pa_type )
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: pa.StructArray ) -> pa.StructArray:
"""simple docstring"""
@no_op_if_value_is_null
def path_to_bytes(_SCREAMING_SNAKE_CASE: Any ):
with xopen(_SCREAMING_SNAKE_CASE , "rb" ) as f:
UpperCamelCase_ = f.read()
return bytes_
UpperCamelCase_ = 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() , )
UpperCamelCase_ = pa.array(
[os.path.basename(_SCREAMING_SNAKE_CASE ) if path is not None else None for path in storage.field("path" ).to_pylist()] , type=pa.string() , )
UpperCamelCase_ = pa.StructArray.from_arrays([bytes_array, path_array] , ["bytes", "path"] , mask=bytes_array.is_null() )
return array_cast(_SCREAMING_SNAKE_CASE , self.pa_type )
| 371 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _UpperCamelCase :
def __init__( self: str ) -> Any:
"""simple docstring"""
UpperCamelCase_ = ""
UpperCamelCase_ = ""
UpperCamelCase_ = []
UpperCamelCase_ = 0
UpperCamelCase_ = 256
UpperCamelCase_ = 0
UpperCamelCase_ = 0
UpperCamelCase_ = 0
UpperCamelCase_ = 0
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: Dict ) -> str:
"""simple docstring"""
UpperCamelCase_ = cva.imread(_SCREAMING_SNAKE_CASE , 0 )
UpperCamelCase_ = copy.deepcopy(self.img )
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = plt.hist(self.img.ravel() , 256 , [0, 256] , label="x" )
UpperCamelCase_ = np.sum(_SCREAMING_SNAKE_CASE )
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
UpperCamelCase_ = x[i] / self.k
self.sk += prk
UpperCamelCase_ = (self.L - 1) * self.sk
if self.rem != 0:
UpperCamelCase_ = int(last % last )
UpperCamelCase_ = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = int(np.ma.count(self.img ) / self.img[1].size )
UpperCamelCase_ = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
UpperCamelCase_ = self.img[j][i]
if num != self.last_list[num]:
UpperCamelCase_ = self.last_list[num]
cva.imwrite("output_data/output.jpg" , self.img )
def lowercase ( self: Any ) -> Optional[Any]:
"""simple docstring"""
plt.hist(self.img.ravel() , 256 , [0, 256] )
def lowercase ( self: Tuple ) -> Union[str, Any]:
"""simple docstring"""
cva.imshow("Output-Image" , self.img )
cva.imshow("Input-Image" , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
_UpperCAmelCase = os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
_UpperCAmelCase = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 328 | 0 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
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 MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class _a :
"""simple docstring"""
def __init__( self : List[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Tuple=13 , UpperCAmelCase : List[Any]=32 , UpperCAmelCase : Union[str, Any]=2 , UpperCAmelCase : Union[str, Any]=3 , UpperCAmelCase : Any=16 , UpperCAmelCase : Optional[int]=[1, 2, 1] , UpperCAmelCase : List[Any]=[2, 2, 4] , UpperCAmelCase : Optional[int]=2 , UpperCAmelCase : Tuple=2.0 , UpperCAmelCase : Any=True , UpperCAmelCase : List[Any]=0.0 , UpperCAmelCase : int=0.0 , UpperCAmelCase : str=0.1 , UpperCAmelCase : List[str]="gelu" , UpperCAmelCase : List[str]=False , UpperCAmelCase : List[str]=True , UpperCAmelCase : Optional[Any]=0.02 , UpperCAmelCase : Optional[int]=1E-5 , UpperCAmelCase : List[str]=True , UpperCAmelCase : Any=None , UpperCAmelCase : int=True , UpperCAmelCase : int=10 , UpperCAmelCase : List[Any]=8 , UpperCAmelCase : Dict=["stage1", "stage2", "stage3"] , UpperCAmelCase : Optional[Any]=[1, 2, 3] , ):
A_ = parent
A_ = batch_size
A_ = image_size
A_ = patch_size
A_ = num_channels
A_ = embed_dim
A_ = depths
A_ = num_heads
A_ = window_size
A_ = mlp_ratio
A_ = qkv_bias
A_ = hidden_dropout_prob
A_ = attention_probs_dropout_prob
A_ = drop_path_rate
A_ = hidden_act
A_ = use_absolute_embeddings
A_ = patch_norm
A_ = layer_norm_eps
A_ = initializer_range
A_ = is_training
A_ = scope
A_ = use_labels
A_ = type_sequence_label_size
A_ = encoder_stride
A_ = out_features
A_ = out_indices
def __A ( self : List[str] ):
A_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ = None
if self.use_labels:
A_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ = self.get_config()
return config, pixel_values, labels
def __A ( self : List[str] ):
return MaskFormerSwinConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def __A ( self : str , UpperCAmelCase : Dict , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str ):
A_ = MaskFormerSwinModel(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
A_ = model(UpperCAmelCase )
A_ = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
A_ = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def __A ( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Union[str, Any] ):
A_ = MaskFormerSwinBackbone(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
A_ = model(UpperCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(UpperCAmelCase ):
A_ = ["stem"]
A_ = MaskFormerSwinBackbone(config=UpperCAmelCase )
def __A ( self : Optional[Any] ):
A_ = self.prepare_config_and_inputs()
A_ , A_ , A_ = config_and_inputs
A_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _a ( snake_case_ , snake_case_ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
_lowerCamelCase : str = {'feature-extraction': MaskFormerSwinModel} if is_torch_available() else {}
_lowerCamelCase : int = False
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : Tuple = False
_lowerCamelCase : Any = False
_lowerCamelCase : List[str] = False
def __A ( self : Union[str, Any] ):
A_ = MaskFormerSwinModelTester(self )
A_ = ConfigTester(self , config_class=UpperCAmelCase , embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
"`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"
" `nn.DataParallel`"
) )
def __A ( self : Dict ):
pass
def __A ( self : Optional[Any] ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __A ( self : List[str] ):
return
def __A ( self : Union[str, Any] ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase )
def __A ( self : Dict ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*UpperCAmelCase )
@unittest.skip("Swin does not use inputs_embeds" )
def __A ( self : Dict ):
pass
@unittest.skip("Swin does not support feedforward chunking" )
def __A ( self : List[str] ):
pass
def __A ( self : List[str] ):
A_ , A_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ = model_class(UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase , nn.Linear ) )
def __A ( self : Union[str, Any] ):
A_ , A_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ = model_class(UpperCAmelCase )
A_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ = [*signature.parameters.keys()]
A_ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , UpperCAmelCase )
@unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn't support output_attentions" )
def __A ( self : Any ):
pass
@unittest.skip(reason="MaskFormerSwin is only used as an internal backbone" )
def __A ( self : Union[str, Any] ):
pass
def __A ( self : int , UpperCAmelCase : str , UpperCAmelCase : Dict , UpperCAmelCase : Dict , UpperCAmelCase : List[Any] ):
A_ = model_class(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
with torch.no_grad():
A_ = model(**self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) )
A_ = outputs.hidden_states
A_ = getattr(
self.model_tester , "expected_num_hidden_layers" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(UpperCAmelCase ) , UpperCAmelCase )
# Swin has a different seq_length
A_ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
A_ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def __A ( self : Optional[Any] ):
A_ , A_ = self.model_tester.prepare_config_and_inputs_for_common()
A_ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
A_ = True
self.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A_ = True
self.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def __A ( self : Optional[int] ):
A_ , A_ = self.model_tester.prepare_config_and_inputs_for_common()
A_ = 3
A_ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
A_ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
A_ = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
A_ = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
A_ = True
self.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A_ = True
self.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , (padded_height, padded_width) )
@unittest.skip(reason="MaskFormerSwin doesn't have pretrained checkpoints" )
def __A ( self : Tuple ):
pass
@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin" )
def __A ( self : int ):
pass
@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin" )
def __A ( self : Any ):
pass
def __A ( self : Any ):
A_ , A_ = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(UpperCAmelCase : Union[str, Any] ):
A_ = 0
return t
def check_equivalence(UpperCAmelCase : Dict , UpperCAmelCase : Tuple , UpperCAmelCase : List[str] , UpperCAmelCase : Any={} ):
with torch.no_grad():
A_ = model(**UpperCAmelCase , return_dict=UpperCAmelCase , **UpperCAmelCase )
A_ = model(**UpperCAmelCase , return_dict=UpperCAmelCase , **UpperCAmelCase ).to_tuple()
def recursive_check(UpperCAmelCase : Optional[Any] , UpperCAmelCase : Any ):
if isinstance(UpperCAmelCase , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(UpperCAmelCase , UpperCAmelCase ):
recursive_check(UpperCAmelCase , UpperCAmelCase )
elif isinstance(UpperCAmelCase , UpperCAmelCase ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(UpperCAmelCase , UpperCAmelCase )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(UpperCAmelCase ) , set_nan_tensor_to_zero(UpperCAmelCase ) , atol=1E-5 ) , msg=(
"Tuple and dict output are not equal. Difference:"
f''' {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:'''
f''' {torch.isnan(UpperCAmelCase ).any()} and `inf`: {torch.isinf(UpperCAmelCase )}. Dict has'''
f''' `nan`: {torch.isnan(UpperCAmelCase ).any()} and `inf`: {torch.isinf(UpperCAmelCase )}.'''
) , )
recursive_check(UpperCAmelCase , UpperCAmelCase )
for model_class in self.all_model_classes:
A_ = model_class(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
A_ = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase )
A_ = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase )
check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
A_ = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )
A_ = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )
check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
A_ = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase )
A_ = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase )
check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , {"output_hidden_states": True} )
A_ = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )
A_ = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )
check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , {"output_hidden_states": True} )
@require_torch
class _a ( unittest.TestCase , snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Tuple = (MaskFormerSwinBackbone,) if is_torch_available() else ()
_lowerCamelCase : List[Any] = MaskFormerSwinConfig
def __A ( self : Optional[int] ):
A_ = MaskFormerSwinModelTester(self )
def __A ( self : Optional[int] ):
A_ , A_ = self.model_tester.prepare_config_and_inputs_for_common()
A_ = inputs_dict["pixel_values"].shape[0]
for backbone_class in self.all_model_classes:
A_ = backbone_class(UpperCAmelCase )
backbone.to(UpperCAmelCase )
backbone.eval()
A_ = backbone(**UpperCAmelCase )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , UpperCAmelCase )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
A_ = backbone(**UpperCAmelCase , output_hidden_states=UpperCAmelCase )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
A_ , A_ , A_ = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
A_ = backbone(**UpperCAmelCase , output_attentions=UpperCAmelCase )
self.assertIsNotNone(outputs.attentions )
| 329 |
from maths.prime_factors import prime_factors
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = f'''Input value of [number={number}] must be an integer'''
raise TypeError(__UpperCamelCase )
if number < 1:
raise ValueError("Input must be a positive integer" )
return -1 if len(prime_factors(__UpperCamelCase ) ) % 2 else 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 329 | 1 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _a ( snake_case_ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase : List[str] = AudioLDMPipeline
_lowerCamelCase : Union[str, Any] = TEXT_TO_AUDIO_PARAMS
_lowerCamelCase : Dict = TEXT_TO_AUDIO_BATCH_PARAMS
_lowerCamelCase : Union[str, Any] = frozenset(
[
'num_inference_steps',
'num_waveforms_per_prompt',
'generator',
'latents',
'output_type',
'return_dict',
'callback',
'callback_steps',
] )
def __A ( self : Union[str, Any] ):
torch.manual_seed(0 )
A_ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=(32, 64) , class_embed_type="simple_projection" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=UpperCAmelCase , )
A_ = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=UpperCAmelCase , set_alpha_to_one=UpperCAmelCase , )
torch.manual_seed(0 )
A_ = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
torch.manual_seed(0 )
A_ = ClapTextConfig(
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 , projection_dim=32 , )
A_ = ClapTextModelWithProjection(UpperCAmelCase )
A_ = RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta" , model_max_length=77 )
A_ = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=16000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=UpperCAmelCase , )
A_ = SpeechTaHifiGan(UpperCAmelCase )
A_ = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"vocoder": vocoder,
}
return components
def __A ( self : Tuple , UpperCAmelCase : str , UpperCAmelCase : Optional[int]=0 ):
if str(UpperCAmelCase ).startswith("mps" ):
A_ = torch.manual_seed(UpperCAmelCase )
else:
A_ = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
A_ = {
"prompt": "A hammer hitting a wooden surface",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
}
return inputs
def __A ( self : Union[str, Any] ):
A_ = "cpu" # ensure determinism for the device-dependent torch.Generator
A_ = self.get_dummy_components()
A_ = AudioLDMPipeline(**UpperCAmelCase )
A_ = audioldm_pipe.to(UpperCAmelCase )
audioldm_pipe.set_progress_bar_config(disable=UpperCAmelCase )
A_ = self.get_dummy_inputs(UpperCAmelCase )
A_ = audioldm_pipe(**UpperCAmelCase )
A_ = output.audios[0]
assert audio.ndim == 1
assert len(UpperCAmelCase ) == 256
A_ = audio[:10]
A_ = np.array(
[-0.0_050, 0.0_050, -0.0_060, 0.0_033, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_033] )
assert np.abs(audio_slice - expected_slice ).max() < 1E-2
def __A ( self : Optional[Any] ):
A_ = self.get_dummy_components()
A_ = AudioLDMPipeline(**UpperCAmelCase )
A_ = audioldm_pipe.to(UpperCAmelCase )
A_ = audioldm_pipe.to(UpperCAmelCase )
audioldm_pipe.set_progress_bar_config(disable=UpperCAmelCase )
A_ = self.get_dummy_inputs(UpperCAmelCase )
A_ = 3 * [inputs["prompt"]]
# forward
A_ = audioldm_pipe(**UpperCAmelCase )
A_ = output.audios[0]
A_ = self.get_dummy_inputs(UpperCAmelCase )
A_ = 3 * [inputs.pop("prompt" )]
A_ = audioldm_pipe.tokenizer(
UpperCAmelCase , padding="max_length" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=UpperCAmelCase , return_tensors="pt" , )
A_ = text_inputs["input_ids"].to(UpperCAmelCase )
A_ = audioldm_pipe.text_encoder(
UpperCAmelCase , )
A_ = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
A_ = F.normalize(UpperCAmelCase , dim=-1 )
A_ = prompt_embeds
# forward
A_ = audioldm_pipe(**UpperCAmelCase )
A_ = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1E-2
def __A ( self : str ):
A_ = self.get_dummy_components()
A_ = AudioLDMPipeline(**UpperCAmelCase )
A_ = audioldm_pipe.to(UpperCAmelCase )
A_ = audioldm_pipe.to(UpperCAmelCase )
audioldm_pipe.set_progress_bar_config(disable=UpperCAmelCase )
A_ = self.get_dummy_inputs(UpperCAmelCase )
A_ = 3 * ["this is a negative prompt"]
A_ = negative_prompt
A_ = 3 * [inputs["prompt"]]
# forward
A_ = audioldm_pipe(**UpperCAmelCase )
A_ = output.audios[0]
A_ = self.get_dummy_inputs(UpperCAmelCase )
A_ = 3 * [inputs.pop("prompt" )]
A_ = []
for p in [prompt, negative_prompt]:
A_ = audioldm_pipe.tokenizer(
UpperCAmelCase , padding="max_length" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=UpperCAmelCase , return_tensors="pt" , )
A_ = text_inputs["input_ids"].to(UpperCAmelCase )
A_ = audioldm_pipe.text_encoder(
UpperCAmelCase , )
A_ = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
A_ = F.normalize(UpperCAmelCase , dim=-1 )
embeds.append(UpperCAmelCase )
A_ , A_ = embeds
# forward
A_ = audioldm_pipe(**UpperCAmelCase )
A_ = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1E-2
def __A ( self : Any ):
A_ = "cpu" # ensure determinism for the device-dependent torch.Generator
A_ = self.get_dummy_components()
A_ = PNDMScheduler(skip_prk_steps=UpperCAmelCase )
A_ = AudioLDMPipeline(**UpperCAmelCase )
A_ = audioldm_pipe.to(UpperCAmelCase )
audioldm_pipe.set_progress_bar_config(disable=UpperCAmelCase )
A_ = self.get_dummy_inputs(UpperCAmelCase )
A_ = "egg cracking"
A_ = audioldm_pipe(**UpperCAmelCase , negative_prompt=UpperCAmelCase )
A_ = output.audios[0]
assert audio.ndim == 1
assert len(UpperCAmelCase ) == 256
A_ = audio[:10]
A_ = np.array(
[-0.0_051, 0.0_050, -0.0_060, 0.0_034, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_032] )
assert np.abs(audio_slice - expected_slice ).max() < 1E-2
def __A ( self : List[Any] ):
A_ = "cpu" # ensure determinism for the device-dependent torch.Generator
A_ = self.get_dummy_components()
A_ = PNDMScheduler(skip_prk_steps=UpperCAmelCase )
A_ = AudioLDMPipeline(**UpperCAmelCase )
A_ = audioldm_pipe.to(UpperCAmelCase )
audioldm_pipe.set_progress_bar_config(disable=UpperCAmelCase )
A_ = "A hammer hitting a wooden surface"
# test num_waveforms_per_prompt=1 (default)
A_ = audioldm_pipe(UpperCAmelCase , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
A_ = 2
A_ = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
A_ = 2
A_ = audioldm_pipe(UpperCAmelCase , num_inference_steps=2 , num_waveforms_per_prompt=UpperCAmelCase ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
A_ = 2
A_ = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=UpperCAmelCase ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def __A ( self : List[Any] ):
A_ = "cpu" # ensure determinism for the device-dependent torch.Generator
A_ = self.get_dummy_components()
A_ = AudioLDMPipeline(**UpperCAmelCase )
A_ = audioldm_pipe.to(UpperCAmelCase )
audioldm_pipe.set_progress_bar_config(disable=UpperCAmelCase )
A_ = audioldm_pipe.vocoder.config.sampling_rate
A_ = self.get_dummy_inputs(UpperCAmelCase )
A_ = audioldm_pipe(audio_length_in_s=0.016 , **UpperCAmelCase )
A_ = output.audios[0]
assert audio.ndim == 1
assert len(UpperCAmelCase ) / vocoder_sampling_rate == 0.016
A_ = audioldm_pipe(audio_length_in_s=0.032 , **UpperCAmelCase )
A_ = output.audios[0]
assert audio.ndim == 1
assert len(UpperCAmelCase ) / vocoder_sampling_rate == 0.032
def __A ( self : Union[str, Any] ):
A_ = self.get_dummy_components()
A_ = AudioLDMPipeline(**UpperCAmelCase )
A_ = audioldm_pipe.to(UpperCAmelCase )
audioldm_pipe.set_progress_bar_config(disable=UpperCAmelCase )
A_ = ["hey"]
A_ = audioldm_pipe(UpperCAmelCase , num_inference_steps=1 )
A_ = output.audios.shape
assert audio_shape == (1, 256)
A_ = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
A_ = SpeechTaHifiGan(UpperCAmelCase ).to(UpperCAmelCase )
A_ = audioldm_pipe(UpperCAmelCase , num_inference_steps=1 )
A_ = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def __A ( self : List[str] ):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=UpperCAmelCase )
def __A ( self : List[str] ):
self._test_inference_batch_single_identical(test_mean_pixel_difference=UpperCAmelCase )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def __A ( self : str ):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=UpperCAmelCase )
@slow
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : Dict ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __A ( self : List[str] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Dict="cpu" , UpperCAmelCase : List[str]=torch.floataa , UpperCAmelCase : Dict=0 ):
A_ = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
A_ = np.random.RandomState(UpperCAmelCase ).standard_normal((1, 8, 128, 16) )
A_ = torch.from_numpy(UpperCAmelCase ).to(device=UpperCAmelCase , dtype=UpperCAmelCase )
A_ = {
"prompt": "A hammer hitting a wooden surface",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 2.5,
}
return inputs
def __A ( self : List[Any] ):
A_ = AudioLDMPipeline.from_pretrained("cvssp/audioldm" )
A_ = audioldm_pipe.to(UpperCAmelCase )
audioldm_pipe.set_progress_bar_config(disable=UpperCAmelCase )
A_ = self.get_inputs(UpperCAmelCase )
A_ = 25
A_ = audioldm_pipe(**UpperCAmelCase ).audios[0]
assert audio.ndim == 1
assert len(UpperCAmelCase ) == 81920
A_ = audio[77230:77240]
A_ = np.array(
[-0.4_884, -0.4_607, 0.0_023, 0.5_007, 0.5_896, 0.5_151, 0.3_813, -0.0_208, -0.3_687, -0.4_315] )
A_ = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1E-2
def __A ( self : str ):
A_ = AudioLDMPipeline.from_pretrained("cvssp/audioldm" )
A_ = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
A_ = audioldm_pipe.to(UpperCAmelCase )
audioldm_pipe.set_progress_bar_config(disable=UpperCAmelCase )
A_ = self.get_inputs(UpperCAmelCase )
A_ = audioldm_pipe(**UpperCAmelCase ).audios[0]
assert audio.ndim == 1
assert len(UpperCAmelCase ) == 81920
A_ = audio[27780:27790]
A_ = np.array([-0.2_131, -0.0_873, -0.0_124, -0.0_189, 0.0_569, 0.1_373, 0.1_883, 0.2_886, 0.3_297, 0.2_212] )
A_ = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3E-2
| 329 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__a :int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__a :Any = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F"{len(upper_files)} files contain uppercase characters:")
print('\n'.join(upper_files) + '\n')
__a :Tuple = [file for file in filepaths if ' ' in file]
if space_files:
print(F"{len(space_files)} files contain space characters:")
print('\n'.join(space_files) + '\n')
__a :str = [file for file in filepaths if '-' in file]
if hyphen_files:
print(F"{len(hyphen_files)} files contain hyphen characters:")
print('\n'.join(hyphen_files) + '\n')
__a :List[str] = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F"{len(nodir_files)} files are not in a directory:")
print('\n'.join(nodir_files) + '\n')
__a :Any = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 329 | 1 |
def __snake_case ( __UpperCamelCase : Optional[int] ):
"""simple docstring"""
A_ = 1
A_ = 2
while i * i <= n:
A_ = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_divisors
def __snake_case ( ):
"""simple docstring"""
A_ = 1
A_ = 1
while True:
i += 1
t_num += i
if count_divisors(__UpperCamelCase ) > 500:
break
return t_num
if __name__ == "__main__":
print(solution())
| 329 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a :Union[str, Any] = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a :Optional[int] = [
'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BioGptForCausalLM',
'BioGptForTokenClassification',
'BioGptForSequenceClassification',
'BioGptModel',
'BioGptPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
from .tokenization_biogpt import BioGptTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_biogpt import (
BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptPreTrainedModel,
)
else:
import sys
__a :str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 329 | 1 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
__a :int = logging.get_logger(__name__)
class _a :
"""simple docstring"""
_lowerCamelCase : str
_lowerCamelCase : str = None
@staticmethod
def __A ( ):
raise NotImplementedError
def __A ( self : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : int , UpperCAmelCase : str , **UpperCAmelCase : List[str] ):
raise NotImplementedError
def __A ( self : Any , UpperCAmelCase : str ):
raise NotImplementedError
def __A ( self : List[Any] ):
if not self.is_available():
raise RuntimeError(
f'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.''' )
@classmethod
def __A ( cls : Tuple ):
return f'''`pip install {cls.pip_package or cls.name}`'''
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : List[Any] = 'optuna'
@staticmethod
def __A ( ):
return is_optuna_available()
def __A ( self : List[Any] , UpperCAmelCase : Any , UpperCAmelCase : int , UpperCAmelCase : str , **UpperCAmelCase : List[Any] ):
return run_hp_search_optuna(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
def __A ( self : Optional[Any] , UpperCAmelCase : Dict ):
return default_hp_space_optuna(UpperCAmelCase )
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : str = 'ray'
_lowerCamelCase : Optional[Any] = '\'ray[tune]\''
@staticmethod
def __A ( ):
return is_ray_available()
def __A ( self : List[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : str , **UpperCAmelCase : Dict ):
return run_hp_search_ray(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
def __A ( self : Tuple , UpperCAmelCase : Union[str, Any] ):
return default_hp_space_ray(UpperCAmelCase )
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = 'sigopt'
@staticmethod
def __A ( ):
return is_sigopt_available()
def __A ( self : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : int , UpperCAmelCase : str , **UpperCAmelCase : int ):
return run_hp_search_sigopt(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
def __A ( self : Union[str, Any] , UpperCAmelCase : Any ):
return default_hp_space_sigopt(UpperCAmelCase )
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = 'wandb'
@staticmethod
def __A ( ):
return is_wandb_available()
def __A ( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : int , UpperCAmelCase : str , **UpperCAmelCase : List[str] ):
return run_hp_search_wandb(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
def __A ( self : Optional[int] , UpperCAmelCase : List[str] ):
return default_hp_space_wandb(UpperCAmelCase )
__a :List[Any] = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def __snake_case ( ):
"""simple docstring"""
A_ = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(__UpperCamelCase ) > 0:
A_ = available_backends[0].name
if len(__UpperCamelCase ) > 1:
logger.info(
f'''{len(__UpperCamelCase )} hyperparameter search backends available. Using {name} as the default.''' )
return name
raise RuntimeError(
"No hyperparameter search backend available.\n"
+ "\n".join(
f''' - To install {backend.name} run {backend.pip_install()}'''
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 329 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def __snake_case ( __UpperCamelCase : Union[str, Any] ):
"""simple docstring"""
if is_torch_version("<" ,"2.0.0" ) or not hasattr(__UpperCamelCase ,"_dynamo" ):
return False
return isinstance(__UpperCamelCase ,torch._dynamo.eval_frame.OptimizedModule )
def __snake_case ( __UpperCamelCase : List[str] ,__UpperCamelCase : bool = True ):
"""simple docstring"""
A_ = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
A_ = is_compiled_module(__UpperCamelCase )
if is_compiled:
A_ = model
A_ = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = model.module
if not keep_fpaa_wrapper:
A_ = getattr(__UpperCamelCase ,"forward" )
A_ = model.__dict__.pop("_original_forward" ,__UpperCamelCase )
if original_forward is not None:
while hasattr(__UpperCamelCase ,"__wrapped__" ):
A_ = forward.__wrapped__
if forward == original_forward:
break
A_ = forward
if getattr(__UpperCamelCase ,"_converted_to_transformer_engine" ,__UpperCamelCase ):
convert_model(__UpperCamelCase ,to_transformer_engine=__UpperCamelCase )
if is_compiled:
A_ = model
A_ = compiled_model
return model
def __snake_case ( ):
"""simple docstring"""
PartialState().wait_for_everyone()
def __snake_case ( __UpperCamelCase : Optional[Any] ,__UpperCamelCase : Any ):
"""simple docstring"""
if PartialState().distributed_type == DistributedType.TPU:
xm.save(__UpperCamelCase ,__UpperCamelCase )
elif PartialState().local_process_index == 0:
torch.save(__UpperCamelCase ,__UpperCamelCase )
@contextmanager
def __snake_case ( **__UpperCamelCase : Any ):
"""simple docstring"""
for key, value in kwargs.items():
A_ = str(__UpperCamelCase )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def __snake_case ( __UpperCamelCase : Optional[Any] ):
"""simple docstring"""
if not hasattr(__UpperCamelCase ,"__qualname__" ) and not hasattr(__UpperCamelCase ,"__name__" ):
A_ = getattr(__UpperCamelCase ,"__class__" ,__UpperCamelCase )
if hasattr(__UpperCamelCase ,"__qualname__" ):
return obj.__qualname__
if hasattr(__UpperCamelCase ,"__name__" ):
return obj.__name__
return str(__UpperCamelCase )
def __snake_case ( __UpperCamelCase : Any ,__UpperCamelCase : Optional[Any] ):
"""simple docstring"""
for key, value in source.items():
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = destination.setdefault(__UpperCamelCase ,{} )
merge_dicts(__UpperCamelCase ,__UpperCamelCase )
else:
A_ = value
return destination
def __snake_case ( __UpperCamelCase : int = None ):
"""simple docstring"""
if port is None:
A_ = 2_9500
with socket.socket(socket.AF_INET ,socket.SOCK_STREAM ) as s:
return s.connect_ex(("localhost", port) ) == 0
| 329 | 1 |
import string
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
for key in range(len(string.ascii_uppercase ) ):
A_ = ""
for symbol in message:
if symbol in string.ascii_uppercase:
A_ = string.ascii_uppercase.find(__UpperCamelCase )
A_ = num - key
if num < 0:
A_ = num + len(string.ascii_uppercase )
A_ = translated + string.ascii_uppercase[num]
else:
A_ = translated + symbol
print(f'''Decryption using Key #{key}: {translated}''' )
def __snake_case ( ):
"""simple docstring"""
A_ = input("Encrypted message: " )
A_ = message.upper()
decrypt(__UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 329 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : int ):
A_ = tempfile.mkdtemp()
A_ = BlipImageProcessor()
A_ = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )
A_ = BlipProcessor(UpperCAmelCase , UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
def __A ( self : Optional[int] , **UpperCAmelCase : Union[str, Any] ):
return AutoProcessor.from_pretrained(self.tmpdirname , **UpperCAmelCase ).tokenizer
def __A ( self : Optional[Any] , **UpperCAmelCase : int ):
return AutoProcessor.from_pretrained(self.tmpdirname , **UpperCAmelCase ).image_processor
def __A ( self : Any ):
shutil.rmtree(self.tmpdirname )
def __A ( self : Dict ):
A_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
A_ = [Image.fromarray(np.moveaxis(UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __A ( self : Any ):
A_ = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
A_ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
A_ = self.get_image_processor(do_normalize=UpperCAmelCase , padding_value=1.0 )
A_ = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , UpperCAmelCase )
def __A ( self : Dict ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = self.prepare_image_inputs()
A_ = image_processor(UpperCAmelCase , return_tensors="np" )
A_ = processor(images=UpperCAmelCase , return_tensors="np" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __A ( self : int ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = "lower newer"
A_ = processor(text=UpperCAmelCase )
A_ = tokenizer(UpperCAmelCase , return_token_type_ids=UpperCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __A ( self : Tuple ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = "lower newer"
A_ = self.prepare_image_inputs()
A_ = processor(text=UpperCAmelCase , images=UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(UpperCAmelCase ):
processor()
def __A ( self : Any ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A_ = processor.batch_decode(UpperCAmelCase )
A_ = tokenizer.batch_decode(UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
def __A ( self : Optional[Any] ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = "lower newer"
A_ = self.prepare_image_inputs()
A_ = processor(text=UpperCAmelCase , images=UpperCAmelCase )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
| 329 | 1 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __snake_case ( *__UpperCamelCase : str ):
"""simple docstring"""
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = list(__UpperCamelCase )
for i in range(len(__UpperCamelCase ) ):
A_ = 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 __snake_case ( __UpperCamelCase : Exception ):
"""simple docstring"""
A_ = [
"CUDA out of memory.", # CUDA OOM
"cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.", # CUDNN SNAFU
"DefaultCPUAllocator: can't allocate memory", # CPU OOM
]
if isinstance(__UpperCamelCase ,__UpperCamelCase ) and len(exception.args ) == 1:
return any(err in exception.args[0] for err in _statements )
return False
def __snake_case ( __UpperCamelCase : callable = None ,__UpperCamelCase : int = 128 ):
"""simple docstring"""
if function is None:
return functools.partial(__UpperCamelCase ,starting_batch_size=__UpperCamelCase )
A_ = starting_batch_size
def decorator(*__UpperCamelCase : int ,**__UpperCamelCase : Union[str, Any] ):
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()
A_ = list(inspect.signature(__UpperCamelCase ).parameters.keys() )
# Guard against user error
if len(__UpperCamelCase ) < (len(__UpperCamelCase ) + 1):
A_ = ", ".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(__UpperCamelCase ,*__UpperCamelCase ,**__UpperCamelCase )
except Exception as e:
if should_reduce_batch_size(__UpperCamelCase ):
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
| 329 |
import math
__a :Union[str, Any] = 10
__a :Union[str, Any] = 7
__a :int = BALLS_PER_COLOUR * NUM_COLOURS
def __snake_case ( __UpperCamelCase : int = 20 ):
"""simple docstring"""
A_ = math.comb(__UpperCamelCase ,__UpperCamelCase )
A_ = math.comb(NUM_BALLS - BALLS_PER_COLOUR ,__UpperCamelCase )
A_ = NUM_COLOURS * (1 - missing_colour / total)
return f'''{result:.9f}'''
if __name__ == "__main__":
print(solution(20))
| 329 | 1 |
from math import isqrt, loga
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
A_ = [True] * max_number
for i in range(2 ,isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 ,__UpperCamelCase ,__UpperCamelCase ):
A_ = False
return [i for i in range(2 ,__UpperCamelCase ) if is_prime[i]]
def __snake_case ( __UpperCamelCase : int = 80_0800 ,__UpperCamelCase : int = 80_0800 ):
"""simple docstring"""
A_ = degree * loga(__UpperCamelCase )
A_ = int(__UpperCamelCase )
A_ = calculate_prime_numbers(__UpperCamelCase )
A_ = 0
A_ = 0
A_ = len(__UpperCamelCase ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(F"{solution() = }")
| 329 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__a :Optional[Any] = logging.get_logger(__name__)
__a :Any = {'vocab_file': 'vocab.txt'}
__a :Any = {
'vocab_file': {
'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
'YituTech/conv-bert-medium-small': (
'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
),
'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
}
}
__a :List[str] = {
'YituTech/conv-bert-base': 512,
'YituTech/conv-bert-medium-small': 512,
'YituTech/conv-bert-small': 512,
}
__a :List[str] = {
'YituTech/conv-bert-base': {'do_lower_case': True},
'YituTech/conv-bert-medium-small': {'do_lower_case': True},
'YituTech/conv-bert-small': {'do_lower_case': True},
}
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Tuple = VOCAB_FILES_NAMES
_lowerCamelCase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase : int = PRETRAINED_INIT_CONFIGURATION
_lowerCamelCase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase : Union[str, Any] = ConvBertTokenizer
def __init__( self : Optional[int] , UpperCAmelCase : Union[str, Any]=None , UpperCAmelCase : Union[str, Any]=None , UpperCAmelCase : Optional[Any]=True , UpperCAmelCase : int="[UNK]" , UpperCAmelCase : str="[SEP]" , UpperCAmelCase : Union[str, Any]="[PAD]" , UpperCAmelCase : Tuple="[CLS]" , UpperCAmelCase : Tuple="[MASK]" , UpperCAmelCase : Any=True , UpperCAmelCase : Union[str, Any]=None , **UpperCAmelCase : List[str] , ):
super().__init__(
UpperCAmelCase , tokenizer_file=UpperCAmelCase , do_lower_case=UpperCAmelCase , unk_token=UpperCAmelCase , sep_token=UpperCAmelCase , pad_token=UpperCAmelCase , cls_token=UpperCAmelCase , mask_token=UpperCAmelCase , tokenize_chinese_chars=UpperCAmelCase , strip_accents=UpperCAmelCase , **UpperCAmelCase , )
A_ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , UpperCAmelCase ) != do_lower_case
or normalizer_state.get("strip_accents" , UpperCAmelCase ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , UpperCAmelCase ) != tokenize_chinese_chars
):
A_ = getattr(UpperCAmelCase , normalizer_state.pop("type" ) )
A_ = do_lower_case
A_ = strip_accents
A_ = tokenize_chinese_chars
A_ = normalizer_class(**UpperCAmelCase )
A_ = do_lower_case
def __A ( self : List[str] , UpperCAmelCase : Any , UpperCAmelCase : Dict=None ):
A_ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __A ( self : Optional[Any] , UpperCAmelCase : List[int] , UpperCAmelCase : Optional[List[int]] = None ):
A_ = [self.sep_token_id]
A_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __A ( self : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : Optional[str] = None ):
A_ = self._tokenizer.model.save(UpperCAmelCase , name=UpperCAmelCase )
return tuple(UpperCAmelCase )
| 329 | 1 |
def __snake_case ( __UpperCamelCase : int = 6008_5147_5143 ):
"""simple docstring"""
try:
A_ = int(__UpperCamelCase )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
raise ValueError("Parameter n must be greater than or equal to one." )
A_ = 1
A_ = 2
while i * i <= n:
while n % i == 0:
A_ = i
n //= i
i += 1
if n > 1:
A_ = n
return int(__UpperCamelCase )
if __name__ == "__main__":
print(F"{solution() = }")
| 329 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__a :Optional[Any] = logging.get_logger(__name__)
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : List[str] , *UpperCAmelCase : int , **UpperCAmelCase : Optional[int] ):
warnings.warn(
"The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use VideoMAEImageProcessor instead." , UpperCAmelCase , )
super().__init__(*UpperCAmelCase , **UpperCAmelCase )
| 329 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
__a :Any = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
__a :Optional[Any] = TaTokenizerFast
__a :Dict = {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a :Dict = [
'MT5EncoderModel',
'MT5ForConditionalGeneration',
'MT5ForQuestionAnswering',
'MT5Model',
'MT5PreTrainedModel',
'MT5Stack',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a :List[Any] = ['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a :Any = ['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
__a :str = _LazyModule(
__name__,
globals()['__file__'],
_import_structure,
extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
module_spec=__spec__,
)
| 329 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _a :
"""simple docstring"""
@staticmethod
def __A ( *UpperCAmelCase : Union[str, Any] , **UpperCAmelCase : Union[str, Any] ):
pass
@is_pipeline_test
@require_vision
class _a ( unittest.TestCase ):
"""simple docstring"""
@require_torch
def __A ( self : List[str] ):
A_ = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , )
A_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
A_ = image_classifier(UpperCAmelCase , candidate_labels=["a", "b", "c"] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(UpperCAmelCase ) , [
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}],
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "c"}, {"score": 0.333, "label": "b"}],
] , )
A_ = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
] , )
@require_tf
def __A ( self : int ):
A_ = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , framework="tf" )
A_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
A_ = image_classifier(UpperCAmelCase , candidate_labels=["a", "b", "c"] )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}] , )
A_ = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
] , )
@slow
@require_torch
def __A ( self : Any ):
A_ = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , )
# This is an image of 2 cats with remotes and no planes
A_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
A_ = image_classifier(UpperCAmelCase , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
A_ = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , )
@slow
@require_tf
def __A ( self : Optional[Any] ):
A_ = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , framework="tf" )
# This is an image of 2 cats with remotes and no planes
A_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
A_ = image_classifier(UpperCAmelCase , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
A_ = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , )
| 329 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceClassification,
TFTrainer,
TFTrainingArguments,
)
from transformers.utils import logging as hf_logging
hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : str ,__UpperCamelCase : str ,__UpperCamelCase : PreTrainedTokenizer ,__UpperCamelCase : int ,__UpperCamelCase : Optional[int] = None ,):
"""simple docstring"""
A_ = {}
if train_file is not None:
A_ = [train_file]
if eval_file is not None:
A_ = [eval_file]
if test_file is not None:
A_ = [test_file]
A_ = datasets.load_dataset("csv" ,data_files=__UpperCamelCase )
A_ = list(ds[list(files.keys() )[0]].features.keys() )
A_ = features_name.pop(__UpperCamelCase )
A_ = list(set(ds[list(files.keys() )[0]][label_name] ) )
A_ = {label: i for i, label in enumerate(__UpperCamelCase )}
A_ = tokenizer.model_input_names
A_ = {}
if len(__UpperCamelCase ) == 1:
for k in files.keys():
A_ = ds[k].map(
lambda __UpperCamelCase : tokenizer.batch_encode_plus(
example[features_name[0]] ,truncation=__UpperCamelCase ,max_length=__UpperCamelCase ,padding="max_length" ) ,batched=__UpperCamelCase ,)
elif len(__UpperCamelCase ) == 2:
for k in files.keys():
A_ = ds[k].map(
lambda __UpperCamelCase : tokenizer.batch_encode_plus(
(example[features_name[0]], example[features_name[1]]) ,truncation=__UpperCamelCase ,max_length=__UpperCamelCase ,padding="max_length" ,) ,batched=__UpperCamelCase ,)
def gen_train():
for ex in transformed_ds[datasets.Split.TRAIN]:
A_ = {k: v for k, v in ex.items() if k in input_names}
A_ = labelaid[ex[label_name]]
yield (d, label)
def gen_val():
for ex in transformed_ds[datasets.Split.VALIDATION]:
A_ = {k: v for k, v in ex.items() if k in input_names}
A_ = labelaid[ex[label_name]]
yield (d, label)
def gen_test():
for ex in transformed_ds[datasets.Split.TEST]:
A_ = {k: v for k, v in ex.items() if k in input_names}
A_ = labelaid[ex[label_name]]
yield (d, label)
A_ = (
tf.data.Dataset.from_generator(
__UpperCamelCase ,({k: tf.intaa for k in input_names}, tf.intaa) ,({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) ,)
if datasets.Split.TRAIN in transformed_ds
else None
)
if train_ds is not None:
A_ = train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )
A_ = (
tf.data.Dataset.from_generator(
__UpperCamelCase ,({k: tf.intaa for k in input_names}, tf.intaa) ,({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) ,)
if datasets.Split.VALIDATION in transformed_ds
else None
)
if val_ds is not None:
A_ = val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )
A_ = (
tf.data.Dataset.from_generator(
__UpperCamelCase ,({k: tf.intaa for k in input_names}, tf.intaa) ,({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) ,)
if datasets.Split.TEST in transformed_ds
else None
)
if test_ds is not None:
A_ = test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )
return train_ds, val_ds, test_ds, labelaid
__a :Dict = logging.getLogger(__name__)
@dataclass
class _a :
"""simple docstring"""
_lowerCamelCase : int = field(metadata={'help': 'Which column contains the label'} )
_lowerCamelCase : str = field(default=snake_case_ , metadata={'help': 'The path of the training file'} )
_lowerCamelCase : Optional[str] = field(default=snake_case_ , metadata={'help': 'The path of the development file'} )
_lowerCamelCase : Optional[str] = field(default=snake_case_ , metadata={'help': 'The path of the test file'} )
_lowerCamelCase : int = field(
default=1_2_8 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
_lowerCamelCase : bool = field(
default=snake_case_ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
@dataclass
class _a :
"""simple docstring"""
_lowerCamelCase : str = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
_lowerCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
_lowerCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
_lowerCamelCase : bool = field(default=snake_case_ , metadata={'help': 'Set this flag to use fast tokenization.'} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_lowerCamelCase : Optional[str] = field(
default=snake_case_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
def __snake_case ( ):
"""simple docstring"""
A_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
A_ , A_ , A_ = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
" --overwrite_output_dir to overcome." )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" ,datefmt="%m/%d/%Y %H:%M:%S" ,level=logging.INFO ,)
logger.info(
f'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1 )}, '''
f'''16-bits training: {training_args.fpaa}''' )
logger.info(f'''Training/evaluation parameters {training_args}''' )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
A_ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,cache_dir=model_args.cache_dir ,)
A_ , A_ , A_ , A_ = get_tfds(
train_file=data_args.train_file ,eval_file=data_args.dev_file ,test_file=data_args.test_file ,tokenizer=__UpperCamelCase ,label_column_id=data_args.label_column_id ,max_seq_length=data_args.max_seq_length ,)
A_ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path ,num_labels=len(__UpperCamelCase ) ,labelaid=__UpperCamelCase ,idalabel={id: label for label, id in labelaid.items()} ,finetuning_task="text-classification" ,cache_dir=model_args.cache_dir ,)
with training_args.strategy.scope():
A_ = TFAutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path ,from_pt=bool(".bin" in model_args.model_name_or_path ) ,config=__UpperCamelCase ,cache_dir=model_args.cache_dir ,)
def compute_metrics(__UpperCamelCase : EvalPrediction ) -> Dict:
A_ = np.argmax(p.predictions ,axis=1 )
return {"acc": (preds == p.label_ids).mean()}
# Initialize our Trainer
A_ = TFTrainer(
model=__UpperCamelCase ,args=__UpperCamelCase ,train_dataset=__UpperCamelCase ,eval_dataset=__UpperCamelCase ,compute_metrics=__UpperCamelCase ,)
# Training
if training_args.do_train:
trainer.train()
trainer.save_model()
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
A_ = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
A_ = trainer.evaluate()
A_ = os.path.join(training_args.output_dir ,"eval_results.txt" )
with open(__UpperCamelCase ,"w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in result.items():
logger.info(f''' {key} = {value}''' )
writer.write(f'''{key} = {value}\n''' )
results.update(__UpperCamelCase )
return results
if __name__ == "__main__":
main()
| 329 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers 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_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def __snake_case ( __UpperCamelCase : Optional[Any] ,__UpperCamelCase : Dict=10 ):
"""simple docstring"""
A_ = []
for _ in range(__UpperCamelCase ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
return lrs
def __snake_case ( __UpperCamelCase : Any ,__UpperCamelCase : Tuple=10 ):
"""simple docstring"""
A_ = []
for step in range(__UpperCamelCase ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
if step == num_steps // 2:
with tempfile.TemporaryDirectory() as tmpdirname:
A_ = os.path.join(__UpperCamelCase ,"schedule.bin" )
torch.save(scheduler.state_dict() ,__UpperCamelCase )
A_ = torch.load(__UpperCamelCase )
scheduler.load_state_dict(__UpperCamelCase )
return lrs
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : Any , UpperCAmelCase : int , UpperCAmelCase : List[Any] , UpperCAmelCase : List[str] ):
self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) )
for a, b in zip(UpperCAmelCase , UpperCAmelCase ):
self.assertAlmostEqual(UpperCAmelCase , UpperCAmelCase , delta=UpperCAmelCase )
def __A ( self : List[Any] ):
A_ = torch.tensor([0.1, -0.2, -0.1] , requires_grad=UpperCAmelCase )
A_ = torch.tensor([0.4, 0.2, -0.5] )
A_ = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
A_ = AdamW(params=[w] , lr=2E-1 , weight_decay=0.0 )
for _ in range(100 ):
A_ = criterion(UpperCAmelCase , UpperCAmelCase )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
def __A ( self : Dict ):
A_ = torch.tensor([0.1, -0.2, -0.1] , requires_grad=UpperCAmelCase )
A_ = torch.tensor([0.4, 0.2, -0.5] )
A_ = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
A_ = Adafactor(
params=[w] , lr=1E-2 , eps=(1E-30, 1E-3) , clip_threshold=1.0 , decay_rate=-0.8 , betaa=UpperCAmelCase , weight_decay=0.0 , relative_step=UpperCAmelCase , scale_parameter=UpperCAmelCase , warmup_init=UpperCAmelCase , )
for _ in range(1000 ):
A_ = criterion(UpperCAmelCase , UpperCAmelCase )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = nn.Linear(5_0 , 5_0 ) if is_torch_available() else None
_lowerCamelCase : Any = AdamW(m.parameters() , lr=1_0.0 ) if is_torch_available() else None
_lowerCamelCase : Any = 1_0
def __A ( self : str , UpperCAmelCase : int , UpperCAmelCase : Any , UpperCAmelCase : Tuple , UpperCAmelCase : Dict=None ):
self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) )
for a, b in zip(UpperCAmelCase , UpperCAmelCase ):
self.assertAlmostEqual(UpperCAmelCase , UpperCAmelCase , delta=UpperCAmelCase , msg=UpperCAmelCase )
def __A ( self : List[Any] ):
A_ = {"num_warmup_steps": 2, "num_training_steps": 10}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
A_ = {
get_constant_schedule: ({}, [10.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{"num_warmup_steps": 4},
[0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, "num_cycles": 2},
[0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, "power": 2.0, "lr_end": 1E-7},
[0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
),
get_inverse_sqrt_schedule: (
{"num_warmup_steps": 2},
[0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
),
}
for scheduler_func, data in scheds.items():
A_ , A_ = data
A_ = scheduler_func(self.optimizer , **UpperCAmelCase )
self.assertEqual(len([scheduler.get_lr()[0]] ) , 1 )
A_ = unwrap_schedule(UpperCAmelCase , self.num_steps )
self.assertListAlmostEqual(
UpperCAmelCase , UpperCAmelCase , tol=1E-2 , msg=f'''failed for {scheduler_func} in normal scheduler''' , )
A_ = scheduler_func(self.optimizer , **UpperCAmelCase )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(UpperCAmelCase ) # wrap to test picklability of the schedule
A_ = unwrap_and_save_reload_schedule(UpperCAmelCase , self.num_steps )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase , msg=f'''failed for {scheduler_func} in save and reload''' )
class _a :
"""simple docstring"""
def __init__( self : List[str] , UpperCAmelCase : List[str] ):
A_ = fn
def __call__( self : Union[str, Any] , *UpperCAmelCase : str , **UpperCAmelCase : Optional[Any] ):
return self.fn(*UpperCAmelCase , **UpperCAmelCase )
@classmethod
def __A ( self : Dict , UpperCAmelCase : List[str] ):
A_ = list(map(self , scheduler.lr_lambdas ) )
| 329 | 1 |
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
__a :Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
__a :Any = '\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_ ):
"""simple docstring"""
_lowerCamelCase : Union[PIL.Image.Image, np.ndarray]
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : List[Any] , UpperCAmelCase : PriorTransformer , UpperCAmelCase : CLIPVisionModel , UpperCAmelCase : CLIPImageProcessor , UpperCAmelCase : HeunDiscreteScheduler , UpperCAmelCase : ShapERenderer , ):
super().__init__()
self.register_modules(
prior=UpperCAmelCase , image_encoder=UpperCAmelCase , image_processor=UpperCAmelCase , scheduler=UpperCAmelCase , renderer=UpperCAmelCase , )
def __A ( self : List[Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[str] , UpperCAmelCase : int ):
if latents is None:
A_ = randn_tensor(UpperCAmelCase , generator=UpperCAmelCase , device=UpperCAmelCase , dtype=UpperCAmelCase )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
A_ = latents.to(UpperCAmelCase )
A_ = latents * scheduler.init_noise_sigma
return latents
def __A ( self : int , UpperCAmelCase : Optional[Any]=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
A_ = torch.device(f'''cuda:{gpu_id}''' )
A_ = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(UpperCAmelCase , UpperCAmelCase )
@property
def __A ( self : List[Any] ):
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(UpperCAmelCase , "_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 __A ( self : int , UpperCAmelCase : Any , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : Dict , ):
if isinstance(UpperCAmelCase , UpperCAmelCase ) and isinstance(image[0] , torch.Tensor ):
A_ = torch.cat(UpperCAmelCase , axis=0 ) if image[0].ndim == 4 else torch.stack(UpperCAmelCase , axis=0 )
if not isinstance(UpperCAmelCase , torch.Tensor ):
A_ = self.image_processor(UpperCAmelCase , return_tensors="pt" ).pixel_values[0].unsqueeze(0 )
A_ = image.to(dtype=self.image_encoder.dtype , device=UpperCAmelCase )
A_ = self.image_encoder(UpperCAmelCase )["last_hidden_state"]
A_ = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
A_ = image_embeds.repeat_interleave(UpperCAmelCase , dim=0 )
if do_classifier_free_guidance:
A_ = torch.zeros_like(UpperCAmelCase )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
A_ = torch.cat([negative_image_embeds, image_embeds] )
return image_embeds
@torch.no_grad()
@replace_example_docstring(UpperCAmelCase )
def __call__( self : Optional[int] , UpperCAmelCase : Union[PIL.Image.Image, List[PIL.Image.Image]] , UpperCAmelCase : int = 1 , UpperCAmelCase : int = 25 , UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , UpperCAmelCase : Optional[torch.FloatTensor] = None , UpperCAmelCase : float = 4.0 , UpperCAmelCase : int = 64 , UpperCAmelCase : Optional[str] = "pil" , UpperCAmelCase : bool = True , ):
if isinstance(UpperCAmelCase , PIL.Image.Image ):
A_ = 1
elif isinstance(UpperCAmelCase , torch.Tensor ):
A_ = image.shape[0]
elif isinstance(UpperCAmelCase , UpperCAmelCase ) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image) ):
A_ = len(UpperCAmelCase )
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(UpperCAmelCase )}''' )
A_ = self._execution_device
A_ = batch_size * num_images_per_prompt
A_ = guidance_scale > 1.0
A_ = self._encode_image(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# prior
self.scheduler.set_timesteps(UpperCAmelCase , device=UpperCAmelCase )
A_ = self.scheduler.timesteps
A_ = self.prior.config.num_embeddings
A_ = self.prior.config.embedding_dim
A_ = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , 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
A_ = latents.reshape(latents.shape[0] , UpperCAmelCase , UpperCAmelCase )
for i, t in enumerate(self.progress_bar(UpperCAmelCase ) ):
# expand the latents if we are doing classifier free guidance
A_ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
A_ = self.scheduler.scale_model_input(UpperCAmelCase , UpperCAmelCase )
A_ = self.prior(
UpperCAmelCase , timestep=UpperCAmelCase , proj_embedding=UpperCAmelCase , ).predicted_image_embedding
# remove the variance
A_ , A_ = noise_pred.split(
scaled_model_input.shape[2] , dim=2 ) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
A_ , A_ = noise_pred.chunk(2 )
A_ = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
A_ = self.scheduler.step(
UpperCAmelCase , timestep=UpperCAmelCase , sample=UpperCAmelCase , ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=UpperCAmelCase )
A_ = []
for i, latent in enumerate(UpperCAmelCase ):
print()
A_ = self.renderer.decode(
latent[None, :] , UpperCAmelCase , size=UpperCAmelCase , ray_batch_size=4096 , n_coarse_samples=64 , n_fine_samples=128 , )
images.append(UpperCAmelCase )
A_ = torch.stack(UpperCAmelCase )
if output_type not in ["np", "pil"]:
raise ValueError(f'''Only the output types `pil` and `np` are supported not output_type={output_type}''' )
A_ = images.cpu().numpy()
if output_type == "pil":
A_ = [self.numpy_to_pil(UpperCAmelCase ) 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=UpperCAmelCase )
| 329 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp_seed,
temporary_assignment,
zip_dict,
)
from .utils import require_tf, require_torch
def __snake_case ( __UpperCamelCase : Optional[int] ): # picklable for multiprocessing
"""simple docstring"""
return x.sum()
def __snake_case ( __UpperCamelCase : List[str] ): # picklable for multiprocessing
"""simple docstring"""
return i + 1
@dataclass
class _a :
"""simple docstring"""
_lowerCamelCase : int
_lowerCamelCase : str
class _a ( snake_case_ ):
"""simple docstring"""
def __A ( self : Dict ):
A_ = {}
A_ = []
A_ = 1
A_ = [1, 2]
A_ = {"a": 1, "b": 2}
A_ = {"a": [1, 2], "b": [3, 4]}
A_ = {"a": {"1": 1}, "b": 2}
A_ = {"a": 1, "b": 2, "c": 3, "d": 4}
A_ = {}
A_ = []
A_ = 2
A_ = [2, 3]
A_ = {"a": 2, "b": 3}
A_ = {"a": [2, 3], "b": [4, 5]}
A_ = {"a": {"1": 2}, "b": 3}
A_ = {"a": 2, "b": 3, "c": 4, "d": 5}
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
A_ = 2
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
A_ = {"a": np.eye(2 ), "b": np.zeros(3 ), "c": np.ones(2 )}
A_ = {"a": 2, "b": 0, "c": 2}
A_ = {
"a": np.eye(2 ).astype(UpperCAmelCase ),
"b": np.zeros(3 ).astype(UpperCAmelCase ),
"c": np.ones(2 ).astype(UpperCAmelCase ),
}
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , map_numpy=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(UpperCAmelCase , UpperCAmelCase , map_numpy=UpperCAmelCase ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , map_numpy=UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(UpperCAmelCase , UpperCAmelCase , map_numpy=UpperCAmelCase , num_proc=UpperCAmelCase ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
with self.assertRaises(UpperCAmelCase ): # can't pickle a local lambda
map_nested(lambda UpperCAmelCase : x + 1 , UpperCAmelCase , num_proc=UpperCAmelCase )
def __A ( self : List[str] ):
A_ = {"a": 1, "b": 2}
A_ = {"a": 3, "b": 4}
A_ = {"a": 5, "b": 6}
A_ = sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))] )
self.assertEqual(sorted(zip_dict(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ) , UpperCAmelCase )
def __A ( self : Any ):
class _a :
"""simple docstring"""
_lowerCamelCase : int = 'bar'
A_ = Foo()
self.assertEqual(foo.my_attr , "bar" )
with temporary_assignment(UpperCAmelCase , "my_attr" , "BAR" ):
self.assertEqual(foo.my_attr , "BAR" )
self.assertEqual(foo.my_attr , "bar" )
@pytest.mark.parametrize(
"iterable_length, num_proc, expected_num_proc" ,[
(1, None, 1),
(1, 1, 1),
(2, None, 1),
(2, 1, 1),
(2, 2, 1),
(2, 3, 1),
(3, 2, 1),
(16, 16, 16),
(16, 17, 16),
(17, 16, 16),
] ,)
def __snake_case ( __UpperCamelCase : Optional[int] ,__UpperCamelCase : Tuple ,__UpperCamelCase : List[Any] ):
"""simple docstring"""
with patch("datasets.utils.py_utils._single_map_nested" ) as mock_single_map_nested, patch(
"datasets.parallel.parallel.Pool" ) as mock_multiprocessing_pool:
A_ = {f'''{i}''': i for i in range(__UpperCamelCase )}
A_ = map_nested(lambda __UpperCamelCase : x + 10 ,__UpperCamelCase ,num_proc=__UpperCamelCase ,parallel_min_length=16 )
if expected_num_proc == 1:
assert mock_single_map_nested.called
assert not mock_multiprocessing_pool.called
else:
assert not mock_single_map_nested.called
assert mock_multiprocessing_pool.called
assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc
class _a ( snake_case_ ):
"""simple docstring"""
@require_tf
def __A ( self : Union[str, Any] ):
import tensorflow as tf
from tensorflow.keras import layers
A_ = layers.Dense(2 )
def gen_random_output():
A_ = tf.random.uniform((1, 3) )
return model(UpperCAmelCase ).numpy()
with temp_seed(42 , set_tensorflow=UpperCAmelCase ):
A_ = gen_random_output()
with temp_seed(42 , set_tensorflow=UpperCAmelCase ):
A_ = gen_random_output()
A_ = gen_random_output()
np.testing.assert_equal(UpperCAmelCase , UpperCAmelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@require_torch
def __A ( self : Optional[int] ):
import torch
def gen_random_output():
A_ = torch.nn.Linear(3 , 2 )
A_ = torch.rand(1 , 3 )
return model(UpperCAmelCase ).detach().numpy()
with temp_seed(42 , set_pytorch=UpperCAmelCase ):
A_ = gen_random_output()
with temp_seed(42 , set_pytorch=UpperCAmelCase ):
A_ = gen_random_output()
A_ = gen_random_output()
np.testing.assert_equal(UpperCAmelCase , UpperCAmelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
def __A ( self : Any ):
def gen_random_output():
return np.random.rand(1 , 3 )
with temp_seed(42 ):
A_ = gen_random_output()
with temp_seed(42 ):
A_ = gen_random_output()
A_ = gen_random_output()
np.testing.assert_equal(UpperCAmelCase , UpperCAmelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@pytest.mark.parametrize("input_data" ,[{}] )
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
A_ = NestedDataStructure(__UpperCamelCase ).data
assert output_data == input_data
@pytest.mark.parametrize(
"data, expected_output" ,[
({}, []),
([], []),
("foo", ["foo"]),
(["foo", "bar"], ["foo", "bar"]),
([["foo", "bar"]], ["foo", "bar"]),
([[["foo"], ["bar"]]], ["foo", "bar"]),
([[["foo"], "bar"]], ["foo", "bar"]),
({"a": 1, "b": 2}, [1, 2]),
({"a": [1, 2], "b": [3, 4]}, [1, 2, 3, 4]),
({"a": [[1, 2]], "b": [[3, 4]]}, [1, 2, 3, 4]),
({"a": [[1, 2]], "b": [3, 4]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [[[3], [4]]]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [[3, 4]]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [3, 4]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [3, [4]]}, [1, 2, 3, 4]),
({"a": {"1": 1}, "b": 2}, [1, 2]),
({"a": {"1": [1]}, "b": 2}, [1, 2]),
({"a": {"1": [1]}, "b": [2]}, [1, 2]),
] ,)
def __snake_case ( __UpperCamelCase : Dict ,__UpperCamelCase : Any ):
"""simple docstring"""
A_ = NestedDataStructure(__UpperCamelCase ).flatten()
assert output == expected_output
def __snake_case ( ):
"""simple docstring"""
A_ = A(x=1 ,y="foobar" )
A_ = {"x": 1, "y": "foobar"}
assert asdict(__UpperCamelCase ) == expected_output
A_ = {"a": {"b": A(x=10 ,y="foo" )}, "c": [A(x=20 ,y="bar" )]}
A_ = {"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
assert asdict(__UpperCamelCase ) == expected_output
with pytest.raises(__UpperCamelCase ):
asdict([1, A(x=10 ,y="foo" )] )
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
return text.split()
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
yield (time.time(), content)
time.sleep(2 )
yield (time.time(), content)
def __snake_case ( ):
"""simple docstring"""
with Pool(2 ) as pool:
A_ = list(iflatmap_unordered(__UpperCamelCase ,_split_text ,kwargs_iterable=[{"text": "hello there"}] * 10 ) )
assert out.count("hello" ) == 10
assert out.count("there" ) == 10
assert len(__UpperCamelCase ) == 20
# check multiprocess from pathos (uses dill for pickling)
with multiprocess.Pool(2 ) as pool:
A_ = list(iflatmap_unordered(__UpperCamelCase ,_split_text ,kwargs_iterable=[{"text": "hello there"}] * 10 ) )
assert out.count("hello" ) == 10
assert out.count("there" ) == 10
assert len(__UpperCamelCase ) == 20
# check that we get items as fast as possible
with Pool(2 ) as pool:
A_ = []
for yield_time, content in iflatmap_unordered(
__UpperCamelCase ,_aseconds_generator_of_aitems_with_timing ,kwargs_iterable=[{"content": "a"}, {"content": "b"}] ):
assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
out.append(__UpperCamelCase )
assert out.count("a" ) == 2
assert out.count("b" ) == 2
assert len(__UpperCamelCase ) == 4
| 329 | 1 |
from maths.prime_factors import prime_factors
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = f'''Input value of [number={number}] must be an integer'''
raise TypeError(__UpperCamelCase )
if number < 1:
raise ValueError("Input must be a positive integer" )
return -1 if len(prime_factors(__UpperCamelCase ) ) % 2 else 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 329 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F) #
or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F) #
or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F) #
or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F) #
or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F) #
or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F) #
): #
return True
return False
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
for char in word:
A_ = ord(__UpperCamelCase )
if not _is_chinese_char(__UpperCamelCase ):
return 0
return 1
def __snake_case ( __UpperCamelCase : List[str] ):
"""simple docstring"""
A_ = set()
for token in tokens:
A_ = len(__UpperCamelCase ) > 1 and is_chinese(__UpperCamelCase )
if chinese_word:
word_set.add(__UpperCamelCase )
A_ = list(__UpperCamelCase )
return word_list
def __snake_case ( __UpperCamelCase : List[str] ,__UpperCamelCase : set() ):
"""simple docstring"""
if not chinese_word_set:
return bert_tokens
A_ = max([len(__UpperCamelCase ) for w in chinese_word_set] )
A_ = bert_tokens
A_ , A_ = 0, len(__UpperCamelCase )
while start < end:
A_ = True
if is_chinese(bert_word[start] ):
A_ = min(end - start ,__UpperCamelCase )
for i in range(__UpperCamelCase ,1 ,-1 ):
A_ = "".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 ,start + i ):
A_ = "##" + bert_word[j]
A_ = start + i
A_ = False
break
if single_word:
start += 1
return bert_word
def __snake_case ( __UpperCamelCase : List[str] ,__UpperCamelCase : LTP ,__UpperCamelCase : BertTokenizer ):
"""simple docstring"""
A_ = []
for i in range(0 ,len(__UpperCamelCase ) ,100 ):
A_ = ltp_tokenizer.seg(lines[i : i + 100] )[0]
A_ = [get_chinese_word(__UpperCamelCase ) for r in res]
ltp_res.extend(__UpperCamelCase )
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
A_ = []
for i in range(0 ,len(__UpperCamelCase ) ,100 ):
A_ = bert_tokenizer(lines[i : i + 100] ,add_special_tokens=__UpperCamelCase ,truncation=__UpperCamelCase ,max_length=512 )
bert_res.extend(res["input_ids"] )
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
A_ = []
for input_ids, chinese_word in zip(__UpperCamelCase ,__UpperCamelCase ):
A_ = []
for id in input_ids:
A_ = bert_tokenizer._convert_id_to_token(__UpperCamelCase )
input_tokens.append(__UpperCamelCase )
A_ = add_sub_symbol(__UpperCamelCase ,__UpperCamelCase )
A_ = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__UpperCamelCase ):
if token[:2] == "##":
A_ = token[2:]
# save chinese tokens' pos
if len(__UpperCamelCase ) == 1 and _is_chinese_char(ord(__UpperCamelCase ) ):
ref_id.append(__UpperCamelCase )
ref_ids.append(__UpperCamelCase )
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
return ref_ids
def __snake_case ( __UpperCamelCase : Dict ):
"""simple docstring"""
with open(args.file_name ,"r" ,encoding="utf-8" ) as f:
A_ = f.readlines()
A_ = [line.strip() for line in data if len(__UpperCamelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
A_ = LTP(args.ltp ) # faster in GPU device
A_ = BertTokenizer.from_pretrained(args.bert )
A_ = prepare_ref(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
with open(args.save_path ,"w" ,encoding="utf-8" ) as f:
A_ = [json.dumps(__UpperCamelCase ) + "\n" for ref in ref_ids]
f.writelines(__UpperCamelCase )
if __name__ == "__main__":
__a :List[Any] = argparse.ArgumentParser(description='prepare_chinese_ref')
parser.add_argument(
'--file_name',
type=str,
default='./resources/chinese-demo.txt',
help='file need process, same as training data in lm',
)
parser.add_argument(
'--ltp', type=str, default='./resources/ltp', help='resources for LTP tokenizer, usually a path'
)
parser.add_argument('--bert', type=str, default='./resources/robert', help='resources for Bert tokenizer')
parser.add_argument('--save_path', type=str, default='./resources/ref.txt', help='path to save res')
__a :Dict = parser.parse_args()
main(args)
| 329 | 1 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class _a :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Union[str, Any]=13 , UpperCAmelCase : Tuple=7 , UpperCAmelCase : Tuple=True , UpperCAmelCase : Tuple=True , UpperCAmelCase : Union[str, Any]=True , UpperCAmelCase : List[Any]=True , UpperCAmelCase : Tuple=99 , UpperCAmelCase : List[Any]=24 , UpperCAmelCase : List[Any]=2 , UpperCAmelCase : Optional[int]=6 , UpperCAmelCase : List[Any]=37 , UpperCAmelCase : List[str]="gelu" , UpperCAmelCase : Union[str, Any]=0.1 , UpperCAmelCase : List[str]=0.1 , UpperCAmelCase : Tuple=512 , UpperCAmelCase : Dict=16 , UpperCAmelCase : int=2 , UpperCAmelCase : Dict=0.02 , UpperCAmelCase : List[Any]=3 , UpperCAmelCase : int=None , UpperCAmelCase : List[Any]=1000 , ):
A_ = parent
A_ = batch_size
A_ = seq_length
A_ = is_training
A_ = use_input_mask
A_ = use_token_type_ids
A_ = use_labels
A_ = vocab_size
A_ = hidden_size
A_ = num_hidden_layers
A_ = num_attention_heads
A_ = intermediate_size
A_ = hidden_act
A_ = hidden_dropout_prob
A_ = attention_probs_dropout_prob
A_ = max_position_embeddings
A_ = type_vocab_size
A_ = type_sequence_label_size
A_ = initializer_range
A_ = num_labels
A_ = scope
A_ = range_bbox
def __A ( self : str ):
A_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
A_ = bbox[i, j, 3]
A_ = bbox[i, j, 1]
A_ = t
if bbox[i, j, 2] < bbox[i, j, 0]:
A_ = bbox[i, j, 2]
A_ = bbox[i, j, 0]
A_ = t
A_ = None
if self.use_input_mask:
A_ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
A_ = None
if self.use_token_type_ids:
A_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
A_ = None
A_ = None
if self.use_labels:
A_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A_ = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def __A ( self : int ):
return LiltConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def __A ( self : Tuple , UpperCAmelCase : List[str] , UpperCAmelCase : Tuple , UpperCAmelCase : List[Any] , UpperCAmelCase : str , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , ):
A_ = LiltModel(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
A_ = model(UpperCAmelCase , bbox=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase )
A_ = model(UpperCAmelCase , bbox=UpperCAmelCase , token_type_ids=UpperCAmelCase )
A_ = model(UpperCAmelCase , bbox=UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __A ( self : List[str] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , UpperCAmelCase : int , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str , ):
A_ = self.num_labels
A_ = LiltForTokenClassification(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
A_ = model(
UpperCAmelCase , bbox=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __A ( self : int , UpperCAmelCase : Tuple , UpperCAmelCase : Tuple , UpperCAmelCase : Any , UpperCAmelCase : Any , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any] , ):
A_ = LiltForQuestionAnswering(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
A_ = model(
UpperCAmelCase , bbox=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __A ( self : Dict ):
A_ = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) = config_and_inputs
A_ = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class _a ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase : int = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
_lowerCamelCase : Union[str, Any] = (
{
'feature-extraction': LiltModel,
'question-answering': LiltForQuestionAnswering,
'text-classification': LiltForSequenceClassification,
'token-classification': LiltForTokenClassification,
'zero-shot': LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
_lowerCamelCase : Union[str, Any] = False
_lowerCamelCase : Any = False
def __A ( self : Optional[int] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[int] ):
return True
def __A ( self : Dict ):
A_ = LiltModelTester(self )
A_ = ConfigTester(self , config_class=UpperCAmelCase , hidden_size=37 )
def __A ( self : Optional[int] ):
self.config_tester.run_common_tests()
def __A ( self : Union[str, Any] ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase )
def __A ( self : int ):
A_ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
A_ = type
self.model_tester.create_and_check_model(*UpperCAmelCase )
def __A ( self : int ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase )
def __A ( self : Union[str, Any] ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase )
@slow
def __A ( self : List[str] ):
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ = LiltModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
@require_torch
@slow
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : Any ):
A_ = LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base" ).to(UpperCAmelCase )
A_ = torch.tensor([[1, 2]] , device=UpperCAmelCase )
A_ = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=UpperCAmelCase )
# forward pass
with torch.no_grad():
A_ = model(input_ids=UpperCAmelCase , bbox=UpperCAmelCase )
A_ = torch.Size([1, 2, 768] )
A_ = torch.tensor(
[[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]] , device=UpperCAmelCase , )
self.assertTrue(outputs.last_hidden_state.shape , UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , UpperCAmelCase , atol=1E-3 ) )
| 329 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def __snake_case ( __UpperCamelCase : Features ):
"""simple docstring"""
A_ = np.inf
def set_batch_size(__UpperCamelCase : FeatureType ) -> None:
nonlocal batch_size
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = min(__UpperCamelCase ,config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = min(__UpperCamelCase ,config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(__UpperCamelCase ,__UpperCamelCase ) and feature.dtype == "binary":
A_ = min(__UpperCamelCase ,config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(__UpperCamelCase ,__UpperCamelCase )
return None if batch_size is np.inf else batch_size
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : Tuple , UpperCAmelCase : NestedDataStructureLike[PathLike] , UpperCAmelCase : Optional[NamedSplit] = None , UpperCAmelCase : Optional[Features] = None , UpperCAmelCase : str = None , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : Optional[int] = None , **UpperCAmelCase : Tuple , ):
super().__init__(
UpperCAmelCase , split=UpperCAmelCase , features=UpperCAmelCase , cache_dir=UpperCAmelCase , keep_in_memory=UpperCAmelCase , streaming=UpperCAmelCase , num_proc=UpperCAmelCase , **UpperCAmelCase , )
A_ = path_or_paths if isinstance(UpperCAmelCase , UpperCAmelCase ) else {self.split: path_or_paths}
A_ = _PACKAGED_DATASETS_MODULES["parquet"][1]
A_ = Parquet(
cache_dir=UpperCAmelCase , data_files=UpperCAmelCase , features=UpperCAmelCase , hash=UpperCAmelCase , **UpperCAmelCase , )
def __A ( self : Optional[Any] ):
# Build iterable dataset
if self.streaming:
A_ = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
A_ = None
A_ = None
A_ = None
A_ = None
self.builder.download_and_prepare(
download_config=UpperCAmelCase , download_mode=UpperCAmelCase , verification_mode=UpperCAmelCase , base_path=UpperCAmelCase , num_proc=self.num_proc , )
A_ = self.builder.as_dataset(
split=self.split , verification_mode=UpperCAmelCase , in_memory=self.keep_in_memory )
return dataset
class _a :
"""simple docstring"""
def __init__( self : Any , UpperCAmelCase : Dataset , UpperCAmelCase : Union[PathLike, BinaryIO] , UpperCAmelCase : Optional[int] = None , **UpperCAmelCase : List[Any] , ):
A_ = dataset
A_ = path_or_buf
A_ = batch_size or get_writer_batch_size(dataset.features )
A_ = parquet_writer_kwargs
def __A ( self : int ):
A_ = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with open(self.path_or_buf , "wb+" ) as buffer:
A_ = self._write(file_obj=UpperCAmelCase , batch_size=UpperCAmelCase , **self.parquet_writer_kwargs )
else:
A_ = self._write(file_obj=self.path_or_buf , batch_size=UpperCAmelCase , **self.parquet_writer_kwargs )
return written
def __A ( self : Tuple , UpperCAmelCase : BinaryIO , UpperCAmelCase : int , **UpperCAmelCase : Optional[Any] ):
A_ = 0
A_ = parquet_writer_kwargs.pop("path_or_buf" , UpperCAmelCase )
A_ = self.dataset.features.arrow_schema
A_ = pq.ParquetWriter(UpperCAmelCase , schema=UpperCAmelCase , **UpperCAmelCase )
for offset in logging.tqdm(
range(0 , len(self.dataset ) , UpperCAmelCase ) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating parquet from Arrow format" , ):
A_ = query_table(
table=self.dataset._data , key=slice(UpperCAmelCase , offset + batch_size ) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(UpperCAmelCase )
written += batch.nbytes
writer.close()
return written
| 329 | 1 |
from __future__ import annotations
from typing import Any
class _a :
"""simple docstring"""
def __init__( self : str , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : float = 0 ):
A_ , A_ = row, column
A_ = [[default_value for c in range(UpperCAmelCase )] for r in range(UpperCAmelCase )]
def __str__( self : List[Any] ):
A_ = f'''Matrix consist of {self.row} rows and {self.column} columns\n'''
# Make string identifier
A_ = 0
for row_vector in self.array:
for obj in row_vector:
A_ = max(UpperCAmelCase , len(str(UpperCAmelCase ) ) )
A_ = f'''%{max_element_length}s'''
# Make string and return
def single_line(UpperCAmelCase : list[float] ) -> str:
nonlocal string_format_identifier
A_ = "["
line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
line += "]"
return line
s += "\n".join(single_line(UpperCAmelCase ) for row_vector in self.array )
return s
def __repr__( self : Dict ):
return str(self )
def __A ( self : Dict , UpperCAmelCase : tuple[int, int] ):
if not (isinstance(UpperCAmelCase , (list, tuple) ) and len(UpperCAmelCase ) == 2):
return False
elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
return False
else:
return True
def __getitem__( self : Dict , UpperCAmelCase : tuple[int, int] ):
assert self.validate_indicies(UpperCAmelCase )
return self.array[loc[0]][loc[1]]
def __setitem__( self : Tuple , UpperCAmelCase : tuple[int, int] , UpperCAmelCase : float ):
assert self.validate_indicies(UpperCAmelCase )
A_ = value
def __add__( self : Optional[int] , UpperCAmelCase : Matrix ):
assert isinstance(UpperCAmelCase , UpperCAmelCase )
assert self.row == another.row and self.column == another.column
# Add
A_ = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
A_ = self[r, c] + another[r, c]
return result
def __neg__( self : List[str] ):
A_ = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
A_ = -self[r, c]
return result
def __sub__( self : str , UpperCAmelCase : Matrix ):
return self + (-another)
def __mul__( self : Union[str, Any] , UpperCAmelCase : int | float | Matrix ):
if isinstance(UpperCAmelCase , (int, float) ): # Scalar multiplication
A_ = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
A_ = self[r, c] * another
return result
elif isinstance(UpperCAmelCase , UpperCAmelCase ): # Matrix multiplication
assert self.column == another.row
A_ = Matrix(self.row , another.column )
for r in range(self.row ):
for c in range(another.column ):
for i in range(self.column ):
result[r, c] += self[r, i] * another[i, c]
return result
else:
A_ = f'''Unsupported type given for another ({type(UpperCAmelCase )})'''
raise TypeError(UpperCAmelCase )
def __A ( self : Tuple ):
A_ = Matrix(self.column , self.row )
for r in range(self.row ):
for c in range(self.column ):
A_ = self[r, c]
return result
def __A ( self : Any , UpperCAmelCase : Matrix , UpperCAmelCase : Matrix ):
assert isinstance(UpperCAmelCase , UpperCAmelCase ) and isinstance(UpperCAmelCase , UpperCAmelCase )
assert self.row == self.column == u.row == v.row # u, v should be column vector
assert u.column == v.column == 1 # u, v should be column vector
# Calculate
A_ = v.transpose()
A_ = (v_t * self * u)[0, 0] + 1
if numerator_factor == 0:
return None # It's not invertable
return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))
# Testing
if __name__ == "__main__":
def __snake_case ( ):
"""simple docstring"""
A_ = Matrix(3 ,3 ,0 )
for i in range(3 ):
A_ = 1
print(f'''a^(-1) is {ainv}''' )
# u, v
A_ = Matrix(3 ,1 ,0 )
A_ , A_ , A_ = 1, 2, -3
A_ = Matrix(3 ,1 ,0 )
A_ , A_ , A_ = 4, -2, 5
print(f'''u is {u}''' )
print(f'''v is {v}''' )
print(f'''uv^T is {u * v.transpose()}''' )
# Sherman Morrison
print(f'''(a + uv^T)^(-1) is {ainv.sherman_morrison(__UpperCamelCase ,__UpperCamelCase )}''' )
def __snake_case ( ):
"""simple docstring"""
import doctest
doctest.testmod()
testa()
| 329 |
from __future__ import annotations
def __snake_case ( __UpperCamelCase : int = 4 ):
"""simple docstring"""
A_ = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
return reverse_row(transpose(__UpperCamelCase ) )
# OR.. transpose(reverse_column(matrix))
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
return reverse_row(reverse_column(__UpperCamelCase ) )
# OR.. reverse_column(reverse_row(matrix))
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
return reverse_column(transpose(__UpperCamelCase ) )
# OR.. transpose(reverse_row(matrix))
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
A_ = [list(__UpperCamelCase ) for x in zip(*__UpperCamelCase )]
return matrix
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
A_ = matrix[::-1]
return matrix
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
A_ = [x[::-1] for x in matrix]
return matrix
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
for i in matrix:
print(*__UpperCamelCase )
if __name__ == "__main__":
__a :Any = make_matrix()
print('\norigin:\n')
print_matrix(matrix)
print('\nrotate 90 counterclockwise:\n')
print_matrix(rotate_aa(matrix))
__a :Any = make_matrix()
print('\norigin:\n')
print_matrix(matrix)
print('\nrotate 180:\n')
print_matrix(rotate_aaa(matrix))
__a :Any = make_matrix()
print('\norigin:\n')
print_matrix(matrix)
print('\nrotate 270 counterclockwise:\n')
print_matrix(rotate_aaa(matrix))
| 329 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__a :List[str] = TypeVar('T')
class _a ( Generic[T] ):
"""simple docstring"""
def __init__( self : Any , UpperCAmelCase : T ):
A_ = data
A_ = None
def __str__( self : Union[str, Any] ):
return f'''{self.data}'''
class _a ( Generic[T] ):
"""simple docstring"""
def __init__( self : Union[str, Any] ):
A_ = None
def __iter__( self : Dict ):
A_ = self.top
while node:
yield node.data
A_ = node.next
def __str__( self : Tuple ):
return "->".join([str(UpperCAmelCase ) for item in self] )
def __len__( self : Dict ):
return len(tuple(iter(self ) ) )
def __A ( self : Optional[int] ):
return self.top is None
def __A ( self : Dict , UpperCAmelCase : T ):
A_ = Node(UpperCAmelCase )
if not self.is_empty():
A_ = self.top
A_ = node
def __A ( self : Any ):
if self.is_empty():
raise IndexError("pop from empty stack" )
assert isinstance(self.top , UpperCAmelCase )
A_ = self.top
A_ = self.top.next
return pop_node.data
def __A ( self : Union[str, Any] ):
if self.is_empty():
raise IndexError("peek from empty stack" )
assert self.top is not None
return self.top.data
def __A ( self : List[str] ):
A_ = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 329 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ['torch', 'transformers', 'onnx']
def __init__( self : List[Any] , *UpperCAmelCase : Union[str, Any] , **UpperCAmelCase : str ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Tuple , *UpperCAmelCase : Tuple , **UpperCAmelCase : Union[str, Any] ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Dict , *UpperCAmelCase : List[Any] , **UpperCAmelCase : Tuple ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Tuple = ['torch', 'transformers', 'onnx']
def __init__( self : Optional[Any] , *UpperCAmelCase : List[Any] , **UpperCAmelCase : List[Any] ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : List[Any] , *UpperCAmelCase : Union[str, Any] , **UpperCAmelCase : str ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Tuple , *UpperCAmelCase : Union[str, Any] , **UpperCAmelCase : int ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Any = ['torch', 'transformers', 'onnx']
def __init__( self : Dict , *UpperCAmelCase : Optional[int] , **UpperCAmelCase : Optional[int] ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Union[str, Any] , *UpperCAmelCase : Tuple , **UpperCAmelCase : Optional[int] ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Tuple , *UpperCAmelCase : Optional[int] , **UpperCAmelCase : int ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : List[str] = ['torch', 'transformers', 'onnx']
def __init__( self : List[Any] , *UpperCAmelCase : List[Any] , **UpperCAmelCase : int ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Any , *UpperCAmelCase : List[Any] , **UpperCAmelCase : str ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Optional[int] , *UpperCAmelCase : str , **UpperCAmelCase : int ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Dict = ['torch', 'transformers', 'onnx']
def __init__( self : str , *UpperCAmelCase : int , **UpperCAmelCase : Tuple ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Optional[int] , *UpperCAmelCase : str , **UpperCAmelCase : Dict ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : int , *UpperCAmelCase : Optional[int] , **UpperCAmelCase : List[str] ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : List[Any] = ['torch', 'transformers', 'onnx']
def __init__( self : str , *UpperCAmelCase : str , **UpperCAmelCase : List[Any] ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : List[Any] , *UpperCAmelCase : Optional[Any] , **UpperCAmelCase : List[Any] ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Optional[int] , *UpperCAmelCase : List[str] , **UpperCAmelCase : int ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
| 329 | 1 |
from collections.abc import Sequence
def __snake_case ( __UpperCamelCase : Sequence[int] | None = None ):
"""simple docstring"""
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
A_ = nums[0]
for i in range(1 ,len(__UpperCamelCase ) ):
A_ = nums[i]
A_ = max(__UpperCamelCase ,ans + num ,__UpperCamelCase )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
__a :Union[str, Any] = int(input('Enter number of elements : ').strip())
__a :Dict = list(map(int, input('\nEnter the numbers : ').strip().split()))[:n]
print(max_subsequence_sum(array))
| 329 |
import itertools
import math
def __snake_case ( __UpperCamelCase : 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(__UpperCamelCase ) + 1 ) ,6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __snake_case ( ):
"""simple docstring"""
A_ = 2
while True:
if is_prime(__UpperCamelCase ):
yield num
num += 1
def __snake_case ( __UpperCamelCase : int = 1_0001 ):
"""simple docstring"""
return next(itertools.islice(prime_generator() ,nth - 1 ,__UpperCamelCase ) )
if __name__ == "__main__":
print(F"{solution() = }")
| 329 | 1 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _a :
"""simple docstring"""
@staticmethod
def __A ( *UpperCAmelCase : Union[str, Any] , **UpperCAmelCase : Union[str, Any] ):
pass
@is_pipeline_test
@require_vision
class _a ( unittest.TestCase ):
"""simple docstring"""
@require_torch
def __A ( self : List[str] ):
A_ = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , )
A_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
A_ = image_classifier(UpperCAmelCase , candidate_labels=["a", "b", "c"] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(UpperCAmelCase ) , [
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}],
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "c"}, {"score": 0.333, "label": "b"}],
] , )
A_ = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
] , )
@require_tf
def __A ( self : int ):
A_ = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , framework="tf" )
A_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
A_ = image_classifier(UpperCAmelCase , candidate_labels=["a", "b", "c"] )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}] , )
A_ = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
] , )
@slow
@require_torch
def __A ( self : Any ):
A_ = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , )
# This is an image of 2 cats with remotes and no planes
A_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
A_ = image_classifier(UpperCAmelCase , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
A_ = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , )
@slow
@require_tf
def __A ( self : Optional[Any] ):
A_ = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , framework="tf" )
# This is an image of 2 cats with remotes and no planes
A_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
A_ = image_classifier(UpperCAmelCase , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
A_ = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , )
| 329 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class _a :
"""simple docstring"""
def __init__( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : List[str]=13 , UpperCAmelCase : Tuple=7 , UpperCAmelCase : int=True , UpperCAmelCase : Dict=True , UpperCAmelCase : Union[str, Any]=True , UpperCAmelCase : List[str]=True , UpperCAmelCase : Optional[Any]=99 , UpperCAmelCase : str=32 , UpperCAmelCase : Dict=2 , UpperCAmelCase : List[str]=4 , UpperCAmelCase : Optional[int]=37 , UpperCAmelCase : Optional[int]="gelu" , UpperCAmelCase : List[str]=0.1 , UpperCAmelCase : Union[str, Any]=0.1 , UpperCAmelCase : Any=512 , UpperCAmelCase : int=16 , UpperCAmelCase : Any=2 , UpperCAmelCase : Union[str, Any]=0.02 , UpperCAmelCase : Union[str, Any]=3 , UpperCAmelCase : Union[str, Any]=4 , UpperCAmelCase : List[Any]=None , ):
A_ = parent
A_ = 13
A_ = 7
A_ = True
A_ = True
A_ = True
A_ = True
A_ = 99
A_ = 384
A_ = 2
A_ = 4
A_ = 37
A_ = "gelu"
A_ = 0.1
A_ = 0.1
A_ = 512
A_ = 16
A_ = 2
A_ = 0.02
A_ = 3
A_ = 4
A_ = 128
A_ = 2
A_ = 9
A_ = 1
A_ = None
def __A ( self : Optional[int] ):
A_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ = None
if self.use_input_mask:
A_ = random_attention_mask([self.batch_size, self.seq_length] )
A_ = None
if self.use_token_type_ids:
A_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
A_ = None
A_ = None
A_ = None
if self.use_labels:
A_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A_ = ids_tensor([self.batch_size] , self.num_choices )
A_ = ConvBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=UpperCAmelCase , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __A ( self : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : int , UpperCAmelCase : Optional[int] , UpperCAmelCase : int , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : int ):
A_ = TFConvBertModel(config=UpperCAmelCase )
A_ = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
A_ = [input_ids, input_mask]
A_ = model(UpperCAmelCase )
A_ = model(UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __A ( self : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Tuple , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : str , UpperCAmelCase : Tuple ):
A_ = TFConvBertForMaskedLM(config=UpperCAmelCase )
A_ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
A_ = model(UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self : Dict , UpperCAmelCase : Any , UpperCAmelCase : List[str] , UpperCAmelCase : Any , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Any , UpperCAmelCase : int ):
A_ = self.num_labels
A_ = TFConvBertForSequenceClassification(config=UpperCAmelCase )
A_ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
A_ = model(UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __A ( self : Any , UpperCAmelCase : Any , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : List[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] , UpperCAmelCase : str ):
A_ = self.num_choices
A_ = TFConvBertForMultipleChoice(config=UpperCAmelCase )
A_ = tf.tile(tf.expand_dims(UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
A_ = tf.tile(tf.expand_dims(UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
A_ = tf.tile(tf.expand_dims(UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
A_ = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
A_ = model(UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __A ( self : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Any , UpperCAmelCase : Tuple , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : Any , UpperCAmelCase : str ):
A_ = self.num_labels
A_ = TFConvBertForTokenClassification(config=UpperCAmelCase )
A_ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
A_ = model(UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __A ( self : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : str ):
A_ = TFConvBertForQuestionAnswering(config=UpperCAmelCase )
A_ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
A_ = model(UpperCAmelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __A ( self : List[str] ):
A_ = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) = config_and_inputs
A_ = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class _a ( snake_case_ , snake_case_ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
_lowerCamelCase : Any = (
{
'feature-extraction': TFConvBertModel,
'fill-mask': TFConvBertForMaskedLM,
'question-answering': TFConvBertForQuestionAnswering,
'text-classification': TFConvBertForSequenceClassification,
'token-classification': TFConvBertForTokenClassification,
'zero-shot': TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
_lowerCamelCase : Dict = False
_lowerCamelCase : Optional[int] = False
_lowerCamelCase : Dict = False
def __A ( self : List[str] ):
A_ = TFConvBertModelTester(self )
A_ = ConfigTester(self , config_class=UpperCAmelCase , hidden_size=37 )
def __A ( self : Tuple ):
self.config_tester.run_common_tests()
def __A ( self : Tuple ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase )
def __A ( self : Dict ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase )
def __A ( self : List[Any] ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase )
def __A ( self : Dict ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase )
def __A ( self : int ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase )
def __A ( self : List[Any] ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase )
@slow
def __A ( self : str ):
A_ , A_ = self.model_tester.prepare_config_and_inputs_for_common()
A_ = True
A_ = True
if hasattr(UpperCAmelCase , "use_cache" ):
A_ = True
A_ = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length )
A_ = getattr(self.model_tester , "key_length" , UpperCAmelCase )
for model_class in self.all_model_classes:
A_ = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase )
A_ = model_class(UpperCAmelCase )
A_ = len(model(UpperCAmelCase ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(UpperCAmelCase , saved_model=UpperCAmelCase )
A_ = os.path.join(UpperCAmelCase , "saved_model" , "1" )
A_ = tf.keras.models.load_model(UpperCAmelCase )
A_ = model(UpperCAmelCase )
if self.is_encoder_decoder:
A_ = outputs["encoder_hidden_states"]
A_ = outputs["encoder_attentions"]
else:
A_ = outputs["hidden_states"]
A_ = outputs["attentions"]
self.assertEqual(len(UpperCAmelCase ) , UpperCAmelCase )
A_ = getattr(
self.model_tester , "expected_num_hidden_layers" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(UpperCAmelCase ) , UpperCAmelCase )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def __A ( self : List[str] ):
A_ = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
self.assertIsNotNone(UpperCAmelCase )
def __A ( self : Any ):
A_ , A_ = self.model_tester.prepare_config_and_inputs_for_common()
A_ = True
A_ = getattr(self.model_tester , "decoder_seq_length" , self.model_tester.seq_length )
A_ = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length )
A_ = getattr(self.model_tester , "key_length" , UpperCAmelCase )
A_ = getattr(self.model_tester , "key_length" , UpperCAmelCase )
def check_decoder_attentions_output(UpperCAmelCase : Optional[int] ):
A_ = len(UpperCAmelCase )
self.assertEqual(out_len % 2 , 0 )
A_ = outputs.decoder_attentions
self.assertEqual(len(UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(UpperCAmelCase : Optional[Any] ):
A_ = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
A_ = True
A_ = False
A_ = model_class(UpperCAmelCase )
A_ = model(self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) )
A_ = len(UpperCAmelCase )
self.assertEqual(config.output_hidden_states , UpperCAmelCase )
check_encoder_attentions_output(UpperCAmelCase )
if self.is_encoder_decoder:
A_ = model_class(UpperCAmelCase )
A_ = model(self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) )
self.assertEqual(config.output_hidden_states , UpperCAmelCase )
check_decoder_attentions_output(UpperCAmelCase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
A_ = True
A_ = model_class(UpperCAmelCase )
A_ = model(self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) )
self.assertEqual(config.output_hidden_states , UpperCAmelCase )
check_encoder_attentions_output(UpperCAmelCase )
# Check attention is always last and order is fine
A_ = True
A_ = True
A_ = model_class(UpperCAmelCase )
A_ = model(self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(UpperCAmelCase ) )
self.assertEqual(model.config.output_hidden_states , UpperCAmelCase )
check_encoder_attentions_output(UpperCAmelCase )
@require_tf
class _a ( unittest.TestCase ):
"""simple docstring"""
@slow
def __A ( self : Dict ):
A_ = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
A_ = tf.constant([[0, 1, 2, 3, 4, 5]] )
A_ = model(UpperCAmelCase )[0]
A_ = [1, 6, 768]
self.assertEqual(output.shape , UpperCAmelCase )
A_ = tf.constant(
[
[
[-0.03_475_493, -0.4_686_034, -0.30_638_832],
[0.22_637_248, -0.26_988_646, -0.7_423_424],
[0.10_324_868, -0.45_013_508, -0.58_280_784],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , UpperCAmelCase , atol=1E-4 )
| 329 | 1 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
class _a :
"""simple docstring"""
def __init__( self : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Any=2 , UpperCAmelCase : str=32 , UpperCAmelCase : List[str]=16 , UpperCAmelCase : int=3 , UpperCAmelCase : Any=True , UpperCAmelCase : int=True , UpperCAmelCase : List[str]=32 , UpperCAmelCase : int=4 , UpperCAmelCase : Tuple=[0, 1, 2, 3] , UpperCAmelCase : List[str]=4 , UpperCAmelCase : str=37 , UpperCAmelCase : Any="gelu" , UpperCAmelCase : List[Any]=0.1 , UpperCAmelCase : Dict=0.1 , UpperCAmelCase : List[str]=0.02 , UpperCAmelCase : List[str]=3 , UpperCAmelCase : int=[1, 384, 24, 24] , UpperCAmelCase : Tuple=True , UpperCAmelCase : Any=None , ):
A_ = parent
A_ = batch_size
A_ = image_size
A_ = patch_size
A_ = num_channels
A_ = is_training
A_ = use_labels
A_ = hidden_size
A_ = num_hidden_layers
A_ = backbone_out_indices
A_ = num_attention_heads
A_ = intermediate_size
A_ = hidden_act
A_ = hidden_dropout_prob
A_ = attention_probs_dropout_prob
A_ = initializer_range
A_ = num_labels
A_ = backbone_featmap_shape
A_ = scope
A_ = is_hybrid
# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
A_ = (image_size // patch_size) ** 2
A_ = num_patches + 1
def __A ( self : str ):
A_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ = None
if self.use_labels:
A_ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
A_ = self.get_config()
return config, pixel_values, labels
def __A ( self : List[str] ):
A_ = {
"global_padding": "same",
"layer_type": "bottleneck",
"depths": [3, 4, 9],
"out_features": ["stage1", "stage2", "stage3"],
"embedding_dynamic_padding": True,
"hidden_sizes": [96, 192, 384, 768],
"num_groups": 2,
}
return DPTConfig(
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 , backbone_out_indices=self.backbone_out_indices , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=UpperCAmelCase , initializer_range=self.initializer_range , is_hybrid=self.is_hybrid , backbone_config=UpperCAmelCase , backbone_featmap_shape=self.backbone_featmap_shape , )
def __A ( self : int , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int] ):
A_ = DPTModel(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
A_ = model(UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __A ( self : str , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : str ):
A_ = self.num_labels
A_ = DPTForDepthEstimation(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
A_ = model(UpperCAmelCase )
self.parent.assertEqual(result.predicted_depth.shape , (self.batch_size, self.image_size, self.image_size) )
def __A ( self : Any , UpperCAmelCase : Optional[int] , UpperCAmelCase : str , UpperCAmelCase : List[Any] ):
A_ = self.num_labels
A_ = DPTForSemanticSegmentation(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
A_ = model(UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def __A ( self : int ):
A_ = self.prepare_config_and_inputs()
A_ , A_ , A_ = config_and_inputs
A_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _a ( snake_case_ , snake_case_ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
_lowerCamelCase : Dict = (
{
'depth-estimation': DPTForDepthEstimation,
'feature-extraction': DPTModel,
'image-segmentation': DPTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
_lowerCamelCase : List[Any] = False
_lowerCamelCase : List[str] = False
_lowerCamelCase : List[str] = False
def __A ( self : Optional[int] ):
A_ = DPTModelTester(self )
A_ = ConfigTester(self , config_class=UpperCAmelCase , has_text_modality=UpperCAmelCase , hidden_size=37 )
def __A ( self : List[str] ):
self.config_tester.run_common_tests()
@unittest.skip(reason="DPT does not use inputs_embeds" )
def __A ( self : Any ):
pass
def __A ( self : List[str] ):
A_ , A_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ = model_class(UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase , nn.Linear ) )
def __A ( self : Union[str, Any] ):
A_ , A_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ = model_class(UpperCAmelCase )
A_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ = [*signature.parameters.keys()]
A_ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , UpperCAmelCase )
def __A ( self : str ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase )
def __A ( self : Tuple ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_depth_estimation(*UpperCAmelCase )
def __A ( self : List[Any] ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase )
def __A ( self : Union[str, Any] ):
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
A_ , A_ = self.model_tester.prepare_config_and_inputs_for_common()
A_ = True
if model_class in get_values(UpperCAmelCase ):
continue
A_ = model_class(UpperCAmelCase )
model.to(UpperCAmelCase )
model.train()
A_ = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )
A_ = model(**UpperCAmelCase ).loss
loss.backward()
def __A ( self : int ):
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
A_ , A_ = self.model_tester.prepare_config_and_inputs_for_common()
A_ = False
A_ = True
if model_class in get_values(UpperCAmelCase ) or not model_class.supports_gradient_checkpointing:
continue
A_ = model_class(UpperCAmelCase )
model.to(UpperCAmelCase )
model.gradient_checkpointing_enable()
model.train()
A_ = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )
A_ = model(**UpperCAmelCase ).loss
loss.backward()
def __A ( self : List[str] ):
A_ , A_ = self.model_tester.prepare_config_and_inputs_for_common()
A_ = _config_zero_init(UpperCAmelCase )
for model_class in self.all_model_classes:
A_ = model_class(config=UpperCAmelCase )
# Skip the check for the backbone
A_ = []
for name, module in model.named_modules():
if module.__class__.__name__ == "DPTViTHybridEmbeddings":
A_ = [f'''{name}.{key}''' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def __A ( self : Tuple ):
pass
@slow
def __A ( self : Optional[Any] ):
for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
A_ = DPTModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
def __A ( self : Optional[int] ):
# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
A_ , A_ = self.model_tester.prepare_config_and_inputs_for_common()
A_ = "add"
with self.assertRaises(UpperCAmelCase ):
A_ = DPTForDepthEstimation(UpperCAmelCase )
def __snake_case ( ):
"""simple docstring"""
A_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
@slow
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : str ):
A_ = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas" )
A_ = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas" ).to(UpperCAmelCase )
A_ = prepare_img()
A_ = image_processor(images=UpperCAmelCase , return_tensors="pt" ).to(UpperCAmelCase )
# forward pass
with torch.no_grad():
A_ = model(**UpperCAmelCase )
A_ = outputs.predicted_depth
# verify the predicted depth
A_ = torch.Size((1, 384, 384) )
self.assertEqual(predicted_depth.shape , UpperCAmelCase )
A_ = torch.tensor(
[[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]] ).to(UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100 , UpperCAmelCase , atol=1E-4 ) )
| 329 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a :Dict = logging.get_logger(__name__)
__a :int = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
),
'google/realm-cc-news-pretrained-encoder': (
'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'
),
'google/realm-cc-news-pretrained-scorer': (
'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'
),
'google/realm-cc-news-pretrained-openqa': (
'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'
),
'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json',
'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json',
'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json',
'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json',
# See all REALM models at https://huggingface.co/models?filter=realm
}
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : List[Any] = 'realm'
def __init__( self : Union[str, Any] , UpperCAmelCase : Optional[Any]=30522 , UpperCAmelCase : List[str]=768 , UpperCAmelCase : Optional[Any]=128 , UpperCAmelCase : str=12 , UpperCAmelCase : Dict=12 , UpperCAmelCase : Optional[Any]=8 , UpperCAmelCase : Any=3072 , UpperCAmelCase : Union[str, Any]="gelu_new" , UpperCAmelCase : List[Any]=0.1 , UpperCAmelCase : Dict=0.1 , UpperCAmelCase : int=512 , UpperCAmelCase : Tuple=2 , UpperCAmelCase : Union[str, Any]=0.02 , UpperCAmelCase : Union[str, Any]=1E-12 , UpperCAmelCase : List[Any]=256 , UpperCAmelCase : Optional[int]=10 , UpperCAmelCase : List[str]=1E-3 , UpperCAmelCase : Any=5 , UpperCAmelCase : List[Any]=320 , UpperCAmelCase : Optional[Any]=13353718 , UpperCAmelCase : Tuple=5000 , UpperCAmelCase : List[str]=1 , UpperCAmelCase : Union[str, Any]=0 , UpperCAmelCase : Union[str, Any]=2 , **UpperCAmelCase : List[str] , ):
super().__init__(pad_token_id=UpperCAmelCase , bos_token_id=UpperCAmelCase , eos_token_id=UpperCAmelCase , **UpperCAmelCase )
# Common config
A_ = vocab_size
A_ = max_position_embeddings
A_ = hidden_size
A_ = retriever_proj_size
A_ = num_hidden_layers
A_ = num_attention_heads
A_ = num_candidates
A_ = intermediate_size
A_ = hidden_act
A_ = hidden_dropout_prob
A_ = attention_probs_dropout_prob
A_ = initializer_range
A_ = type_vocab_size
A_ = layer_norm_eps
# Reader config
A_ = span_hidden_size
A_ = max_span_width
A_ = reader_layer_norm_eps
A_ = reader_beam_size
A_ = reader_seq_len
# Retrieval config
A_ = num_block_records
A_ = searcher_beam_size
| 329 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__a :Any = logging.get_logger(__name__)
__a :str = {
'post_extract_proj': 'feature_projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.upsample.0': 'encoder.upsample.projection',
'encoder.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'layer_norm',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
def __snake_case ( __UpperCamelCase : int ,__UpperCamelCase : List[Any] ,__UpperCamelCase : Optional[Any] ,__UpperCamelCase : Optional[int] ,__UpperCamelCase : str ):
"""simple docstring"""
for attribute in key.split("." ):
A_ = getattr(__UpperCamelCase ,__UpperCamelCase )
if weight_type is not None:
A_ = getattr(__UpperCamelCase ,__UpperCamelCase ).shape
else:
A_ = hf_pointer.shape
assert hf_shape == value.shape, (
f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
f''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
A_ = value
elif weight_type == "weight_g":
A_ = value
elif weight_type == "weight_v":
A_ = value
elif weight_type == "bias":
A_ = value
else:
A_ = value
logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : Optional[int] ,__UpperCamelCase : Any ):
"""simple docstring"""
A_ = []
A_ = fairseq_model.state_dict()
A_ = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
A_ = False
if "conv_layers" in name:
load_conv_layer(
__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,hf_model.config.feat_extract_norm == "group" ,)
A_ = True
else:
for key, mapped_key in MAPPING.items():
A_ = "sew." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
A_ = True
if "*" in mapped_key:
A_ = name.split(__UpperCamelCase )[0].split("." )[-2]
A_ = mapped_key.replace("*" ,__UpperCamelCase )
if "weight_g" in name:
A_ = "weight_g"
elif "weight_v" in name:
A_ = "weight_v"
elif "weight" in name:
A_ = "weight"
elif "bias" in name:
A_ = "bias"
else:
A_ = None
set_recursively(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
continue
if not is_used:
unused_weights.append(__UpperCamelCase )
logger.warning(f'''Unused weights: {unused_weights}''' )
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : Dict ,__UpperCamelCase : Union[str, Any] ,__UpperCamelCase : int ,__UpperCamelCase : Union[str, Any] ):
"""simple docstring"""
A_ = full_name.split("conv_layers." )[-1]
A_ = name.split("." )
A_ = int(items[0] )
A_ = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
A_ = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
A_ = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
A_ = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
A_ = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__UpperCamelCase )
def __snake_case ( __UpperCamelCase : List[Any] ,__UpperCamelCase : Any ):
"""simple docstring"""
A_ = SEWConfig()
if is_finetuned:
A_ = model.wav_encoder.wav_model.cfg
else:
A_ = model.cfg
A_ = fs_config.conv_bias
A_ = eval(fs_config.conv_feature_layers )
A_ = [x[0] for x in conv_layers]
A_ = [x[1] for x in conv_layers]
A_ = [x[2] for x in conv_layers]
A_ = "gelu"
A_ = "layer" if fs_config.extractor_mode == "layer_norm" else "group"
A_ = 0.0
A_ = fs_config.activation_fn.name
A_ = fs_config.encoder_embed_dim
A_ = 0.02
A_ = fs_config.encoder_ffn_embed_dim
A_ = 1E-5
A_ = fs_config.encoder_layerdrop
A_ = fs_config.encoder_attention_heads
A_ = fs_config.conv_pos_groups
A_ = fs_config.conv_pos
A_ = len(__UpperCamelCase )
A_ = fs_config.encoder_layers
A_ = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
A_ = model.cfg
A_ = fs_config.final_dropout
A_ = fs_config.layerdrop
A_ = fs_config.activation_dropout
A_ = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
A_ = fs_config.attention_dropout
A_ = fs_config.dropout_input
A_ = fs_config.dropout
A_ = fs_config.mask_channel_length
A_ = fs_config.mask_channel_prob
A_ = fs_config.mask_length
A_ = fs_config.mask_prob
A_ = "Wav2Vec2FeatureExtractor"
A_ = "Wav2Vec2CTCTokenizer"
return config
@torch.no_grad()
def __snake_case ( __UpperCamelCase : Tuple ,__UpperCamelCase : Optional[int] ,__UpperCamelCase : int=None ,__UpperCamelCase : List[str]=None ,__UpperCamelCase : str=True ):
"""simple docstring"""
if is_finetuned:
A_ , A_ , A_ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] ,arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
else:
A_ , A_ , A_ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
A_ = SEWConfig.from_pretrained(__UpperCamelCase )
else:
A_ = convert_config(model[0] ,__UpperCamelCase )
A_ = model[0].eval()
A_ = True if config.feat_extract_norm == "layer" else False
A_ = WavaVecaFeatureExtractor(
feature_size=1 ,sampling_rate=1_6000 ,padding_value=0 ,do_normalize=__UpperCamelCase ,return_attention_mask=__UpperCamelCase ,)
if is_finetuned:
if dict_path:
A_ = Dictionary.load(__UpperCamelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
A_ = target_dict.pad_index
A_ = target_dict.bos_index
A_ = target_dict.pad_index
A_ = target_dict.bos_index
A_ = target_dict.eos_index
A_ = len(target_dict.symbols )
A_ = os.path.join(__UpperCamelCase ,"vocab.json" )
if not os.path.isdir(__UpperCamelCase ):
logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__UpperCamelCase ) )
return
os.makedirs(__UpperCamelCase ,exist_ok=__UpperCamelCase )
with open(__UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_handle:
json.dump(target_dict.indices ,__UpperCamelCase )
A_ = WavaVecaCTCTokenizer(
__UpperCamelCase ,unk_token=target_dict.unk_word ,pad_token=target_dict.pad_word ,bos_token=target_dict.bos_word ,eos_token=target_dict.eos_word ,word_delimiter_token="|" ,do_lower_case=__UpperCamelCase ,)
A_ = WavaVecaProcessor(feature_extractor=__UpperCamelCase ,tokenizer=__UpperCamelCase )
processor.save_pretrained(__UpperCamelCase )
A_ = SEWForCTC(__UpperCamelCase )
else:
A_ = SEWModel(__UpperCamelCase )
feature_extractor.save_pretrained(__UpperCamelCase )
recursively_load_weights(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
hf_model.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
__a :Optional[int] = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
__a :int = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 329 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__a :Optional[Any] = logging.get_logger(__name__)
def __snake_case ( __UpperCamelCase : List[str] ,__UpperCamelCase : Any ,__UpperCamelCase : List[str] ,__UpperCamelCase : Optional[Any] ):
"""simple docstring"""
A_ = original_name.split("." )[0]
A_ = key.split("." )
A_ = int(key_list[key_list.index(__UpperCamelCase ) - 2] )
A_ = int(key_list[key_list.index(__UpperCamelCase ) - 1] )
A_ = orig_block_num - offset
A_ = key.replace(f'''{orig_block_num}.{layer_num}.{original_name}''' ,f'''block.{new_block_num}.{layer_num}.{new_name}''' )
return key
def __snake_case ( __UpperCamelCase : Any ):
"""simple docstring"""
A_ = OrderedDict()
A_ , A_ = 0, 0
for key, value in state_dict.items():
if key.startswith("network" ):
A_ = key.replace("network" ,"poolformer.encoder" )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith("bias" ) and "patch_embed" not in key:
patch_emb_offset += 1
A_ = key[: key.find("proj" )]
A_ = key.replace(__UpperCamelCase ,f'''patch_embeddings.{total_embed_found}.''' )
A_ = key.replace("proj" ,"projection" )
if key.endswith("bias" ):
total_embed_found += 1
if "patch_embeddings" in key:
A_ = "poolformer.encoder." + key
if "mlp.fc1" in key:
A_ = replace_key_with_offset(__UpperCamelCase ,__UpperCamelCase ,"mlp.fc1" ,"output.conv1" )
if "mlp.fc2" in key:
A_ = replace_key_with_offset(__UpperCamelCase ,__UpperCamelCase ,"mlp.fc2" ,"output.conv2" )
if "norm1" in key:
A_ = replace_key_with_offset(__UpperCamelCase ,__UpperCamelCase ,"norm1" ,"before_norm" )
if "norm2" in key:
A_ = replace_key_with_offset(__UpperCamelCase ,__UpperCamelCase ,"norm2" ,"after_norm" )
if "layer_scale_1" in key:
A_ = replace_key_with_offset(__UpperCamelCase ,__UpperCamelCase ,"layer_scale_1" ,"layer_scale_1" )
if "layer_scale_2" in key:
A_ = replace_key_with_offset(__UpperCamelCase ,__UpperCamelCase ,"layer_scale_2" ,"layer_scale_2" )
if "head" in key:
A_ = key.replace("head" ,"classifier" )
A_ = value
return new_state_dict
def __snake_case ( ):
"""simple docstring"""
A_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
A_ = Image.open(requests.get(__UpperCamelCase ,stream=__UpperCamelCase ).raw )
return image
@torch.no_grad()
def __snake_case ( __UpperCamelCase : Union[str, Any] ,__UpperCamelCase : List[str] ,__UpperCamelCase : List[Any] ):
"""simple docstring"""
A_ = PoolFormerConfig()
# set attributes based on model_name
A_ = "huggingface/label-files"
A_ = model_name[-3:]
A_ = 1000
A_ = "imagenet-1k-id2label.json"
A_ = (1, 1000)
# set config attributes
A_ = json.load(open(hf_hub_download(__UpperCamelCase ,__UpperCamelCase ,repo_type="dataset" ) ,"r" ) )
A_ = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
A_ = idalabel
A_ = {v: k for k, v in idalabel.items()}
if size == "s12":
A_ = [2, 2, 6, 2]
A_ = [64, 128, 320, 512]
A_ = 4.0
A_ = 0.9
elif size == "s24":
A_ = [4, 4, 12, 4]
A_ = [64, 128, 320, 512]
A_ = 4.0
A_ = 0.9
elif size == "s36":
A_ = [6, 6, 18, 6]
A_ = [64, 128, 320, 512]
A_ = 4.0
A_ = 1E-6
A_ = 0.9
elif size == "m36":
A_ = [6, 6, 18, 6]
A_ = [96, 192, 384, 768]
A_ = 4.0
A_ = 1E-6
A_ = 0.95
elif size == "m48":
A_ = [8, 8, 24, 8]
A_ = [96, 192, 384, 768]
A_ = 4.0
A_ = 1E-6
A_ = 0.95
else:
raise ValueError(f'''Size {size} not supported''' )
# load image processor
A_ = PoolFormerImageProcessor(crop_pct=__UpperCamelCase )
# Prepare image
A_ = prepare_img()
A_ = image_processor(images=__UpperCamelCase ,return_tensors="pt" ).pixel_values
logger.info(f'''Converting model {model_name}...''' )
# load original state dict
A_ = torch.load(__UpperCamelCase ,map_location=torch.device("cpu" ) )
# rename keys
A_ = rename_keys(__UpperCamelCase )
# create HuggingFace model and load state dict
A_ = PoolFormerForImageClassification(__UpperCamelCase )
model.load_state_dict(__UpperCamelCase )
model.eval()
# Define image processor
A_ = PoolFormerImageProcessor(crop_pct=__UpperCamelCase )
A_ = image_processor(images=prepare_img() ,return_tensors="pt" ).pixel_values
# forward pass
A_ = model(__UpperCamelCase )
A_ = outputs.logits
# define expected logit slices for different models
if size == "s12":
A_ = torch.tensor([-0.3045, -0.6758, -0.4869] )
elif size == "s24":
A_ = torch.tensor([0.4402, -0.1374, -0.8045] )
elif size == "s36":
A_ = torch.tensor([-0.6080, -0.5133, -0.5898] )
elif size == "m36":
A_ = torch.tensor([0.3952, 0.2263, -1.2668] )
elif size == "m48":
A_ = torch.tensor([0.1167, -0.0656, -0.3423] )
else:
raise ValueError(f'''Size {size} not supported''' )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3] ,__UpperCamelCase ,atol=1E-2 )
# finally, save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
model.save_pretrained(__UpperCamelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
__a :Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
default='poolformer_s12',
type=str,
help='Name of the model you\'d like to convert.',
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
__a :int = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 329 | 1 |
__a :List[str] = 'Alexander Joslin'
import operator as op
from .stack import Stack
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
A_ = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
A_ = Stack()
A_ = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(__UpperCamelCase ) )
elif i in operators:
# RULE 2
operator_stack.push(__UpperCamelCase )
elif i == ")":
# RULE 4
A_ = operator_stack.peek()
operator_stack.pop()
A_ = operand_stack.peek()
operand_stack.pop()
A_ = operand_stack.peek()
operand_stack.pop()
A_ = operators[opr](__UpperCamelCase ,__UpperCamelCase )
operand_stack.push(__UpperCamelCase )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
__a :str = '(5 + ((4 * 2) * (2 + 3)))'
# answer = 45
print(F"{equation} = {dijkstras_two_stack_algorithm(equation)}")
| 329 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : torch.FloatTensor
_lowerCamelCase : Optional[torch.FloatTensor] = None
def __snake_case ( __UpperCamelCase : Tuple ,__UpperCamelCase : Any=0.999 ,__UpperCamelCase : Any="cosine" ,):
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(__UpperCamelCase : Any ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__UpperCamelCase : int ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
A_ = []
for i in range(__UpperCamelCase ):
A_ = i / num_diffusion_timesteps
A_ = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__UpperCamelCase ) / alpha_bar_fn(__UpperCamelCase ) ,__UpperCamelCase ) )
return torch.tensor(__UpperCamelCase ,dtype=torch.floataa )
class _a ( snake_case_ , snake_case_ ):
"""simple docstring"""
@register_to_config
def __init__( self : Optional[int] , UpperCAmelCase : int = 1000 , UpperCAmelCase : str = "fixed_small_log" , UpperCAmelCase : bool = True , UpperCAmelCase : Optional[float] = 1.0 , UpperCAmelCase : str = "epsilon" , UpperCAmelCase : str = "squaredcos_cap_v2" , ):
if beta_schedule != "squaredcos_cap_v2":
raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'" )
A_ = betas_for_alpha_bar(UpperCAmelCase )
A_ = 1.0 - self.betas
A_ = torch.cumprod(self.alphas , dim=0 )
A_ = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
A_ = 1.0
# setable values
A_ = None
A_ = torch.from_numpy(np.arange(0 , UpperCAmelCase )[::-1].copy() )
A_ = variance_type
def __A ( self : Optional[Any] , UpperCAmelCase : torch.FloatTensor , UpperCAmelCase : Optional[int] = None ):
return sample
def __A ( self : List[Any] , UpperCAmelCase : int , UpperCAmelCase : Union[str, torch.device] = None ):
A_ = num_inference_steps
A_ = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
A_ = (np.arange(0 , UpperCAmelCase ) * step_ratio).round()[::-1].copy().astype(np.intaa )
A_ = torch.from_numpy(UpperCAmelCase ).to(UpperCAmelCase )
def __A ( self : List[Any] , UpperCAmelCase : Dict , UpperCAmelCase : str=None , UpperCAmelCase : Any=None , UpperCAmelCase : List[Any]=None ):
if prev_timestep is None:
A_ = t - 1
A_ = self.alphas_cumprod[t]
A_ = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
A_ = 1 - alpha_prod_t
A_ = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
A_ = self.betas[t]
else:
A_ = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
A_ = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
A_ = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
A_ = torch.log(torch.clamp(UpperCAmelCase , min=1E-20 ) )
A_ = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
A_ = variance.log()
A_ = beta.log()
A_ = (predicted_variance + 1) / 2
A_ = frac * max_log + (1 - frac) * min_log
return variance
def __A ( self : int , UpperCAmelCase : torch.FloatTensor , UpperCAmelCase : int , UpperCAmelCase : torch.FloatTensor , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : Dict=None , UpperCAmelCase : bool = True , ):
A_ = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
A_ , A_ = torch.split(UpperCAmelCase , sample.shape[1] , dim=1 )
else:
A_ = None
# 1. compute alphas, betas
if prev_timestep is None:
A_ = t - 1
A_ = self.alphas_cumprod[t]
A_ = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
A_ = 1 - alpha_prod_t
A_ = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
A_ = self.betas[t]
A_ = self.alphas[t]
else:
A_ = 1 - alpha_prod_t / alpha_prod_t_prev
A_ = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
A_ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
A_ = model_output
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
" for the UnCLIPScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
A_ = torch.clamp(
UpperCAmelCase , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
A_ = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
A_ = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
A_ = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
A_ = 0
if t > 0:
A_ = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=UpperCAmelCase , device=model_output.device )
A_ = self._get_variance(
UpperCAmelCase , predicted_variance=UpperCAmelCase , prev_timestep=UpperCAmelCase , )
if self.variance_type == "fixed_small_log":
A_ = variance
elif self.variance_type == "learned_range":
A_ = (0.5 * variance).exp()
else:
raise ValueError(
f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
" for the UnCLIPScheduler." )
A_ = variance * variance_noise
A_ = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=UpperCAmelCase , pred_original_sample=UpperCAmelCase )
def __A ( self : Optional[Any] , UpperCAmelCase : torch.FloatTensor , UpperCAmelCase : torch.FloatTensor , UpperCAmelCase : torch.IntTensor , ):
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
A_ = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
A_ = timesteps.to(original_samples.device )
A_ = alphas_cumprod[timesteps] ** 0.5
A_ = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
A_ = sqrt_alpha_prod.unsqueeze(-1 )
A_ = (1 - alphas_cumprod[timesteps]) ** 0.5
A_ = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
A_ = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
A_ = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 329 | 1 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def __snake_case ( __UpperCamelCase : Union[dict, list, tuple, torch.Tensor] ):
"""simple docstring"""
A_ = []
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
for v in tree.values():
shapes.extend(_fetch_dims(__UpperCamelCase ) )
elif isinstance(__UpperCamelCase ,(list, tuple) ):
for t in tree:
shapes.extend(_fetch_dims(__UpperCamelCase ) )
elif isinstance(__UpperCamelCase ,torch.Tensor ):
shapes.append(tree.shape )
else:
raise ValueError("Not supported" )
return shapes
@torch.jit.ignore
def __snake_case ( __UpperCamelCase : int ,__UpperCamelCase : Tuple[int, ...] ):
"""simple docstring"""
A_ = []
for d in reversed(__UpperCamelCase ):
idx.append(flat_idx % d )
A_ = flat_idx // d
return tuple(reversed(__UpperCamelCase ) )
@torch.jit.ignore
def __snake_case ( __UpperCamelCase : Sequence[int] ,__UpperCamelCase : Sequence[int] ,__UpperCamelCase : Sequence[int] ,__UpperCamelCase : Optional[Sequence[bool]] = None ,__UpperCamelCase : Optional[Sequence[bool]] = None ,):
"""simple docstring"""
def reduce_edge_list(__UpperCamelCase : List[bool] ) -> None:
A_ = True
for i in range(len(__UpperCamelCase ) ):
A_ = -1 * (i + 1)
l[reversed_idx] &= tally
A_ = l[reversed_idx]
if start_edges is None:
A_ = [s == 0 for s in start]
reduce_edge_list(__UpperCamelCase )
if end_edges is None:
A_ = [e == (d - 1) for e, d in zip(__UpperCamelCase ,__UpperCamelCase )]
reduce_edge_list(__UpperCamelCase )
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(__UpperCamelCase ) == 0:
return [()]
elif len(__UpperCamelCase ) == 1:
return [(slice(start[0] ,end[0] + 1 ),)]
A_ = []
A_ = []
# Dimensions common to start and end can be selected directly
for s, e in zip(__UpperCamelCase ,__UpperCamelCase ):
if s == e:
path_list.append(slice(__UpperCamelCase ,s + 1 ) )
else:
break
A_ = tuple(__UpperCamelCase )
A_ = len(__UpperCamelCase )
# start == end, and we're done
if divergence_idx == len(__UpperCamelCase ):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
A_ = start[divergence_idx]
return tuple(
path + (slice(__UpperCamelCase ,sdi + 1 ),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :] ,[d - 1 for d in dims[divergence_idx + 1 :]] ,dims[divergence_idx + 1 :] ,start_edges=start_edges[divergence_idx + 1 :] ,end_edges=[True for _ in end_edges[divergence_idx + 1 :]] ,) )
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
A_ = end[divergence_idx]
return tuple(
path + (slice(__UpperCamelCase ,edi + 1 ),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]] ,end[divergence_idx + 1 :] ,dims[divergence_idx + 1 :] ,start_edges=[True for _ in start_edges[divergence_idx + 1 :]] ,end_edges=end_edges[divergence_idx + 1 :] ,) )
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] ,end[divergence_idx] + 1 ),) )
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] ,end[divergence_idx] ),) )
slices.extend(lower() )
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper() )
slices.append(path + (slice(start[divergence_idx] + 1 ,end[divergence_idx] + 1 ),) )
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper() )
A_ = end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1 ,end[divergence_idx] ),) )
slices.extend(lower() )
return slices
@torch.jit.ignore
def __snake_case ( __UpperCamelCase : torch.Tensor ,__UpperCamelCase : int ,__UpperCamelCase : int ,__UpperCamelCase : int ):
"""simple docstring"""
A_ = t.shape[:no_batch_dims]
A_ = list(_flat_idx_to_idx(__UpperCamelCase ,__UpperCamelCase ) )
# _get_minimal_slice_set is inclusive
A_ = list(_flat_idx_to_idx(flat_end - 1 ,__UpperCamelCase ) )
# Get an ordered list of slices to perform
A_ = _get_minimal_slice_set(
__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,)
A_ = [t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def __snake_case ( __UpperCamelCase : Callable ,__UpperCamelCase : Dict[str, Any] ,__UpperCamelCase : int ,__UpperCamelCase : int ,__UpperCamelCase : bool = False ,__UpperCamelCase : Any = None ,__UpperCamelCase : bool = False ,):
"""simple docstring"""
if not (len(__UpperCamelCase ) > 0):
raise ValueError("Must provide at least one input" )
A_ = [shape[:no_batch_dims] for shape in _fetch_dims(__UpperCamelCase )]
A_ = tuple([max(__UpperCamelCase ) for s in zip(*__UpperCamelCase )] )
def _prep_inputs(__UpperCamelCase : torch.Tensor ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
A_ = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
A_ = t.reshape(-1 ,*t.shape[no_batch_dims:] )
else:
A_ = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
A_ = tensor_tree_map(_prep_inputs ,__UpperCamelCase )
A_ = None
if _out is not None:
A_ = tensor_tree_map(lambda __UpperCamelCase : t.view([-1] + list(t.shape[no_batch_dims:] ) ) ,_out )
A_ = 1
for d in orig_batch_dims:
flat_batch_dim *= d
A_ = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(__UpperCamelCase : torch.Tensor ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
A_ = 0
A_ = prepped_outputs
for _ in range(__UpperCamelCase ):
# Chunk the input
if not low_mem:
A_ = _select_chunk
else:
A_ = partial(
_chunk_slice ,flat_start=__UpperCamelCase ,flat_end=min(__UpperCamelCase ,i + chunk_size ) ,no_batch_dims=len(__UpperCamelCase ) ,)
A_ = tensor_tree_map(__UpperCamelCase ,__UpperCamelCase )
# Run the layer on the chunk
A_ = layer(**__UpperCamelCase )
# Allocate space for the output
if out is None:
A_ = tensor_tree_map(lambda __UpperCamelCase : t.new_zeros((flat_batch_dim,) + t.shape[1:] ) ,__UpperCamelCase )
# Put the chunk in its pre-allocated space
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
def assign(__UpperCamelCase : dict ,__UpperCamelCase : dict ) -> None:
for k, v in da.items():
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
assign(__UpperCamelCase ,da[k] )
else:
if _add_into_out:
v[i : i + chunk_size] += da[k]
else:
A_ = da[k]
assign(__UpperCamelCase ,__UpperCamelCase )
elif isinstance(__UpperCamelCase ,__UpperCamelCase ):
for xa, xa in zip(__UpperCamelCase ,__UpperCamelCase ):
if _add_into_out:
xa[i : i + chunk_size] += xa
else:
A_ = xa
elif isinstance(__UpperCamelCase ,torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
A_ = output_chunk
else:
raise ValueError("Not supported" )
i += chunk_size
A_ = tensor_tree_map(lambda __UpperCamelCase : t.view(orig_batch_dims + t.shape[1:] ) ,__UpperCamelCase )
return out
class _a :
"""simple docstring"""
def __init__( self : Tuple , UpperCAmelCase : int = 512 , ):
A_ = max_chunk_size
A_ = None
A_ = None
def __A ( self : int , UpperCAmelCase : Callable , UpperCAmelCase : tuple , UpperCAmelCase : int ):
logging.info("Tuning chunk size..." )
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
A_ = [2**l for l in range(int(math.log(self.max_chunk_size , 2 ) ) + 1 )]
A_ = [c for c in candidates if c > min_chunk_size]
A_ = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(UpperCAmelCase : int ) -> bool:
try:
with torch.no_grad():
fn(*UpperCAmelCase , chunk_size=UpperCAmelCase )
return True
except RuntimeError:
return False
A_ = 0
A_ = len(UpperCAmelCase ) - 1
while i > min_viable_chunk_size_index:
A_ = test_chunk_size(candidates[i] )
if not viable:
A_ = (min_viable_chunk_size_index + i) // 2
else:
A_ = i
A_ = (i + len(UpperCAmelCase ) - 1) // 2
return candidates[min_viable_chunk_size_index]
def __A ( self : Dict , UpperCAmelCase : Iterable , UpperCAmelCase : Iterable ):
A_ = True
for aa, aa in zip(UpperCAmelCase , UpperCAmelCase ):
assert type(UpperCAmelCase ) == type(UpperCAmelCase )
if isinstance(UpperCAmelCase , (list, tuple) ):
consistent &= self._compare_arg_caches(UpperCAmelCase , UpperCAmelCase )
elif isinstance(UpperCAmelCase , UpperCAmelCase ):
A_ = [v for _, v in sorted(aa.items() , key=lambda UpperCAmelCase : x[0] )]
A_ = [v for _, v in sorted(aa.items() , key=lambda UpperCAmelCase : x[0] )]
consistent &= self._compare_arg_caches(UpperCAmelCase , UpperCAmelCase )
else:
consistent &= aa == aa
return consistent
def __A ( self : str , UpperCAmelCase : Callable , UpperCAmelCase : tuple , UpperCAmelCase : int , ):
A_ = True
A_ = tree_map(lambda UpperCAmelCase : a.shape if isinstance(UpperCAmelCase , torch.Tensor ) else a , UpperCAmelCase , UpperCAmelCase )
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data ) == len(UpperCAmelCase )
A_ = self._compare_arg_caches(self.cached_arg_data , UpperCAmelCase )
else:
# Otherwise, we can reuse the precomputed value
A_ = False
if not consistent:
A_ = self._determine_favorable_chunk_size(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , )
A_ = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 329 |
from math import isqrt, loga
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
A_ = [True] * max_number
for i in range(2 ,isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 ,__UpperCamelCase ,__UpperCamelCase ):
A_ = False
return [i for i in range(2 ,__UpperCamelCase ) if is_prime[i]]
def __snake_case ( __UpperCamelCase : int = 80_0800 ,__UpperCamelCase : int = 80_0800 ):
"""simple docstring"""
A_ = degree * loga(__UpperCamelCase )
A_ = int(__UpperCamelCase )
A_ = calculate_prime_numbers(__UpperCamelCase )
A_ = 0
A_ = 0
A_ = len(__UpperCamelCase ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(F"{solution() = }")
| 329 | 1 |
# flake8: noqa
# Lint as: python3
__a :int = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
| 329 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__a :str = logging.get_logger(__name__)
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : str ):
"""simple docstring"""
A_ = RobertaPreLayerNormConfig.from_pretrained(
__UpperCamelCase ,architectures=["RobertaPreLayerNormForMaskedLM"] )
# convert state_dict
A_ = torch.load(hf_hub_download(repo_id=__UpperCamelCase ,filename="pytorch_model.bin" ) )
A_ = {}
for tensor_key, tensor_value in original_state_dict.items():
# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
if tensor_key.startswith("roberta." ):
A_ = "roberta_prelayernorm." + tensor_key[len("roberta." ) :]
# The original implementation contains weights which are not used, remove them from the state_dict
if tensor_key.endswith(".self.LayerNorm.weight" ) or tensor_key.endswith(".self.LayerNorm.bias" ):
continue
A_ = tensor_value
A_ = RobertaPreLayerNormForMaskedLM.from_pretrained(
pretrained_model_name_or_path=__UpperCamelCase ,config=__UpperCamelCase ,state_dict=__UpperCamelCase )
model.save_pretrained(__UpperCamelCase )
# convert tokenizer
A_ = AutoTokenizer.from_pretrained(__UpperCamelCase )
tokenizer.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
__a :Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint-repo',
default=None,
type=str,
required=True,
help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
__a :Any = parser.parse_args()
convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)
| 329 | 1 |
__a :Optional[int] = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
__a :List[str] = {
'm': 0,
'km': 3,
'Mm': 6,
'Gm': 9,
'Tm': 12,
'Pm': 15,
'Em': 18,
'Zm': 21,
'Ym': 24,
}
def __snake_case ( __UpperCamelCase : float ,__UpperCamelCase : str ,__UpperCamelCase : str ):
"""simple docstring"""
A_ = from_type.lower().strip("s" )
A_ = to_type.lower().strip("s" )
A_ = UNIT_SYMBOL.get(__UpperCamelCase ,__UpperCamelCase )
A_ = UNIT_SYMBOL.get(__UpperCamelCase ,__UpperCamelCase )
if from_sanitized not in METRIC_CONVERSION:
A_ = (
f'''Invalid \'from_type\' value: {from_type!r}.\n'''
f'''Conversion abbreviations are: {", ".join(__UpperCamelCase )}'''
)
raise ValueError(__UpperCamelCase )
if to_sanitized not in METRIC_CONVERSION:
A_ = (
f'''Invalid \'to_type\' value: {to_type!r}.\n'''
f'''Conversion abbreviations are: {", ".join(__UpperCamelCase )}'''
)
raise ValueError(__UpperCamelCase )
A_ = METRIC_CONVERSION[from_sanitized]
A_ = METRIC_CONVERSION[to_sanitized]
A_ = 1
if from_exponent > to_exponent:
A_ = from_exponent - to_exponent
else:
A_ = -(to_exponent - from_exponent)
return value * pow(10 ,__UpperCamelCase )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 329 |
from maths.prime_factors import prime_factors
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = f'''Input value of [number={number}] must be an integer'''
raise TypeError(__UpperCamelCase )
if number < 1:
raise ValueError("Input must be a positive integer" )
return -1 if len(prime_factors(__UpperCamelCase ) ) % 2 else 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 329 | 1 |
import os
from distutils.util import strtobool
def __snake_case ( __UpperCamelCase : Optional[Any] ,__UpperCamelCase : str ):
"""simple docstring"""
for e in env_keys:
A_ = int(os.environ.get(__UpperCamelCase ,-1 ) )
if val >= 0:
return val
return default
def __snake_case ( __UpperCamelCase : List[str] ,__UpperCamelCase : Any=False ):
"""simple docstring"""
A_ = os.environ.get(__UpperCamelCase ,str(__UpperCamelCase ) )
return strtobool(__UpperCamelCase ) == 1 # As its name indicates `strtobool` actually returns an int...
def __snake_case ( __UpperCamelCase : List[Any] ,__UpperCamelCase : List[Any]="no" ):
"""simple docstring"""
A_ = os.environ.get(__UpperCamelCase ,str(__UpperCamelCase ) )
return value
| 329 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__a :int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__a :Any = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F"{len(upper_files)} files contain uppercase characters:")
print('\n'.join(upper_files) + '\n')
__a :Tuple = [file for file in filepaths if ' ' in file]
if space_files:
print(F"{len(space_files)} files contain space characters:")
print('\n'.join(space_files) + '\n')
__a :str = [file for file in filepaths if '-' in file]
if hyphen_files:
print(F"{len(hyphen_files)} files contain hyphen characters:")
print('\n'.join(hyphen_files) + '\n')
__a :List[str] = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F"{len(nodir_files)} files are not in a directory:")
print('\n'.join(nodir_files) + '\n')
__a :Any = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 329 | 1 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
__a :Union[str, Any] = logging.get_logger(__name__)
def __snake_case ( __UpperCamelCase : Dict ):
"""simple docstring"""
A_ = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
A_ = 128
elif "12-12" in model_name:
A_ = 12
A_ = 12
elif "14-14" in model_name:
A_ = 14
A_ = 14
elif "16-16" in model_name:
A_ = 16
A_ = 16
else:
raise ValueError("Model not supported" )
A_ = "huggingface/label-files"
if "speech-commands" in model_name:
A_ = 35
A_ = "speech-commands-v2-id2label.json"
else:
A_ = 527
A_ = "audioset-id2label.json"
A_ = json.load(open(hf_hub_download(__UpperCamelCase ,__UpperCamelCase ,repo_type="dataset" ) ,"r" ) )
A_ = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
A_ = idalabel
A_ = {v: k for k, v in idalabel.items()}
return config
def __snake_case ( __UpperCamelCase : List[str] ):
"""simple docstring"""
if "module.v" in name:
A_ = name.replace("module.v" ,"audio_spectrogram_transformer" )
if "cls_token" in name:
A_ = name.replace("cls_token" ,"embeddings.cls_token" )
if "dist_token" in name:
A_ = name.replace("dist_token" ,"embeddings.distillation_token" )
if "pos_embed" in name:
A_ = name.replace("pos_embed" ,"embeddings.position_embeddings" )
if "patch_embed.proj" in name:
A_ = name.replace("patch_embed.proj" ,"embeddings.patch_embeddings.projection" )
# transformer blocks
if "blocks" in name:
A_ = name.replace("blocks" ,"encoder.layer" )
if "attn.proj" in name:
A_ = name.replace("attn.proj" ,"attention.output.dense" )
if "attn" in name:
A_ = name.replace("attn" ,"attention.self" )
if "norm1" in name:
A_ = name.replace("norm1" ,"layernorm_before" )
if "norm2" in name:
A_ = name.replace("norm2" ,"layernorm_after" )
if "mlp.fc1" in name:
A_ = name.replace("mlp.fc1" ,"intermediate.dense" )
if "mlp.fc2" in name:
A_ = name.replace("mlp.fc2" ,"output.dense" )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
A_ = name.replace("audio_spectrogram_transformer.norm" ,"audio_spectrogram_transformer.layernorm" )
# classifier head
if "module.mlp_head.0" in name:
A_ = name.replace("module.mlp_head.0" ,"classifier.layernorm" )
if "module.mlp_head.1" in name:
A_ = name.replace("module.mlp_head.1" ,"classifier.dense" )
return name
def __snake_case ( __UpperCamelCase : Optional[Any] ,__UpperCamelCase : Dict ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
A_ = orig_state_dict.pop(__UpperCamelCase )
if "qkv" in key:
A_ = key.split("." )
A_ = int(key_split[3] )
A_ = config.hidden_size
if "weight" in key:
A_ = val[:dim, :]
A_ = val[dim : dim * 2, :]
A_ = val[-dim:, :]
else:
A_ = val[:dim]
A_ = val[dim : dim * 2]
A_ = val[-dim:]
else:
A_ = val
return orig_state_dict
def __snake_case ( __UpperCamelCase : Tuple ):
"""simple docstring"""
A_ = [
"module.v.head.weight",
"module.v.head.bias",
"module.v.head_dist.weight",
"module.v.head_dist.bias",
]
for k in ignore_keys:
state_dict.pop(__UpperCamelCase ,__UpperCamelCase )
@torch.no_grad()
def __snake_case ( __UpperCamelCase : int ,__UpperCamelCase : List[str] ,__UpperCamelCase : str=False ):
"""simple docstring"""
A_ = get_audio_spectrogram_transformer_config(__UpperCamelCase )
A_ = {
"ast-finetuned-audioset-10-10-0.4593": (
"https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"
),
"ast-finetuned-audioset-10-10-0.450": (
"https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"
),
"ast-finetuned-audioset-10-10-0.448": (
"https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"
),
"ast-finetuned-audioset-10-10-0.448-v2": (
"https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"
),
"ast-finetuned-audioset-12-12-0.447": (
"https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"
),
"ast-finetuned-audioset-14-14-0.443": (
"https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"
),
"ast-finetuned-audioset-16-16-0.442": (
"https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"
),
"ast-finetuned-speech-commands-v2": (
"https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"
),
}
# load original state_dict
A_ = model_name_to_url[model_name]
A_ = torch.hub.load_state_dict_from_url(__UpperCamelCase ,map_location="cpu" )
# remove some keys
remove_keys(__UpperCamelCase )
# rename some keys
A_ = convert_state_dict(__UpperCamelCase ,__UpperCamelCase )
# load 🤗 model
A_ = ASTForAudioClassification(__UpperCamelCase )
model.eval()
model.load_state_dict(__UpperCamelCase )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
A_ = -4.267_7393 if "speech-commands" not in model_name else -6.84_5978
A_ = 4.568_9974 if "speech-commands" not in model_name else 5.565_4526
A_ = 1024 if "speech-commands" not in model_name else 128
A_ = ASTFeatureExtractor(mean=__UpperCamelCase ,std=__UpperCamelCase ,max_length=__UpperCamelCase )
if "speech-commands" in model_name:
A_ = load_dataset("speech_commands" ,"v0.02" ,split="validation" )
A_ = dataset[0]["audio"]["array"]
else:
A_ = hf_hub_download(
repo_id="nielsr/audio-spectogram-transformer-checkpoint" ,filename="sample_audio.flac" ,repo_type="dataset" ,)
A_ , A_ = torchaudio.load(__UpperCamelCase )
A_ = waveform.squeeze().numpy()
A_ = feature_extractor(__UpperCamelCase ,sampling_rate=1_6000 ,return_tensors="pt" )
# forward pass
A_ = model(**__UpperCamelCase )
A_ = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
A_ = torch.tensor([-0.8760, -7.0042, -8.6602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
A_ = torch.tensor([-1.1986, -7.0903, -8.2718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
A_ = torch.tensor([-2.6128, -8.0080, -9.4344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
A_ = torch.tensor([-1.5080, -7.4534, -8.8917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
A_ = torch.tensor([-0.5050, -6.5833, -8.0843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
A_ = torch.tensor([-0.3826, -7.0336, -8.2413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
A_ = torch.tensor([-1.2113, -6.9101, -8.3470] )
elif model_name == "ast-finetuned-speech-commands-v2":
A_ = torch.tensor([6.1589, -8.0566, -8.7984] )
else:
raise ValueError("Unknown model name" )
if not torch.allclose(logits[0, :3] ,__UpperCamelCase ,atol=1E-4 ):
raise ValueError("Logits don't match" )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(__UpperCamelCase )
print(f'''Saving feature extractor to {pytorch_dump_folder_path}''' )
feature_extractor.save_pretrained(__UpperCamelCase )
if push_to_hub:
print("Pushing model and feature extractor to the hub..." )
model.push_to_hub(f'''MIT/{model_name}''' )
feature_extractor.push_to_hub(f'''MIT/{model_name}''' )
if __name__ == "__main__":
__a :Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='ast-finetuned-audioset-10-10-0.4593',
type=str,
help='Name of the Audio Spectrogram Transformer model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
__a :List[str] = parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 329 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a :Union[str, Any] = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a :Optional[int] = [
'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BioGptForCausalLM',
'BioGptForTokenClassification',
'BioGptForSequenceClassification',
'BioGptModel',
'BioGptPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
from .tokenization_biogpt import BioGptTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_biogpt import (
BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptPreTrainedModel,
)
else:
import sys
__a :str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 329 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DeformableDetrImageProcessor
class _a ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Dict , UpperCAmelCase : List[Any] , UpperCAmelCase : Union[str, Any]=7 , UpperCAmelCase : Any=3 , UpperCAmelCase : Any=30 , UpperCAmelCase : Tuple=400 , UpperCAmelCase : Any=True , UpperCAmelCase : int=None , UpperCAmelCase : Optional[Any]=True , UpperCAmelCase : Optional[Any]=[0.5, 0.5, 0.5] , UpperCAmelCase : Optional[Any]=[0.5, 0.5, 0.5] , UpperCAmelCase : Optional[int]=True , UpperCAmelCase : List[Any]=1 / 255 , UpperCAmelCase : Dict=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
A_ = size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
A_ = parent
A_ = batch_size
A_ = num_channels
A_ = min_resolution
A_ = max_resolution
A_ = do_resize
A_ = size
A_ = do_normalize
A_ = image_mean
A_ = image_std
A_ = do_rescale
A_ = rescale_factor
A_ = do_pad
def __A ( self : int ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def __A ( self : Any , UpperCAmelCase : List[str] , UpperCAmelCase : str=False ):
if not batched:
A_ = image_inputs[0]
if isinstance(UpperCAmelCase , Image.Image ):
A_ , A_ = image.size
else:
A_ , A_ = image.shape[1], image.shape[2]
if w < h:
A_ = int(self.size["shortest_edge"] * h / w )
A_ = self.size["shortest_edge"]
elif w > h:
A_ = self.size["shortest_edge"]
A_ = int(self.size["shortest_edge"] * w / h )
else:
A_ = self.size["shortest_edge"]
A_ = self.size["shortest_edge"]
else:
A_ = []
for image in image_inputs:
A_ , A_ = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
A_ = max(UpperCAmelCase , key=lambda UpperCAmelCase : item[0] )[0]
A_ = max(UpperCAmelCase , key=lambda UpperCAmelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class _a ( snake_case_ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase : Tuple = DeformableDetrImageProcessor if is_vision_available() else None
def __A ( self : int ):
A_ = DeformableDetrImageProcessingTester(self )
@property
def __A ( self : Tuple ):
return self.image_processor_tester.prepare_image_processor_dict()
def __A ( self : Any ):
A_ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCAmelCase , "image_mean" ) )
self.assertTrue(hasattr(UpperCAmelCase , "image_std" ) )
self.assertTrue(hasattr(UpperCAmelCase , "do_normalize" ) )
self.assertTrue(hasattr(UpperCAmelCase , "do_resize" ) )
self.assertTrue(hasattr(UpperCAmelCase , "do_rescale" ) )
self.assertTrue(hasattr(UpperCAmelCase , "do_pad" ) )
self.assertTrue(hasattr(UpperCAmelCase , "size" ) )
def __A ( self : str ):
A_ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 1333} )
self.assertEqual(image_processor.do_pad , UpperCAmelCase )
A_ = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=UpperCAmelCase )
self.assertEqual(image_processor.size , {"shortest_edge": 42, "longest_edge": 84} )
self.assertEqual(image_processor.do_pad , UpperCAmelCase )
def __A ( self : str ):
pass
def __A ( self : Tuple ):
# Initialize image_processing
A_ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase , Image.Image )
# Test not batched input
A_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
A_ , A_ = self.image_processor_tester.get_expected_values(UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A_ , A_ = self.image_processor_tester.get_expected_values(UpperCAmelCase , batched=UpperCAmelCase )
A_ = image_processing(UpperCAmelCase , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __A ( self : Dict ):
# Initialize image_processing
A_ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase , numpify=UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase , np.ndarray )
# Test not batched input
A_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
A_ , A_ = self.image_processor_tester.get_expected_values(UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A_ = image_processing(UpperCAmelCase , return_tensors="pt" ).pixel_values
A_ , A_ = self.image_processor_tester.get_expected_values(UpperCAmelCase , batched=UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __A ( self : str ):
# Initialize image_processing
A_ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase , torchify=UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase , torch.Tensor )
# Test not batched input
A_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
A_ , A_ = self.image_processor_tester.get_expected_values(UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A_ = image_processing(UpperCAmelCase , return_tensors="pt" ).pixel_values
A_ , A_ = self.image_processor_tester.get_expected_values(UpperCAmelCase , batched=UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def __A ( self : List[str] ):
# prepare image and target
A_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
A_ = json.loads(f.read() )
A_ = {"image_id": 39769, "annotations": target}
# encode them
A_ = DeformableDetrImageProcessor()
A_ = image_processing(images=UpperCAmelCase , annotations=UpperCAmelCase , return_tensors="pt" )
# verify pixel values
A_ = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["pixel_values"].shape , UpperCAmelCase )
A_ = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , UpperCAmelCase , atol=1E-4 ) )
# verify area
A_ = torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , UpperCAmelCase ) )
# verify boxes
A_ = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , UpperCAmelCase )
A_ = torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , UpperCAmelCase , atol=1E-3 ) )
# verify image_id
A_ = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , UpperCAmelCase ) )
# verify is_crowd
A_ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , UpperCAmelCase ) )
# verify class_labels
A_ = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , UpperCAmelCase ) )
# verify orig_size
A_ = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , UpperCAmelCase ) )
# verify size
A_ = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , UpperCAmelCase ) )
@slow
def __A ( self : Optional[int] ):
# prepare image, target and masks_path
A_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f:
A_ = json.loads(f.read() )
A_ = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}
A_ = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
A_ = DeformableDetrImageProcessor(format="coco_panoptic" )
A_ = image_processing(images=UpperCAmelCase , annotations=UpperCAmelCase , masks_path=UpperCAmelCase , return_tensors="pt" )
# verify pixel values
A_ = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["pixel_values"].shape , UpperCAmelCase )
A_ = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , UpperCAmelCase , atol=1E-4 ) )
# verify area
A_ = torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , UpperCAmelCase ) )
# verify boxes
A_ = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , UpperCAmelCase )
A_ = torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , UpperCAmelCase , atol=1E-3 ) )
# verify image_id
A_ = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , UpperCAmelCase ) )
# verify is_crowd
A_ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , UpperCAmelCase ) )
# verify class_labels
A_ = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , UpperCAmelCase ) )
# verify masks
A_ = 822873
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , UpperCAmelCase )
# verify orig_size
A_ = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , UpperCAmelCase ) )
# verify size
A_ = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , UpperCAmelCase ) )
| 329 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def __snake_case ( __UpperCamelCase : Union[str, Any] ):
"""simple docstring"""
if is_torch_version("<" ,"2.0.0" ) or not hasattr(__UpperCamelCase ,"_dynamo" ):
return False
return isinstance(__UpperCamelCase ,torch._dynamo.eval_frame.OptimizedModule )
def __snake_case ( __UpperCamelCase : List[str] ,__UpperCamelCase : bool = True ):
"""simple docstring"""
A_ = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
A_ = is_compiled_module(__UpperCamelCase )
if is_compiled:
A_ = model
A_ = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = model.module
if not keep_fpaa_wrapper:
A_ = getattr(__UpperCamelCase ,"forward" )
A_ = model.__dict__.pop("_original_forward" ,__UpperCamelCase )
if original_forward is not None:
while hasattr(__UpperCamelCase ,"__wrapped__" ):
A_ = forward.__wrapped__
if forward == original_forward:
break
A_ = forward
if getattr(__UpperCamelCase ,"_converted_to_transformer_engine" ,__UpperCamelCase ):
convert_model(__UpperCamelCase ,to_transformer_engine=__UpperCamelCase )
if is_compiled:
A_ = model
A_ = compiled_model
return model
def __snake_case ( ):
"""simple docstring"""
PartialState().wait_for_everyone()
def __snake_case ( __UpperCamelCase : Optional[Any] ,__UpperCamelCase : Any ):
"""simple docstring"""
if PartialState().distributed_type == DistributedType.TPU:
xm.save(__UpperCamelCase ,__UpperCamelCase )
elif PartialState().local_process_index == 0:
torch.save(__UpperCamelCase ,__UpperCamelCase )
@contextmanager
def __snake_case ( **__UpperCamelCase : Any ):
"""simple docstring"""
for key, value in kwargs.items():
A_ = str(__UpperCamelCase )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def __snake_case ( __UpperCamelCase : Optional[Any] ):
"""simple docstring"""
if not hasattr(__UpperCamelCase ,"__qualname__" ) and not hasattr(__UpperCamelCase ,"__name__" ):
A_ = getattr(__UpperCamelCase ,"__class__" ,__UpperCamelCase )
if hasattr(__UpperCamelCase ,"__qualname__" ):
return obj.__qualname__
if hasattr(__UpperCamelCase ,"__name__" ):
return obj.__name__
return str(__UpperCamelCase )
def __snake_case ( __UpperCamelCase : Any ,__UpperCamelCase : Optional[Any] ):
"""simple docstring"""
for key, value in source.items():
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = destination.setdefault(__UpperCamelCase ,{} )
merge_dicts(__UpperCamelCase ,__UpperCamelCase )
else:
A_ = value
return destination
def __snake_case ( __UpperCamelCase : int = None ):
"""simple docstring"""
if port is None:
A_ = 2_9500
with socket.socket(socket.AF_INET ,socket.SOCK_STREAM ) as s:
return s.connect_ex(("localhost", port) ) == 0
| 329 | 1 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
__a :Dict = False
__a :int = True
__a :Any = False
if __name__ == "__main__":
__a :Any = argparse.ArgumentParser()
parser.add_argument(
'--repo_path',
default=None,
type=str,
required=True,
help='The config json file corresponding to the architecture.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
__a :Union[str, Any] = parser.parse_args()
__a :int = {
'image_size': 'sample_size',
'num_res_blocks': 'layers_per_block',
'block_channels': 'block_out_channels',
'down_blocks': 'down_block_types',
'up_blocks': 'up_block_types',
'downscale_freq_shift': 'freq_shift',
'resnet_num_groups': 'norm_num_groups',
'resnet_act_fn': 'act_fn',
'resnet_eps': 'norm_eps',
'num_head_channels': 'attention_head_dim',
}
__a :Dict = {
'time_steps': 'time_proj',
'mid': 'mid_block',
'downsample_blocks': 'down_blocks',
'upsample_blocks': 'up_blocks',
}
__a :Dict = '' if has_file(args.repo_path, 'config.json') else 'unet'
with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
__a :Tuple = reader.read()
__a :Optional[int] = json.loads(text)
if do_only_config:
for key in config_parameters_to_change.keys():
config.pop(key, None)
if has_file(args.repo_path, 'config.json'):
__a :Any = UNetaDModel(**config)
else:
__a :str = UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
__a :List[str] = class_name(**config)
if do_only_config:
model.save_config(os.path.join(args.repo_path, subfolder))
__a :List[str] = dict(model.config)
if do_only_renaming:
for key, value in config_parameters_to_change.items():
if key in config:
__a :List[str] = config[key]
del config[key]
__a :Optional[Any] = [k.replace('UNetRes', '') for k in config['down_block_types']]
__a :Union[str, Any] = [k.replace('UNetRes', '') for k in config['up_block_types']]
if do_only_weights:
__a :int = torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))
__a :Optional[int] = {}
for param_key, param_value in state_dict.items():
if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
continue
__a :List[Any] = False
for key, new_key in key_parameters_to_change.items():
if not has_changed and param_key.split('.')[0] == key:
__a :Tuple = param_value
__a :List[Any] = True
if not has_changed:
__a :str = param_value
model.load_state_dict(new_state_dict)
model.save_pretrained(os.path.join(args.repo_path, subfolder))
| 329 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : int ):
A_ = tempfile.mkdtemp()
A_ = BlipImageProcessor()
A_ = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )
A_ = BlipProcessor(UpperCAmelCase , UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
def __A ( self : Optional[int] , **UpperCAmelCase : Union[str, Any] ):
return AutoProcessor.from_pretrained(self.tmpdirname , **UpperCAmelCase ).tokenizer
def __A ( self : Optional[Any] , **UpperCAmelCase : int ):
return AutoProcessor.from_pretrained(self.tmpdirname , **UpperCAmelCase ).image_processor
def __A ( self : Any ):
shutil.rmtree(self.tmpdirname )
def __A ( self : Dict ):
A_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
A_ = [Image.fromarray(np.moveaxis(UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __A ( self : Any ):
A_ = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
A_ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
A_ = self.get_image_processor(do_normalize=UpperCAmelCase , padding_value=1.0 )
A_ = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , UpperCAmelCase )
def __A ( self : Dict ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = self.prepare_image_inputs()
A_ = image_processor(UpperCAmelCase , return_tensors="np" )
A_ = processor(images=UpperCAmelCase , return_tensors="np" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __A ( self : int ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = "lower newer"
A_ = processor(text=UpperCAmelCase )
A_ = tokenizer(UpperCAmelCase , return_token_type_ids=UpperCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __A ( self : Tuple ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = "lower newer"
A_ = self.prepare_image_inputs()
A_ = processor(text=UpperCAmelCase , images=UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(UpperCAmelCase ):
processor()
def __A ( self : Any ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A_ = processor.batch_decode(UpperCAmelCase )
A_ = tokenizer.batch_decode(UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
def __A ( self : Optional[Any] ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = "lower newer"
A_ = self.prepare_image_inputs()
A_ = processor(text=UpperCAmelCase , images=UpperCAmelCase )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
| 329 | 1 |
from __future__ import annotations
def __snake_case ( __UpperCamelCase : int | str ):
"""simple docstring"""
A_ = str(__UpperCamelCase )
return n == n[::-1]
def __snake_case ( __UpperCamelCase : int = 100_0000 ):
"""simple docstring"""
A_ = 0
for i in range(1 ,__UpperCamelCase ):
if is_palindrome(__UpperCamelCase ) and is_palindrome(bin(__UpperCamelCase ).split("b" )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 329 |
import math
__a :Union[str, Any] = 10
__a :Union[str, Any] = 7
__a :int = BALLS_PER_COLOUR * NUM_COLOURS
def __snake_case ( __UpperCamelCase : int = 20 ):
"""simple docstring"""
A_ = math.comb(__UpperCamelCase ,__UpperCamelCase )
A_ = math.comb(NUM_BALLS - BALLS_PER_COLOUR ,__UpperCamelCase )
A_ = NUM_COLOURS * (1 - missing_colour / total)
return f'''{result:.9f}'''
if __name__ == "__main__":
print(solution(20))
| 329 | 1 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
__a :Any = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import deepcopy # noqa
from parameterized import parameterized # noqa
from transformers import TrainingArguments, is_torch_available # noqa
from transformers.deepspeed import is_deepspeed_available # noqa
from transformers.file_utils import WEIGHTS_NAME # noqa
from transformers.testing_utils import ( # noqa
CaptureLogger,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
mockenv_context,
require_deepspeed,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
from transformers.trainer_utils import set_seed # noqa
set_seed(42)
__a :List[Any] = {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}
__a :Optional[Any] = 'zero2'
__a :Union[str, Any] = 'zero3'
__a :Tuple = [ZEROa, ZEROa]
def __snake_case ( __UpperCamelCase : Any ,__UpperCamelCase : int ,__UpperCamelCase : Optional[Any] ):
"""simple docstring"""
A_ = parameterized.to_safe_name("_".join(str(__UpperCamelCase ) for x in param.args ) )
return f'''{func.__name__}_{param_based_name}'''
# Cartesian-product of zero stages with models to test
__a :List[Any] = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class _a ( snake_case_ ):
"""simple docstring"""
@parameterized.expand(UpperCAmelCase , name_func=UpperCAmelCase )
def __A ( self : Tuple , UpperCAmelCase : List[Any] , UpperCAmelCase : int ):
self.run_and_check(
stage=UpperCAmelCase , model=UpperCAmelCase , distributed=UpperCAmelCase , fpaa=UpperCAmelCase , )
@require_torch_multi_gpu
@parameterized.expand(UpperCAmelCase , name_func=UpperCAmelCase )
def __A ( self : Union[str, Any] , UpperCAmelCase : List[str] , UpperCAmelCase : int ):
self.run_and_check(
stage=UpperCAmelCase , model=UpperCAmelCase , distributed=UpperCAmelCase , fpaa=UpperCAmelCase , )
@parameterized.expand(UpperCAmelCase , name_func=UpperCAmelCase )
def __A ( self : str , UpperCAmelCase : Optional[int] , UpperCAmelCase : List[str] ):
self.run_and_check(
stage=UpperCAmelCase , model=UpperCAmelCase , distributed=UpperCAmelCase , fpaa=UpperCAmelCase , )
@require_torch_multi_gpu
@parameterized.expand(UpperCAmelCase , name_func=UpperCAmelCase )
def __A ( self : Dict , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] ):
self.run_and_check(
stage=UpperCAmelCase , model=UpperCAmelCase , distributed=UpperCAmelCase , fpaa=UpperCAmelCase , )
def __A ( self : Union[str, Any] , UpperCAmelCase : Optional[Any] ):
# XXX: run_asr is premature and doesn't save any results
# so all we check for now is that the process didn't fail
pass
def __A ( self : Tuple , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : int = 10 , UpperCAmelCase : bool = True , UpperCAmelCase : bool = True , UpperCAmelCase : bool = True , ):
A_ = models[model]
A_ = self.run_trainer(
stage=UpperCAmelCase , model_name=UpperCAmelCase , eval_steps=UpperCAmelCase , num_train_epochs=1 , distributed=UpperCAmelCase , fpaa=UpperCAmelCase , )
self.do_checks(UpperCAmelCase )
return output_dir
def __A ( self : Optional[int] , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : int = 10 , UpperCAmelCase : int = 1 , UpperCAmelCase : bool = True , UpperCAmelCase : bool = True , ):
A_ = self.get_auto_remove_tmp_dir("./xxx" , after=UpperCAmelCase )
A_ = f'''
--model_name_or_path {model_name}
--dataset_name hf-internal-testing/librispeech_asr_dummy
--dataset_config_name clean
--train_split_name validation
--validation_split_name validation
--output_dir {output_dir}
--num_train_epochs {str(UpperCAmelCase )}
--per_device_train_batch_size 2
--per_device_eval_batch_size 2
--evaluation_strategy steps
--learning_rate 5e-4
--warmup_steps 8
--orthography timit
--preprocessing_num_workers 1
--group_by_length
--freeze_feature_extractor
--report_to none
--save_steps 0
--eval_steps {eval_steps}
--report_to none
'''.split()
if fpaa:
args.extend(["--fp16"] )
# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
# hence the separate config files
A_ = f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
A_ = [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
A_ = self.get_launcher(UpperCAmelCase )
A_ = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(UpperCAmelCase , env=self.get_env() )
return output_dir
def __A ( self : int , UpperCAmelCase : Optional[Any]=False ):
# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
# - it won't be able to handle that
# 2. for now testing with just 2 gpus max (since some quality tests may give different
# results with mode gpus because we use very little data)
A_ = min(2 , get_gpu_count() ) if distributed else 1
return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()
| 329 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__a :Optional[Any] = logging.get_logger(__name__)
__a :Any = {'vocab_file': 'vocab.txt'}
__a :Any = {
'vocab_file': {
'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
'YituTech/conv-bert-medium-small': (
'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
),
'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
}
}
__a :List[str] = {
'YituTech/conv-bert-base': 512,
'YituTech/conv-bert-medium-small': 512,
'YituTech/conv-bert-small': 512,
}
__a :List[str] = {
'YituTech/conv-bert-base': {'do_lower_case': True},
'YituTech/conv-bert-medium-small': {'do_lower_case': True},
'YituTech/conv-bert-small': {'do_lower_case': True},
}
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Tuple = VOCAB_FILES_NAMES
_lowerCamelCase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase : int = PRETRAINED_INIT_CONFIGURATION
_lowerCamelCase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase : Union[str, Any] = ConvBertTokenizer
def __init__( self : Optional[int] , UpperCAmelCase : Union[str, Any]=None , UpperCAmelCase : Union[str, Any]=None , UpperCAmelCase : Optional[Any]=True , UpperCAmelCase : int="[UNK]" , UpperCAmelCase : str="[SEP]" , UpperCAmelCase : Union[str, Any]="[PAD]" , UpperCAmelCase : Tuple="[CLS]" , UpperCAmelCase : Tuple="[MASK]" , UpperCAmelCase : Any=True , UpperCAmelCase : Union[str, Any]=None , **UpperCAmelCase : List[str] , ):
super().__init__(
UpperCAmelCase , tokenizer_file=UpperCAmelCase , do_lower_case=UpperCAmelCase , unk_token=UpperCAmelCase , sep_token=UpperCAmelCase , pad_token=UpperCAmelCase , cls_token=UpperCAmelCase , mask_token=UpperCAmelCase , tokenize_chinese_chars=UpperCAmelCase , strip_accents=UpperCAmelCase , **UpperCAmelCase , )
A_ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , UpperCAmelCase ) != do_lower_case
or normalizer_state.get("strip_accents" , UpperCAmelCase ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , UpperCAmelCase ) != tokenize_chinese_chars
):
A_ = getattr(UpperCAmelCase , normalizer_state.pop("type" ) )
A_ = do_lower_case
A_ = strip_accents
A_ = tokenize_chinese_chars
A_ = normalizer_class(**UpperCAmelCase )
A_ = do_lower_case
def __A ( self : List[str] , UpperCAmelCase : Any , UpperCAmelCase : Dict=None ):
A_ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __A ( self : Optional[Any] , UpperCAmelCase : List[int] , UpperCAmelCase : Optional[List[int]] = None ):
A_ = [self.sep_token_id]
A_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __A ( self : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : Optional[str] = None ):
A_ = self._tokenizer.model.save(UpperCAmelCase , name=UpperCAmelCase )
return tuple(UpperCAmelCase )
| 329 | 1 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
A_ = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"_float_tensor",
"decoder.output_projection.weight",
]
for k in ignore_keys:
state_dict.pop(__UpperCamelCase ,__UpperCamelCase )
def __snake_case ( __UpperCamelCase : Union[str, Any] ):
"""simple docstring"""
A_ , A_ = emb.weight.shape
A_ = nn.Linear(__UpperCamelCase ,__UpperCamelCase ,bias=__UpperCamelCase )
A_ = emb.weight.data
return lin_layer
def __snake_case ( __UpperCamelCase : Any ,__UpperCamelCase : Dict="facebook/mbart-large-en-ro" ,__UpperCamelCase : Union[str, Any]=False ,__UpperCamelCase : Dict=False ):
"""simple docstring"""
A_ = torch.load(__UpperCamelCase ,map_location="cpu" )["model"]
remove_ignore_keys_(__UpperCamelCase )
A_ = state_dict["encoder.embed_tokens.weight"].shape[0]
A_ = MBartConfig.from_pretrained(__UpperCamelCase ,vocab_size=__UpperCamelCase )
if mbart_aa and finetuned:
A_ = "relu"
A_ = state_dict["decoder.embed_tokens.weight"]
A_ = MBartForConditionalGeneration(__UpperCamelCase )
model.model.load_state_dict(__UpperCamelCase )
if finetuned:
A_ = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
__a :Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
)
parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument(
'--hf_config',
default='facebook/mbart-large-cc25',
type=str,
help='Which huggingface architecture to use: mbart-large',
)
parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
__a :List[Any] = parser.parse_args()
__a :Union[str, Any] = convert_fairseq_mbart_checkpoint_from_disk(
args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
)
model.save_pretrained(args.pytorch_dump_folder_path)
| 329 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__a :Optional[Any] = logging.get_logger(__name__)
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : List[str] , *UpperCAmelCase : int , **UpperCAmelCase : Optional[int] ):
warnings.warn(
"The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use VideoMAEImageProcessor instead." , UpperCAmelCase , )
super().__init__(*UpperCAmelCase , **UpperCAmelCase )
| 329 | 1 |
def __snake_case ( __UpperCamelCase : list ):
"""simple docstring"""
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]" )
if len(__UpperCamelCase ) == 0:
raise ValueError("Input list must be a non empty list" )
if len(__UpperCamelCase ) == 1:
return True
A_ = series[1] - series[0]
for index in range(len(__UpperCamelCase ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def __snake_case ( __UpperCamelCase : list ):
"""simple docstring"""
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]" )
if len(__UpperCamelCase ) == 0:
raise ValueError("Input list must be a non empty list" )
A_ = 0
for val in series:
answer += val
return answer / len(__UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 329 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _a :
"""simple docstring"""
@staticmethod
def __A ( *UpperCAmelCase : Union[str, Any] , **UpperCAmelCase : Union[str, Any] ):
pass
@is_pipeline_test
@require_vision
class _a ( unittest.TestCase ):
"""simple docstring"""
@require_torch
def __A ( self : List[str] ):
A_ = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , )
A_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
A_ = image_classifier(UpperCAmelCase , candidate_labels=["a", "b", "c"] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(UpperCAmelCase ) , [
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}],
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "c"}, {"score": 0.333, "label": "b"}],
] , )
A_ = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
] , )
@require_tf
def __A ( self : int ):
A_ = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , framework="tf" )
A_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
A_ = image_classifier(UpperCAmelCase , candidate_labels=["a", "b", "c"] )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}] , )
A_ = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
[
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
{"score": 0.333, "label": ANY(UpperCAmelCase )},
],
] , )
@slow
@require_torch
def __A ( self : Any ):
A_ = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , )
# This is an image of 2 cats with remotes and no planes
A_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
A_ = image_classifier(UpperCAmelCase , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
A_ = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , )
@slow
@require_tf
def __A ( self : Optional[Any] ):
A_ = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , framework="tf" )
# This is an image of 2 cats with remotes and no planes
A_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
A_ = image_classifier(UpperCAmelCase , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
A_ = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , )
| 329 | 1 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def __snake_case ( ):
"""simple docstring"""
A_ = 9
A_ = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
A_ = kruskal(__UpperCamelCase ,__UpperCamelCase )
A_ = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(__UpperCamelCase ) == sorted(__UpperCamelCase )
| 329 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers 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_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def __snake_case ( __UpperCamelCase : Optional[Any] ,__UpperCamelCase : Dict=10 ):
"""simple docstring"""
A_ = []
for _ in range(__UpperCamelCase ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
return lrs
def __snake_case ( __UpperCamelCase : Any ,__UpperCamelCase : Tuple=10 ):
"""simple docstring"""
A_ = []
for step in range(__UpperCamelCase ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
if step == num_steps // 2:
with tempfile.TemporaryDirectory() as tmpdirname:
A_ = os.path.join(__UpperCamelCase ,"schedule.bin" )
torch.save(scheduler.state_dict() ,__UpperCamelCase )
A_ = torch.load(__UpperCamelCase )
scheduler.load_state_dict(__UpperCamelCase )
return lrs
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : Any , UpperCAmelCase : int , UpperCAmelCase : List[Any] , UpperCAmelCase : List[str] ):
self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) )
for a, b in zip(UpperCAmelCase , UpperCAmelCase ):
self.assertAlmostEqual(UpperCAmelCase , UpperCAmelCase , delta=UpperCAmelCase )
def __A ( self : List[Any] ):
A_ = torch.tensor([0.1, -0.2, -0.1] , requires_grad=UpperCAmelCase )
A_ = torch.tensor([0.4, 0.2, -0.5] )
A_ = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
A_ = AdamW(params=[w] , lr=2E-1 , weight_decay=0.0 )
for _ in range(100 ):
A_ = criterion(UpperCAmelCase , UpperCAmelCase )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
def __A ( self : Dict ):
A_ = torch.tensor([0.1, -0.2, -0.1] , requires_grad=UpperCAmelCase )
A_ = torch.tensor([0.4, 0.2, -0.5] )
A_ = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
A_ = Adafactor(
params=[w] , lr=1E-2 , eps=(1E-30, 1E-3) , clip_threshold=1.0 , decay_rate=-0.8 , betaa=UpperCAmelCase , weight_decay=0.0 , relative_step=UpperCAmelCase , scale_parameter=UpperCAmelCase , warmup_init=UpperCAmelCase , )
for _ in range(1000 ):
A_ = criterion(UpperCAmelCase , UpperCAmelCase )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = nn.Linear(5_0 , 5_0 ) if is_torch_available() else None
_lowerCamelCase : Any = AdamW(m.parameters() , lr=1_0.0 ) if is_torch_available() else None
_lowerCamelCase : Any = 1_0
def __A ( self : str , UpperCAmelCase : int , UpperCAmelCase : Any , UpperCAmelCase : Tuple , UpperCAmelCase : Dict=None ):
self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) )
for a, b in zip(UpperCAmelCase , UpperCAmelCase ):
self.assertAlmostEqual(UpperCAmelCase , UpperCAmelCase , delta=UpperCAmelCase , msg=UpperCAmelCase )
def __A ( self : List[Any] ):
A_ = {"num_warmup_steps": 2, "num_training_steps": 10}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
A_ = {
get_constant_schedule: ({}, [10.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{"num_warmup_steps": 4},
[0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, "num_cycles": 2},
[0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, "power": 2.0, "lr_end": 1E-7},
[0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
),
get_inverse_sqrt_schedule: (
{"num_warmup_steps": 2},
[0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
),
}
for scheduler_func, data in scheds.items():
A_ , A_ = data
A_ = scheduler_func(self.optimizer , **UpperCAmelCase )
self.assertEqual(len([scheduler.get_lr()[0]] ) , 1 )
A_ = unwrap_schedule(UpperCAmelCase , self.num_steps )
self.assertListAlmostEqual(
UpperCAmelCase , UpperCAmelCase , tol=1E-2 , msg=f'''failed for {scheduler_func} in normal scheduler''' , )
A_ = scheduler_func(self.optimizer , **UpperCAmelCase )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(UpperCAmelCase ) # wrap to test picklability of the schedule
A_ = unwrap_and_save_reload_schedule(UpperCAmelCase , self.num_steps )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase , msg=f'''failed for {scheduler_func} in save and reload''' )
class _a :
"""simple docstring"""
def __init__( self : List[str] , UpperCAmelCase : List[str] ):
A_ = fn
def __call__( self : Union[str, Any] , *UpperCAmelCase : str , **UpperCAmelCase : Optional[Any] ):
return self.fn(*UpperCAmelCase , **UpperCAmelCase )
@classmethod
def __A ( self : Dict , UpperCAmelCase : List[str] ):
A_ = list(map(self , scheduler.lr_lambdas ) )
| 329 | 1 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
__a :Optional[Any] = logging.get_logger(__name__)
@add_end_docstrings(snake_case_ )
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : List[Any] , *UpperCAmelCase : Any , **UpperCAmelCase : int ):
super().__init__(*UpperCAmelCase , **UpperCAmelCase )
requires_backends(self , "vision" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == "tf" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def __A ( self : Optional[int] , UpperCAmelCase : int=None , UpperCAmelCase : List[Any]=None , UpperCAmelCase : List[Any]=None ):
A_ = {}
A_ = {}
if prompt is not None:
A_ = prompt
if generate_kwargs is not None:
A_ = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
A_ = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"
" please use only one" )
A_ = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self : str , UpperCAmelCase : Union[str, List[str], "Image.Image", List["Image.Image"]] , **UpperCAmelCase : str ):
return super().__call__(UpperCAmelCase , **UpperCAmelCase )
def __A ( self : int , UpperCAmelCase : List[str] , UpperCAmelCase : Union[str, Any]=None ):
A_ = load_image(UpperCAmelCase )
if prompt is not None:
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
raise ValueError(
f'''Received an invalid text input, got - {type(UpperCAmelCase )} - but expected a single string. '''
"Note also that one single text can be provided for conditional image to text generation." )
A_ = self.model.config.model_type
if model_type == "git":
A_ = self.image_processor(images=UpperCAmelCase , return_tensors=self.framework )
A_ = self.tokenizer(text=UpperCAmelCase , add_special_tokens=UpperCAmelCase ).input_ids
A_ = [self.tokenizer.cls_token_id] + input_ids
A_ = torch.tensor(UpperCAmelCase ).unsqueeze(0 )
model_inputs.update({"input_ids": input_ids} )
elif model_type == "pix2struct":
A_ = self.image_processor(images=UpperCAmelCase , header_text=UpperCAmelCase , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
A_ = self.image_processor(images=UpperCAmelCase , return_tensors=self.framework )
A_ = self.tokenizer(UpperCAmelCase , return_tensors=self.framework )
model_inputs.update(UpperCAmelCase )
else:
raise ValueError(f'''Model type {model_type} does not support conditional text generation''' )
else:
A_ = self.image_processor(images=UpperCAmelCase , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
A_ = None
return model_inputs
def __A ( self : Dict , UpperCAmelCase : str , UpperCAmelCase : str=None ):
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs["input_ids"] , UpperCAmelCase )
and all(x is None for x in model_inputs["input_ids"] )
):
A_ = None
if generate_kwargs is None:
A_ = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
A_ = model_inputs.pop(self.model.main_input_name )
A_ = self.model.generate(UpperCAmelCase , **UpperCAmelCase , **UpperCAmelCase )
return model_outputs
def __A ( self : Optional[Any] , UpperCAmelCase : Union[str, Any] ):
A_ = []
for output_ids in model_outputs:
A_ = {
"generated_text": self.tokenizer.decode(
UpperCAmelCase , skip_special_tokens=UpperCAmelCase , )
}
records.append(UpperCAmelCase )
return records
| 329 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp_seed,
temporary_assignment,
zip_dict,
)
from .utils import require_tf, require_torch
def __snake_case ( __UpperCamelCase : Optional[int] ): # picklable for multiprocessing
"""simple docstring"""
return x.sum()
def __snake_case ( __UpperCamelCase : List[str] ): # picklable for multiprocessing
"""simple docstring"""
return i + 1
@dataclass
class _a :
"""simple docstring"""
_lowerCamelCase : int
_lowerCamelCase : str
class _a ( snake_case_ ):
"""simple docstring"""
def __A ( self : Dict ):
A_ = {}
A_ = []
A_ = 1
A_ = [1, 2]
A_ = {"a": 1, "b": 2}
A_ = {"a": [1, 2], "b": [3, 4]}
A_ = {"a": {"1": 1}, "b": 2}
A_ = {"a": 1, "b": 2, "c": 3, "d": 4}
A_ = {}
A_ = []
A_ = 2
A_ = [2, 3]
A_ = {"a": 2, "b": 3}
A_ = {"a": [2, 3], "b": [4, 5]}
A_ = {"a": {"1": 2}, "b": 3}
A_ = {"a": 2, "b": 3, "c": 4, "d": 5}
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase )
A_ = 2
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
A_ = {"a": np.eye(2 ), "b": np.zeros(3 ), "c": np.ones(2 )}
A_ = {"a": 2, "b": 0, "c": 2}
A_ = {
"a": np.eye(2 ).astype(UpperCAmelCase ),
"b": np.zeros(3 ).astype(UpperCAmelCase ),
"c": np.ones(2 ).astype(UpperCAmelCase ),
}
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , map_numpy=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(UpperCAmelCase , UpperCAmelCase , map_numpy=UpperCAmelCase ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
self.assertEqual(map_nested(UpperCAmelCase , UpperCAmelCase , map_numpy=UpperCAmelCase , num_proc=UpperCAmelCase ) , UpperCAmelCase )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(UpperCAmelCase , UpperCAmelCase , map_numpy=UpperCAmelCase , num_proc=UpperCAmelCase ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
with self.assertRaises(UpperCAmelCase ): # can't pickle a local lambda
map_nested(lambda UpperCAmelCase : x + 1 , UpperCAmelCase , num_proc=UpperCAmelCase )
def __A ( self : List[str] ):
A_ = {"a": 1, "b": 2}
A_ = {"a": 3, "b": 4}
A_ = {"a": 5, "b": 6}
A_ = sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))] )
self.assertEqual(sorted(zip_dict(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ) , UpperCAmelCase )
def __A ( self : Any ):
class _a :
"""simple docstring"""
_lowerCamelCase : int = 'bar'
A_ = Foo()
self.assertEqual(foo.my_attr , "bar" )
with temporary_assignment(UpperCAmelCase , "my_attr" , "BAR" ):
self.assertEqual(foo.my_attr , "BAR" )
self.assertEqual(foo.my_attr , "bar" )
@pytest.mark.parametrize(
"iterable_length, num_proc, expected_num_proc" ,[
(1, None, 1),
(1, 1, 1),
(2, None, 1),
(2, 1, 1),
(2, 2, 1),
(2, 3, 1),
(3, 2, 1),
(16, 16, 16),
(16, 17, 16),
(17, 16, 16),
] ,)
def __snake_case ( __UpperCamelCase : Optional[int] ,__UpperCamelCase : Tuple ,__UpperCamelCase : List[Any] ):
"""simple docstring"""
with patch("datasets.utils.py_utils._single_map_nested" ) as mock_single_map_nested, patch(
"datasets.parallel.parallel.Pool" ) as mock_multiprocessing_pool:
A_ = {f'''{i}''': i for i in range(__UpperCamelCase )}
A_ = map_nested(lambda __UpperCamelCase : x + 10 ,__UpperCamelCase ,num_proc=__UpperCamelCase ,parallel_min_length=16 )
if expected_num_proc == 1:
assert mock_single_map_nested.called
assert not mock_multiprocessing_pool.called
else:
assert not mock_single_map_nested.called
assert mock_multiprocessing_pool.called
assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc
class _a ( snake_case_ ):
"""simple docstring"""
@require_tf
def __A ( self : Union[str, Any] ):
import tensorflow as tf
from tensorflow.keras import layers
A_ = layers.Dense(2 )
def gen_random_output():
A_ = tf.random.uniform((1, 3) )
return model(UpperCAmelCase ).numpy()
with temp_seed(42 , set_tensorflow=UpperCAmelCase ):
A_ = gen_random_output()
with temp_seed(42 , set_tensorflow=UpperCAmelCase ):
A_ = gen_random_output()
A_ = gen_random_output()
np.testing.assert_equal(UpperCAmelCase , UpperCAmelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@require_torch
def __A ( self : Optional[int] ):
import torch
def gen_random_output():
A_ = torch.nn.Linear(3 , 2 )
A_ = torch.rand(1 , 3 )
return model(UpperCAmelCase ).detach().numpy()
with temp_seed(42 , set_pytorch=UpperCAmelCase ):
A_ = gen_random_output()
with temp_seed(42 , set_pytorch=UpperCAmelCase ):
A_ = gen_random_output()
A_ = gen_random_output()
np.testing.assert_equal(UpperCAmelCase , UpperCAmelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
def __A ( self : Any ):
def gen_random_output():
return np.random.rand(1 , 3 )
with temp_seed(42 ):
A_ = gen_random_output()
with temp_seed(42 ):
A_ = gen_random_output()
A_ = gen_random_output()
np.testing.assert_equal(UpperCAmelCase , UpperCAmelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@pytest.mark.parametrize("input_data" ,[{}] )
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
A_ = NestedDataStructure(__UpperCamelCase ).data
assert output_data == input_data
@pytest.mark.parametrize(
"data, expected_output" ,[
({}, []),
([], []),
("foo", ["foo"]),
(["foo", "bar"], ["foo", "bar"]),
([["foo", "bar"]], ["foo", "bar"]),
([[["foo"], ["bar"]]], ["foo", "bar"]),
([[["foo"], "bar"]], ["foo", "bar"]),
({"a": 1, "b": 2}, [1, 2]),
({"a": [1, 2], "b": [3, 4]}, [1, 2, 3, 4]),
({"a": [[1, 2]], "b": [[3, 4]]}, [1, 2, 3, 4]),
({"a": [[1, 2]], "b": [3, 4]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [[[3], [4]]]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [[3, 4]]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [3, 4]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [3, [4]]}, [1, 2, 3, 4]),
({"a": {"1": 1}, "b": 2}, [1, 2]),
({"a": {"1": [1]}, "b": 2}, [1, 2]),
({"a": {"1": [1]}, "b": [2]}, [1, 2]),
] ,)
def __snake_case ( __UpperCamelCase : Dict ,__UpperCamelCase : Any ):
"""simple docstring"""
A_ = NestedDataStructure(__UpperCamelCase ).flatten()
assert output == expected_output
def __snake_case ( ):
"""simple docstring"""
A_ = A(x=1 ,y="foobar" )
A_ = {"x": 1, "y": "foobar"}
assert asdict(__UpperCamelCase ) == expected_output
A_ = {"a": {"b": A(x=10 ,y="foo" )}, "c": [A(x=20 ,y="bar" )]}
A_ = {"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
assert asdict(__UpperCamelCase ) == expected_output
with pytest.raises(__UpperCamelCase ):
asdict([1, A(x=10 ,y="foo" )] )
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
return text.split()
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
yield (time.time(), content)
time.sleep(2 )
yield (time.time(), content)
def __snake_case ( ):
"""simple docstring"""
with Pool(2 ) as pool:
A_ = list(iflatmap_unordered(__UpperCamelCase ,_split_text ,kwargs_iterable=[{"text": "hello there"}] * 10 ) )
assert out.count("hello" ) == 10
assert out.count("there" ) == 10
assert len(__UpperCamelCase ) == 20
# check multiprocess from pathos (uses dill for pickling)
with multiprocess.Pool(2 ) as pool:
A_ = list(iflatmap_unordered(__UpperCamelCase ,_split_text ,kwargs_iterable=[{"text": "hello there"}] * 10 ) )
assert out.count("hello" ) == 10
assert out.count("there" ) == 10
assert len(__UpperCamelCase ) == 20
# check that we get items as fast as possible
with Pool(2 ) as pool:
A_ = []
for yield_time, content in iflatmap_unordered(
__UpperCamelCase ,_aseconds_generator_of_aitems_with_timing ,kwargs_iterable=[{"content": "a"}, {"content": "b"}] ):
assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
out.append(__UpperCamelCase )
assert out.count("a" ) == 2
assert out.count("b" ) == 2
assert len(__UpperCamelCase ) == 4
| 329 | 1 |
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be positive" )
return sum(
divisor for divisor in range(1 ,input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 329 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F) #
or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F) #
or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F) #
or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F) #
or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F) #
or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F) #
): #
return True
return False
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
for char in word:
A_ = ord(__UpperCamelCase )
if not _is_chinese_char(__UpperCamelCase ):
return 0
return 1
def __snake_case ( __UpperCamelCase : List[str] ):
"""simple docstring"""
A_ = set()
for token in tokens:
A_ = len(__UpperCamelCase ) > 1 and is_chinese(__UpperCamelCase )
if chinese_word:
word_set.add(__UpperCamelCase )
A_ = list(__UpperCamelCase )
return word_list
def __snake_case ( __UpperCamelCase : List[str] ,__UpperCamelCase : set() ):
"""simple docstring"""
if not chinese_word_set:
return bert_tokens
A_ = max([len(__UpperCamelCase ) for w in chinese_word_set] )
A_ = bert_tokens
A_ , A_ = 0, len(__UpperCamelCase )
while start < end:
A_ = True
if is_chinese(bert_word[start] ):
A_ = min(end - start ,__UpperCamelCase )
for i in range(__UpperCamelCase ,1 ,-1 ):
A_ = "".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 ,start + i ):
A_ = "##" + bert_word[j]
A_ = start + i
A_ = False
break
if single_word:
start += 1
return bert_word
def __snake_case ( __UpperCamelCase : List[str] ,__UpperCamelCase : LTP ,__UpperCamelCase : BertTokenizer ):
"""simple docstring"""
A_ = []
for i in range(0 ,len(__UpperCamelCase ) ,100 ):
A_ = ltp_tokenizer.seg(lines[i : i + 100] )[0]
A_ = [get_chinese_word(__UpperCamelCase ) for r in res]
ltp_res.extend(__UpperCamelCase )
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
A_ = []
for i in range(0 ,len(__UpperCamelCase ) ,100 ):
A_ = bert_tokenizer(lines[i : i + 100] ,add_special_tokens=__UpperCamelCase ,truncation=__UpperCamelCase ,max_length=512 )
bert_res.extend(res["input_ids"] )
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
A_ = []
for input_ids, chinese_word in zip(__UpperCamelCase ,__UpperCamelCase ):
A_ = []
for id in input_ids:
A_ = bert_tokenizer._convert_id_to_token(__UpperCamelCase )
input_tokens.append(__UpperCamelCase )
A_ = add_sub_symbol(__UpperCamelCase ,__UpperCamelCase )
A_ = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__UpperCamelCase ):
if token[:2] == "##":
A_ = token[2:]
# save chinese tokens' pos
if len(__UpperCamelCase ) == 1 and _is_chinese_char(ord(__UpperCamelCase ) ):
ref_id.append(__UpperCamelCase )
ref_ids.append(__UpperCamelCase )
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
return ref_ids
def __snake_case ( __UpperCamelCase : Dict ):
"""simple docstring"""
with open(args.file_name ,"r" ,encoding="utf-8" ) as f:
A_ = f.readlines()
A_ = [line.strip() for line in data if len(__UpperCamelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
A_ = LTP(args.ltp ) # faster in GPU device
A_ = BertTokenizer.from_pretrained(args.bert )
A_ = prepare_ref(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
with open(args.save_path ,"w" ,encoding="utf-8" ) as f:
A_ = [json.dumps(__UpperCamelCase ) + "\n" for ref in ref_ids]
f.writelines(__UpperCamelCase )
if __name__ == "__main__":
__a :List[Any] = argparse.ArgumentParser(description='prepare_chinese_ref')
parser.add_argument(
'--file_name',
type=str,
default='./resources/chinese-demo.txt',
help='file need process, same as training data in lm',
)
parser.add_argument(
'--ltp', type=str, default='./resources/ltp', help='resources for LTP tokenizer, usually a path'
)
parser.add_argument('--bert', type=str, default='./resources/robert', help='resources for Bert tokenizer')
parser.add_argument('--save_path', type=str, default='./resources/ref.txt', help='path to save res')
__a :Dict = parser.parse_args()
main(args)
| 329 | 1 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__a :List[Any] = logging.get_logger(__name__)
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : List[Any] = ['input_values', 'attention_mask']
def __init__( self : List[str] , UpperCAmelCase : int = 1 , UpperCAmelCase : int = 16000 , UpperCAmelCase : float = 0.0 , UpperCAmelCase : bool = False , UpperCAmelCase : int = 80 , UpperCAmelCase : int = 16 , UpperCAmelCase : int = 64 , UpperCAmelCase : str = "hann_window" , UpperCAmelCase : float = 1.0 , UpperCAmelCase : float = 80 , UpperCAmelCase : float = 7600 , UpperCAmelCase : float = 1E-10 , UpperCAmelCase : int = 2 , UpperCAmelCase : bool = True , **UpperCAmelCase : List[Any] , ):
super().__init__(feature_size=UpperCAmelCase , sampling_rate=UpperCAmelCase , padding_value=UpperCAmelCase , **UpperCAmelCase )
A_ = do_normalize
A_ = return_attention_mask
A_ = num_mel_bins
A_ = hop_length
A_ = win_length
A_ = win_function
A_ = frame_signal_scale
A_ = fmin
A_ = fmax
A_ = mel_floor
A_ = reduction_factor
A_ = win_length * sampling_rate // 1000
A_ = hop_length * sampling_rate // 1000
A_ = optimal_fft_length(self.sample_size )
A_ = (self.n_fft // 2) + 1
A_ = window_function(window_length=self.sample_size , name=self.win_function , periodic=UpperCAmelCase )
A_ = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.num_mel_bins , min_frequency=self.fmin , max_frequency=self.fmax , sampling_rate=self.sampling_rate , norm="slaney" , mel_scale="slaney" , )
if frame_signal_scale != 1.0:
warnings.warn(
"The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers" , UpperCAmelCase , )
if reduction_factor != 2.0:
warnings.warn(
"The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers" , UpperCAmelCase , )
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def __A ( UpperCAmelCase : List[np.ndarray] , UpperCAmelCase : List[np.ndarray] , UpperCAmelCase : float = 0.0 ):
if attention_mask is not None:
A_ = np.array(UpperCAmelCase , np.intaa )
A_ = []
for vector, length in zip(UpperCAmelCase , attention_mask.sum(-1 ) ):
A_ = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
A_ = padding_value
normed_input_values.append(UpperCAmelCase )
else:
A_ = [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def __A ( self : Tuple , UpperCAmelCase : np.ndarray , ):
A_ = spectrogram(
UpperCAmelCase , window=self.window , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , mel_filters=self.mel_filters , mel_floor=self.mel_floor , log_mel="log10" , )
return log_mel_spec.T
def __call__( self : List[str] , UpperCAmelCase : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , UpperCAmelCase : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : bool = False , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : Optional[bool] = None , UpperCAmelCase : Optional[Union[str, TensorType]] = None , UpperCAmelCase : Optional[int] = None , **UpperCAmelCase : int , ):
if audio is None and audio_target is None:
raise ValueError("You must provide either `audio` or `audio_target` values." )
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
f''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
f''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
"It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
if audio is not None:
A_ = self._process_audio(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase , )
else:
A_ = None
if audio_target is not None:
A_ = self._process_audio(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase , )
if inputs is None:
return inputs_target
else:
A_ = inputs_target["input_values"]
A_ = inputs_target.get("attention_mask" )
if decoder_attention_mask is not None:
A_ = decoder_attention_mask
return inputs
def __A ( self : List[Any] , UpperCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , UpperCAmelCase : bool = False , UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : bool = False , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : Optional[bool] = None , UpperCAmelCase : Optional[Union[str, TensorType]] = None , **UpperCAmelCase : int , ):
A_ = isinstance(UpperCAmelCase , np.ndarray ) and len(speech.shape ) > 1
if is_batched_numpy and len(speech.shape ) > 2:
raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' )
A_ = is_batched_numpy or (
isinstance(UpperCAmelCase , (list, tuple) ) and (isinstance(speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
A_ = [np.asarray(UpperCAmelCase , dtype=np.floataa ) for speech in speech]
elif not is_batched and not isinstance(UpperCAmelCase , np.ndarray ):
A_ = np.asarray(UpperCAmelCase , dtype=np.floataa )
elif isinstance(UpperCAmelCase , np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
A_ = speech.astype(np.floataa )
# always return batch
if not is_batched:
A_ = [speech]
# needed to make pad() work on spectrogram inputs
A_ = self.feature_size
# convert into correct format for padding
if is_target:
A_ = [self._extract_mel_features(UpperCAmelCase ) for waveform in speech]
A_ = BatchFeature({"input_values": features} )
A_ = self.num_mel_bins
else:
A_ = BatchFeature({"input_values": speech} )
A_ = self.pad(
UpperCAmelCase , padding=UpperCAmelCase , max_length=UpperCAmelCase , truncation=UpperCAmelCase , pad_to_multiple_of=UpperCAmelCase , return_attention_mask=UpperCAmelCase , **UpperCAmelCase , )
A_ = feature_size_hack
# convert input values to correct format
A_ = padded_inputs["input_values"]
if not isinstance(input_values[0] , np.ndarray ):
A_ = [np.asarray(UpperCAmelCase , dtype=np.floataa ) for array in input_values]
elif (
not isinstance(UpperCAmelCase , np.ndarray )
and isinstance(input_values[0] , np.ndarray )
and input_values[0].dtype is np.dtype(np.floataa )
):
A_ = [array.astype(np.floataa ) for array in input_values]
elif isinstance(UpperCAmelCase , np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
A_ = input_values.astype(np.floataa )
# convert attention_mask to correct format
A_ = padded_inputs.get("attention_mask" )
if attention_mask is not None:
A_ = [np.asarray(UpperCAmelCase , dtype=np.intaa ) for array in attention_mask]
# zero-mean and unit-variance normalization
if not is_target and self.do_normalize:
A_ = (
attention_mask
if self._get_padding_strategies(UpperCAmelCase , max_length=UpperCAmelCase ) is not PaddingStrategy.DO_NOT_PAD
else None
)
A_ = self.zero_mean_unit_var_norm(
padded_inputs["input_values"] , attention_mask=UpperCAmelCase , padding_value=self.padding_value )
if return_tensors is not None:
A_ = padded_inputs.convert_to_tensors(UpperCAmelCase )
return padded_inputs
def __A ( self : int ):
A_ = super().to_dict()
# Don't serialize these as they are derived from the other properties.
A_ = ["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
for name in names:
if name in output:
del output[name]
return output
| 329 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def __snake_case ( __UpperCamelCase : Features ):
"""simple docstring"""
A_ = np.inf
def set_batch_size(__UpperCamelCase : FeatureType ) -> None:
nonlocal batch_size
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = min(__UpperCamelCase ,config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = min(__UpperCamelCase ,config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(__UpperCamelCase ,__UpperCamelCase ) and feature.dtype == "binary":
A_ = min(__UpperCamelCase ,config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(__UpperCamelCase ,__UpperCamelCase )
return None if batch_size is np.inf else batch_size
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : Tuple , UpperCAmelCase : NestedDataStructureLike[PathLike] , UpperCAmelCase : Optional[NamedSplit] = None , UpperCAmelCase : Optional[Features] = None , UpperCAmelCase : str = None , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : Optional[int] = None , **UpperCAmelCase : Tuple , ):
super().__init__(
UpperCAmelCase , split=UpperCAmelCase , features=UpperCAmelCase , cache_dir=UpperCAmelCase , keep_in_memory=UpperCAmelCase , streaming=UpperCAmelCase , num_proc=UpperCAmelCase , **UpperCAmelCase , )
A_ = path_or_paths if isinstance(UpperCAmelCase , UpperCAmelCase ) else {self.split: path_or_paths}
A_ = _PACKAGED_DATASETS_MODULES["parquet"][1]
A_ = Parquet(
cache_dir=UpperCAmelCase , data_files=UpperCAmelCase , features=UpperCAmelCase , hash=UpperCAmelCase , **UpperCAmelCase , )
def __A ( self : Optional[Any] ):
# Build iterable dataset
if self.streaming:
A_ = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
A_ = None
A_ = None
A_ = None
A_ = None
self.builder.download_and_prepare(
download_config=UpperCAmelCase , download_mode=UpperCAmelCase , verification_mode=UpperCAmelCase , base_path=UpperCAmelCase , num_proc=self.num_proc , )
A_ = self.builder.as_dataset(
split=self.split , verification_mode=UpperCAmelCase , in_memory=self.keep_in_memory )
return dataset
class _a :
"""simple docstring"""
def __init__( self : Any , UpperCAmelCase : Dataset , UpperCAmelCase : Union[PathLike, BinaryIO] , UpperCAmelCase : Optional[int] = None , **UpperCAmelCase : List[Any] , ):
A_ = dataset
A_ = path_or_buf
A_ = batch_size or get_writer_batch_size(dataset.features )
A_ = parquet_writer_kwargs
def __A ( self : int ):
A_ = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with open(self.path_or_buf , "wb+" ) as buffer:
A_ = self._write(file_obj=UpperCAmelCase , batch_size=UpperCAmelCase , **self.parquet_writer_kwargs )
else:
A_ = self._write(file_obj=self.path_or_buf , batch_size=UpperCAmelCase , **self.parquet_writer_kwargs )
return written
def __A ( self : Tuple , UpperCAmelCase : BinaryIO , UpperCAmelCase : int , **UpperCAmelCase : Optional[Any] ):
A_ = 0
A_ = parquet_writer_kwargs.pop("path_or_buf" , UpperCAmelCase )
A_ = self.dataset.features.arrow_schema
A_ = pq.ParquetWriter(UpperCAmelCase , schema=UpperCAmelCase , **UpperCAmelCase )
for offset in logging.tqdm(
range(0 , len(self.dataset ) , UpperCAmelCase ) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating parquet from Arrow format" , ):
A_ = query_table(
table=self.dataset._data , key=slice(UpperCAmelCase , offset + batch_size ) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(UpperCAmelCase )
written += batch.nbytes
writer.close()
return written
| 329 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : int , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[Any] ):
A_ = dataset
A_ = process
A_ = params
def __len__( self : List[str] ):
return len(self.dataset )
def __getitem__( self : int , UpperCAmelCase : Tuple ):
A_ = self.dataset[i]
A_ = self.process(UpperCAmelCase , **self.params )
return processed
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any]=None ):
A_ = loader
A_ = infer
A_ = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
A_ = None
A_ = loader_batch_size
# Internal bookkeeping
A_ = None
A_ = None
def __len__( self : Union[str, Any] ):
return len(self.loader )
def __iter__( self : int ):
A_ = iter(self.loader )
return self
def __A ( self : int ):
if isinstance(self._loader_batch_data , torch.Tensor ):
# Batch data is simple tensor, just fetch the slice
A_ = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
A_ = {}
for k, element in self._loader_batch_data.items():
if isinstance(UpperCAmelCase , UpperCAmelCase ):
# Convert ModelOutput to tuple first
A_ = element.to_tuple()
if isinstance(element[0] , torch.Tensor ):
A_ = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
A_ = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCAmelCase , UpperCAmelCase ):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] , torch.Tensor ):
A_ = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
A_ = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if element is None:
# This can happen for optional data that get passed around
A_ = None
elif isinstance(element[self._loader_batch_index] , torch.Tensor ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
A_ = element[self._loader_batch_index].unsqueeze(0 )
elif isinstance(element[self._loader_batch_index] , np.ndarray ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
A_ = np.expand_dims(element[self._loader_batch_index] , 0 )
else:
# This is typically a list, so no need to `unsqueeze`.
A_ = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
A_ = self._loader_batch_data.__class__(UpperCAmelCase )
self._loader_batch_index += 1
return result
def __A ( self : List[str] ):
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
A_ = next(self.iterator )
A_ = self.infer(UpperCAmelCase , **self.params )
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(UpperCAmelCase , torch.Tensor ):
A_ = processed
else:
A_ = list(processed.keys() )[0]
A_ = processed[key]
if isinstance(UpperCAmelCase , UpperCAmelCase ):
A_ = len(UpperCAmelCase )
else:
A_ = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
A_ = observed_batch_size
# Setting internal index to unwrap the batch
A_ = processed
A_ = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : Tuple , UpperCAmelCase : Dict , UpperCAmelCase : List[str] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[str]=None ):
super().__init__(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def __iter__( self : Any ):
A_ = iter(self.loader )
A_ = None
return self
def __A ( self : Dict ):
if self.subiterator is None:
A_ = self.infer(next(self.iterator ) , **self.params )
try:
# Try to return next item
A_ = next(self.subiterator )
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
A_ = self.infer(next(self.iterator ) , **self.params )
A_ = next(self.subiterator )
return processed
class _a ( snake_case_ ):
"""simple docstring"""
def __iter__( self : List[str] ):
A_ = iter(self.loader )
return self
def __A ( self : Optional[Any] ):
# Extremely similar to PipelineIterator in its unpacking mechanism
# BUT, we have an extra required item which is the presence of `is_last`
# That is because everything is flattened by `PipelineChunkIterator` we
# need to keep track of how to regroup here in the original `process`
# boundaries so that `process` and `postprocess` see the same data.
# This iterator accumulates items (possibly while unbatching) until it
# its a `is_last` and then just passes it on to the caller.
A_ = False
A_ = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
A_ = self.loader_batch_item()
A_ = item.pop("is_last" )
accumulator.append(UpperCAmelCase )
if is_last:
return accumulator
while not is_last:
A_ = self.infer(next(self.iterator ) , **self.params )
if self.loader_batch_size is not None:
if isinstance(UpperCAmelCase , torch.Tensor ):
A_ = processed
else:
A_ = list(processed.keys() )[0]
A_ = processed[key]
if isinstance(UpperCAmelCase , UpperCAmelCase ):
A_ = len(UpperCAmelCase )
else:
A_ = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
A_ = observed_batch_size
A_ = processed
A_ = 0
while self._loader_batch_index < self.loader_batch_size:
A_ = self.loader_batch_item()
A_ = item.pop("is_last" )
accumulator.append(UpperCAmelCase )
if is_last:
return accumulator
else:
A_ = processed
A_ = item.pop("is_last" )
accumulator.append(UpperCAmelCase )
return accumulator
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : Optional[int] , UpperCAmelCase : Dataset , UpperCAmelCase : str ):
A_ = dataset
A_ = key
def __len__( self : Any ):
return len(self.dataset )
def __getitem__( self : List[str] , UpperCAmelCase : Tuple ):
return self.dataset[i][self.key]
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : Any , UpperCAmelCase : Dataset , UpperCAmelCase : str , UpperCAmelCase : str ):
A_ = dataset
A_ = keya
A_ = keya
def __len__( self : List[str] ):
return len(self.dataset )
def __getitem__( self : Optional[Any] , UpperCAmelCase : Tuple ):
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 329 |
from __future__ import annotations
def __snake_case ( __UpperCamelCase : int = 4 ):
"""simple docstring"""
A_ = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
return reverse_row(transpose(__UpperCamelCase ) )
# OR.. transpose(reverse_column(matrix))
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
return reverse_row(reverse_column(__UpperCamelCase ) )
# OR.. reverse_column(reverse_row(matrix))
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
return reverse_column(transpose(__UpperCamelCase ) )
# OR.. transpose(reverse_row(matrix))
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
A_ = [list(__UpperCamelCase ) for x in zip(*__UpperCamelCase )]
return matrix
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
A_ = matrix[::-1]
return matrix
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
A_ = [x[::-1] for x in matrix]
return matrix
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
for i in matrix:
print(*__UpperCamelCase )
if __name__ == "__main__":
__a :Any = make_matrix()
print('\norigin:\n')
print_matrix(matrix)
print('\nrotate 90 counterclockwise:\n')
print_matrix(rotate_aa(matrix))
__a :Any = make_matrix()
print('\norigin:\n')
print_matrix(matrix)
print('\nrotate 180:\n')
print_matrix(rotate_aaa(matrix))
__a :Any = make_matrix()
print('\norigin:\n')
print_matrix(matrix)
print('\nrotate 270 counterclockwise:\n')
print_matrix(rotate_aaa(matrix))
| 329 | 1 |
from functools import reduce
__a :Optional[Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def __snake_case ( __UpperCamelCase : str = N ):
"""simple docstring"""
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda __UpperCamelCase ,__UpperCamelCase : str(int(__UpperCamelCase ) * int(__UpperCamelCase ) ) ,n[i : i + 13] ) )
for i in range(len(__UpperCamelCase ) - 12 ) )
if __name__ == "__main__":
print(F"{solution() = }")
| 329 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ['torch', 'transformers', 'onnx']
def __init__( self : List[Any] , *UpperCAmelCase : Union[str, Any] , **UpperCAmelCase : str ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Tuple , *UpperCAmelCase : Tuple , **UpperCAmelCase : Union[str, Any] ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Dict , *UpperCAmelCase : List[Any] , **UpperCAmelCase : Tuple ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Tuple = ['torch', 'transformers', 'onnx']
def __init__( self : Optional[Any] , *UpperCAmelCase : List[Any] , **UpperCAmelCase : List[Any] ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : List[Any] , *UpperCAmelCase : Union[str, Any] , **UpperCAmelCase : str ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Tuple , *UpperCAmelCase : Union[str, Any] , **UpperCAmelCase : int ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Any = ['torch', 'transformers', 'onnx']
def __init__( self : Dict , *UpperCAmelCase : Optional[int] , **UpperCAmelCase : Optional[int] ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Union[str, Any] , *UpperCAmelCase : Tuple , **UpperCAmelCase : Optional[int] ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Tuple , *UpperCAmelCase : Optional[int] , **UpperCAmelCase : int ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : List[str] = ['torch', 'transformers', 'onnx']
def __init__( self : List[Any] , *UpperCAmelCase : List[Any] , **UpperCAmelCase : int ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Any , *UpperCAmelCase : List[Any] , **UpperCAmelCase : str ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Optional[int] , *UpperCAmelCase : str , **UpperCAmelCase : int ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Dict = ['torch', 'transformers', 'onnx']
def __init__( self : str , *UpperCAmelCase : int , **UpperCAmelCase : Tuple ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Optional[int] , *UpperCAmelCase : str , **UpperCAmelCase : Dict ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : int , *UpperCAmelCase : Optional[int] , **UpperCAmelCase : List[str] ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : List[Any] = ['torch', 'transformers', 'onnx']
def __init__( self : str , *UpperCAmelCase : str , **UpperCAmelCase : List[Any] ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : List[Any] , *UpperCAmelCase : Optional[Any] , **UpperCAmelCase : List[Any] ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Optional[int] , *UpperCAmelCase : List[str] , **UpperCAmelCase : int ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
| 329 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __snake_case ( __UpperCamelCase : int = 3 ):
"""simple docstring"""
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
raise TypeError("number of qubits must be a integer." )
if number_of_qubits <= 0:
raise ValueError("number of qubits must be > 0." )
if math.floor(__UpperCamelCase ) != number_of_qubits:
raise ValueError("number of qubits must be exact integer." )
if number_of_qubits > 10:
raise ValueError("number of qubits too large to simulate(>10)." )
A_ = QuantumRegister(__UpperCamelCase ,"qr" )
A_ = ClassicalRegister(__UpperCamelCase ,"cr" )
A_ = QuantumCircuit(__UpperCamelCase ,__UpperCamelCase )
A_ = number_of_qubits
for i in range(__UpperCamelCase ):
quantum_circuit.h(number_of_qubits - i - 1 )
counter -= 1
for j in range(__UpperCamelCase ):
quantum_circuit.cp(np.pi / 2 ** (counter - j) ,__UpperCamelCase ,__UpperCamelCase )
for k in range(number_of_qubits // 2 ):
quantum_circuit.swap(__UpperCamelCase ,number_of_qubits - k - 1 )
# measure all the qubits
quantum_circuit.measure(__UpperCamelCase ,__UpperCamelCase )
# simulate with 10000 shots
A_ = Aer.get_backend("qasm_simulator" )
A_ = execute(__UpperCamelCase ,__UpperCamelCase ,shots=1_0000 )
return job.result().get_counts(__UpperCamelCase )
if __name__ == "__main__":
print(
F"Total count for quantum fourier transform state is: \
{quantum_fourier_transform(3)}"
)
| 329 |
import itertools
import math
def __snake_case ( __UpperCamelCase : 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(__UpperCamelCase ) + 1 ) ,6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __snake_case ( ):
"""simple docstring"""
A_ = 2
while True:
if is_prime(__UpperCamelCase ):
yield num
num += 1
def __snake_case ( __UpperCamelCase : int = 1_0001 ):
"""simple docstring"""
return next(itertools.islice(prime_generator() ,nth - 1 ,__UpperCamelCase ) )
if __name__ == "__main__":
print(F"{solution() = }")
| 329 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union
from packaging import version
from ..utils import is_torch_available, logging
if is_torch_available():
import torch
__a :Dict = logging.get_logger(__name__)
@dataclass
class _a :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase : str=False , UpperCAmelCase : Optional[int]=False , UpperCAmelCase : List[str]=6.0 , UpperCAmelCase : int=None , UpperCAmelCase : List[str]=False , UpperCAmelCase : Tuple=False , UpperCAmelCase : Any=None , UpperCAmelCase : Any="fp4" , UpperCAmelCase : Any=False , **UpperCAmelCase : Tuple , ):
A_ = load_in_abit
A_ = load_in_abit
A_ = llm_inta_threshold
A_ = llm_inta_skip_modules
A_ = llm_inta_enable_fpaa_cpu_offload
A_ = llm_inta_has_fpaa_weight
A_ = bnb_abit_quant_type
A_ = bnb_abit_use_double_quant
if bnb_abit_compute_dtype is None:
A_ = torch.floataa
elif isinstance(UpperCAmelCase , UpperCAmelCase ):
A_ = getattr(UpperCAmelCase , UpperCAmelCase )
elif isinstance(UpperCAmelCase , torch.dtype ):
A_ = bnb_abit_compute_dtype
else:
raise ValueError("bnb_4bit_compute_dtype must be a string or a torch.dtype" )
self.post_init()
def __A ( self : int ):
if not isinstance(self.llm_inta_threshold , UpperCAmelCase ):
raise ValueError("llm_int8_threshold must be a float" )
if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules , UpperCAmelCase ):
raise ValueError("llm_int8_skip_modules must be a list of strings" )
if not isinstance(self.llm_inta_enable_fpaa_cpu_offload , UpperCAmelCase ):
raise ValueError("llm_int8_enable_fp32_cpu_offload must be a boolean" )
if not isinstance(self.llm_inta_has_fpaa_weight , UpperCAmelCase ):
raise ValueError("llm_int8_has_fp16_weight must be a boolean" )
if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype , torch.dtype ):
raise ValueError("bnb_4bit_compute_dtype must be torch.dtype" )
if not isinstance(self.bnb_abit_quant_type , UpperCAmelCase ):
raise ValueError("bnb_4bit_quant_type must be a string" )
if not isinstance(self.bnb_abit_use_double_quant , UpperCAmelCase ):
raise ValueError("bnb_4bit_use_double_quant must be a boolean" )
if self.load_in_abit and not version.parse(importlib.metadata.version("bitsandbytes" ) ) >= version.parse(
"0.39.0" ):
raise ValueError(
"4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version" )
def __A ( self : Tuple ):
return self.load_in_abit or self.load_in_abit
def __A ( self : Union[str, Any] ):
if self.load_in_abit:
return "llm_int8"
elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
return "fp4"
elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
return "nf4"
else:
return None
@classmethod
def __A ( cls : Any , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str , **UpperCAmelCase : List[Any] ):
A_ = cls(**UpperCAmelCase )
A_ = []
for key, value in kwargs.items():
if hasattr(UpperCAmelCase , UpperCAmelCase ):
setattr(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
to_remove.append(UpperCAmelCase )
for key in to_remove:
kwargs.pop(UpperCAmelCase , UpperCAmelCase )
if return_unused_kwargs:
return config, kwargs
else:
return config
def __A ( self : int , UpperCAmelCase : Union[str, os.PathLike] ):
with open(UpperCAmelCase , "w" , encoding="utf-8" ) as writer:
A_ = self.to_dict()
A_ = json.dumps(UpperCAmelCase , indent=2 , sort_keys=UpperCAmelCase ) + "\n"
writer.write(UpperCAmelCase )
def __A ( self : Any ):
A_ = copy.deepcopy(self.__dict__ )
A_ = str(output["bnb_4bit_compute_dtype"] ).split("." )[1]
return output
def __repr__( self : Dict ):
return f'''{self.__class__.__name__} {self.to_json_string()}'''
def __A ( self : Any , UpperCAmelCase : bool = True ):
if use_diff is True:
A_ = self.to_diff_dict()
else:
A_ = self.to_dict()
return json.dumps(UpperCAmelCase , indent=2 , sort_keys=UpperCAmelCase ) + "\n"
def __A ( self : Union[str, Any] ):
A_ = self.to_dict()
# get the default config dict
A_ = BitsAndBytesConfig().to_dict()
A_ = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
A_ = value
return serializable_config_dict
| 329 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class _a :
"""simple docstring"""
def __init__( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : List[str]=13 , UpperCAmelCase : Tuple=7 , UpperCAmelCase : int=True , UpperCAmelCase : Dict=True , UpperCAmelCase : Union[str, Any]=True , UpperCAmelCase : List[str]=True , UpperCAmelCase : Optional[Any]=99 , UpperCAmelCase : str=32 , UpperCAmelCase : Dict=2 , UpperCAmelCase : List[str]=4 , UpperCAmelCase : Optional[int]=37 , UpperCAmelCase : Optional[int]="gelu" , UpperCAmelCase : List[str]=0.1 , UpperCAmelCase : Union[str, Any]=0.1 , UpperCAmelCase : Any=512 , UpperCAmelCase : int=16 , UpperCAmelCase : Any=2 , UpperCAmelCase : Union[str, Any]=0.02 , UpperCAmelCase : Union[str, Any]=3 , UpperCAmelCase : Union[str, Any]=4 , UpperCAmelCase : List[Any]=None , ):
A_ = parent
A_ = 13
A_ = 7
A_ = True
A_ = True
A_ = True
A_ = True
A_ = 99
A_ = 384
A_ = 2
A_ = 4
A_ = 37
A_ = "gelu"
A_ = 0.1
A_ = 0.1
A_ = 512
A_ = 16
A_ = 2
A_ = 0.02
A_ = 3
A_ = 4
A_ = 128
A_ = 2
A_ = 9
A_ = 1
A_ = None
def __A ( self : Optional[int] ):
A_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ = None
if self.use_input_mask:
A_ = random_attention_mask([self.batch_size, self.seq_length] )
A_ = None
if self.use_token_type_ids:
A_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
A_ = None
A_ = None
A_ = None
if self.use_labels:
A_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A_ = ids_tensor([self.batch_size] , self.num_choices )
A_ = ConvBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=UpperCAmelCase , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __A ( self : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : int , UpperCAmelCase : Optional[int] , UpperCAmelCase : int , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : int ):
A_ = TFConvBertModel(config=UpperCAmelCase )
A_ = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
A_ = [input_ids, input_mask]
A_ = model(UpperCAmelCase )
A_ = model(UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __A ( self : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Tuple , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : str , UpperCAmelCase : Tuple ):
A_ = TFConvBertForMaskedLM(config=UpperCAmelCase )
A_ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
A_ = model(UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self : Dict , UpperCAmelCase : Any , UpperCAmelCase : List[str] , UpperCAmelCase : Any , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Any , UpperCAmelCase : int ):
A_ = self.num_labels
A_ = TFConvBertForSequenceClassification(config=UpperCAmelCase )
A_ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
A_ = model(UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __A ( self : Any , UpperCAmelCase : Any , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : List[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] , UpperCAmelCase : str ):
A_ = self.num_choices
A_ = TFConvBertForMultipleChoice(config=UpperCAmelCase )
A_ = tf.tile(tf.expand_dims(UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
A_ = tf.tile(tf.expand_dims(UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
A_ = tf.tile(tf.expand_dims(UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
A_ = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
A_ = model(UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __A ( self : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Any , UpperCAmelCase : Tuple , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : Any , UpperCAmelCase : str ):
A_ = self.num_labels
A_ = TFConvBertForTokenClassification(config=UpperCAmelCase )
A_ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
A_ = model(UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __A ( self : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : str ):
A_ = TFConvBertForQuestionAnswering(config=UpperCAmelCase )
A_ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
A_ = model(UpperCAmelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __A ( self : List[str] ):
A_ = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) = config_and_inputs
A_ = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class _a ( snake_case_ , snake_case_ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
_lowerCamelCase : Any = (
{
'feature-extraction': TFConvBertModel,
'fill-mask': TFConvBertForMaskedLM,
'question-answering': TFConvBertForQuestionAnswering,
'text-classification': TFConvBertForSequenceClassification,
'token-classification': TFConvBertForTokenClassification,
'zero-shot': TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
_lowerCamelCase : Dict = False
_lowerCamelCase : Optional[int] = False
_lowerCamelCase : Dict = False
def __A ( self : List[str] ):
A_ = TFConvBertModelTester(self )
A_ = ConfigTester(self , config_class=UpperCAmelCase , hidden_size=37 )
def __A ( self : Tuple ):
self.config_tester.run_common_tests()
def __A ( self : Tuple ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase )
def __A ( self : Dict ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase )
def __A ( self : List[Any] ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase )
def __A ( self : Dict ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase )
def __A ( self : int ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase )
def __A ( self : List[Any] ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase )
@slow
def __A ( self : str ):
A_ , A_ = self.model_tester.prepare_config_and_inputs_for_common()
A_ = True
A_ = True
if hasattr(UpperCAmelCase , "use_cache" ):
A_ = True
A_ = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length )
A_ = getattr(self.model_tester , "key_length" , UpperCAmelCase )
for model_class in self.all_model_classes:
A_ = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase )
A_ = model_class(UpperCAmelCase )
A_ = len(model(UpperCAmelCase ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(UpperCAmelCase , saved_model=UpperCAmelCase )
A_ = os.path.join(UpperCAmelCase , "saved_model" , "1" )
A_ = tf.keras.models.load_model(UpperCAmelCase )
A_ = model(UpperCAmelCase )
if self.is_encoder_decoder:
A_ = outputs["encoder_hidden_states"]
A_ = outputs["encoder_attentions"]
else:
A_ = outputs["hidden_states"]
A_ = outputs["attentions"]
self.assertEqual(len(UpperCAmelCase ) , UpperCAmelCase )
A_ = getattr(
self.model_tester , "expected_num_hidden_layers" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(UpperCAmelCase ) , UpperCAmelCase )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def __A ( self : List[str] ):
A_ = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
self.assertIsNotNone(UpperCAmelCase )
def __A ( self : Any ):
A_ , A_ = self.model_tester.prepare_config_and_inputs_for_common()
A_ = True
A_ = getattr(self.model_tester , "decoder_seq_length" , self.model_tester.seq_length )
A_ = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length )
A_ = getattr(self.model_tester , "key_length" , UpperCAmelCase )
A_ = getattr(self.model_tester , "key_length" , UpperCAmelCase )
def check_decoder_attentions_output(UpperCAmelCase : Optional[int] ):
A_ = len(UpperCAmelCase )
self.assertEqual(out_len % 2 , 0 )
A_ = outputs.decoder_attentions
self.assertEqual(len(UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(UpperCAmelCase : Optional[Any] ):
A_ = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
A_ = True
A_ = False
A_ = model_class(UpperCAmelCase )
A_ = model(self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) )
A_ = len(UpperCAmelCase )
self.assertEqual(config.output_hidden_states , UpperCAmelCase )
check_encoder_attentions_output(UpperCAmelCase )
if self.is_encoder_decoder:
A_ = model_class(UpperCAmelCase )
A_ = model(self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) )
self.assertEqual(config.output_hidden_states , UpperCAmelCase )
check_decoder_attentions_output(UpperCAmelCase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
A_ = True
A_ = model_class(UpperCAmelCase )
A_ = model(self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) )
self.assertEqual(config.output_hidden_states , UpperCAmelCase )
check_encoder_attentions_output(UpperCAmelCase )
# Check attention is always last and order is fine
A_ = True
A_ = True
A_ = model_class(UpperCAmelCase )
A_ = model(self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(UpperCAmelCase ) )
self.assertEqual(model.config.output_hidden_states , UpperCAmelCase )
check_encoder_attentions_output(UpperCAmelCase )
@require_tf
class _a ( unittest.TestCase ):
"""simple docstring"""
@slow
def __A ( self : Dict ):
A_ = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
A_ = tf.constant([[0, 1, 2, 3, 4, 5]] )
A_ = model(UpperCAmelCase )[0]
A_ = [1, 6, 768]
self.assertEqual(output.shape , UpperCAmelCase )
A_ = tf.constant(
[
[
[-0.03_475_493, -0.4_686_034, -0.30_638_832],
[0.22_637_248, -0.26_988_646, -0.7_423_424],
[0.10_324_868, -0.45_013_508, -0.58_280_784],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , UpperCAmelCase , atol=1E-4 )
| 329 | 1 |
__a :List[str] = 0 # The first color of the flag.
__a :int = 1 # The second color of the flag.
__a :Optional[int] = 2 # The third color of the flag.
__a :Any = (red, white, blue)
def __snake_case ( __UpperCamelCase : list ):
"""simple docstring"""
if not sequence:
return []
if len(__UpperCamelCase ) == 1:
return list(__UpperCamelCase )
A_ = 0
A_ = len(__UpperCamelCase ) - 1
A_ = 0
while mid <= high:
if sequence[mid] == colors[0]:
A_ , A_ = sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
A_ , A_ = sequence[high], sequence[mid]
high -= 1
else:
A_ = f'''The elements inside the sequence must contains only {colors} values'''
raise ValueError(__UpperCamelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
__a :str = input('Enter numbers separated by commas:\n').strip()
__a :Tuple = [int(item.strip()) for item in user_input.split(',')]
print(F"{dutch_national_flag_sort(unsorted)}")
| 329 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a :Dict = logging.get_logger(__name__)
__a :int = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
),
'google/realm-cc-news-pretrained-encoder': (
'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'
),
'google/realm-cc-news-pretrained-scorer': (
'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'
),
'google/realm-cc-news-pretrained-openqa': (
'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'
),
'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json',
'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json',
'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json',
'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json',
# See all REALM models at https://huggingface.co/models?filter=realm
}
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : List[Any] = 'realm'
def __init__( self : Union[str, Any] , UpperCAmelCase : Optional[Any]=30522 , UpperCAmelCase : List[str]=768 , UpperCAmelCase : Optional[Any]=128 , UpperCAmelCase : str=12 , UpperCAmelCase : Dict=12 , UpperCAmelCase : Optional[Any]=8 , UpperCAmelCase : Any=3072 , UpperCAmelCase : Union[str, Any]="gelu_new" , UpperCAmelCase : List[Any]=0.1 , UpperCAmelCase : Dict=0.1 , UpperCAmelCase : int=512 , UpperCAmelCase : Tuple=2 , UpperCAmelCase : Union[str, Any]=0.02 , UpperCAmelCase : Union[str, Any]=1E-12 , UpperCAmelCase : List[Any]=256 , UpperCAmelCase : Optional[int]=10 , UpperCAmelCase : List[str]=1E-3 , UpperCAmelCase : Any=5 , UpperCAmelCase : List[Any]=320 , UpperCAmelCase : Optional[Any]=13353718 , UpperCAmelCase : Tuple=5000 , UpperCAmelCase : List[str]=1 , UpperCAmelCase : Union[str, Any]=0 , UpperCAmelCase : Union[str, Any]=2 , **UpperCAmelCase : List[str] , ):
super().__init__(pad_token_id=UpperCAmelCase , bos_token_id=UpperCAmelCase , eos_token_id=UpperCAmelCase , **UpperCAmelCase )
# Common config
A_ = vocab_size
A_ = max_position_embeddings
A_ = hidden_size
A_ = retriever_proj_size
A_ = num_hidden_layers
A_ = num_attention_heads
A_ = num_candidates
A_ = intermediate_size
A_ = hidden_act
A_ = hidden_dropout_prob
A_ = attention_probs_dropout_prob
A_ = initializer_range
A_ = type_vocab_size
A_ = layer_norm_eps
# Reader config
A_ = span_hidden_size
A_ = max_span_width
A_ = reader_layer_norm_eps
A_ = reader_beam_size
A_ = reader_seq_len
# Retrieval config
A_ = num_block_records
A_ = searcher_beam_size
| 329 | 1 |
class _a :
"""simple docstring"""
def __init__( self : Dict , UpperCAmelCase : list[int] ):
A_ = len(UpperCAmelCase )
A_ = [0] * len_array
if len_array > 0:
A_ = array[0]
for i in range(1 , UpperCAmelCase ):
A_ = self.prefix_sum[i - 1] + array[i]
def __A ( self : List[str] , UpperCAmelCase : int , UpperCAmelCase : int ):
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def __A ( self : Tuple , UpperCAmelCase : int ):
A_ = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(UpperCAmelCase )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 329 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__a :Optional[Any] = logging.get_logger(__name__)
def __snake_case ( __UpperCamelCase : List[str] ,__UpperCamelCase : Any ,__UpperCamelCase : List[str] ,__UpperCamelCase : Optional[Any] ):
"""simple docstring"""
A_ = original_name.split("." )[0]
A_ = key.split("." )
A_ = int(key_list[key_list.index(__UpperCamelCase ) - 2] )
A_ = int(key_list[key_list.index(__UpperCamelCase ) - 1] )
A_ = orig_block_num - offset
A_ = key.replace(f'''{orig_block_num}.{layer_num}.{original_name}''' ,f'''block.{new_block_num}.{layer_num}.{new_name}''' )
return key
def __snake_case ( __UpperCamelCase : Any ):
"""simple docstring"""
A_ = OrderedDict()
A_ , A_ = 0, 0
for key, value in state_dict.items():
if key.startswith("network" ):
A_ = key.replace("network" ,"poolformer.encoder" )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith("bias" ) and "patch_embed" not in key:
patch_emb_offset += 1
A_ = key[: key.find("proj" )]
A_ = key.replace(__UpperCamelCase ,f'''patch_embeddings.{total_embed_found}.''' )
A_ = key.replace("proj" ,"projection" )
if key.endswith("bias" ):
total_embed_found += 1
if "patch_embeddings" in key:
A_ = "poolformer.encoder." + key
if "mlp.fc1" in key:
A_ = replace_key_with_offset(__UpperCamelCase ,__UpperCamelCase ,"mlp.fc1" ,"output.conv1" )
if "mlp.fc2" in key:
A_ = replace_key_with_offset(__UpperCamelCase ,__UpperCamelCase ,"mlp.fc2" ,"output.conv2" )
if "norm1" in key:
A_ = replace_key_with_offset(__UpperCamelCase ,__UpperCamelCase ,"norm1" ,"before_norm" )
if "norm2" in key:
A_ = replace_key_with_offset(__UpperCamelCase ,__UpperCamelCase ,"norm2" ,"after_norm" )
if "layer_scale_1" in key:
A_ = replace_key_with_offset(__UpperCamelCase ,__UpperCamelCase ,"layer_scale_1" ,"layer_scale_1" )
if "layer_scale_2" in key:
A_ = replace_key_with_offset(__UpperCamelCase ,__UpperCamelCase ,"layer_scale_2" ,"layer_scale_2" )
if "head" in key:
A_ = key.replace("head" ,"classifier" )
A_ = value
return new_state_dict
def __snake_case ( ):
"""simple docstring"""
A_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
A_ = Image.open(requests.get(__UpperCamelCase ,stream=__UpperCamelCase ).raw )
return image
@torch.no_grad()
def __snake_case ( __UpperCamelCase : Union[str, Any] ,__UpperCamelCase : List[str] ,__UpperCamelCase : List[Any] ):
"""simple docstring"""
A_ = PoolFormerConfig()
# set attributes based on model_name
A_ = "huggingface/label-files"
A_ = model_name[-3:]
A_ = 1000
A_ = "imagenet-1k-id2label.json"
A_ = (1, 1000)
# set config attributes
A_ = json.load(open(hf_hub_download(__UpperCamelCase ,__UpperCamelCase ,repo_type="dataset" ) ,"r" ) )
A_ = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
A_ = idalabel
A_ = {v: k for k, v in idalabel.items()}
if size == "s12":
A_ = [2, 2, 6, 2]
A_ = [64, 128, 320, 512]
A_ = 4.0
A_ = 0.9
elif size == "s24":
A_ = [4, 4, 12, 4]
A_ = [64, 128, 320, 512]
A_ = 4.0
A_ = 0.9
elif size == "s36":
A_ = [6, 6, 18, 6]
A_ = [64, 128, 320, 512]
A_ = 4.0
A_ = 1E-6
A_ = 0.9
elif size == "m36":
A_ = [6, 6, 18, 6]
A_ = [96, 192, 384, 768]
A_ = 4.0
A_ = 1E-6
A_ = 0.95
elif size == "m48":
A_ = [8, 8, 24, 8]
A_ = [96, 192, 384, 768]
A_ = 4.0
A_ = 1E-6
A_ = 0.95
else:
raise ValueError(f'''Size {size} not supported''' )
# load image processor
A_ = PoolFormerImageProcessor(crop_pct=__UpperCamelCase )
# Prepare image
A_ = prepare_img()
A_ = image_processor(images=__UpperCamelCase ,return_tensors="pt" ).pixel_values
logger.info(f'''Converting model {model_name}...''' )
# load original state dict
A_ = torch.load(__UpperCamelCase ,map_location=torch.device("cpu" ) )
# rename keys
A_ = rename_keys(__UpperCamelCase )
# create HuggingFace model and load state dict
A_ = PoolFormerForImageClassification(__UpperCamelCase )
model.load_state_dict(__UpperCamelCase )
model.eval()
# Define image processor
A_ = PoolFormerImageProcessor(crop_pct=__UpperCamelCase )
A_ = image_processor(images=prepare_img() ,return_tensors="pt" ).pixel_values
# forward pass
A_ = model(__UpperCamelCase )
A_ = outputs.logits
# define expected logit slices for different models
if size == "s12":
A_ = torch.tensor([-0.3045, -0.6758, -0.4869] )
elif size == "s24":
A_ = torch.tensor([0.4402, -0.1374, -0.8045] )
elif size == "s36":
A_ = torch.tensor([-0.6080, -0.5133, -0.5898] )
elif size == "m36":
A_ = torch.tensor([0.3952, 0.2263, -1.2668] )
elif size == "m48":
A_ = torch.tensor([0.1167, -0.0656, -0.3423] )
else:
raise ValueError(f'''Size {size} not supported''' )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3] ,__UpperCamelCase ,atol=1E-2 )
# finally, save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
model.save_pretrained(__UpperCamelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
__a :Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
default='poolformer_s12',
type=str,
help='Name of the model you\'d like to convert.',
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
__a :int = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 329 | 1 |
import os
from pathlib import Path
def __snake_case ( ):
"""simple docstring"""
from torch.utils.cpp_extension import load
A_ = Path(__UpperCamelCase ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
A_ = [
root / filename
for filename in [
"vision.cpp",
os.path.join("cpu" ,"ms_deform_attn_cpu.cpp" ),
os.path.join("cuda" ,"ms_deform_attn_cuda.cu" ),
]
]
load(
"MultiScaleDeformableAttention" ,__UpperCamelCase ,with_cuda=__UpperCamelCase ,extra_include_paths=[str(__UpperCamelCase )] ,extra_cflags=["-DWITH_CUDA=1"] ,extra_cuda_cflags=[
"-DCUDA_HAS_FP16=1",
"-D__CUDA_NO_HALF_OPERATORS__",
"-D__CUDA_NO_HALF_CONVERSIONS__",
"-D__CUDA_NO_HALF2_OPERATORS__",
] ,)
import MultiScaleDeformableAttention as MSDA
return MSDA
| 329 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : torch.FloatTensor
_lowerCamelCase : Optional[torch.FloatTensor] = None
def __snake_case ( __UpperCamelCase : Tuple ,__UpperCamelCase : Any=0.999 ,__UpperCamelCase : Any="cosine" ,):
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(__UpperCamelCase : Any ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__UpperCamelCase : int ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
A_ = []
for i in range(__UpperCamelCase ):
A_ = i / num_diffusion_timesteps
A_ = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__UpperCamelCase ) / alpha_bar_fn(__UpperCamelCase ) ,__UpperCamelCase ) )
return torch.tensor(__UpperCamelCase ,dtype=torch.floataa )
class _a ( snake_case_ , snake_case_ ):
"""simple docstring"""
@register_to_config
def __init__( self : Optional[int] , UpperCAmelCase : int = 1000 , UpperCAmelCase : str = "fixed_small_log" , UpperCAmelCase : bool = True , UpperCAmelCase : Optional[float] = 1.0 , UpperCAmelCase : str = "epsilon" , UpperCAmelCase : str = "squaredcos_cap_v2" , ):
if beta_schedule != "squaredcos_cap_v2":
raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'" )
A_ = betas_for_alpha_bar(UpperCAmelCase )
A_ = 1.0 - self.betas
A_ = torch.cumprod(self.alphas , dim=0 )
A_ = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
A_ = 1.0
# setable values
A_ = None
A_ = torch.from_numpy(np.arange(0 , UpperCAmelCase )[::-1].copy() )
A_ = variance_type
def __A ( self : Optional[Any] , UpperCAmelCase : torch.FloatTensor , UpperCAmelCase : Optional[int] = None ):
return sample
def __A ( self : List[Any] , UpperCAmelCase : int , UpperCAmelCase : Union[str, torch.device] = None ):
A_ = num_inference_steps
A_ = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
A_ = (np.arange(0 , UpperCAmelCase ) * step_ratio).round()[::-1].copy().astype(np.intaa )
A_ = torch.from_numpy(UpperCAmelCase ).to(UpperCAmelCase )
def __A ( self : List[Any] , UpperCAmelCase : Dict , UpperCAmelCase : str=None , UpperCAmelCase : Any=None , UpperCAmelCase : List[Any]=None ):
if prev_timestep is None:
A_ = t - 1
A_ = self.alphas_cumprod[t]
A_ = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
A_ = 1 - alpha_prod_t
A_ = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
A_ = self.betas[t]
else:
A_ = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
A_ = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
A_ = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
A_ = torch.log(torch.clamp(UpperCAmelCase , min=1E-20 ) )
A_ = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
A_ = variance.log()
A_ = beta.log()
A_ = (predicted_variance + 1) / 2
A_ = frac * max_log + (1 - frac) * min_log
return variance
def __A ( self : int , UpperCAmelCase : torch.FloatTensor , UpperCAmelCase : int , UpperCAmelCase : torch.FloatTensor , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : Dict=None , UpperCAmelCase : bool = True , ):
A_ = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
A_ , A_ = torch.split(UpperCAmelCase , sample.shape[1] , dim=1 )
else:
A_ = None
# 1. compute alphas, betas
if prev_timestep is None:
A_ = t - 1
A_ = self.alphas_cumprod[t]
A_ = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
A_ = 1 - alpha_prod_t
A_ = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
A_ = self.betas[t]
A_ = self.alphas[t]
else:
A_ = 1 - alpha_prod_t / alpha_prod_t_prev
A_ = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
A_ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
A_ = model_output
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
" for the UnCLIPScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
A_ = torch.clamp(
UpperCAmelCase , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
A_ = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
A_ = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
A_ = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
A_ = 0
if t > 0:
A_ = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=UpperCAmelCase , device=model_output.device )
A_ = self._get_variance(
UpperCAmelCase , predicted_variance=UpperCAmelCase , prev_timestep=UpperCAmelCase , )
if self.variance_type == "fixed_small_log":
A_ = variance
elif self.variance_type == "learned_range":
A_ = (0.5 * variance).exp()
else:
raise ValueError(
f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
" for the UnCLIPScheduler." )
A_ = variance * variance_noise
A_ = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=UpperCAmelCase , pred_original_sample=UpperCAmelCase )
def __A ( self : Optional[Any] , UpperCAmelCase : torch.FloatTensor , UpperCAmelCase : torch.FloatTensor , UpperCAmelCase : torch.IntTensor , ):
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
A_ = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
A_ = timesteps.to(original_samples.device )
A_ = alphas_cumprod[timesteps] ** 0.5
A_ = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
A_ = sqrt_alpha_prod.unsqueeze(-1 )
A_ = (1 - alphas_cumprod[timesteps]) ** 0.5
A_ = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
A_ = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
A_ = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 329 | 1 |
from __future__ import annotations
from typing import Any
class _a ( snake_case_ ):
"""simple docstring"""
pass
class _a :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase : Any ):
A_ = data
A_ = None
def __iter__( self : Optional[Any] ):
A_ = self
A_ = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(UpperCAmelCase )
yield node.data
A_ = node.next_node
@property
def __A ( self : int ):
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
__a :Union[str, Any] = Node(1)
__a :Optional[Any] = Node(2)
__a :List[str] = Node(3)
__a :Dict = Node(4)
print(root_node.has_loop) # False
__a :Optional[int] = root_node.next_node
print(root_node.has_loop) # True
__a :int = Node(5)
__a :Any = Node(6)
__a :Optional[int] = Node(5)
__a :Union[str, Any] = Node(6)
print(root_node.has_loop) # False
__a :Dict = Node(1)
print(root_node.has_loop) # False
| 329 |
from math import isqrt, loga
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
A_ = [True] * max_number
for i in range(2 ,isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 ,__UpperCamelCase ,__UpperCamelCase ):
A_ = False
return [i for i in range(2 ,__UpperCamelCase ) if is_prime[i]]
def __snake_case ( __UpperCamelCase : int = 80_0800 ,__UpperCamelCase : int = 80_0800 ):
"""simple docstring"""
A_ = degree * loga(__UpperCamelCase )
A_ = int(__UpperCamelCase )
A_ = calculate_prime_numbers(__UpperCamelCase )
A_ = 0
A_ = 0
A_ = len(__UpperCamelCase ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(F"{solution() = }")
| 329 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__a :Any = logging.get_logger(__name__)
__a :List[Any] = {
'microsoft/focalnet-tiny': 'https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json',
}
class _a ( snake_case_ , snake_case_ ):
"""simple docstring"""
_lowerCamelCase : str = 'focalnet'
def __init__( self : Union[str, Any] , UpperCAmelCase : List[str]=224 , UpperCAmelCase : List[Any]=4 , UpperCAmelCase : Dict=3 , UpperCAmelCase : Optional[int]=96 , UpperCAmelCase : List[str]=False , UpperCAmelCase : Tuple=[192, 384, 768, 768] , UpperCAmelCase : Tuple=[2, 2, 6, 2] , UpperCAmelCase : Tuple=[2, 2, 2, 2] , UpperCAmelCase : List[str]=[3, 3, 3, 3] , UpperCAmelCase : List[Any]="gelu" , UpperCAmelCase : str=4.0 , UpperCAmelCase : Any=0.0 , UpperCAmelCase : Optional[int]=0.1 , UpperCAmelCase : Dict=False , UpperCAmelCase : Tuple=1E-4 , UpperCAmelCase : Optional[int]=False , UpperCAmelCase : List[Any]=False , UpperCAmelCase : Tuple=False , UpperCAmelCase : Optional[Any]=0.02 , UpperCAmelCase : List[str]=1E-5 , UpperCAmelCase : Any=32 , UpperCAmelCase : List[str]=None , UpperCAmelCase : Dict=None , **UpperCAmelCase : List[str] , ):
super().__init__(**UpperCAmelCase )
A_ = image_size
A_ = patch_size
A_ = num_channels
A_ = embed_dim
A_ = use_conv_embed
A_ = hidden_sizes
A_ = depths
A_ = focal_levels
A_ = focal_windows
A_ = hidden_act
A_ = mlp_ratio
A_ = hidden_dropout_prob
A_ = drop_path_rate
A_ = use_layerscale
A_ = layerscale_value
A_ = use_post_layernorm
A_ = use_post_layernorm_in_modulation
A_ = normalize_modulator
A_ = initializer_range
A_ = layer_norm_eps
A_ = encoder_stride
A_ = ["stem"] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
A_ , A_ = get_aligned_output_features_output_indices(
out_features=UpperCAmelCase , out_indices=UpperCAmelCase , stage_names=self.stage_names )
| 329 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__a :str = logging.get_logger(__name__)
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : str ):
"""simple docstring"""
A_ = RobertaPreLayerNormConfig.from_pretrained(
__UpperCamelCase ,architectures=["RobertaPreLayerNormForMaskedLM"] )
# convert state_dict
A_ = torch.load(hf_hub_download(repo_id=__UpperCamelCase ,filename="pytorch_model.bin" ) )
A_ = {}
for tensor_key, tensor_value in original_state_dict.items():
# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
if tensor_key.startswith("roberta." ):
A_ = "roberta_prelayernorm." + tensor_key[len("roberta." ) :]
# The original implementation contains weights which are not used, remove them from the state_dict
if tensor_key.endswith(".self.LayerNorm.weight" ) or tensor_key.endswith(".self.LayerNorm.bias" ):
continue
A_ = tensor_value
A_ = RobertaPreLayerNormForMaskedLM.from_pretrained(
pretrained_model_name_or_path=__UpperCamelCase ,config=__UpperCamelCase ,state_dict=__UpperCamelCase )
model.save_pretrained(__UpperCamelCase )
# convert tokenizer
A_ = AutoTokenizer.from_pretrained(__UpperCamelCase )
tokenizer.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
__a :Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint-repo',
default=None,
type=str,
required=True,
help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
__a :Any = parser.parse_args()
convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)
| 329 | 1 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__a :str = logging.get_logger(__name__)
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : str ):
"""simple docstring"""
A_ = RobertaPreLayerNormConfig.from_pretrained(
__UpperCamelCase ,architectures=["RobertaPreLayerNormForMaskedLM"] )
# convert state_dict
A_ = torch.load(hf_hub_download(repo_id=__UpperCamelCase ,filename="pytorch_model.bin" ) )
A_ = {}
for tensor_key, tensor_value in original_state_dict.items():
# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
if tensor_key.startswith("roberta." ):
A_ = "roberta_prelayernorm." + tensor_key[len("roberta." ) :]
# The original implementation contains weights which are not used, remove them from the state_dict
if tensor_key.endswith(".self.LayerNorm.weight" ) or tensor_key.endswith(".self.LayerNorm.bias" ):
continue
A_ = tensor_value
A_ = RobertaPreLayerNormForMaskedLM.from_pretrained(
pretrained_model_name_or_path=__UpperCamelCase ,config=__UpperCamelCase ,state_dict=__UpperCamelCase )
model.save_pretrained(__UpperCamelCase )
# convert tokenizer
A_ = AutoTokenizer.from_pretrained(__UpperCamelCase )
tokenizer.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
__a :Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint-repo',
default=None,
type=str,
required=True,
help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
__a :Any = parser.parse_args()
convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)
| 329 |
from maths.prime_factors import prime_factors
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = f'''Input value of [number={number}] must be an integer'''
raise TypeError(__UpperCamelCase )
if number < 1:
raise ValueError("Input must be a positive integer" )
return -1 if len(prime_factors(__UpperCamelCase ) ) % 2 else 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 329 | 1 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
__a :Optional[int] = TypeVar('T')
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
return (position - 1) // 2
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
return (2 * position) + 1
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
return (2 * position) + 2
class _a ( Generic[T] ):
"""simple docstring"""
def __init__( self : int ):
A_ = []
A_ = {}
A_ = 0
def __len__( self : Tuple ):
return self.elements
def __repr__( self : Union[str, Any] ):
return str(self.heap )
def __A ( self : List[str] ):
# Check if the priority queue is empty
return self.elements == 0
def __A ( self : int , UpperCAmelCase : T , UpperCAmelCase : int ):
# Add an element with given priority to the queue
self.heap.append((elem, weight) )
A_ = self.elements
self.elements += 1
self._bubble_up(UpperCAmelCase )
def __A ( self : str ):
# Remove and return the element with lowest weight (highest priority)
if self.elements > 1:
self._swap_nodes(0 , self.elements - 1 )
A_ , A_ = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
A_ , A_ = self.heap[0]
self._bubble_down(UpperCAmelCase )
return elem
def __A ( self : Tuple , UpperCAmelCase : T , UpperCAmelCase : int ):
# Update the weight of the given key
A_ = self.position_map[elem]
A_ = (elem, weight)
if position > 0:
A_ = get_parent_position(UpperCAmelCase )
A_ , A_ = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(UpperCAmelCase )
else:
self._bubble_down(UpperCAmelCase )
else:
self._bubble_down(UpperCAmelCase )
def __A ( self : Optional[int] , UpperCAmelCase : T ):
# Place a node at the proper position (upward movement) [to be used internally
# only]
A_ = self.position_map[elem]
if curr_pos == 0:
return None
A_ = get_parent_position(UpperCAmelCase )
A_ , A_ = self.heap[curr_pos]
A_ , A_ = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(UpperCAmelCase , UpperCAmelCase )
return self._bubble_up(UpperCAmelCase )
return None
def __A ( self : str , UpperCAmelCase : T ):
# Place a node at the proper position (downward movement) [to be used
# internally only]
A_ = self.position_map[elem]
A_ , A_ = self.heap[curr_pos]
A_ = get_child_left_position(UpperCAmelCase )
A_ = get_child_right_position(UpperCAmelCase )
if child_left_position < self.elements and child_right_position < self.elements:
A_ , A_ = self.heap[child_left_position]
A_ , A_ = self.heap[child_right_position]
if child_right_weight < child_left_weight and child_right_weight < weight:
self._swap_nodes(UpperCAmelCase , UpperCAmelCase )
return self._bubble_down(UpperCAmelCase )
if child_left_position < self.elements:
A_ , A_ = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(UpperCAmelCase , UpperCAmelCase )
return self._bubble_down(UpperCAmelCase )
else:
return None
if child_right_position < self.elements:
A_ , A_ = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(UpperCAmelCase , UpperCAmelCase )
return self._bubble_down(UpperCAmelCase )
return None
def __A ( self : Optional[int] , UpperCAmelCase : int , UpperCAmelCase : int ):
# Swap the nodes at the given positions
A_ = self.heap[nodea_pos][0]
A_ = self.heap[nodea_pos][0]
A_ , A_ = (
self.heap[nodea_pos],
self.heap[nodea_pos],
)
A_ = nodea_pos
A_ = nodea_pos
class _a ( Generic[T] ):
"""simple docstring"""
def __init__( self : Dict ):
A_ = {}
A_ = 0
def __repr__( self : Tuple ):
return str(self.connections )
def __len__( self : List[str] ):
return self.nodes
def __A ( self : Optional[int] , UpperCAmelCase : T ):
# Add a node in the graph if it is not in the graph
if node not in self.connections:
A_ = {}
self.nodes += 1
def __A ( self : Any , UpperCAmelCase : T , UpperCAmelCase : T , UpperCAmelCase : int ):
# Add an edge between 2 nodes in the graph
self.add_node(UpperCAmelCase )
self.add_node(UpperCAmelCase )
A_ = weight
A_ = weight
def __snake_case ( __UpperCamelCase : GraphUndirectedWeighted[T] ,):
"""simple docstring"""
A_ = {node: maxsize for node in graph.connections}
A_ = {node: None for node in graph.connections}
A_ = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(__UpperCamelCase ,__UpperCamelCase )
if priority_queue.is_empty():
return dist, parent
# initialization
A_ = priority_queue.extract_min()
A_ = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
A_ = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(__UpperCamelCase ,dist[neighbour] )
A_ = node
# running prim's algorithm
while not priority_queue.is_empty():
A_ = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
A_ = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(__UpperCamelCase ,dist[neighbour] )
A_ = node
return dist, parent
| 329 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__a :int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__a :Any = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F"{len(upper_files)} files contain uppercase characters:")
print('\n'.join(upper_files) + '\n')
__a :Tuple = [file for file in filepaths if ' ' in file]
if space_files:
print(F"{len(space_files)} files contain space characters:")
print('\n'.join(space_files) + '\n')
__a :str = [file for file in filepaths if '-' in file]
if hyphen_files:
print(F"{len(hyphen_files)} files contain hyphen characters:")
print('\n'.join(hyphen_files) + '\n')
__a :List[str] = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F"{len(nodir_files)} files are not in a directory:")
print('\n'.join(nodir_files) + '\n')
__a :Any = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 329 | 1 |
def __snake_case ( __UpperCamelCase : list[int] ,__UpperCamelCase : list[int] ,__UpperCamelCase : int ):
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__UpperCamelCase ) )
def __snake_case ( __UpperCamelCase : list[list[int]] ,__UpperCamelCase : int ,__UpperCamelCase : list[int] ,__UpperCamelCase : int ):
"""simple docstring"""
if index == len(__UpperCamelCase ):
return True
# Recursive Step
for i in range(__UpperCamelCase ):
if valid_coloring(graph[index] ,__UpperCamelCase ,__UpperCamelCase ):
# Color current vertex
A_ = i
# Validate coloring
if util_color(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,index + 1 ):
return True
# Backtrack
A_ = -1
return False
def __snake_case ( __UpperCamelCase : list[list[int]] ,__UpperCamelCase : int ):
"""simple docstring"""
A_ = [-1] * len(__UpperCamelCase )
if util_color(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,0 ):
return colored_vertices
return []
| 329 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a :Union[str, Any] = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a :Optional[int] = [
'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BioGptForCausalLM',
'BioGptForTokenClassification',
'BioGptForSequenceClassification',
'BioGptModel',
'BioGptPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
from .tokenization_biogpt import BioGptTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_biogpt import (
BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptPreTrainedModel,
)
else:
import sys
__a :str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 329 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__a :List[Any] = logging.get_logger(__name__)
def __snake_case ( __UpperCamelCase : Tuple ):
"""simple docstring"""
A_ = OrderedDict()
for key, value in state_dict.items():
if key.startswith("module.encoder" ):
A_ = key.replace("module.encoder" ,"glpn.encoder" )
if key.startswith("module.decoder" ):
A_ = key.replace("module.decoder" ,"decoder.stages" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
A_ = key[key.find("patch_embed" ) + len("patch_embed" )]
A_ = key.replace(f'''patch_embed{idx}''' ,f'''patch_embeddings.{int(__UpperCamelCase )-1}''' )
if "norm" in key:
A_ = key.replace("norm" ,"layer_norm" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
A_ = key[key.find("glpn.encoder.layer_norm" ) + len("glpn.encoder.layer_norm" )]
A_ = key.replace(f'''layer_norm{idx}''' ,f'''layer_norm.{int(__UpperCamelCase )-1}''' )
if "layer_norm1" in key:
A_ = key.replace("layer_norm1" ,"layer_norm_1" )
if "layer_norm2" in key:
A_ = key.replace("layer_norm2" ,"layer_norm_2" )
if "block" in key:
# replace for example block1 by block.0
A_ = key[key.find("block" ) + len("block" )]
A_ = key.replace(f'''block{idx}''' ,f'''block.{int(__UpperCamelCase )-1}''' )
if "attn.q" in key:
A_ = key.replace("attn.q" ,"attention.self.query" )
if "attn.proj" in key:
A_ = key.replace("attn.proj" ,"attention.output.dense" )
if "attn" in key:
A_ = key.replace("attn" ,"attention.self" )
if "fc1" in key:
A_ = key.replace("fc1" ,"dense1" )
if "fc2" in key:
A_ = key.replace("fc2" ,"dense2" )
if "linear_pred" in key:
A_ = key.replace("linear_pred" ,"classifier" )
if "linear_fuse" in key:
A_ = key.replace("linear_fuse.conv" ,"linear_fuse" )
A_ = key.replace("linear_fuse.bn" ,"batch_norm" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
A_ = key[key.find("linear_c" ) + len("linear_c" )]
A_ = key.replace(f'''linear_c{idx}''' ,f'''linear_c.{int(__UpperCamelCase )-1}''' )
if "bot_conv" in key:
A_ = key.replace("bot_conv" ,"0.convolution" )
if "skip_conv1" in key:
A_ = key.replace("skip_conv1" ,"1.convolution" )
if "skip_conv2" in key:
A_ = key.replace("skip_conv2" ,"2.convolution" )
if "fusion1" in key:
A_ = key.replace("fusion1" ,"1.fusion" )
if "fusion2" in key:
A_ = key.replace("fusion2" ,"2.fusion" )
if "fusion3" in key:
A_ = key.replace("fusion3" ,"3.fusion" )
if "fusion" in key and "conv" in key:
A_ = key.replace("conv" ,"convolutional_layer" )
if key.startswith("module.last_layer_depth" ):
A_ = key.replace("module.last_layer_depth" ,"head.head" )
A_ = value
return new_state_dict
def __snake_case ( __UpperCamelCase : Tuple ,__UpperCamelCase : Dict ):
"""simple docstring"""
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
A_ = state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight''' )
A_ = state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias''' )
# next, add keys and values (in that order) to the state dict
A_ = kv_weight[
: config.hidden_sizes[i], :
]
A_ = kv_bias[: config.hidden_sizes[i]]
A_ = kv_weight[
config.hidden_sizes[i] :, :
]
A_ = kv_bias[config.hidden_sizes[i] :]
def __snake_case ( ):
"""simple docstring"""
A_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
A_ = Image.open(requests.get(__UpperCamelCase ,stream=__UpperCamelCase ).raw )
return image
@torch.no_grad()
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : Tuple ,__UpperCamelCase : str=False ,__UpperCamelCase : Any=None ):
"""simple docstring"""
A_ = GLPNConfig(hidden_sizes=[64, 128, 320, 512] ,decoder_hidden_size=64 ,depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
A_ = GLPNImageProcessor()
# prepare image
A_ = prepare_img()
A_ = image_processor(images=__UpperCamelCase ,return_tensors="pt" ).pixel_values
logger.info("Converting model..." )
# load original state dict
A_ = torch.load(__UpperCamelCase ,map_location=torch.device("cpu" ) )
# rename keys
A_ = rename_keys(__UpperCamelCase )
# key and value matrices need special treatment
read_in_k_v(__UpperCamelCase ,__UpperCamelCase )
# create HuggingFace model and load state dict
A_ = GLPNForDepthEstimation(__UpperCamelCase )
model.load_state_dict(__UpperCamelCase )
model.eval()
# forward pass
A_ = model(__UpperCamelCase )
A_ = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
A_ = torch.tensor(
[[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]] )
elif "kitti" in model_name:
A_ = torch.tensor(
[[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] )
else:
raise ValueError(f'''Unknown model name: {model_name}''' )
A_ = torch.Size([1, 480, 640] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] ,__UpperCamelCase ,atol=1E-4 )
print("Looks ok!" )
# finally, push to hub if required
if push_to_hub:
logger.info("Pushing model and image processor to the hub..." )
model.push_to_hub(
repo_path_or_name=Path(__UpperCamelCase ,__UpperCamelCase ) ,organization="nielsr" ,commit_message="Add model" ,use_temp_dir=__UpperCamelCase ,)
image_processor.push_to_hub(
repo_path_or_name=Path(__UpperCamelCase ,__UpperCamelCase ) ,organization="nielsr" ,commit_message="Add image processor" ,use_temp_dir=__UpperCamelCase ,)
if __name__ == "__main__":
__a :Dict = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path',
default=None,
type=str,
help='Path to the original PyTorch checkpoint (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
)
parser.add_argument(
'--model_name',
default='glpn-kitti',
type=str,
help='Name of the model in case you\'re pushing to the hub.',
)
__a :Dict = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 329 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def __snake_case ( __UpperCamelCase : Union[str, Any] ):
"""simple docstring"""
if is_torch_version("<" ,"2.0.0" ) or not hasattr(__UpperCamelCase ,"_dynamo" ):
return False
return isinstance(__UpperCamelCase ,torch._dynamo.eval_frame.OptimizedModule )
def __snake_case ( __UpperCamelCase : List[str] ,__UpperCamelCase : bool = True ):
"""simple docstring"""
A_ = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
A_ = is_compiled_module(__UpperCamelCase )
if is_compiled:
A_ = model
A_ = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = model.module
if not keep_fpaa_wrapper:
A_ = getattr(__UpperCamelCase ,"forward" )
A_ = model.__dict__.pop("_original_forward" ,__UpperCamelCase )
if original_forward is not None:
while hasattr(__UpperCamelCase ,"__wrapped__" ):
A_ = forward.__wrapped__
if forward == original_forward:
break
A_ = forward
if getattr(__UpperCamelCase ,"_converted_to_transformer_engine" ,__UpperCamelCase ):
convert_model(__UpperCamelCase ,to_transformer_engine=__UpperCamelCase )
if is_compiled:
A_ = model
A_ = compiled_model
return model
def __snake_case ( ):
"""simple docstring"""
PartialState().wait_for_everyone()
def __snake_case ( __UpperCamelCase : Optional[Any] ,__UpperCamelCase : Any ):
"""simple docstring"""
if PartialState().distributed_type == DistributedType.TPU:
xm.save(__UpperCamelCase ,__UpperCamelCase )
elif PartialState().local_process_index == 0:
torch.save(__UpperCamelCase ,__UpperCamelCase )
@contextmanager
def __snake_case ( **__UpperCamelCase : Any ):
"""simple docstring"""
for key, value in kwargs.items():
A_ = str(__UpperCamelCase )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def __snake_case ( __UpperCamelCase : Optional[Any] ):
"""simple docstring"""
if not hasattr(__UpperCamelCase ,"__qualname__" ) and not hasattr(__UpperCamelCase ,"__name__" ):
A_ = getattr(__UpperCamelCase ,"__class__" ,__UpperCamelCase )
if hasattr(__UpperCamelCase ,"__qualname__" ):
return obj.__qualname__
if hasattr(__UpperCamelCase ,"__name__" ):
return obj.__name__
return str(__UpperCamelCase )
def __snake_case ( __UpperCamelCase : Any ,__UpperCamelCase : Optional[Any] ):
"""simple docstring"""
for key, value in source.items():
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = destination.setdefault(__UpperCamelCase ,{} )
merge_dicts(__UpperCamelCase ,__UpperCamelCase )
else:
A_ = value
return destination
def __snake_case ( __UpperCamelCase : int = None ):
"""simple docstring"""
if port is None:
A_ = 2_9500
with socket.socket(socket.AF_INET ,socket.SOCK_STREAM ) as s:
return s.connect_ex(("localhost", port) ) == 0
| 329 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a :List[str] = {
'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
'tokenization_roberta': ['RobertaTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a :Dict = ['RobertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a :str = [
'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'RobertaForCausalLM',
'RobertaForMaskedLM',
'RobertaForMultipleChoice',
'RobertaForQuestionAnswering',
'RobertaForSequenceClassification',
'RobertaForTokenClassification',
'RobertaModel',
'RobertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a :Dict = [
'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRobertaForCausalLM',
'TFRobertaForMaskedLM',
'TFRobertaForMultipleChoice',
'TFRobertaForQuestionAnswering',
'TFRobertaForSequenceClassification',
'TFRobertaForTokenClassification',
'TFRobertaMainLayer',
'TFRobertaModel',
'TFRobertaPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a :Union[str, Any] = [
'FlaxRobertaForCausalLM',
'FlaxRobertaForMaskedLM',
'FlaxRobertaForMultipleChoice',
'FlaxRobertaForQuestionAnswering',
'FlaxRobertaForSequenceClassification',
'FlaxRobertaForTokenClassification',
'FlaxRobertaModel',
'FlaxRobertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
__a :str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 329 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : int ):
A_ = tempfile.mkdtemp()
A_ = BlipImageProcessor()
A_ = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )
A_ = BlipProcessor(UpperCAmelCase , UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
def __A ( self : Optional[int] , **UpperCAmelCase : Union[str, Any] ):
return AutoProcessor.from_pretrained(self.tmpdirname , **UpperCAmelCase ).tokenizer
def __A ( self : Optional[Any] , **UpperCAmelCase : int ):
return AutoProcessor.from_pretrained(self.tmpdirname , **UpperCAmelCase ).image_processor
def __A ( self : Any ):
shutil.rmtree(self.tmpdirname )
def __A ( self : Dict ):
A_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
A_ = [Image.fromarray(np.moveaxis(UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __A ( self : Any ):
A_ = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
A_ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
A_ = self.get_image_processor(do_normalize=UpperCAmelCase , padding_value=1.0 )
A_ = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , UpperCAmelCase )
def __A ( self : Dict ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = self.prepare_image_inputs()
A_ = image_processor(UpperCAmelCase , return_tensors="np" )
A_ = processor(images=UpperCAmelCase , return_tensors="np" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __A ( self : int ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = "lower newer"
A_ = processor(text=UpperCAmelCase )
A_ = tokenizer(UpperCAmelCase , return_token_type_ids=UpperCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __A ( self : Tuple ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = "lower newer"
A_ = self.prepare_image_inputs()
A_ = processor(text=UpperCAmelCase , images=UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(UpperCAmelCase ):
processor()
def __A ( self : Any ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A_ = processor.batch_decode(UpperCAmelCase )
A_ = tokenizer.batch_decode(UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
def __A ( self : Optional[Any] ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = "lower newer"
A_ = self.prepare_image_inputs()
A_ = processor(text=UpperCAmelCase , images=UpperCAmelCase )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
| 329 | 1 |
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