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import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase = logging.get_logger(__name__)
def _A ( lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Union[str, Any]=False ):
"""simple docstring"""
lowerCAmelCase__ = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias') )
# projection layer + position embeddings
rename_keys.extend(
[
("cls_token", "vit.embeddings.cls_token"),
("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
("pos_embed", "vit.embeddings.position_embeddings"),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
lowerCAmelCase__ = [(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
return rename_keys
def _A ( lowerCAmelCase_ : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
lowerCAmelCase__ = ""
else:
lowerCAmelCase__ = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCAmelCase__ = state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
lowerCAmelCase__ = state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
lowerCAmelCase__ = in_proj_weight[
: config.hidden_size, :
]
lowerCAmelCase__ = in_proj_bias[: config.hidden_size]
lowerCAmelCase__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCAmelCase__ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCAmelCase__ = in_proj_weight[
-config.hidden_size :, :
]
lowerCAmelCase__ = in_proj_bias[-config.hidden_size :]
def _A ( lowerCAmelCase_ : List[str] ):
"""simple docstring"""
lowerCAmelCase__ = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(lowerCAmelCase_ , lowerCAmelCase_ )
def _A ( lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : int ):
"""simple docstring"""
lowerCAmelCase__ = dct.pop(lowerCAmelCase_ )
lowerCAmelCase__ = val
def _A ( ):
"""simple docstring"""
lowerCAmelCase__ = "http://images.cocodataset.org/val2017/000000039769.jpg"
lowerCAmelCase__ = Image.open(requests.get(lowerCAmelCase_ , stream=lowerCAmelCase_ ).raw )
return im
@torch.no_grad()
def _A ( lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str , lowerCAmelCase_ : Any=True ):
"""simple docstring"""
lowerCAmelCase__ = ViTConfig()
# patch_size
if model_name[-1] == "8":
lowerCAmelCase__ = 8
# set labels if required
if not base_model:
lowerCAmelCase__ = 1000
lowerCAmelCase__ = "huggingface/label-files"
lowerCAmelCase__ = "imagenet-1k-id2label.json"
lowerCAmelCase__ = json.load(open(hf_hub_download(lowerCAmelCase_ , lowerCAmelCase_ , repo_type="dataset" ) , "r" ) )
lowerCAmelCase__ = {int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
lowerCAmelCase__ = idalabel
lowerCAmelCase__ = {v: k for k, v in idalabel.items()}
# size of the architecture
if model_name in ["dino_vits8", "dino_vits16"]:
lowerCAmelCase__ = 384
lowerCAmelCase__ = 1536
lowerCAmelCase__ = 12
lowerCAmelCase__ = 6
# load original model from torch hub
lowerCAmelCase__ = torch.hub.load("facebookresearch/dino:main" , lowerCAmelCase_ )
original_model.eval()
# load state_dict of original model, remove and rename some keys
lowerCAmelCase__ = original_model.state_dict()
if base_model:
remove_classification_head_(lowerCAmelCase_ )
lowerCAmelCase__ = create_rename_keys(lowerCAmelCase_ , base_model=lowerCAmelCase_ )
for src, dest in rename_keys:
rename_key(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
read_in_q_k_v(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# load HuggingFace model
if base_model:
lowerCAmelCase__ = ViTModel(lowerCAmelCase_ , add_pooling_layer=lowerCAmelCase_ ).eval()
else:
lowerCAmelCase__ = ViTForImageClassification(lowerCAmelCase_ ).eval()
model.load_state_dict(lowerCAmelCase_ )
# Check outputs on an image, prepared by ViTImageProcessor
lowerCAmelCase__ = ViTImageProcessor()
lowerCAmelCase__ = image_processor(images=prepare_img() , return_tensors="pt" )
lowerCAmelCase__ = encoding["pixel_values"]
lowerCAmelCase__ = model(lowerCAmelCase_ )
if base_model:
lowerCAmelCase__ = original_model(lowerCAmelCase_ )
assert torch.allclose(lowerCAmelCase_ , outputs.last_hidden_state[:, 0, :] , atol=1E-1 )
else:
lowerCAmelCase__ = original_model(lowerCAmelCase_ )
assert logits.shape == outputs.logits.shape
assert torch.allclose(lowerCAmelCase_ , outputs.logits , atol=1E-3 )
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(lowerCAmelCase_ )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='dino_vitb16',
type=str,
help='Name of the model trained with DINO 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(
'--base_model',
action='store_true',
help='Whether to only convert the base model (no projection head weights).',
)
parser.set_defaults(base_model=True)
UpperCamelCase = parser.parse_args()
convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
| 61
|
from collections import defaultdict
def a__ (__lowercase :str , __lowercase :str ) -> bool:
_A : Union[str, Any] = first_str.lower().strip()
_A : int = second_str.lower().strip()
# Remove whitespace
_A : int = first_str.replace(''' ''' , '''''' )
_A : Optional[Any] = second_str.replace(''' ''' , '''''' )
# Strings of different lengths are not anagrams
if len(__lowercase ) != len(__lowercase ):
return False
# Default values for count should be 0
_A : defaultdict[str, int] = defaultdict(__lowercase )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(__lowercase ) ):
count[first_str[i]] += 1
count[second_str[i]] -= 1
return all(_count == 0 for _count in count.values() )
if __name__ == "__main__":
from doctest import testmod
testmod()
_UpperCamelCase : Dict =input('Enter the first string ').strip()
_UpperCamelCase : Union[str, Any] =input('Enter the second string ').strip()
_UpperCamelCase : Dict =check_anagrams(input_a, input_b)
print(f'''{input_a} and {input_b} are {"" if status else "not "}anagrams.''')
| 206
| 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
a : List[Any] = '''\
'''
a : List[Any] = '''
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.
For more information, see https://huggingface.co/docs/transformers/perplexity
'''
a : Union[str, Any] = '''
Args:
model_id (str): model used for calculating Perplexity
NOTE: Perplexity can only be calculated for causal language models.
This includes models such as gpt2, causal variations of bert,
causal versions of t5, and more (the full list can be found
in the AutoModelForCausalLM documentation here:
https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )
input_texts (list of str): input text, each separate text snippet
is one list entry.
batch_size (int): the batch size to run texts through the model. Defaults to 16.
add_start_token (bool): whether to add the start token to the texts,
so the perplexity can include the probability of the first word. Defaults to True.
device (str): device to run on, defaults to \'cuda\' when available
Returns:
perplexity: dictionary containing the perplexity scores for the texts
in the input list, as well as the mean perplexity. If one of the input texts is
longer than the max input length of the model, then it is truncated to the
max length for the perplexity computation.
Examples:
Example 1:
>>> perplexity = datasets.load_metric("perplexity")
>>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]
>>> results = perplexity.compute(model_id=\'gpt2\',
... add_start_token=False,
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
[\'perplexities\', \'mean_perplexity\']
>>> print(round(results["mean_perplexity"], 2))
78.22
>>> print(round(results["perplexities"][0], 2))
11.11
Example 2:
>>> perplexity = datasets.load_metric("perplexity")
>>> input_texts = datasets.load_dataset("wikitext",
... "wikitext-2-raw-v1",
... split="test")["text"][:50] # doctest:+ELLIPSIS
[...]
>>> input_texts = [s for s in input_texts if s!=\'\']
>>> results = perplexity.compute(model_id=\'gpt2\',
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
[\'perplexities\', \'mean_perplexity\']
>>> print(round(results["mean_perplexity"], 2))
60.35
>>> print(round(results["perplexities"][0], 2))
81.12
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
def _snake_case ( self : Any ) ->List[str]:
'''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 _snake_case ( self : Optional[int] , __UpperCamelCase : Tuple , __UpperCamelCase : List[Any] , __UpperCamelCase : int = 16 , __UpperCamelCase : bool = True , __UpperCamelCase : Any=None ) ->Union[str, Any]:
'''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(__UpperCamelCase )
_UpperCAmelCase = model.to(__UpperCamelCase )
_UpperCAmelCase = AutoTokenizer.from_pretrained(__UpperCamelCase )
# 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(__UpperCamelCase ) > 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(
__UpperCamelCase , add_special_tokens=__UpperCamelCase , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=__UpperCamelCase , return_tensors="""pt""" , return_attention_mask=__UpperCamelCase , ).to(__UpperCamelCase )
_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(__UpperCamelCase ) , __UpperCamelCase ) ):
_UpperCAmelCase = min(start_index + batch_size , len(__UpperCamelCase ) )
_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(__UpperCamelCase )
_UpperCAmelCase = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
_UpperCAmelCase = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(__UpperCamelCase ), attn_mask] , dim=1 )
_UpperCAmelCase = encoded_batch
with torch.no_grad():
_UpperCAmelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase ).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 ) , __UpperCamelCase ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(__UpperCamelCase )}
| 717
|
"""simple docstring"""
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
a : int = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def _UpperCamelCase ( _A ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = test_results.split(""" """ )
_UpperCAmelCase = 0
_UpperCAmelCase = 0
# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
# When it is too long, those signs are not present.
_UpperCAmelCase = expressions[-2] if """=""" in expressions[-1] else expressions[-1]
for i, expression in enumerate(_A ):
if "failed" in expression:
failed += int(expressions[i - 1] )
if "passed" in expression:
success += int(expressions[i - 1] )
return failed, success, time_spent
def _UpperCamelCase ( _A ) -> int:
"""simple docstring"""
_UpperCAmelCase = {}
_UpperCAmelCase = None
_UpperCAmelCase = False
for line in failures_short_lines.split("""\n""" ):
if re.search(R"""_ \[doctest\]""" , _A ):
_UpperCAmelCase = True
_UpperCAmelCase = line.split(""" """ )[2]
elif in_error and not line.split(""" """ )[0].isdigit():
_UpperCAmelCase = line
_UpperCAmelCase = False
return failures
class a_ :
def __init__( self : Union[str, Any] , __UpperCamelCase : str , __UpperCamelCase : Dict ) ->Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = title
_UpperCAmelCase = doc_test_results["""time_spent"""].split(""",""" )[0]
_UpperCAmelCase = doc_test_results["""success"""]
_UpperCAmelCase = doc_test_results["""failures"""]
_UpperCAmelCase = self.n_success + self.n_failures
# Failures and success of the modeling tests
_UpperCAmelCase = doc_test_results
@property
def _snake_case ( self : int ) ->str:
'''simple docstring'''
_UpperCAmelCase = [self._time_spent]
_UpperCAmelCase = 0
for time in time_spent:
_UpperCAmelCase = time.split(""":""" )
# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
if len(__UpperCamelCase ) == 1:
_UpperCAmelCase = [0, 0, time_parts[0]]
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase = int(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] )
total_secs += hours * 36_00 + minutes * 60 + seconds
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase = total_secs // 36_00, (total_secs % 36_00) // 60, total_secs % 60
return f"""{int(__UpperCamelCase )}h{int(__UpperCamelCase )}m{int(__UpperCamelCase )}s"""
@property
def _snake_case ( self : List[Any] ) ->Dict:
'''simple docstring'''
return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
@property
def _snake_case ( self : Optional[Any] ) ->Dict:
'''simple docstring'''
return {
"type": "section",
"text": {
"type": "plain_text",
"text": f"""🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.""",
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": f"""https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}""",
},
}
@property
def _snake_case ( self : Union[str, Any] ) ->Dict:
'''simple docstring'''
return {
"type": "section",
"text": {
"type": "plain_text",
"text": (
f"""There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in"""
f""" {self.time}."""
),
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": f"""https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}""",
},
}
@property
def _snake_case ( self : List[str] ) ->Dict:
'''simple docstring'''
_UpperCAmelCase = 40
_UpperCAmelCase = {k: v["""failed"""] for k, v in doc_test_results.items() if isinstance(__UpperCamelCase , __UpperCamelCase )}
_UpperCAmelCase = """"""
for category, failures in category_failures.items():
if len(__UpperCamelCase ) == 0:
continue
if report != "":
report += "\n\n"
report += f"""*{category} failures*:""".ljust(line_length // 2 ).rjust(line_length // 2 ) + "\n"
report += "`"
report += "`\n`".join(__UpperCamelCase )
report += "`"
return {
"type": "section",
"text": {
"type": "mrkdwn",
"text": f"""The following examples had failures:\n\n\n{report}\n""",
},
}
@property
def _snake_case ( self : Union[str, Any] ) ->str:
'''simple docstring'''
_UpperCAmelCase = [self.header]
if self.n_failures > 0:
blocks.append(self.failures )
if self.n_failures > 0:
blocks.extend([self.category_failures] )
if self.n_failures == 0:
blocks.append(self.no_failures )
return json.dumps(__UpperCamelCase )
@staticmethod
def _snake_case ( ) ->Any:
'''simple docstring'''
_UpperCAmelCase = [
{
"""type""": """section""",
"""text""": {
"""type""": """plain_text""",
"""text""": """There was an issue running the tests.""",
},
"""accessory""": {
"""type""": """button""",
"""text""": {"""type""": """plain_text""", """text""": """Check Action results""", """emoji""": True},
"""url""": f"""https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}""",
},
}
]
print("""Sending the following payload""" )
print(json.dumps({"""blocks""": json.loads(__UpperCamelCase )} ) )
client.chat_postMessage(
channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""] , text="""There was an issue running the tests.""" , blocks=__UpperCamelCase , )
def _snake_case ( self : Tuple ) ->Optional[Any]:
'''simple docstring'''
print("""Sending the following payload""" )
print(json.dumps({"""blocks""": json.loads(self.payload )} ) )
_UpperCAmelCase = f"""{self.n_failures} failures out of {self.n_tests} tests,""" if self.n_failures else """All tests passed."""
_UpperCAmelCase = client.chat_postMessage(
channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""] , blocks=self.payload , text=__UpperCamelCase , )
def _snake_case ( self : List[Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Any , __UpperCamelCase : Any , __UpperCamelCase : List[str] ) ->str:
'''simple docstring'''
_UpperCAmelCase = """"""
for key, value in failures.items():
_UpperCAmelCase = value[:2_00] + """ [Truncated]""" if len(__UpperCamelCase ) > 2_50 else value
failures_text += f"""*{key}*\n_{value}_\n\n"""
_UpperCAmelCase = job_name
_UpperCAmelCase = {"""type""": """section""", """text""": {"""type""": """mrkdwn""", """text""": text}}
if job_link is not None:
_UpperCAmelCase = {
"""type""": """button""",
"""text""": {"""type""": """plain_text""", """text""": """GitHub Action job""", """emoji""": True},
"""url""": job_link,
}
return [
{"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
content,
{"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
]
def _snake_case ( self : int ) ->Optional[Any]:
'''simple docstring'''
if self.thread_ts is None:
raise ValueError("""Can only post reply if a post has been made.""" )
_UpperCAmelCase = self.doc_test_results.pop("""job_link""" )
self.doc_test_results.pop("""failures""" )
self.doc_test_results.pop("""success""" )
self.doc_test_results.pop("""time_spent""" )
_UpperCAmelCase = sorted(self.doc_test_results.items() , key=lambda __UpperCamelCase : t[0] )
for job, job_result in sorted_dict:
if len(job_result["""failures"""] ):
_UpperCAmelCase = f"""*Num failures* :{len(job_result["failed"] )} \n"""
_UpperCAmelCase = job_result["""failures"""]
_UpperCAmelCase = self.get_reply_blocks(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , text=__UpperCamelCase )
print("""Sending the following reply""" )
print(json.dumps({"""blocks""": blocks} ) )
client.chat_postMessage(
channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""] , text=f"""Results for {job}""" , blocks=__UpperCamelCase , thread_ts=self.thread_ts["""ts"""] , )
time.sleep(1 )
def _UpperCamelCase ( ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = os.environ["""GITHUB_RUN_ID"""]
_UpperCAmelCase = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"""
_UpperCAmelCase = requests.get(_A ).json()
_UpperCAmelCase = {}
try:
jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
_UpperCAmelCase = math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0 )
for i in range(_A ):
_UpperCAmelCase = requests.get(url + F"""&page={i + 2}""" ).json()
jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
return jobs
except Exception as e:
print("""Unknown error, could not fetch links.""" , _A )
return {}
def _UpperCamelCase ( _A ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = {}
if os.path.exists(_A ):
_UpperCAmelCase = os.listdir(_A )
for file in files:
try:
with open(os.path.join(_A , _A ) , encoding="""utf-8""" ) as f:
_UpperCAmelCase = f.read()
except UnicodeDecodeError as e:
raise ValueError(F"""Could not open {os.path.join(_A , _A )}.""" ) from e
return _artifact
def _UpperCamelCase ( ) -> int:
"""simple docstring"""
class a_ :
def __init__( self : List[Any] , __UpperCamelCase : str ) ->Tuple:
'''simple docstring'''
_UpperCAmelCase = name
_UpperCAmelCase = []
def __str__( self : int ) ->Optional[Any]:
'''simple docstring'''
return self.name
def _snake_case ( self : Dict , __UpperCamelCase : str ) ->int:
'''simple docstring'''
self.paths.append({"""name""": self.name, """path""": path} )
_UpperCAmelCase = {}
_UpperCAmelCase = filter(os.path.isdir , os.listdir() )
for directory in directories:
_UpperCAmelCase = directory
if artifact_name not in _available_artifacts:
_UpperCAmelCase = Artifact(_A )
_available_artifacts[artifact_name].add_path(_A )
return _available_artifacts
if __name__ == "__main__":
a : Dict = get_job_links()
a : Dict = retrieve_available_artifacts()
a : Optional[int] = collections.OrderedDict(
[
('''*.py''', '''API Examples'''),
('''*.md''', '''MD Examples'''),
]
)
# This dict will contain all the information relative to each doc test category:
# - failed: list of failed tests
# - failures: dict in the format 'test': 'error_message'
a : Dict = {
v: {
'''failed''': [],
'''failures''': {},
}
for v in docs.values()
}
# Link to the GitHub Action job
a : int = github_actions_job_links.get('''run_doctests''')
a : Tuple = available_artifacts['''doc_tests_gpu_test_reports'''].paths[0]
a : Optional[Any] = retrieve_artifact(artifact_path['''name'''])
if "stats" in artifact:
a , a , a : str = handle_test_results(artifact['''stats'''])
a : Tuple = failed
a : int = success
a : Any = time_spent[1:-1] + ''', '''
a : Dict = extract_first_line_failure(artifact['''failures_short'''])
for line in artifact["summary_short"].split('''\n'''):
if re.search('''FAILED''', line):
a : List[Any] = line.replace('''FAILED ''', '''''')
a : Tuple = line.split()[0].replace('''\n''', '''''')
if "::" in line:
a , a : Union[str, Any] = line.split('''::''')
else:
a , a : Optional[Any] = line, line
for file_regex in docs.keys():
if fnmatch(file_path, file_regex):
a : List[Any] = docs[file_regex]
doc_test_results[category]["failed"].append(test)
a : Optional[Any] = all_failures[test] if test in all_failures else '''N/A'''
a : List[str] = failure
break
a : List[Any] = Message('''🤗 Results of the doc tests.''', doc_test_results)
message.post()
message.post_reply()
| 19
| 0
|
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__magic_name__ = {
'''vocab_file''': {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'''
),
'''facebook/dpr-ctx_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'''
),
'''facebook/dpr-ctx_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'''
),
},
}
__magic_name__ = {
'''vocab_file''': {
'''facebook/dpr-question_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'''
),
'''facebook/dpr-question_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''facebook/dpr-question_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'''
),
'''facebook/dpr-question_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'''
),
},
}
__magic_name__ = {
'''vocab_file''': {
'''facebook/dpr-reader-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'''
),
'''facebook/dpr-reader-multiset-base''': (
'''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''facebook/dpr-reader-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'''
),
'''facebook/dpr-reader-multiset-base''': (
'''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'''
),
},
}
__magic_name__ = {
'''facebook/dpr-ctx_encoder-single-nq-base''': 512,
'''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
__magic_name__ = {
'''facebook/dpr-question_encoder-single-nq-base''': 512,
'''facebook/dpr-question_encoder-multiset-base''': 512,
}
__magic_name__ = {
'''facebook/dpr-reader-single-nq-base''': 512,
'''facebook/dpr-reader-multiset-base''': 512,
}
__magic_name__ = {
'''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
'''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
__magic_name__ = {
'''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
'''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
__magic_name__ = {
'''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
'''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}
class _SCREAMING_SNAKE_CASE ( __UpperCamelCase ):
_A : List[Any] = VOCAB_FILES_NAMES
_A : Optional[int] = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
_A : List[str] = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A : List[str] = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class _SCREAMING_SNAKE_CASE ( __UpperCamelCase ):
_A : Dict = VOCAB_FILES_NAMES
_A : Any = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
_A : Dict = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A : Optional[int] = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
__magic_name__ = collections.namedtuple(
'''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)
__magic_name__ = collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])
__magic_name__ = r'''
Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
with the format:
```
[CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
```
Args:
questions (`str` or `List[str]`):
The questions to be encoded. You can specify one question for many passages. In this case, the question
will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
`titles` or `texts`.
titles (`str` or `List[str]`):
The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
texts (`str` or `List[str]`):
The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Activates and controls padding. Accepts the following values:
- `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
if provided).
- `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
Activates and controls truncation. Accepts the following values:
- `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
the maximum acceptable input length for the model if that argument is not provided. This will truncate
token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
of pairs) is provided.
- `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the first
sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the
second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
max_length (`int`, *optional*):
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `\'tf\'`: Return TensorFlow `tf.constant` objects.
- `\'pt\'`: Return PyTorch `torch.Tensor` objects.
- `\'np\'`: Return Numpy `np.ndarray` objects.
return_attention_mask (`bool`, *optional*):
Whether or not to return the attention mask. If not set, will return the attention mask according to the
specific tokenizer\'s default, defined by the `return_outputs` attribute.
[What are attention masks?](../glossary#attention-mask)
Returns:
`Dict[str, List[List[int]]]`: A dictionary with the following keys:
- `input_ids`: List of token ids to be fed to a model.
- `attention_mask`: List of indices specifying which tokens should be attended to by the model.
'''
@add_start_docstrings(__UpperCamelCase )
class _SCREAMING_SNAKE_CASE :
def __call__( self , lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = False , lowerCamelCase = False , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , **lowerCamelCase , ):
if titles is None and texts is None:
return super().__call__(
lowerCamelCase , padding=lowerCamelCase , truncation=lowerCamelCase , max_length=lowerCamelCase , return_tensors=lowerCamelCase , return_attention_mask=lowerCamelCase , **lowerCamelCase , )
elif titles is None or texts is None:
snake_case__ = titles if texts is None else texts
return super().__call__(
lowerCamelCase , lowerCamelCase , padding=lowerCamelCase , truncation=lowerCamelCase , max_length=lowerCamelCase , return_tensors=lowerCamelCase , return_attention_mask=lowerCamelCase , **lowerCamelCase , )
snake_case__ = titles if not isinstance(lowerCamelCase , lowerCamelCase ) else [titles]
snake_case__ = texts if not isinstance(lowerCamelCase , lowerCamelCase ) else [texts]
snake_case__ = len(lowerCamelCase )
snake_case__ = questions if not isinstance(lowerCamelCase , lowerCamelCase ) else [questions] * n_passages
if len(lowerCamelCase ) != len(lowerCamelCase ):
raise ValueError(
F"""There should be as many titles than texts but got {len(lowerCamelCase )} titles and {len(lowerCamelCase )} texts.""" )
snake_case__ = super().__call__(lowerCamelCase , lowerCamelCase , padding=lowerCamelCase , truncation=lowerCamelCase )["input_ids"]
snake_case__ = super().__call__(lowerCamelCase , add_special_tokens=lowerCamelCase , padding=lowerCamelCase , truncation=lowerCamelCase )["input_ids"]
snake_case__ = {
"input_ids": [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(lowerCamelCase , lowerCamelCase )
]
}
if return_attention_mask is not False:
snake_case__ = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
snake_case__ = attention_mask
return self.pad(lowerCamelCase , padding=lowerCamelCase , max_length=lowerCamelCase , return_tensors=lowerCamelCase )
def A_ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase = 16 , lowerCamelCase = 64 , lowerCamelCase = 4 , ):
snake_case__ = reader_input["input_ids"]
snake_case__ , snake_case__ , snake_case__ = reader_output[:3]
snake_case__ = len(lowerCamelCase )
snake_case__ = sorted(range(lowerCamelCase ) , reverse=lowerCamelCase , key=relevance_logits.__getitem__ )
snake_case__ = []
for doc_id in sorted_docs:
snake_case__ = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
snake_case__ = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
snake_case__ = sequence_ids.index(self.pad_token_id )
else:
snake_case__ = len(lowerCamelCase )
snake_case__ = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=lowerCamelCase , top_spans=lowerCamelCase , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=lowerCamelCase , start_index=lowerCamelCase , end_index=lowerCamelCase , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(lowerCamelCase ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def A_ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , ):
snake_case__ = []
for start_index, start_score in enumerate(lowerCamelCase ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
snake_case__ = sorted(lowerCamelCase , key=lambda lowerCamelCase : x[1] , reverse=lowerCamelCase )
snake_case__ = []
for (start_index, end_index), score in scores:
if start_index > end_index:
raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]""" )
snake_case__ = end_index - start_index + 1
if length > max_answer_length:
raise ValueError(F"""Span is too long: {length} > {max_answer_length}""" )
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(lowerCamelCase ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(__UpperCamelCase )
class _SCREAMING_SNAKE_CASE ( __UpperCamelCase , __UpperCamelCase ):
_A : Any = VOCAB_FILES_NAMES
_A : Any = READER_PRETRAINED_VOCAB_FILES_MAP
_A : Tuple = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A : Optional[int] = READER_PRETRAINED_INIT_CONFIGURATION
_A : Any = ['input_ids', 'attention_mask']
| 276
|
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__magic_name__ = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.14.0''', '''To fix: pip install -r examples/pytorch/audio-classification/requirements.txt''')
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 16_000 ):
snake_case__ = int(round(sample_rate * max_length ) )
if len(__lowerCAmelCase ) <= sample_length:
return wav
snake_case__ = randint(0 , len(__lowerCAmelCase ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class _SCREAMING_SNAKE_CASE :
_A : Optional[str] = field(default=__UpperCamelCase , metadata={'help': 'Name of a dataset from the datasets package'} )
_A : Optional[str] = field(
default=__UpperCamelCase , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
_A : Optional[str] = field(
default=__UpperCamelCase , metadata={'help': 'A file containing the training audio paths and labels.'} )
_A : Optional[str] = field(
default=__UpperCamelCase , metadata={'help': 'A file containing the validation audio paths and labels.'} )
_A : str = field(
default='train' , metadata={
'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
} , )
_A : str = field(
default='validation' , metadata={
'help': (
'The name of the training data set split to use (via the datasets library). Defaults to \'validation\''
)
} , )
_A : str = field(
default='audio' , metadata={'help': 'The name of the dataset column containing the audio data. Defaults to \'audio\''} , )
_A : str = field(
default='label' , metadata={'help': 'The name of the dataset column containing the labels. Defaults to \'label\''} )
_A : Optional[int] = field(
default=__UpperCamelCase , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
_A : Optional[int] = field(
default=__UpperCamelCase , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
_A : float = field(
default=20 , metadata={'help': 'Audio clips will be randomly cut to this length during training if the value is set.'} , )
@dataclass
class _SCREAMING_SNAKE_CASE :
_A : str = field(
default='facebook/wav2vec2-base' , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} , )
_A : Optional[str] = field(
default=__UpperCamelCase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
_A : Optional[str] = field(
default=__UpperCamelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from the Hub'} )
_A : str = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
_A : Optional[str] = field(
default=__UpperCamelCase , metadata={'help': 'Name or path of preprocessor config.'} )
_A : bool = field(
default=__UpperCamelCase , metadata={'help': 'Whether to freeze the feature encoder layers of the model.'} )
_A : bool = field(
default=__UpperCamelCase , metadata={'help': 'Whether to generate an attention mask in the feature extractor.'} )
_A : bool = field(
default=__UpperCamelCase , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
_A : Optional[bool] = field(
default=__UpperCamelCase , metadata={'help': 'Whether to freeze the feature extractor layers of the model.'} )
_A : bool = field(
default=__UpperCamelCase , metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'} , )
def A_ ( self ):
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
"The argument `--freeze_feature_extractor` is deprecated and "
"will be removed in a future version. Use `--freeze_feature_encoder`"
"instead. Setting `freeze_feature_encoder==True`." , lowerCamelCase , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
"The argument `--freeze_feature_extractor` is deprecated and "
"should not be used in combination with `--freeze_feature_encoder`."
"Only make use of `--freeze_feature_encoder`." )
def SCREAMING_SNAKE_CASE__ ( ):
# 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.
snake_case__ = 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.
snake_case__ , snake_case__ , snake_case__ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case__ , snake_case__ , snake_case__ = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_audio_classification" , __lowerCAmelCase , __lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
snake_case__ = training_args.get_process_log_level()
logger.setLevel(__lowerCAmelCase )
transformers.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} """
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
snake_case__ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case__ = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
"Use --overwrite_output_dir to train from scratch." )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )
# Initialize our dataset and prepare it for the audio classification task.
snake_case__ = DatasetDict()
snake_case__ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
snake_case__ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"""--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. """
"Make sure to set `--audio_column_name` to the correct audio column - one of "
F"""{", ".join(raw_datasets["train"].column_names )}.""" )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"""--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. """
"Make sure to set `--label_column_name` to the correct text column - one of "
F"""{", ".join(raw_datasets["train"].column_names )}.""" )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
snake_case__ = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
snake_case__ = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
snake_case__ = feature_extractor.model_input_names[0]
def train_transforms(__lowerCAmelCase ):
snake_case__ = []
for audio in batch[data_args.audio_column_name]:
snake_case__ = random_subsample(
audio["array"] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(__lowerCAmelCase )
snake_case__ = feature_extractor(__lowerCAmelCase , sampling_rate=feature_extractor.sampling_rate )
snake_case__ = {model_input_name: inputs.get(__lowerCAmelCase )}
snake_case__ = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(__lowerCAmelCase ):
snake_case__ = [audio["array"] for audio in batch[data_args.audio_column_name]]
snake_case__ = feature_extractor(__lowerCAmelCase , sampling_rate=feature_extractor.sampling_rate )
snake_case__ = {model_input_name: inputs.get(__lowerCAmelCase )}
snake_case__ = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
snake_case__ = raw_datasets["train"].features[data_args.label_column_name].names
snake_case__ , snake_case__ = {}, {}
for i, label in enumerate(__lowerCAmelCase ):
snake_case__ = str(__lowerCAmelCase )
snake_case__ = label
# Load the accuracy metric from the datasets package
snake_case__ = evaluate.load("accuracy" )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(__lowerCAmelCase ):
snake_case__ = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=__lowerCAmelCase , references=eval_pred.label_ids )
snake_case__ = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(__lowerCAmelCase ) , labelaid=__lowerCAmelCase , idalabel=__lowerCAmelCase , finetuning_task="audio-classification" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
snake_case__ = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
snake_case__ = (
raw_datasets["train"].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(__lowerCAmelCase , output_all_columns=__lowerCAmelCase )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
snake_case__ = (
raw_datasets["eval"].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(__lowerCAmelCase , output_all_columns=__lowerCAmelCase )
# Initialize our trainer
snake_case__ = Trainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=raw_datasets["train"] if training_args.do_train else None , eval_dataset=raw_datasets["eval"] if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , )
# Training
if training_args.do_train:
snake_case__ = None
if training_args.resume_from_checkpoint is not None:
snake_case__ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case__ = last_checkpoint
snake_case__ = trainer.train(resume_from_checkpoint=__lowerCAmelCase )
trainer.save_model()
trainer.log_metrics("train" , train_result.metrics )
trainer.save_metrics("train" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
snake_case__ = trainer.evaluate()
trainer.log_metrics("eval" , __lowerCAmelCase )
trainer.save_metrics("eval" , __lowerCAmelCase )
# Write model card and (optionally) push to hub
snake_case__ = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "audio-classification",
"dataset": data_args.dataset_name,
"tags": ["audio-classification"],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__lowerCAmelCase )
else:
trainer.create_model_card(**__lowerCAmelCase )
if __name__ == "__main__":
main()
| 276
| 1
|
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = FlaxAutoencoderKL
@property
def snake_case__ ( self : str ):
"""simple docstring"""
snake_case_ = 4
snake_case_ = 3
snake_case_ = (32, 32)
snake_case_ = jax.random.PRNGKey(0 )
snake_case_ = jax.random.uniform(__lowercase , ((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def snake_case__ ( self : Union[str, Any] ):
"""simple docstring"""
snake_case_ = {
"block_out_channels": [32, 64],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 4,
}
snake_case_ = self.dummy_input
return init_dict, inputs_dict
| 716
|
import baseaa
def lowerCamelCase__ ( _A ):
'''simple docstring'''
return baseaa.baaencode(string.encode("utf-8" ) )
def lowerCamelCase__ ( _A ):
'''simple docstring'''
return baseaa.baadecode(_A ).decode("utf-8" )
if __name__ == "__main__":
lowercase__ : str = "Hello World!"
lowercase__ : Tuple = baseaa_encode(test)
print(encoded)
lowercase__ : Optional[Any] = baseaa_decode(encoded)
print(decoded)
| 139
| 0
|
from __future__ import annotations
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : List[str], lowerCamelCase : list[list[int]] ):
'''simple docstring'''
lowercase__ = TypeError(
'''Matrices must be formed from a list of zero or more lists containing at '''
'''least one and the same number of values, each of which must be of type '''
'''int or float.''' )
if len(lowerCamelCase ) != 0:
lowercase__ = len(rows[0] )
if cols == 0:
raise error
for row in rows:
if len(lowerCamelCase ) != cols:
raise error
for value in row:
if not isinstance(lowerCamelCase, (int, float) ):
raise error
lowercase__ = rows
else:
lowercase__ = []
def lowercase__ ( self : Optional[int] ):
'''simple docstring'''
return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]
@property
def lowercase__ ( self : Union[str, Any] ):
'''simple docstring'''
return len(self.rows )
@property
def lowercase__ ( self : Union[str, Any] ):
'''simple docstring'''
return len(self.rows[0] )
@property
def lowercase__ ( self : List[str] ):
'''simple docstring'''
return (self.num_rows, self.num_columns)
@property
def lowercase__ ( self : int ):
'''simple docstring'''
return self.order[0] == self.order[1]
def lowercase__ ( self : List[Any] ):
'''simple docstring'''
lowercase__ = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
for row_num in range(self.num_rows )
]
return Matrix(lowerCamelCase )
def lowercase__ ( self : Union[str, Any] ):
'''simple docstring'''
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0] )
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0]) )
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns ) )
def lowercase__ ( self : List[Any] ):
'''simple docstring'''
return bool(self.determinant() )
def lowercase__ ( self : List[Any], lowerCamelCase : int, lowerCamelCase : int ):
'''simple docstring'''
lowercase__ = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns )
if other_column != column
]
for other_row in range(self.num_rows )
if other_row != row
]
return Matrix(lowerCamelCase ).determinant()
def lowercase__ ( self : str, lowerCamelCase : int, lowerCamelCase : int ):
'''simple docstring'''
if (row + column) % 2 == 0:
return self.get_minor(lowerCamelCase, lowerCamelCase )
return -1 * self.get_minor(lowerCamelCase, lowerCamelCase )
def lowercase__ ( self : List[Any] ):
'''simple docstring'''
return Matrix(
[
[self.get_minor(lowerCamelCase, lowerCamelCase ) for column in range(self.num_columns )]
for row in range(self.num_rows )
] )
def lowercase__ ( self : Dict ):
'''simple docstring'''
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns )
]
for row in range(self.minors().num_rows )
] )
def lowercase__ ( self : Any ):
'''simple docstring'''
lowercase__ = [
[self.cofactors().rows[column][row] for column in range(self.num_columns )]
for row in range(self.num_rows )
]
return Matrix(lowerCamelCase )
def lowercase__ ( self : Any ):
'''simple docstring'''
lowercase__ = self.determinant()
if not determinant:
raise TypeError('''Only matrices with a non-zero determinant have an inverse''' )
return self.adjugate() * (1 / determinant)
def __repr__( self : Tuple ):
'''simple docstring'''
return str(self.rows )
def __str__( self : str ):
'''simple docstring'''
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
return (
"["
+ "\n ".join(
[
'''[''' + '''. '''.join([str(lowerCamelCase ) for value in row] ) + '''.]'''
for row in self.rows
] )
+ "]"
)
def lowercase__ ( self : List[Any], lowerCamelCase : list[int], lowerCamelCase : int | None = None ):
'''simple docstring'''
lowercase__ = TypeError('''Row must be a list containing all ints and/or floats''' )
if not isinstance(lowerCamelCase, lowerCamelCase ):
raise type_error
for value in row:
if not isinstance(lowerCamelCase, (int, float) ):
raise type_error
if len(lowerCamelCase ) != self.num_columns:
raise ValueError(
'''Row must be equal in length to the other rows in the matrix''' )
if position is None:
self.rows.append(lowerCamelCase )
else:
lowercase__ = self.rows[0:position] + [row] + self.rows[position:]
def lowercase__ ( self : List[str], lowerCamelCase : list[int], lowerCamelCase : int | None = None ):
'''simple docstring'''
lowercase__ = TypeError(
'''Column must be a list containing all ints and/or floats''' )
if not isinstance(lowerCamelCase, lowerCamelCase ):
raise type_error
for value in column:
if not isinstance(lowerCamelCase, (int, float) ):
raise type_error
if len(lowerCamelCase ) != self.num_rows:
raise ValueError(
'''Column must be equal in length to the other columns in the matrix''' )
if position is None:
lowercase__ = [self.rows[i] + [column[i]] for i in range(self.num_rows )]
else:
lowercase__ = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows )
]
def __eq__( self : Union[str, Any], lowerCamelCase : object ):
'''simple docstring'''
if not isinstance(lowerCamelCase, lowerCamelCase ):
return NotImplemented
return self.rows == other.rows
def __ne__( self : int, lowerCamelCase : object ):
'''simple docstring'''
return not self == other
def __neg__( self : Tuple ):
'''simple docstring'''
return self * -1
def __add__( self : List[str], lowerCamelCase : Matrix ):
'''simple docstring'''
if self.order != other.order:
raise ValueError('''Addition requires matrices of the same order''' )
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __sub__( self : Tuple, lowerCamelCase : Matrix ):
'''simple docstring'''
if self.order != other.order:
raise ValueError('''Subtraction requires matrices of the same order''' )
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __mul__( self : str, lowerCamelCase : Matrix | int | float ):
'''simple docstring'''
if isinstance(lowerCamelCase, (int, float) ):
return Matrix(
[[int(element * other ) for element in row] for row in self.rows] )
elif isinstance(lowerCamelCase, lowerCamelCase ):
if self.num_columns != other.num_rows:
raise ValueError(
'''The number of columns in the first matrix must '''
'''be equal to the number of rows in the second''' )
return Matrix(
[
[Matrix.dot_product(lowerCamelCase, lowerCamelCase ) for column in other.columns()]
for row in self.rows
] )
else:
raise TypeError(
'''A Matrix can only be multiplied by an int, float, or another matrix''' )
def __pow__( self : List[str], lowerCamelCase : int ):
'''simple docstring'''
if not isinstance(lowerCamelCase, lowerCamelCase ):
raise TypeError('''A Matrix can only be raised to the power of an int''' )
if not self.is_square:
raise ValueError('''Only square matrices can be raised to a power''' )
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable():
return self.inverse() ** (-other)
raise ValueError(
'''Only invertable matrices can be raised to a negative power''' )
lowercase__ = self
for _ in range(other - 1 ):
result *= self
return result
@classmethod
def lowercase__ ( cls : List[Any], lowerCamelCase : list[int], lowerCamelCase : list[int] ):
'''simple docstring'''
return sum(row[i] * column[i] for i in range(len(lowerCamelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 183
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Optional[int] = {
'configuration_clap': [
'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ClapAudioConfig',
'ClapConfig',
'ClapTextConfig',
],
'processing_clap': ['ClapProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : str = [
'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ClapModel',
'ClapPreTrainedModel',
'ClapTextModel',
'ClapTextModelWithProjection',
'ClapAudioModel',
'ClapAudioModelWithProjection',
]
A__ : List[Any] = ['ClapFeatureExtractor']
if TYPE_CHECKING:
from .configuration_clap import (
CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
ClapAudioConfig,
ClapConfig,
ClapTextConfig,
)
from .processing_clap import ClapProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clap import ClapFeatureExtractor
from .modeling_clap import (
CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
ClapAudioModel,
ClapAudioModelWithProjection,
ClapModel,
ClapPreTrainedModel,
ClapTextModel,
ClapTextModelWithProjection,
)
else:
import sys
A__ : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 183
| 1
|
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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 RegNetForImageClassification, RegNetModel
from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A_ :
"""simple docstring"""
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=3 , __UpperCAmelCase=32 , __UpperCAmelCase=3 , __UpperCAmelCase=10 , __UpperCAmelCase=[10, 20, 30, 40] , __UpperCAmelCase=[1, 1, 2, 1] , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase="relu" , __UpperCAmelCase=3 , __UpperCAmelCase=None , ) -> Optional[int]:
a : List[str] = parent
a : Any = batch_size
a : Optional[int] = image_size
a : List[str] = num_channels
a : List[Any] = embeddings_size
a : List[str] = hidden_sizes
a : List[Any] = depths
a : Dict = is_training
a : List[str] = use_labels
a : Tuple = hidden_act
a : List[str] = num_labels
a : Dict = scope
a : Optional[int] = len(__UpperCAmelCase )
def lowercase_ ( self ) -> str:
a : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a : List[Any] = None
if self.use_labels:
a : Any = ids_tensor([self.batch_size] , self.num_labels )
a : List[Any] = self.get_config()
return config, pixel_values, labels
def lowercase_ ( self ) -> List[Any]:
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def lowercase_ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Union[str, Any]:
a : List[str] = RegNetModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
a : str = model(__UpperCAmelCase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowercase_ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Any:
a : Dict = self.num_labels
a : Any = RegNetForImageClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
a : Optional[Any] = model(__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase_ ( self ) -> int:
a : Tuple = self.prepare_config_and_inputs()
a , a , a : str = config_and_inputs
a : Optional[int] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class A_ ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
lowercase : Optional[Any] = (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
lowercase : Any = (
{"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
if is_torch_available()
else {}
)
lowercase : str = False
lowercase : Optional[int] = False
lowercase : Optional[Any] = False
lowercase : int = False
def lowercase_ ( self ) -> Optional[int]:
a : str = RegNetModelTester(self )
a : Union[str, Any] = ConfigTester(self , config_class=__UpperCAmelCase , has_text_modality=__UpperCAmelCase )
def lowercase_ ( 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 lowercase_ ( self ) -> Optional[int]:
return
@unittest.skip(reason='RegNet does not use inputs_embeds' )
def lowercase_ ( self ) -> str:
pass
@unittest.skip(reason='RegNet does not support input and output embeddings' )
def lowercase_ ( self ) -> Optional[Any]:
pass
def lowercase_ ( self ) -> Tuple:
a , a : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a : Optional[int] = model_class(__UpperCAmelCase )
a : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a : Union[str, Any] = [*signature.parameters.keys()]
a : List[str] = ['pixel_values']
self.assertListEqual(arg_names[:1] , __UpperCAmelCase )
def lowercase_ ( self ) -> Dict:
a : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowercase_ ( self ) -> str:
a , a : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a : int = model_class(config=__UpperCAmelCase )
for name, module in model.named_modules():
if isinstance(__UpperCAmelCase , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
def lowercase_ ( self ) -> Any:
def check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
a : Any = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
a : Union[str, Any] = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
a : int = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
a : Dict = self.model_tester.num_stages
self.assertEqual(len(__UpperCAmelCase ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
a , a : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
a : Any = ['basic', 'bottleneck']
for model_class in self.all_model_classes:
for layer_type in layers_type:
a : Optional[int] = layer_type
a : Optional[int] = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a : int = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def lowercase_ ( self ) -> Tuple:
a : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase )
@slow
def lowercase_ ( self ) -> Union[str, Any]:
for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a : List[Any] = RegNetModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def A_ ( ) -> Optional[Any]:
a : Any = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class A_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowercase_ ( self ) -> int:
return (
AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def lowercase_ ( self ) -> Union[str, Any]:
a : List[str] = RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(__UpperCAmelCase )
a : Optional[Any] = self.default_image_processor
a : Optional[int] = prepare_img()
a : Tuple = image_processor(images=__UpperCAmelCase , return_tensors='pt' ).to(__UpperCAmelCase )
# forward pass
with torch.no_grad():
a : str = model(**__UpperCAmelCase )
# verify the logits
a : Any = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __UpperCAmelCase )
a : Dict = torch.tensor([-0.4180, -1.5051, -3.4836] ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 509
|
"""simple docstring"""
from collections import Counter
from timeit import timeit
def A_ ( UpperCAmelCase__ = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(' ' , '' ).lower() ).values() ) < 2
def A_ ( UpperCAmelCase__ = "" ) -> bool:
if len(UpperCAmelCase__ ) == 0:
return True
a : Union[str, Any] = input_str.replace(' ' , '' ).lower()
# character_freq_dict: Stores the frequency of every character in the input string
a : dict[str, int] = {}
for character in lower_case_input_str:
a : Optional[Any] = character_freq_dict.get(UpperCAmelCase__ , 0 ) + 1
a : Any = 0
for character_count in character_freq_dict.values():
if character_count % 2:
odd_char += 1
if odd_char > 1:
return False
return True
def A_ ( UpperCAmelCase__ = "" ) -> None:
print('\nFor string = ' , UpperCAmelCase__ , ':' )
print(
'> can_string_be_rearranged_as_palindrome_counter()' , '\tans =' , can_string_be_rearranged_as_palindrome_counter(UpperCAmelCase__ ) , '\ttime =' , timeit(
'z.can_string_be_rearranged_as_palindrome_counter(z.check_str)' , setup='import __main__ as z' , ) , 'seconds' , )
print(
'> can_string_be_rearranged_as_palindrome()' , '\tans =' , can_string_be_rearranged_as_palindrome(UpperCAmelCase__ ) , '\ttime =' , timeit(
'z.can_string_be_rearranged_as_palindrome(z.check_str)' , setup='import __main__ as z' , ) , 'seconds' , )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input(
"Enter string to determine if it can be rearranged as a palindrome or not: "
).strip()
benchmark(check_str)
SCREAMING_SNAKE_CASE__ : List[Any] = can_string_be_rearranged_as_palindrome_counter(check_str)
print(F'{check_str} can {"" if status else "not "}be rearranged as a palindrome')
| 509
| 1
|
"""simple docstring"""
def snake_case__ ( _snake_case : Any , _snake_case : Union[str, Any] ):
"""simple docstring"""
return int(input_a == input_a == 0 )
def snake_case__ ( ):
"""simple docstring"""
print("Truth Table of NOR Gate:" )
print("| Input 1 | Input 2 | Output |" )
print(F'| 0 | 0 | {nor_gate(0 , 0 )} |' )
print(F'| 0 | 1 | {nor_gate(0 , 1 )} |' )
print(F'| 1 | 0 | {nor_gate(1 , 0 )} |' )
print(F'| 1 | 1 | {nor_gate(1 , 1 )} |' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 516
|
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
__snake_case : Union[str, Any] = logging.getLogger(__name__)
@dataclass
class A__(a_ ):
"""simple docstring"""
_A : Optional[float] = field(
default=0.0, metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''} )
_A : bool = field(default=a_, metadata={'''help''': '''Whether to SortishSamler or not.'''} )
_A : bool = field(
default=a_, metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''} )
_A : bool = field(default=a_, metadata={'''help''': '''whether to use adafactor'''} )
_A : Optional[float] = field(
default=a_, metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''} )
_A : Optional[float] = field(
default=a_, metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''} )
_A : Optional[float] = field(default=a_, metadata={'''help''': '''Dropout probability. Goes into model.config.'''} )
_A : Optional[float] = field(
default=a_, metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''} )
_A : Optional[str] = field(
default='''linear''', metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys() )}'''}, )
| 540
| 0
|
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('TEST_SAGEMAKER' , 'False' ) ) is not True , reason='Skipping test because should only be run when releasing minor transformers version' , )
@pytest.mark.usefixtures('sm_env' )
@parameterized_class(
[
{
'framework': 'pytorch',
'script': 'run_glue.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.g4dn.xlarge',
'results': {'train_runtime': 650, 'eval_accuracy': 0.6, 'eval_loss': 0.9},
},
{
'framework': 'tensorflow',
'script': 'run_tf.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.g4dn.xlarge',
'results': {'train_runtime': 600, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
},
] )
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def _UpperCAmelCase ( self ) -> Union[str, Any]:
if self.framework == "pytorch":
subprocess.run(
F'cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'.split() , encoding='''utf-8''' , check=__UpperCAmelCase , )
assert hasattr(self , '''env''' )
def _UpperCAmelCase ( self , __UpperCAmelCase=1 ) -> str:
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=F'{self.env.base_job_name}-single' , instance_count=__UpperCAmelCase , instance_type=self.instance_type , debugger_hook_config=__UpperCAmelCase , hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path} , metric_definitions=self.env.metric_definitions , py_version='''py36''' , )
def _UpperCAmelCase ( self , __UpperCAmelCase ) -> List[Any]:
TrainingJobAnalytics(__UpperCAmelCase ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv' )
def _UpperCAmelCase ( self ) -> str:
# create estimator
_a = self.create_estimator()
# run training
estimator.fit()
# result dataframe
_a = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
_a = list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''] )
_a = list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
_a = (
Session().describe_training_job(estimator.latest_training_job.name ).get('''TrainingTimeInSeconds''' , 999999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy )
assert all(t <= self.results['''eval_loss'''] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F'{estimator.latest_training_job.name}.json' , '''w''' ) as outfile:
json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss} , __UpperCAmelCase )
| 285
|
"""simple docstring"""
def A_ ( _lowerCAmelCase : Dict=2_81_23 ):
"""simple docstring"""
_a = [1] * (limit + 1)
for i in range(2, int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1, limit // i + 1 ):
sum_divs[k * i] += k + i
_a = set()
_a = 0
for n in range(1, limit + 1 ):
if sum_divs[n] > n:
abundants.add(_lowerCAmelCase )
if not any((n - a in abundants) for a in abundants ):
res += n
return res
if __name__ == "__main__":
print(solution())
| 285
| 1
|
"""simple docstring"""
from itertools import count
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : int = 50 ):
'''simple docstring'''
lowerCAmelCase = [1] * min_block_length
for n in count(SCREAMING_SNAKE_CASE ):
fill_count_functions.append(1 )
for block_length in range(SCREAMING_SNAKE_CASE , n + 1 ):
for block_start in range(n - block_length ):
fill_count_functions[n] += fill_count_functions[
n - block_start - block_length - 1
]
fill_count_functions[n] += 1
if fill_count_functions[n] > 1_00_00_00:
break
return n
if __name__ == "__main__":
print(f'{solution() = }')
| 532
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
# See all FNet models at https://huggingface.co/models?filter=fnet
}
class lowercase ( _UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = 'fnet'
def __init__( self , lowercase=32_000 , lowercase=768 , lowercase=12 , lowercase=3_072 , lowercase="gelu_new" , lowercase=0.1 , lowercase=512 , lowercase=4 , lowercase=0.02 , lowercase=1e-12 , lowercase=False , lowercase=512 , lowercase=3 , lowercase=1 , lowercase=2 , **lowercase , ) -> int:
super().__init__(pad_token_id=lowercase , bos_token_id=lowercase , eos_token_id=lowercase , **lowercase )
lowerCAmelCase = vocab_size
lowerCAmelCase = max_position_embeddings
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = initializer_range
lowerCAmelCase = type_vocab_size
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = use_tpu_fourier_optimizations
lowerCAmelCase = tpu_short_seq_length
| 532
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import _LazyModule
UpperCamelCase_ = {"tokenization_tapex": ["TapexTokenizer"]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 710
|
'''simple docstring'''
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 _a :
'''simple docstring'''
A : str = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
A : Optional[str] = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
A : Optional[str] = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
A : Optional[str] = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
A : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether tp freeze the encoder.'''} )
A : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to freeze the embeddings.'''} )
@dataclass
class _a :
'''simple docstring'''
A : str = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
A : Optional[str] = field(
default='''summarization''' , metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''} , )
A : Optional[int] = field(
default=1_024 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
A : Optional[int] = field(
default=128 , metadata={
'''help''': (
'''The maximum total sequence length for target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
A : Optional[int] = field(
default=142 , 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``.'''
)
} , )
A : Optional[int] = field(
default=142 , 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.'''
)
} , )
A : Optional[int] = field(default=-1 , metadata={'''help''': '''# training examples. -1 means use all.'''} )
A : Optional[int] = field(default=-1 , metadata={'''help''': '''# validation examples. -1 means use all.'''} )
A : Optional[int] = field(default=-1 , metadata={'''help''': '''# test examples. -1 means use all.'''} )
A : Optional[str] = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Source language id for translation.'''} )
A : Optional[str] = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Target language id for translation.'''} )
A : Optional[int] = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''# num_beams to use for evaluation.'''} )
A : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''} , )
def lowercase__( __UpperCamelCase: Any ,__UpperCamelCase: List[str] ,__UpperCamelCase: Dict ):
"""simple docstring"""
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 lowercase__( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = 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.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = 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.
SCREAMING_SNAKE_CASE : List[str] = 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 ,)
SCREAMING_SNAKE_CASE : List[str] = ('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 ) )
SCREAMING_SNAKE_CASE : Tuple = 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 ,)
SCREAMING_SNAKE_CASE : List[str] = 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:
SCREAMING_SNAKE_CASE : Optional[int] = 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 ):
SCREAMING_SNAKE_CASE : str = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = 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() )
SCREAMING_SNAKE_CASE : Tuple = SeqaSeqDataset
# Get datasets
SCREAMING_SNAKE_CASE : str = (
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
)
SCREAMING_SNAKE_CASE : Any = (
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
)
SCREAMING_SNAKE_CASE : List[str] = (
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
SCREAMING_SNAKE_CASE : Dict = (
build_compute_metrics_fn(data_args.task ,__UpperCamelCase ) if training_args.predict_with_generate else None
)
SCREAMING_SNAKE_CASE : List[Any] = 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 ,)
SCREAMING_SNAKE_CASE : List[str] = {}
# Training
if training_args.do_train:
logger.info('*** Train ***' )
SCREAMING_SNAKE_CASE : Optional[Any] = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
SCREAMING_SNAKE_CASE : Optional[Any] = train_result.metrics
SCREAMING_SNAKE_CASE : Tuple = 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 ***' )
SCREAMING_SNAKE_CASE : Union[str, Any] = trainer.evaluate(metric_key_prefix='val' )
SCREAMING_SNAKE_CASE : int = data_args.n_val
SCREAMING_SNAKE_CASE : List[Any] = 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 ***' )
SCREAMING_SNAKE_CASE : str = trainer.predict(test_dataset=__UpperCamelCase ,metric_key_prefix='test' )
SCREAMING_SNAKE_CASE : Optional[int] = test_output.metrics
SCREAMING_SNAKE_CASE : Tuple = data_args.n_test
if trainer.is_world_process_zero():
SCREAMING_SNAKE_CASE : Optional[Any] = round(metrics['test_loss'] ,4 )
handle_metrics('test' ,__UpperCamelCase ,training_args.output_dir )
all_metrics.update(__UpperCamelCase )
if training_args.predict_with_generate:
SCREAMING_SNAKE_CASE : str = tokenizer.batch_decode(
test_output.predictions ,skip_special_tokens=__UpperCamelCase ,clean_up_tokenization_spaces=__UpperCamelCase )
SCREAMING_SNAKE_CASE : Union[str, Any] = 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 lowercase__( __UpperCamelCase: Any ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 508
| 0
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class __lowercase ( __lowerCamelCase ):
def __init__( self : List[str] ,*A : Union[str, Any] ,**A : Tuple ):
'''simple docstring'''
warnings.warn(
"""The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use DPTImageProcessor instead.""" ,A ,)
super().__init__(*A ,**A )
| 65
|
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
UpperCAmelCase__ : List[Any] = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(__UpperCamelCase )
# Let's go
UpperCAmelCase__ : int = parser.parse_args()
if not hasattr(__UpperCamelCase , """func""" ):
parser.print_help()
exit(1 )
# Run
UpperCAmelCase__ : Union[str, Any] = args.func(__UpperCamelCase )
service.run()
if __name__ == "__main__":
main()
| 65
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE = {
'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
'convert_funnel_original_tf_checkpoint_to_pytorch': [],
'tokenization_funnel': ['FunnelTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE = ['FunnelTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE = [
'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
'FunnelBaseModel',
'FunnelForMaskedLM',
'FunnelForMultipleChoice',
'FunnelForPreTraining',
'FunnelForQuestionAnswering',
'FunnelForSequenceClassification',
'FunnelForTokenClassification',
'FunnelModel',
'FunnelPreTrainedModel',
'load_tf_weights_in_funnel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE = [
'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFFunnelBaseModel',
'TFFunnelForMaskedLM',
'TFFunnelForMultipleChoice',
'TFFunnelForPreTraining',
'TFFunnelForQuestionAnswering',
'TFFunnelForSequenceClassification',
'TFFunnelForTokenClassification',
'TFFunnelModel',
'TFFunnelPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
from .tokenization_funnel import FunnelTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_funnel_fast import FunnelTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_funnel import (
FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
FunnelPreTrainedModel,
load_tf_weights_in_funnel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_funnel import (
TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
TFFunnelPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 153
|
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : str ,lowerCAmelCase_ : str ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] =get_failure_array(lowerCAmelCase_ )
# 2) Step through text searching for pattern
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[Any] =0, 0 # index into text, pattern
while i < len(lowerCAmelCase_ ):
if pattern[j] == text[i]:
if j == (len(lowerCAmelCase_ ) - 1):
return True
j += 1
# if this is a prefix in our pattern
# just go back far enough to continue
elif j > 0:
SCREAMING_SNAKE_CASE_ : Optional[Any] =failure[j - 1]
continue
i += 1
return False
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : str ) -> list[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any =[0]
SCREAMING_SNAKE_CASE_ : List[str] =0
SCREAMING_SNAKE_CASE_ : int =1
while j < len(lowerCAmelCase_ ):
if pattern[i] == pattern[j]:
i += 1
elif i > 0:
SCREAMING_SNAKE_CASE_ : Optional[int] =failure[i - 1]
continue
j += 1
failure.append(lowerCAmelCase_ )
return failure
if __name__ == "__main__":
# Test 1)
__SCREAMING_SNAKE_CASE = 'abc1abc12'
__SCREAMING_SNAKE_CASE = 'alskfjaldsabc1abc1abc12k23adsfabcabc'
__SCREAMING_SNAKE_CASE = 'alskfjaldsk23adsfabcabc'
assert kmp(pattern, texta) and not kmp(pattern, texta)
# Test 2)
__SCREAMING_SNAKE_CASE = 'ABABX'
__SCREAMING_SNAKE_CASE = 'ABABZABABYABABX'
assert kmp(pattern, text)
# Test 3)
__SCREAMING_SNAKE_CASE = 'AAAB'
__SCREAMING_SNAKE_CASE = 'ABAAAAAB'
assert kmp(pattern, text)
# Test 4)
__SCREAMING_SNAKE_CASE = 'abcdabcy'
__SCREAMING_SNAKE_CASE = 'abcxabcdabxabcdabcdabcy'
assert kmp(pattern, text)
# Test 5)
__SCREAMING_SNAKE_CASE = 'aabaabaaa'
assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
| 153
| 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 re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class lowerCamelCase__ ( a_ ):
__lowerCamelCase = """naver-clova-ix/donut-base-finetuned-docvqa"""
__lowerCamelCase = (
"""This is a tool that answers a question about an document (pdf). It takes an input named `document` which """
"""should be the document containing the information, as well as a `question` that is the question about the """
"""document. It returns a text that contains the answer to the question."""
)
__lowerCamelCase = """document_qa"""
__lowerCamelCase = AutoProcessor
__lowerCamelCase = VisionEncoderDecoderModel
__lowerCamelCase = ["""image""", """text"""]
__lowerCamelCase = ["""text"""]
def __init__( self : Any , *__a : str , **__a : Dict ):
'''simple docstring'''
if not is_vision_available():
raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool.""" )
super().__init__(*__lowerCamelCase , **__lowerCamelCase )
def lowerCamelCase_ ( self : List[str] , __a : "Image" , __a : str ):
'''simple docstring'''
lowerCamelCase__: str = """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
lowerCamelCase__: Optional[int] = task_prompt.replace("""{user_input}""" , __lowerCamelCase )
lowerCamelCase__: int = self.pre_processor.tokenizer(
__lowerCamelCase , add_special_tokens=__lowerCamelCase , return_tensors="""pt""" ).input_ids
lowerCamelCase__: List[str] = self.pre_processor(__lowerCamelCase , return_tensors="""pt""" ).pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def lowerCamelCase_ ( self : Dict , __a : Any ):
'''simple docstring'''
return self.model.generate(
inputs["""pixel_values"""].to(self.device ) , decoder_input_ids=inputs["""decoder_input_ids"""].to(self.device ) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=__lowerCamelCase , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=__lowerCamelCase , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=__lowerCamelCase , ).sequences
def lowerCamelCase_ ( self : Tuple , __a : Dict ):
'''simple docstring'''
lowerCamelCase__: Optional[int] = self.pre_processor.batch_decode(__lowerCamelCase )[0]
lowerCamelCase__: Optional[Any] = sequence.replace(self.pre_processor.tokenizer.eos_token , """""" )
lowerCamelCase__: Optional[Any] = sequence.replace(self.pre_processor.tokenizer.pad_token , """""" )
lowerCamelCase__: Dict = re.sub(R"""<.*?>""" , """""" , __lowerCamelCase , count=1 ).strip() # remove first task start token
lowerCamelCase__: Union[str, Any] = self.pre_processor.tokenajson(__lowerCamelCase )
return sequence["answer"]
| 306
|
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class lowerCamelCase_ ( unittest.TestCase ):
def __init__( self : int ,__lowerCamelCase : List[str] ,__lowerCamelCase : Optional[int]=7 ,__lowerCamelCase : Optional[int]=3 ,__lowerCamelCase : Any=18 ,__lowerCamelCase : List[Any]=30 ,__lowerCamelCase : Optional[Any]=4_00 ,__lowerCamelCase : Dict=True ,__lowerCamelCase : Optional[int]=None ,__lowerCamelCase : int=True ,__lowerCamelCase : List[str]=False ,__lowerCamelCase : Dict=True ,__lowerCamelCase : Dict=True ,__lowerCamelCase : Union[str, Any]=[0.5, 0.5, 0.5] ,__lowerCamelCase : Optional[int]=[0.5, 0.5, 0.5] ,):
'''simple docstring'''
a = parent
a = batch_size
a = num_channels
a = image_size
a = min_resolution
a = max_resolution
a = do_resize
a = size if size is not None else {'''height''': 18, '''width''': 20}
a = do_thumbnail
a = do_align_axis
a = do_pad
a = do_normalize
a = image_mean
a = image_std
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class lowerCamelCase_ ( a_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ = DonutImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
'''simple docstring'''
a = DonutImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
'''simple docstring'''
a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__lowerCamelCase ,'''do_resize''' ) )
self.assertTrue(hasattr(__lowerCamelCase ,'''size''' ) )
self.assertTrue(hasattr(__lowerCamelCase ,'''do_thumbnail''' ) )
self.assertTrue(hasattr(__lowerCamelCase ,'''do_align_long_axis''' ) )
self.assertTrue(hasattr(__lowerCamelCase ,'''do_pad''' ) )
self.assertTrue(hasattr(__lowerCamelCase ,'''do_normalize''' ) )
self.assertTrue(hasattr(__lowerCamelCase ,'''image_mean''' ) )
self.assertTrue(hasattr(__lowerCamelCase ,'''image_std''' ) )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
'''simple docstring'''
a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{'''height''': 18, '''width''': 20} )
a = self.image_processing_class.from_dict(self.image_processor_dict ,size=42 )
self.assertEqual(image_processor.size ,{'''height''': 42, '''width''': 42} )
# Previous config had dimensions in (width, height) order
a = self.image_processing_class.from_dict(self.image_processor_dict ,size=(42, 84) )
self.assertEqual(image_processor.size ,{'''height''': 84, '''width''': 42} )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
'''simple docstring'''
pass
@is_flaky()
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
'''simple docstring'''
a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
a = prepare_image_inputs(self.image_processor_tester ,equal_resolution=__lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCamelCase ,Image.Image )
# Test not batched input
a = 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.size['''height'''],
self.image_processor_tester.size['''width'''],
) ,)
# Test batched
a = image_processing(__lowerCamelCase ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) ,)
@is_flaky()
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
'''simple docstring'''
a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
a = prepare_image_inputs(self.image_processor_tester ,equal_resolution=__lowerCamelCase ,numpify=__lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCamelCase ,np.ndarray )
# Test not batched input
a = 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.size['''height'''],
self.image_processor_tester.size['''width'''],
) ,)
# Test batched
a = image_processing(__lowerCamelCase ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) ,)
@is_flaky()
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
'''simple docstring'''
a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
a = prepare_image_inputs(self.image_processor_tester ,equal_resolution=__lowerCamelCase ,torchify=__lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCamelCase ,torch.Tensor )
# Test not batched input
a = 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.size['''height'''],
self.image_processor_tester.size['''width'''],
) ,)
# Test batched
a = image_processing(__lowerCamelCase ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) ,)
| 387
| 0
|
def _UpperCamelCase (a__ :list[int] , a__ :int ):
"""simple docstring"""
UpperCamelCase__ = len(a__ )
UpperCamelCase__ = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
UpperCamelCase__ = True
# sum is not zero and set is empty then false
for i in range(1 , required_sum + 1 ):
UpperCamelCase__ = False
for i in range(1 , arr_len + 1 ):
for j in range(1 , required_sum + 1 ):
if arr[i - 1] > j:
UpperCamelCase__ = subset[i - 1][j]
if arr[i - 1] <= j:
UpperCamelCase__ = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 702
|
from math import ceil, sqrt
def _UpperCamelCase (a__ :int = 100_0000 ):
"""simple docstring"""
UpperCamelCase__ = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
UpperCamelCase__ = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
UpperCamelCase__ = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(f"""{solution() = }""")
| 548
| 0
|
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer
from ...test_tokenization_common import TokenizerTesterMixin
@require_phonemizer
class lowerCamelCase ( _UpperCamelCase , unittest.TestCase ):
_lowerCAmelCase : Union[str, Any] = WavaVecaPhonemeCTCTokenizer
_lowerCAmelCase : str = False
def A( self):
super().setUp()
__UpperCAmelCase : Optional[int] = (
'''<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː '''
'''ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː '''
'''ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 '''
'''oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ '''
'''pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ '''
'''yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ '''
'''əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '''
'''ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ '''
'''ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ '''
'''uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ '''
'''ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ '''
'''ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ '''
'''ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4'''
).split(''' ''')
__UpperCAmelCase : Union[str, Any] = dict(zip(lowercase__ , range(len(lowercase__))))
__UpperCAmelCase : List[Any] = {'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}
__UpperCAmelCase : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''])
with open(self.vocab_file , '''w''' , encoding='''utf-8''') as fp:
fp.write(json.dumps(lowercase__) + '''\n''')
def A( self , lowercase__ , lowercase__=False , lowercase__=2_0 , lowercase__=5):
__UpperCAmelCase : str = [(i, tokenizer.decode([i] , clean_up_tokenization_spaces=lowercase__)) for i in range(len(lowercase__))]
__UpperCAmelCase : int = list(filter(lambda lowercase__: [t[0]] == tokenizer.encode(t[1] , do_phonemize=lowercase__) , lowercase__))
if max_length is not None and len(lowercase__) > max_length:
__UpperCAmelCase : Tuple = toks[:max_length]
if min_length is not None and len(lowercase__) < min_length and len(lowercase__) > 0:
while len(lowercase__) < min_length:
__UpperCAmelCase : List[Any] = toks + toks
# toks_str = [t[1] for t in toks]
__UpperCAmelCase : List[str] = [t[0] for t in toks]
# Ensure consistency
__UpperCAmelCase : Optional[int] = tokenizer.decode(lowercase__ , clean_up_tokenization_spaces=lowercase__)
if " " not in output_txt and len(lowercase__) > 1:
__UpperCAmelCase : Optional[Any] = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=lowercase__)
+ ''' '''
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=lowercase__)
)
if with_prefix_space:
__UpperCAmelCase : Optional[Any] = ''' ''' + output_txt
__UpperCAmelCase : str = tokenizer.encode(lowercase__ , add_special_tokens=lowercase__)
return output_txt, output_ids
def A( self , **lowercase__):
kwargs.update(self.special_tokens_map)
return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname , **lowercase__)
def A( self):
__UpperCAmelCase : str = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')
# check adding a single token
tokenizer.add_tokens('''xxx''')
__UpperCAmelCase : int = tokenizer('''m xxx ɪ''' , do_phonemize=lowercase__).input_ids
self.assertEqual(lowercase__ , [1_3, 3_9_2, 1_7]) # xxx should be last token
tokenizer.add_tokens(['''aaa''', '''bbb''', '''ccc'''])
__UpperCAmelCase : Optional[int] = tokenizer('''m aaa ɪ ccc''' , do_phonemize=lowercase__).input_ids
self.assertEqual(lowercase__ , [1_3, 3_9_3, 1_7, 3_9_5]) # aaa and ccc should be after xxx and 2 after aaa
__UpperCAmelCase : str = tokenizer('''maɪ c''' , do_phonemize=lowercase__).input_ids
self.assertEqual(lowercase__ , [3, 2_0_0]) # mai should be <unk> (=3)
def A( self):
__UpperCAmelCase : List[Any] = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')
__UpperCAmelCase : List[str] = '''Hello how are you'''
__UpperCAmelCase : Union[str, Any] = tokenizer.phonemize(lowercase__ , phonemizer_lang='''en-us''')
self.assertEqual(lowercase__ , '''h ə l oʊ h aʊ ɑːɹ j uː''')
def A( self):
__UpperCAmelCase : int = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')
__UpperCAmelCase : List[str] = '''Hello how are you'''
__UpperCAmelCase : Optional[Any] = tokenizer.phonemize(lowercase__ , phonemizer_lang='''en-us''')
self.assertEqual(tokenizer(lowercase__).input_ids , tokenizer(lowercase__ , do_phonemize=lowercase__).input_ids)
def A( self):
__UpperCAmelCase : int = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')
__UpperCAmelCase : Any = '''Hello how are you'''
__UpperCAmelCase : Any = tokenizer.phonemize(lowercase__ , phonemizer_lang='''en-us''')
__UpperCAmelCase : Union[str, Any] = tokenizer.decode(tokenizer(lowercase__).input_ids)
self.assertEqual(lowercase__ , lowercase__)
def A( self):
__UpperCAmelCase : List[str] = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')
__UpperCAmelCase : Dict = [
[1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 8, 9_8],
[2_4, 2_2, 5, 2_4, 2_2, 5, 7_7],
]
__UpperCAmelCase : int = tokenizer.decode(sample_ids[0])
__UpperCAmelCase : Optional[Any] = tokenizer.batch_decode(lowercase__)
self.assertEqual(lowercase__ , batch_tokens[0])
self.assertEqual(lowercase__ , ['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ'''])
def A( self):
__UpperCAmelCase : Dict = self.tokenizer_class.from_pretrained(
'''facebook/wav2vec2-lv-60-espeak-cv-ft''' , word_delimiter_token='''|''')
tokenizer.add_tokens('''|''')
__UpperCAmelCase : Optional[Any] = '''Hello how are you'''
__UpperCAmelCase : Union[str, Any] = tokenizer.phonemize(lowercase__ , phonemizer_lang='''en-us''')
self.assertEqual(lowercase__ , '''h ə l oʊ | h aʊ | ɑːɹ | j uː |''')
def A( self):
__UpperCAmelCase : Optional[int] = self.tokenizer_class.from_pretrained(
'''facebook/wav2vec2-lv-60-espeak-cv-ft''' , word_delimiter_token='''|''')
tokenizer.add_tokens('''|''')
__UpperCAmelCase : Dict = '''Hello how are you'''
__UpperCAmelCase : Dict = tokenizer.phonemize(lowercase__ , phonemizer_lang='''en-us''')
self.assertEqual(tokenizer(lowercase__).input_ids , tokenizer(lowercase__ , do_phonemize=lowercase__).input_ids)
def A( self):
__UpperCAmelCase : Dict = self.tokenizer_class.from_pretrained(
'''facebook/wav2vec2-lv-60-espeak-cv-ft''' , word_delimiter_token='''|''')
tokenizer.add_tokens('''|''')
# fmt: off
__UpperCAmelCase : Dict = [
[1_1, 5, 1_5, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 1_5, 8, tokenizer.word_delimiter_token_id, 9_8],
[tokenizer.word_delimiter_token_id, 2_4, 2_2, tokenizer.word_delimiter_token_id, 5, 2_4, 2_2, 5, 7_7],
]
# fmt: on
# decode with word_del_token filter
__UpperCAmelCase : List[str] = tokenizer.decode(sample_ids[0])
__UpperCAmelCase : str = tokenizer.batch_decode(lowercase__)
self.assertEqual(lowercase__ , batch_tokens[0])
self.assertEqual(lowercase__ , ['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ'''])
# decode with no word_del_token filter
__UpperCAmelCase : Optional[Any] = tokenizer.decode(sample_ids[0] , filter_word_delimiter_token=lowercase__)
__UpperCAmelCase : List[str] = tokenizer.batch_decode(lowercase__ , filter_word_delimiter_token=lowercase__)
self.assertEqual(lowercase__ , batch_tokens[0])
self.assertEqual(lowercase__ , ['''k s ɾ | ɾ l | ɭʲ''', '''| j ð | s j ð s oːɹ'''])
def A( self):
__UpperCAmelCase : Tuple = self.tokenizer_class.from_pretrained(
'''facebook/wav2vec2-lv-60-espeak-cv-ft''' , word_delimiter_token='''|''')
tokenizer.add_tokens('''|''')
__UpperCAmelCase : Any = '''Hello how are you'''
__UpperCAmelCase : List[str] = tokenizer.phonemize(lowercase__ , phonemizer_lang='''en-us''')
__UpperCAmelCase : Union[str, Any] = tokenizer.decode(tokenizer(lowercase__).input_ids , filter_word_delimiter_token=lowercase__)
self.assertEqual(lowercase__ , lowercase__)
def A( self):
__UpperCAmelCase : List[str] = self.tokenizer_class.from_pretrained(
'''facebook/wav2vec2-lv-60-espeak-cv-ft''' , word_delimiter_token='''|''')
tokenizer.add_tokens('''|''')
__UpperCAmelCase : Optional[int] = '''Hello how are you'''
__UpperCAmelCase : Optional[Any] = tokenizer.phonemize(lowercase__ , phonemizer_lang='''en-us''')
__UpperCAmelCase : Optional[int] = tokenizer.decode(tokenizer(lowercase__).input_ids , filter_word_delimiter_token=lowercase__)
self.assertEqual(''' '''.join([p.strip() for p in phonemes.split(''' |''')]).strip() , lowercase__)
def A( self):
__UpperCAmelCase : List[str] = self.tokenizer_class.from_pretrained(
'''facebook/wav2vec2-lv-60-espeak-cv-ft''' , word_delimiter_token=lowercase__)
__UpperCAmelCase : Optional[Any] = '''Hello how are you'''
__UpperCAmelCase : Optional[int] = tokenizer(lowercase__ , phonemizer_lang='''en-us''').input_ids
__UpperCAmelCase : Tuple = tokenizer(lowercase__ , phonemizer_lang='''fr-fr''').input_ids
self.assertNotEqual(lowercase__ , lowercase__)
__UpperCAmelCase : Union[str, Any] = tokenizer.decode(lowercase__)
__UpperCAmelCase : Any = tokenizer.decode(lowercase__)
self.assertEqual(lowercase__ , '''h ə l oʊ h aʊ ɑːɹ j uː''')
self.assertEqual(lowercase__ , '''ɛ l o h aʊ a ʁ j u''')
def A( self):
__UpperCAmelCase : Tuple = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')
__UpperCAmelCase : Tuple = '''Hello how Are you'''
__UpperCAmelCase : Any = '''hello how are you'''
__UpperCAmelCase : Optional[int] = tokenizer(lowercase__).input_ids
__UpperCAmelCase : str = tokenizer(lowercase__).input_ids
self.assertEqual(lowercase__ , lowercase__)
def A( self):
__UpperCAmelCase : int = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')
tokenizer.add_tokens(['''!''', '''?'''])
tokenizer.add_special_tokens({'''cls_token''': '''$$$'''})
# fmt: off
__UpperCAmelCase : Optional[Any] = [
[1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 8, 9_8, 3_9_2, 3_9_2, 3_9_3, 3_9_2, 3_9_2, 3_9_3, 3_9_4, 3_9_4],
[2_4, 2_2, 5, 2_4, 2_2, 5, 7_7, tokenizer.pad_token_id, 3_9_4, 3_9_4],
]
# fmt: on
__UpperCAmelCase : Any = tokenizer.batch_decode(lowercase__)
self.assertEqual(lowercase__ , ['''k s ɾ ɾ l ɭʲ!?!? $$$''', '''j ð s j ð s oːɹ $$$'''])
@staticmethod
def A( lowercase__ , lowercase__):
__UpperCAmelCase : Tuple = [d[key] for d in offsets]
return retrieved_list
def A( self):
__UpperCAmelCase : Any = self.get_tokenizer(word_delimiter_token='''|''')
tokenizer.add_tokens('''|''')
# fmt: off
# ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
__UpperCAmelCase : Dict = [1_1, 5, 5, 5, 1_5, 1_5, tokenizer.pad_token_id, 1_5, 1_5, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 1_5, 8, 8, 8, tokenizer.word_delimiter_token_id, 9_8]
# fmt: on
__UpperCAmelCase : Tuple = tokenizer.decode(lowercase__ , output_char_offsets=lowercase__ , filter_word_delimiter_token=lowercase__)
# check Wav2Vec2CTCTokenizerOutput keys for char
self.assertEqual(len(outputs.keys()) , 2)
self.assertTrue('''text''' in outputs)
self.assertTrue('''char_offsets''' in outputs)
self.assertTrue(isinstance(lowercase__ , lowercase__))
# check that order of chars is correct and identical for both outputs
self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''char_offsets'''] , '''char''')) , outputs.text)
self.assertListEqual(
self.get_from_offsets(outputs['''char_offsets'''] , '''char''') , ['''k''', '''s''', '''ɾ''', '''ɾ''', '''|''', '''ɾ''', '''l''', '''|''', '''ɭʲ'''])
# check that offsets are actually correct for char
# 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
# 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
self.assertListEqual(
self.get_from_offsets(outputs['''char_offsets'''] , '''start_offset''') , [0, 1, 4, 7, 9, 1_1, 1_2, 1_5, 1_6])
self.assertListEqual(
self.get_from_offsets(outputs['''char_offsets'''] , '''end_offset''') , [1, 4, 6, 9, 1_0, 1_2, 1_5, 1_6, 1_7])
def A( self):
__UpperCAmelCase : Dict = self.get_tokenizer(word_delimiter_token='''|''')
def check_list_tuples_equal(lowercase__ , lowercase__):
self.assertTrue(isinstance(lowercase__ , lowercase__))
self.assertTrue(isinstance(outputs_list[0] , lowercase__))
# transform list to ModelOutput
__UpperCAmelCase : str = WavaVecaPhonemeCTCTokenizerOutput(
{k: [d[k] for d in outputs_list] for k in outputs_list[0]})
self.assertListEqual(outputs_batch['''text'''] , outputs_batch_a['''text'''])
def recursive_check(lowercase__ , lowercase__):
if isinstance(lowercase__ , lowercase__):
[recursive_check(lowercase__ , lowercase__) for la, la in zip(lowercase__ , lowercase__)]
self.assertEqual(lowercase__ , lowercase__)
if "char_offsets" in outputs_batch:
recursive_check(outputs_batch['''char_offsets'''] , outputs_batch_a['''char_offsets'''])
# fmt: off
__UpperCAmelCase : int = [
[1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 4, 8, 9_8, 3_2, 3_2, 3_2, 3_2, 4, 3_3, tokenizer.word_delimiter_token_id, 3_2, 3_2, 3_3, 3_4, 3_4],
[2_4, 2_2, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 2_4, 2_2, 2_2, 2_2, 4, 5, 7_7, tokenizer.pad_token_id, 2_2, 2_2, 4, 3_4, 3_4, 3_4, 3_4],
]
# fmt: on
# We assume that `decode` works as expected. All we will check now is
# the output type is correct and the output is identical to `decode`
# char
__UpperCAmelCase : List[Any] = tokenizer.batch_decode(lowercase__ , output_char_offsets=lowercase__)
__UpperCAmelCase : str = [tokenizer.decode(lowercase__ , output_char_offsets=lowercase__) for ids in sample_ids]
check_list_tuples_equal(lowercase__ , lowercase__)
@unittest.skip('''Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes''')
def A( self):
pass
@unittest.skip('''Wav2Vec2PhonemeTokenizer always puts spaces between phonemes''')
def A( self):
pass
@unittest.skip('''encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency''')
def A( self):
pass
@unittest.skip('''Wav2Vec2PhonemeModel has no max model length => no testing''')
def A( self):
pass
def A( self):
__UpperCAmelCase : Dict = self.get_tokenizers(do_lower_case=lowercase__)
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}"):
__UpperCAmelCase : List[str] = tokenizer.vocab_size
__UpperCAmelCase : List[Any] = len(lowercase__)
self.assertNotEqual(lowercase__ , 0)
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
__UpperCAmelCase : List[str] = ['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
__UpperCAmelCase : int = tokenizer.add_tokens(lowercase__)
__UpperCAmelCase : Tuple = tokenizer.vocab_size
__UpperCAmelCase : str = len(lowercase__)
self.assertNotEqual(lowercase__ , 0)
self.assertEqual(lowercase__ , lowercase__)
self.assertEqual(lowercase__ , len(lowercase__))
self.assertEqual(lowercase__ , all_size + len(lowercase__))
__UpperCAmelCase : List[Any] = tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l''' , add_special_tokens=lowercase__)
self.assertGreaterEqual(len(lowercase__) , 4)
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1)
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1)
__UpperCAmelCase : List[str] = {'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
__UpperCAmelCase : str = tokenizer.add_special_tokens(lowercase__)
__UpperCAmelCase : Union[str, Any] = tokenizer.vocab_size
__UpperCAmelCase : Tuple = len(lowercase__)
self.assertNotEqual(lowercase__ , 0)
self.assertEqual(lowercase__ , lowercase__)
self.assertEqual(lowercase__ , len(lowercase__))
self.assertEqual(lowercase__ , all_size_a + len(lowercase__))
__UpperCAmelCase : int = tokenizer.encode(
'''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l''' , add_special_tokens=lowercase__)
self.assertGreaterEqual(len(lowercase__) , 6)
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1)
self.assertGreater(tokens[0] , tokens[1])
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1)
self.assertGreater(tokens[-3] , tokens[-4])
self.assertEqual(tokens[0] , tokenizer.eos_token_id)
self.assertEqual(tokens[-3] , tokenizer.pad_token_id)
@unittest.skip('''The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.''')
def A( self):
pass
@unittest.skip('''The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.''')
def A( self):
pass
def A( self):
# The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
# is not the case for Wav2Vec2PhonemeCTCTokenizer.
__UpperCAmelCase : List[Any] = self.get_tokenizers(fast=lowercase__ , do_lower_case=lowercase__)
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}"):
__UpperCAmelCase : int = ['''ð''', '''ɪ''', '''s''', '''ɪ''', '''z''', '''ɐ''', '''t''', '''ɛ''', '''k''', '''s''', '''t''']
__UpperCAmelCase : Tuple = tokenizer.convert_tokens_to_string(lowercase__)
self.assertIsInstance(output['''text'''] , lowercase__)
| 462
|
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
lowerCAmelCase = datasets.logging.get_logger(__name__)
lowerCAmelCase = """\
@InProceedings{moosavi2019minimum,
author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
year = {2019},
booktitle = {Proceedings of the 57th Annual Meeting of
the Association for Computational Linguistics (Volume 1: Long Papers)},
publisher = {Association for Computational Linguistics},
address = {Florence, Italy},
}
@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}
@INPROCEEDINGS{Bagga98algorithmsfor,
author = {Amit Bagga and Breck Baldwin},
title = {Algorithms for Scoring Coreference Chains},
booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
year = {1998},
pages = {563--566}
}
@INPROCEEDINGS{Luo05oncoreference,
author = {Xiaoqiang Luo},
title = {On coreference resolution performance metrics},
booktitle = {In Proc. of HLT/EMNLP},
year = {2005},
pages = {25--32},
publisher = {URL}
}
@inproceedings{moosavi-strube-2016-coreference,
title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",
author = \"Moosavi, Nafise Sadat and
Strube, Michael\",
booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",
month = aug,
year = \"2016\",
address = \"Berlin, Germany\",
publisher = \"Association for Computational Linguistics\",
url = \"https://www.aclweb.org/anthology/P16-1060\",
doi = \"10.18653/v1/P16-1060\",
pages = \"632--642\",
}
"""
lowerCAmelCase = """\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].
This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column Type Description
1 Document ID This is a variation on the document filename
2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3 Word number
4 Word itself This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5 Part-of-Speech
6 Parse bit This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the \"([pos] [word])\" string (or leaf) and concatenating the items in the rows of that column.
7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"
8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7.
9 Word sense This is the word sense of the word in Column 3.
10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11 Named Entities These columns identifies the spans representing various named entities.
12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N Coreference Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html
Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md
CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
"""
lowerCAmelCase = """
Calculates coreference evaluation metrics.
Args:
predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
Each prediction is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
Each reference is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
keep_singletons: After extracting all mentions of key or system files,
mentions whose corresponding coreference chain is of size one,
are considered as singletons. The default evaluation mode will include
singletons in evaluations if they are included in the key or the system files.
By setting 'keep_singletons=False', all singletons in the key and system files
will be excluded from the evaluation.
NP_only: Most of the recent coreference resolvers only resolve NP mentions and
leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.
min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.
Minimum spans are determined using the MINA algorithm.
Returns:
'mentions': mentions
'muc': MUC metric [Vilain et al, 1995]
'bcub': B-cubed [Bagga and Baldwin, 1998]
'ceafe': CEAFe [Luo et al., 2005]
'lea': LEA [Moosavi and Strube, 2016]
'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)
Examples:
>>> coval = datasets.load_metric('coval')
>>> words = ['bc/cctv/00/cctv_0005 0 0 Thank VBP (TOP(S(VP* thank 01 1 Xu_li * (V*) * -',
... 'bc/cctv/00/cctv_0005 0 1 you PRP (NP*) - - - Xu_li * (ARG1*) (ARG0*) (116)',
... 'bc/cctv/00/cctv_0005 0 2 everyone NN (NP*) - - - Xu_li * (ARGM-DIS*) * (116)',
... 'bc/cctv/00/cctv_0005 0 3 for IN (PP* - - - Xu_li * (ARG2* * -',
... 'bc/cctv/00/cctv_0005 0 4 watching VBG (S(VP*)))) watch 01 1 Xu_li * *) (V*) -',
... 'bc/cctv/00/cctv_0005 0 5 . . *)) - - - Xu_li * * * -']
>>> references = [words]
>>> predictions = [words]
>>> results = coval.compute(predictions=predictions, references=references)
>>> print(results) # doctest:+ELLIPSIS
{'mentions/recall': 1.0,[...] 'conll_score': 100.0}
"""
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_=False , lowercase_=False , lowercase_=True , lowercase_=False , lowercase_="dummy_doc" ) -> Dict:
'''simple docstring'''
__UpperCAmelCase : List[str] = {doc: key_lines}
__UpperCAmelCase : Tuple = {doc: sys_lines}
__UpperCAmelCase : Union[str, Any] = {}
__UpperCAmelCase : Union[str, Any] = 0
__UpperCAmelCase : str = 0
__UpperCAmelCase : int = 0
__UpperCAmelCase : int = 0
__UpperCAmelCase : Union[str, Any] = 0
__UpperCAmelCase : Any = 0
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = reader.get_doc_mentions(lowercase_ , key_doc_lines[doc] , lowercase_ )
key_singletons_num += singletons_num
if NP_only or min_span:
__UpperCAmelCase : List[Any] = reader.set_annotated_parse_trees(lowercase_ , key_doc_lines[doc] , lowercase_ , lowercase_ )
__UpperCAmelCase , __UpperCAmelCase : List[Any] = reader.get_doc_mentions(lowercase_ , sys_doc_lines[doc] , lowercase_ )
sys_singletons_num += singletons_num
if NP_only or min_span:
__UpperCAmelCase : Union[str, Any] = reader.set_annotated_parse_trees(lowercase_ , key_doc_lines[doc] , lowercase_ , lowercase_ )
if remove_nested:
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = reader.remove_nested_coref_mentions(lowercase_ , lowercase_ )
key_nested_coref_num += nested_mentions
key_removed_nested_clusters += removed_clusters
__UpperCAmelCase , __UpperCAmelCase : str = reader.remove_nested_coref_mentions(lowercase_ , lowercase_ )
sys_nested_coref_num += nested_mentions
sys_removed_nested_clusters += removed_clusters
__UpperCAmelCase : List[Any] = reader.get_mention_assignments(lowercase_ , lowercase_ )
__UpperCAmelCase : Optional[Any] = reader.get_mention_assignments(lowercase_ , lowercase_ )
__UpperCAmelCase : Dict = (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)
if remove_nested:
logger.info(
'''Number of removed nested coreferring mentions in the key '''
f"annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}" )
logger.info(
'''Number of resulting singleton clusters in the key '''
f"annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}" )
if not keep_singletons:
logger.info(
f"{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system "
'''files, respectively''' )
return doc_coref_infos
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
__UpperCAmelCase : List[Any] = get_coref_infos(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
__UpperCAmelCase : Union[str, Any] = {}
__UpperCAmelCase : Union[str, Any] = 0
__UpperCAmelCase : str = 0
for name, metric in metrics:
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Dict = evaluator.evaluate_documents(lowercase_ , lowercase_ , beta=1 )
if name in ["muc", "bcub", "ceafe"]:
conll += fa
conll_subparts_num += 1
output_scores.update({f"{name}/recall": recall, f"{name}/precision": precision, f"{name}/f1": fa} )
logger.info(
name.ljust(10 ) , f"Recall: {recall * 100:.2f}" , f" Precision: {precision * 100:.2f}" , f" F1: {fa * 100:.2f}" , )
if conll_subparts_num == 3:
__UpperCAmelCase : Optional[int] = (conll / 3) * 100
logger.info(f"CoNLL score: {conll:.2f}" )
output_scores.update({'''conll_score''': conll} )
return output_scores
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = False
for line in key_lines:
if not line.startswith('''#''' ):
if len(line.split() ) > 6:
__UpperCAmelCase : List[Any] = line.split()[5]
if not parse_col == "-":
__UpperCAmelCase : Optional[Any] = True
break
else:
break
return has_gold_parse
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCamelCase ( datasets.Metric ):
def A( self):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''')),
'''references''': datasets.Sequence(datasets.Value('''string''')),
}) , codebase_urls=['''https://github.com/ns-moosavi/coval'''] , reference_urls=[
'''https://github.com/ns-moosavi/coval''',
'''https://www.aclweb.org/anthology/P16-1060''',
'''http://www.conll.cemantix.org/2012/data.html''',
] , )
def A( self , lowercase__ , lowercase__ , lowercase__=True , lowercase__=False , lowercase__=False , lowercase__=False):
__UpperCAmelCase : List[Any] = [
('''mentions''', evaluator.mentions),
('''muc''', evaluator.muc),
('''bcub''', evaluator.b_cubed),
('''ceafe''', evaluator.ceafe),
('''lea''', evaluator.lea),
]
if min_span:
__UpperCAmelCase : Optional[Any] = util.check_gold_parse_annotation(lowercase__)
if not has_gold_parse:
raise NotImplementedError('''References should have gold parse annotation to use \'min_span\'.''')
# util.parse_key_file(key_file)
# key_file = key_file + ".parsed"
__UpperCAmelCase : Tuple = evaluate(
key_lines=lowercase__ , sys_lines=lowercase__ , metrics=lowercase__ , NP_only=lowercase__ , remove_nested=lowercase__ , keep_singletons=lowercase__ , min_span=lowercase__ , )
return score
| 462
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : str = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AutoformerConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = [
'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'AutoformerForPrediction',
'AutoformerModel',
'AutoformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_autoformer import (
AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_autoformer import (
AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoformerForPrediction,
AutoformerModel,
AutoformerPreTrainedModel,
)
else:
import sys
__A : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 281
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..utils import cached_file
# docstyle-ignore
__A : int = '\nHuman: <<task>>\n\nAssistant: '
__A : List[str] = 'huggingface-tools/default-prompts'
__A : Tuple = {'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}
def snake_case__ ( _lowerCamelCase, _lowerCamelCase, _lowerCamelCase="run" ) ->Optional[int]:
"""simple docstring"""
if prompt_or_repo_id is None:
__lowercase : Any = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search("\\s", _lowerCamelCase ) is not None:
return prompt_or_repo_id
__lowercase : Optional[Any] = cached_file(
_lowerCamelCase, PROMPT_FILES[mode], repo_type="dataset", user_agent={"agent": agent_name} )
with open(_lowerCamelCase, "r", encoding="utf-8" ) as f:
return f.read()
| 281
| 1
|
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__a: List[Any] = logging.get_logger(__name__)
__a: List[str] = {
"""ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = "deta"
SCREAMING_SNAKE_CASE = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self , __lowerCAmelCase=None , __lowerCAmelCase=900 , __lowerCAmelCase=2048 , __lowerCAmelCase=6 , __lowerCAmelCase=2048 , __lowerCAmelCase=8 , __lowerCAmelCase=6 , __lowerCAmelCase=1024 , __lowerCAmelCase=8 , __lowerCAmelCase=0.0 , __lowerCAmelCase=True , __lowerCAmelCase="relu" , __lowerCAmelCase=256 , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.0 , __lowerCAmelCase=0.0 , __lowerCAmelCase=0.0_2 , __lowerCAmelCase=1.0 , __lowerCAmelCase=True , __lowerCAmelCase=False , __lowerCAmelCase="sine" , __lowerCAmelCase=5 , __lowerCAmelCase=4 , __lowerCAmelCase=4 , __lowerCAmelCase=True , __lowerCAmelCase=300 , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase=1 , __lowerCAmelCase=5 , __lowerCAmelCase=2 , __lowerCAmelCase=1 , __lowerCAmelCase=1 , __lowerCAmelCase=5 , __lowerCAmelCase=2 , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.2_5 , **__lowerCAmelCase , ) -> List[Any]:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowercase__ : Union[str, Any] = CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4'''] )
else:
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
lowercase__ : Any = backbone_config.pop('''model_type''' )
lowercase__ : Tuple = CONFIG_MAPPING[backbone_model_type]
lowercase__ : int = config_class.from_dict(__lowerCAmelCase )
lowercase__ : Optional[Any] = backbone_config
lowercase__ : List[str] = num_queries
lowercase__ : List[str] = max_position_embeddings
lowercase__ : Tuple = d_model
lowercase__ : Union[str, Any] = encoder_ffn_dim
lowercase__ : str = encoder_layers
lowercase__ : Any = encoder_attention_heads
lowercase__ : int = decoder_ffn_dim
lowercase__ : Any = decoder_layers
lowercase__ : Optional[Any] = decoder_attention_heads
lowercase__ : Any = dropout
lowercase__ : Optional[int] = attention_dropout
lowercase__ : List[str] = activation_dropout
lowercase__ : int = activation_function
lowercase__ : List[str] = init_std
lowercase__ : Union[str, Any] = init_xavier_std
lowercase__ : List[str] = encoder_layerdrop
lowercase__ : Any = auxiliary_loss
lowercase__ : Any = position_embedding_type
# deformable attributes
lowercase__ : Optional[Any] = num_feature_levels
lowercase__ : List[Any] = encoder_n_points
lowercase__ : Any = decoder_n_points
lowercase__ : List[Any] = two_stage
lowercase__ : Optional[Any] = two_stage_num_proposals
lowercase__ : str = with_box_refine
lowercase__ : Tuple = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError('''If two_stage is True, with_box_refine must be True.''' )
# Hungarian matcher
lowercase__ : int = class_cost
lowercase__ : Dict = bbox_cost
lowercase__ : str = giou_cost
# Loss coefficients
lowercase__ : Any = mask_loss_coefficient
lowercase__ : Optional[int] = dice_loss_coefficient
lowercase__ : Optional[int] = bbox_loss_coefficient
lowercase__ : List[str] = giou_loss_coefficient
lowercase__ : List[Any] = eos_coefficient
lowercase__ : Optional[int] = focal_alpha
super().__init__(is_encoder_decoder=__lowerCAmelCase , **__lowerCAmelCase )
@property
def _lowerCAmelCase( self ) -> int:
return self.encoder_attention_heads
@property
def _lowerCAmelCase( self ) -> int:
return self.d_model
def _lowerCAmelCase( self ) -> Optional[int]:
lowercase__ : str = copy.deepcopy(self.__dict__ )
lowercase__ : List[Any] = self.backbone_config.to_dict()
lowercase__ : int = self.__class__.model_type
return output
| 152
|
'''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ : Optional[int] = 0
lowercase__ : int = len(UpperCAmelCase )
for i in range(n - 1 ):
for j in range(i + 1 , UpperCAmelCase ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def __UpperCamelCase ( UpperCAmelCase ):
if len(UpperCAmelCase ) <= 1:
return arr, 0
lowercase__ : List[str] = len(UpperCAmelCase ) // 2
lowercase__ : Optional[Any] = arr[0:mid]
lowercase__ : Any = arr[mid:]
lowercase__ , lowercase__ : Any = count_inversions_recursive(UpperCAmelCase )
lowercase__ , lowercase__ : List[str] = count_inversions_recursive(UpperCAmelCase )
lowercase__ , lowercase__ : Optional[Any] = _count_cross_inversions(UpperCAmelCase , UpperCAmelCase )
lowercase__ : Optional[Any] = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ):
lowercase__ : Union[str, Any] = []
lowercase__ : List[Any] = 0
while i < len(UpperCAmelCase ) and j < len(UpperCAmelCase ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(UpperCAmelCase ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(UpperCAmelCase ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def __UpperCamelCase ( ):
lowercase__ : str = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
lowercase__ : Dict = count_inversions_bf(UpperCAmelCase )
lowercase__ , lowercase__ : Union[str, Any] = count_inversions_recursive(UpperCAmelCase )
assert num_inversions_bf == num_inversions_recursive == 8
print('''number of inversions = ''' , UpperCAmelCase )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
lowercase__ : Optional[int] = count_inversions_bf(UpperCAmelCase )
lowercase__ , lowercase__ : int = count_inversions_recursive(UpperCAmelCase )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''' , UpperCAmelCase )
# an empty list should also have zero inversions
lowercase__ : Optional[Any] = []
lowercase__ : Any = count_inversions_bf(UpperCAmelCase )
lowercase__ , lowercase__ : List[Any] = count_inversions_recursive(UpperCAmelCase )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''' , UpperCAmelCase )
if __name__ == "__main__":
main()
| 152
| 1
|
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
__UpperCAmelCase : Union[str, Any] = False
__UpperCAmelCase : Any = True
__UpperCAmelCase : Optional[int] = False
if __name__ == "__main__":
__UpperCAmelCase : str = 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.")
__UpperCAmelCase : Tuple = parser.parse_args()
__UpperCAmelCase : Optional[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",
}
__UpperCAmelCase : Optional[int] = {
"time_steps": "time_proj",
"mid": "mid_block",
"downsample_blocks": "down_blocks",
"upsample_blocks": "up_blocks",
}
__UpperCAmelCase : int = "" 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:
__UpperCAmelCase : Tuple = reader.read()
__UpperCAmelCase : str = 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"):
__UpperCAmelCase : Optional[Any] = UNetaDModel(**config)
else:
__UpperCAmelCase : Tuple = UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
__UpperCAmelCase : str = class_name(**config)
if do_only_config:
model.save_config(os.path.join(args.repo_path, subfolder))
__UpperCAmelCase : List[str] = dict(model.config)
if do_only_renaming:
for key, value in config_parameters_to_change.items():
if key in config:
__UpperCAmelCase : Tuple = config[key]
del config[key]
__UpperCAmelCase : Tuple = [k.replace("UNetRes", "") for k in config["down_block_types"]]
__UpperCAmelCase : List[Any] = [k.replace("UNetRes", "") for k in config["up_block_types"]]
if do_only_weights:
__UpperCAmelCase : int = torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))
__UpperCAmelCase : int = {}
for param_key, param_value in state_dict.items():
if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
continue
__UpperCAmelCase : List[str] = False
for key, new_key in key_parameters_to_change.items():
if not has_changed and param_key.split(".")[0] == key:
__UpperCAmelCase : Any = param_value
__UpperCAmelCase : Optional[Any] = True
if not has_changed:
__UpperCAmelCase : Union[str, Any] = param_value
model.load_state_dict(new_state_dict)
model.save_pretrained(os.path.join(args.repo_path, subfolder))
| 714
|
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def A__ ( ) -> Tuple:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
__snake_case: List[str] = """__test_patch_submodule_mock__"""
with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os , _PatchedModuleObj)
assert isinstance(_test_patching.os.path , _PatchedModuleObj)
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path , _PatchedModuleObj)
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os , _PatchedModuleObj)
assert isinstance(_test_patching.renamed_os.path , _PatchedModuleObj)
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path , _PatchedModuleObj)
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def A__ ( ) -> Tuple:
assert _test_patching.open is open
__snake_case: Tuple = """__test_patch_submodule_builtin_mock__"""
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching , """open""" , SCREAMING_SNAKE_CASE__):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def A__ ( ) -> Dict:
# pandas.read_csv is not present in _test_patching
__snake_case: Tuple = """__test_patch_submodule_missing_mock__"""
with patch_submodule(_test_patching , """pandas.read_csv""" , SCREAMING_SNAKE_CASE__):
pass
def A__ ( ) -> int:
# builtin should always be mocked even if they're not in the globals
# in case they're loaded at one point
__snake_case: Tuple = """__test_patch_submodule_missing_builtin_mock__"""
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching , """len""" , SCREAMING_SNAKE_CASE__) is None
with patch_submodule(_test_patching , """len""" , SCREAMING_SNAKE_CASE__):
assert _test_patching.len is mock
assert _test_patching.len is len
def A__ ( ) -> List[Any]:
__snake_case: Optional[int] = """__test_patch_submodule_start_and_stop_mock__"""
__snake_case: Union[str, Any] = patch_submodule(_test_patching , """open""" , SCREAMING_SNAKE_CASE__)
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def A__ ( ) -> List[Any]:
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
__snake_case: int = """__test_patch_submodule_successive_join__"""
__snake_case: Union[str, Any] = """__test_patch_submodule_successive_dirname__"""
__snake_case: Tuple = """__test_patch_submodule_successive_rename__"""
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__):
with patch_submodule(_test_patching , """os.rename""" , SCREAMING_SNAKE_CASE__):
with patch_submodule(_test_patching , """os.path.dirname""" , SCREAMING_SNAKE_CASE__):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching , """os.rename""" , SCREAMING_SNAKE_CASE__):
with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__):
with patch_submodule(_test_patching , """os.path.dirname""" , SCREAMING_SNAKE_CASE__):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def A__ ( ) -> Optional[Any]:
__snake_case: Tuple = """__test_patch_submodule_doesnt_exist_mock__"""
with patch_submodule(_test_patching , """__module_that_doesn_exist__.__attribute_that_doesn_exist__""" , SCREAMING_SNAKE_CASE__):
pass
with patch_submodule(_test_patching , """os.__attribute_that_doesn_exist__""" , SCREAMING_SNAKE_CASE__):
pass
| 155
| 0
|
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def _lowerCAmelCase (_lowercase , _lowercase , _lowercase = 10**-10 ):
"""simple docstring"""
a__ = a
while True:
a__ = Decimal(_lowercase ) - (
Decimal(eval(_lowercase ) ) / Decimal(eval(str(diff(_lowercase ) ) ) ) # noqa: S307
)
# This number dictates the accuracy of the answer
if abs(eval(_lowercase ) ) < precision: # noqa: S307
return float(_lowercase )
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(F"The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}")
# Find root of polynomial
print(F"The root of x**2 - 5*x + 2 = 0 is {newton_raphson('x**2 - 5*x + 2', 0.4)}")
# Find Square Root of 5
print(F"The root of log(x) - 1 = 0 is {newton_raphson('log(x) - 1', 2)}")
# Exponential Roots
print(F"The root of exp(x) - 1 = 0 is {newton_raphson('exp(x) - 1', 0)}")
| 331
|
'''simple docstring'''
def _lowerCAmelCase (_lowercase , _lowercase ):
"""simple docstring"""
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _lowerCAmelCase (_lowercase , _lowercase=0 ):
"""simple docstring"""
return sorted(_lowercase , key=lambda _lowercase : x[column] )
def _lowerCAmelCase (_lowercase , _lowercase , _lowercase=float("inf" ) ):
"""simple docstring"""
for i in range(points_counts - 1 ):
for j in range(i + 1 , _lowercase ):
a__ = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
a__ = current_dis
return min_dis
def _lowerCAmelCase (_lowercase , _lowercase , _lowercase=float("inf" ) ):
"""simple docstring"""
for i in range(min(6 , points_counts - 1 ) , _lowercase ):
for j in range(max(0 , i - 6 ) , _lowercase ):
a__ = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
a__ = current_dis
return min_dis
def _lowerCAmelCase (_lowercase , _lowercase , _lowercase ):
"""simple docstring"""
if points_counts <= 3:
return dis_between_closest_pair(_lowercase , _lowercase )
# recursion
a__ = points_counts // 2
a__ = closest_pair_of_points_sqr(
_lowercase , points_sorted_on_y[:mid] , _lowercase )
a__ = closest_pair_of_points_sqr(
_lowercase , points_sorted_on_y[mid:] , points_counts - mid )
a__ = min(_lowercase , _lowercase )
a__ = []
for point in points_sorted_on_x:
if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
cross_strip.append(_lowercase )
a__ = dis_between_closest_in_strip(
_lowercase , len(_lowercase ) , _lowercase )
return min(_lowercase , _lowercase )
def _lowerCAmelCase (_lowercase , _lowercase ):
"""simple docstring"""
a__ = column_based_sort(_lowercase , column=0 )
a__ = column_based_sort(_lowercase , column=1 )
return (
closest_pair_of_points_sqr(
_lowercase , _lowercase , _lowercase )
) ** 0.5
if __name__ == "__main__":
UpperCamelCase_ : List[Any] = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
print("""Distance:""", closest_pair_of_points(points, len(points)))
| 331
| 1
|
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=32 , snake_case_=3 , snake_case_=4 , snake_case_=[10, 20, 30, 40] , snake_case_=[2, 2, 3, 2] , snake_case_=True , snake_case_=True , snake_case_=37 , snake_case_="gelu" , snake_case_=10 , snake_case_=0.02 , snake_case_=["stage2", "stage3", "stage4"] , snake_case_=[2, 3, 4] , snake_case_=None , ):
_snake_case : Dict = parent
_snake_case : Tuple = batch_size
_snake_case : Optional[int] = image_size
_snake_case : Dict = num_channels
_snake_case : List[Any] = num_stages
_snake_case : str = hidden_sizes
_snake_case : Union[str, Any] = depths
_snake_case : int = is_training
_snake_case : Optional[Any] = use_labels
_snake_case : Tuple = intermediate_size
_snake_case : Tuple = hidden_act
_snake_case : Tuple = num_labels
_snake_case : Optional[Any] = initializer_range
_snake_case : int = out_features
_snake_case : Union[str, Any] = out_indices
_snake_case : Union[str, Any] = scope
def lowerCamelCase__ ( self ):
_snake_case : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_snake_case : Optional[int] = None
if self.use_labels:
_snake_case : List[Any] = ids_tensor([self.batch_size] , self.num_labels )
_snake_case : Optional[Any] = self.get_config()
return config, pixel_values, labels
def lowerCamelCase__ ( self ):
return ConvNextVaConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=snake_case_ , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Union[str, Any] = ConvNextVaModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_snake_case : Optional[Any] = model(snake_case_ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Optional[int] = ConvNextVaForImageClassification(snake_case_ )
model.to(snake_case_ )
model.eval()
_snake_case : str = model(snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase__ ( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Tuple = ConvNextVaBackbone(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_snake_case : Optional[Any] = model(snake_case_ )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
_snake_case : Any = None
_snake_case : str = ConvNextVaBackbone(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_snake_case : str = model(snake_case_ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def lowerCamelCase__ ( self ):
_snake_case : Tuple = self.prepare_config_and_inputs()
_snake_case , _snake_case , _snake_case : str = config_and_inputs
_snake_case : Dict = {"pixel_values": pixel_values}
return config, inputs_dict
def lowerCamelCase__ ( self ):
_snake_case : List[str] = self.prepare_config_and_inputs()
_snake_case , _snake_case , _snake_case : Optional[int] = config_and_inputs
_snake_case : Union[str, Any] = {"pixel_values": pixel_values, "labels": labels}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( _snake_case , _snake_case , unittest.TestCase):
__lowercase : Dict = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
__lowercase : str = (
{"""feature-extraction""": ConvNextVaModel, """image-classification""": ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
__lowercase : Union[str, Any] = False
__lowercase : str = False
__lowercase : Tuple = False
__lowercase : Union[str, Any] = False
__lowercase : Optional[int] = False
def lowerCamelCase__ ( self ):
_snake_case : Any = ConvNextVaModelTester(self )
_snake_case : List[str] = ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ , hidden_size=37 )
def lowerCamelCase__ ( self ):
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 lowerCamelCase__ ( self ):
return
@unittest.skip(reason="ConvNextV2 does not use inputs_embeds" )
def lowerCamelCase__ ( self ):
pass
@unittest.skip(reason="ConvNextV2 does not support input and output embeddings" )
def lowerCamelCase__ ( self ):
pass
@unittest.skip(reason="ConvNextV2 does not use feedforward chunking" )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
_snake_case , _snake_case : List[Any] = self.model_tester.prepare_config_and_inputs_with_labels()
_snake_case : Tuple = True
if model_class.__name__ in [
*get_values(snake_case_ ),
*get_values(snake_case_ ),
]:
continue
_snake_case : str = model_class(snake_case_ )
model.to(snake_case_ )
model.train()
_snake_case : List[Any] = self._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
_snake_case : List[str] = model(**snake_case_ ).loss
loss.backward()
def lowerCamelCase__ ( self ):
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
_snake_case , _snake_case : str = self.model_tester.prepare_config_and_inputs_with_labels()
_snake_case : List[str] = False
_snake_case : Optional[int] = True
if (
model_class.__name__
in [*get_values(snake_case_ ), *get_values(snake_case_ )]
or not model_class.supports_gradient_checkpointing
):
continue
_snake_case : Optional[int] = model_class(snake_case_ )
model.to(snake_case_ )
model.gradient_checkpointing_enable()
model.train()
_snake_case : Optional[int] = self._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
_snake_case : List[Any] = model(**snake_case_ ).loss
loss.backward()
def lowerCamelCase__ ( self ):
_snake_case , _snake_case : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case : Dict = model_class(snake_case_ )
_snake_case : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_snake_case : List[Any] = [*signature.parameters.keys()]
_snake_case : Optional[Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def lowerCamelCase__ ( self ):
def check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Optional[Any] = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_snake_case : Tuple = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
_snake_case : Tuple = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_snake_case : Union[str, Any] = self.model_tester.num_stages
self.assertEqual(len(snake_case_ ) , expected_num_stages + 1 )
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_snake_case , _snake_case : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case : Optional[int] = True
check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_snake_case : Optional[Any] = True
check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ )
def lowerCamelCase__ ( self ):
_snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case_ )
@slow
def lowerCamelCase__ ( self ):
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case : Dict = ConvNextVaModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def a__ ( ):
"""simple docstring"""
_snake_case : Optional[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _UpperCAmelCase ( unittest.TestCase):
@cached_property
def lowerCamelCase__ ( self ):
return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224" ) if is_vision_available() else None
@slow
def lowerCamelCase__ ( self ):
_snake_case : List[Any] = ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224" ).to(snake_case_ )
_snake_case : Any = self.default_image_processor
_snake_case : List[str] = prepare_img()
_snake_case : Tuple = preprocessor(images=snake_case_ , return_tensors="pt" ).to(snake_case_ )
# forward pass
with torch.no_grad():
_snake_case : Union[str, Any] = model(**snake_case_ )
# verify the logits
_snake_case : str = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , snake_case_ )
_snake_case : Any = torch.tensor([0.9996, 0.1966, -0.4386] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case_ , atol=1E-4 ) )
| 87
|
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : Optional[int] = dataset
_snake_case : str = process
_snake_case : int = params
def __len__( self ):
return len(self.dataset )
def __getitem__( self , snake_case_ ):
_snake_case : Union[str, Any] = self.dataset[i]
_snake_case : Optional[Any] = self.process(snake_case_ , **self.params )
return processed
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=None ):
_snake_case : Union[str, Any] = loader
_snake_case : Tuple = infer
_snake_case : List[Any] = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
_snake_case : int = None
_snake_case : int = loader_batch_size
# Internal bookkeeping
_snake_case : Any = None
_snake_case : Dict = None
def __len__( self ):
return len(self.loader )
def __iter__( self ):
_snake_case : int = iter(self.loader )
return self
def lowerCamelCase__ ( self ):
if isinstance(self._loader_batch_data , torch.Tensor ):
# Batch data is simple tensor, just fetch the slice
_snake_case : List[Any] = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
_snake_case : int = {}
for k, element in self._loader_batch_data.items():
if isinstance(snake_case_ , snake_case_ ):
# Convert ModelOutput to tuple first
_snake_case : Tuple = element.to_tuple()
if isinstance(element[0] , torch.Tensor ):
_snake_case : Union[str, Any] = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
_snake_case : int = 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(snake_case_ , snake_case_ ):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] , torch.Tensor ):
_snake_case : Tuple = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
_snake_case : Tuple = 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
_snake_case : Tuple = 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
_snake_case : List[Any] = 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
_snake_case : Union[str, Any] = np.expand_dims(element[self._loader_batch_index] , 0 )
else:
# This is typically a list, so no need to `unsqueeze`.
_snake_case : List[Any] = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
_snake_case : int = self._loader_batch_data.__class__(snake_case_ )
self._loader_batch_index += 1
return result
def lowerCamelCase__ ( self ):
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
_snake_case : Tuple = next(self.iterator )
_snake_case : Any = self.infer(snake_case_ , **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(snake_case_ , torch.Tensor ):
_snake_case : Union[str, Any] = processed
else:
_snake_case : Optional[int] = list(processed.keys() )[0]
_snake_case : List[str] = processed[key]
if isinstance(snake_case_ , snake_case_ ):
_snake_case : Dict = len(snake_case_ )
else:
_snake_case : Optional[int] = 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.
_snake_case : Union[str, Any] = observed_batch_size
# Setting internal index to unwrap the batch
_snake_case : str = processed
_snake_case : List[Any] = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=None ):
super().__init__(snake_case_ , snake_case_ , snake_case_ )
def __iter__( self ):
_snake_case : Tuple = iter(self.loader )
_snake_case : List[Any] = None
return self
def lowerCamelCase__ ( self ):
if self.subiterator is None:
_snake_case : Optional[Any] = self.infer(next(self.iterator ) , **self.params )
try:
# Try to return next item
_snake_case : Union[str, Any] = 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
_snake_case : str = self.infer(next(self.iterator ) , **self.params )
_snake_case : Tuple = next(self.subiterator )
return processed
class _UpperCAmelCase ( _snake_case):
def __iter__( self ):
_snake_case : Optional[Any] = iter(self.loader )
return self
def lowerCamelCase__ ( self ):
# 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.
_snake_case : Optional[Any] = False
_snake_case : Tuple = []
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:
_snake_case : Union[str, Any] = self.loader_batch_item()
_snake_case : str = item.pop("is_last" )
accumulator.append(snake_case_ )
if is_last:
return accumulator
while not is_last:
_snake_case : List[str] = self.infer(next(self.iterator ) , **self.params )
if self.loader_batch_size is not None:
if isinstance(snake_case_ , torch.Tensor ):
_snake_case : Union[str, Any] = processed
else:
_snake_case : Tuple = list(processed.keys() )[0]
_snake_case : Tuple = processed[key]
if isinstance(snake_case_ , snake_case_ ):
_snake_case : Any = len(snake_case_ )
else:
_snake_case : List[Any] = 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.
_snake_case : Dict = observed_batch_size
_snake_case : List[Any] = processed
_snake_case : List[str] = 0
while self._loader_batch_index < self.loader_batch_size:
_snake_case : Union[str, Any] = self.loader_batch_item()
_snake_case : int = item.pop("is_last" )
accumulator.append(snake_case_ )
if is_last:
return accumulator
else:
_snake_case : Dict = processed
_snake_case : Dict = item.pop("is_last" )
accumulator.append(snake_case_ )
return accumulator
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ ):
_snake_case : str = dataset
_snake_case : Any = key
def __len__( self ):
return len(self.dataset )
def __getitem__( self , snake_case_ ):
return self.dataset[i][self.key]
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_case : int = dataset
_snake_case : Any = keya
_snake_case : int = keya
def __len__( self ):
return len(self.dataset )
def __getitem__( self , snake_case_ ):
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 87
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a : List[Any] = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Mask2FormerConfig",
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Tuple = ["Mask2FormerImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Any = [
"MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"Mask2FormerForUniversalSegmentation",
"Mask2FormerModel",
"Mask2FormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_maskaformer import MaskaFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskaformer import (
MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskaFormerForUniversalSegmentation,
MaskaFormerModel,
MaskaFormerPreTrainedModel,
)
else:
import sys
a : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 679
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=A__ ):
A__ = ['torch', 'transformers', 'onnx']
def __init__( self : Any , *_a : Union[str, Any] , **_a : Optional[Any] ) -> int:
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def A ( cls : str , *_a : Optional[int] , **_a : List[Any] ) -> int:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def A ( cls : Optional[Any] , *_a : Dict , **_a : Optional[Any] ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class A__ ( metaclass=A__ ):
A__ = ['torch', 'transformers', 'onnx']
def __init__( self : List[Any] , *_a : Optional[int] , **_a : str ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def A ( cls : Union[str, Any] , *_a : List[str] , **_a : int ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def A ( cls : Optional[int] , *_a : List[str] , **_a : Union[str, Any] ) -> int:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class A__ ( metaclass=A__ ):
A__ = ['torch', 'transformers', 'onnx']
def __init__( self : str , *_a : int , **_a : List[str] ) -> Any:
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def A ( cls : Any , *_a : Optional[int] , **_a : int ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def A ( cls : List[str] , *_a : Dict , **_a : List[Any] ) -> str:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class A__ ( metaclass=A__ ):
A__ = ['torch', 'transformers', 'onnx']
def __init__( self : Optional[int] , *_a : Optional[Any] , **_a : str ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def A ( cls : List[Any] , *_a : Any , **_a : Tuple ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def A ( cls : str , *_a : Dict , **_a : Optional[Any] ) -> int:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class A__ ( metaclass=A__ ):
A__ = ['torch', 'transformers', 'onnx']
def __init__( self : Dict , *_a : Union[str, Any] , **_a : int ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def A ( cls : List[Any] , *_a : Union[str, Any] , **_a : Tuple ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def A ( cls : Union[str, Any] , *_a : Dict , **_a : List[str] ) -> Any:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class A__ ( metaclass=A__ ):
A__ = ['torch', 'transformers', 'onnx']
def __init__( self : Optional[Any] , *_a : Union[str, Any] , **_a : Optional[Any] ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def A ( cls : Optional[Any] , *_a : Optional[Any] , **_a : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def A ( cls : List[Any] , *_a : Optional[int] , **_a : Tuple ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
| 405
| 0
|
"""simple docstring"""
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__lowerCamelCase = 16
__lowerCamelCase = 32
def lowercase ( __UpperCamelCase , __UpperCamelCase = 16 ) -> Union[str, Any]:
__magic_name__ = AutoTokenizer.from_pretrained('''bert-base-cased''' )
__magic_name__ = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(__UpperCamelCase ):
# max_length=None => use the model max length (it's actually the default)
__magic_name__ = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=__UpperCamelCase , max_length=__UpperCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__magic_name__ = datasets.map(
__UpperCamelCase , batched=__UpperCamelCase , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__magic_name__ = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(__UpperCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__magic_name__ = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__magic_name__ = 16
elif accelerator.mixed_precision != "no":
__magic_name__ = 8
else:
__magic_name__ = None
return tokenizer.pad(
__UpperCamelCase , padding='''longest''' , max_length=__UpperCamelCase , pad_to_multiple_of=__UpperCamelCase , return_tensors='''pt''' , )
# Instantiate dataloaders.
__magic_name__ = DataLoader(
tokenized_datasets['''train'''] , shuffle=__UpperCamelCase , collate_fn=__UpperCamelCase , batch_size=__UpperCamelCase )
__magic_name__ = DataLoader(
tokenized_datasets['''validation'''] , shuffle=__UpperCamelCase , collate_fn=__UpperCamelCase , batch_size=__UpperCamelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__lowerCamelCase = mocked_dataloaders # noqa: F811
def lowercase ( __UpperCamelCase , __UpperCamelCase ) -> int:
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , __UpperCamelCase ) == "1":
__magic_name__ = 2
# Initialize accelerator
__magic_name__ = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__magic_name__ = config['''lr''']
__magic_name__ = int(config['''num_epochs'''] )
__magic_name__ = int(config['''seed'''] )
__magic_name__ = int(config['''batch_size'''] )
__magic_name__ = evaluate.load('''glue''' , '''mrpc''' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=__UpperCamelCase )
def inner_training_loop(__UpperCamelCase ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(__UpperCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__magic_name__ = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=__UpperCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__magic_name__ = model.to(accelerator.device )
# Instantiate optimizer
__magic_name__ = AdamW(params=model.parameters() , lr=__UpperCamelCase )
__magic_name__ , __magic_name__ = get_dataloaders(__UpperCamelCase , __UpperCamelCase )
# Instantiate scheduler
__magic_name__ = get_linear_schedule_with_warmup(
optimizer=__UpperCamelCase , num_warmup_steps=100 , num_training_steps=(len(__UpperCamelCase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ = accelerator.prepare(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Now we train the model
for epoch in range(__UpperCamelCase ):
model.train()
for step, batch in enumerate(__UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__magic_name__ = model(**__UpperCamelCase )
__magic_name__ = outputs.loss
accelerator.backward(__UpperCamelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__magic_name__ = model(**__UpperCamelCase )
__magic_name__ = outputs.logits.argmax(dim=-1 )
__magic_name__ , __magic_name__ = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=__UpperCamelCase , references=__UpperCamelCase , )
__magic_name__ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}:''' , __UpperCamelCase )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def lowercase ( ) -> Union[str, Any]:
__magic_name__ = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=__UpperCamelCase , default=__UpperCamelCase , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
__magic_name__ = parser.parse_args()
__magic_name__ = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(__UpperCamelCase , __UpperCamelCase )
if __name__ == "__main__":
main()
| 190
|
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCamelCase = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class _lowercase ( __UpperCAmelCase , unittest.TestCase ):
_lowerCamelCase = ReformerTokenizer
_lowerCamelCase = ReformerTokenizerFast
_lowerCamelCase = True
_lowerCamelCase = False
_lowerCamelCase = True
def lowerCAmelCase__ ( self ):
super().setUp()
__magic_name__ = ReformerTokenizer(UpperCamelCase_ , keep_accents=UpperCamelCase_ )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCAmelCase__ ( self ):
__magic_name__ = '''<s>'''
__magic_name__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase_ ) , UpperCamelCase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase_ ) , UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
__magic_name__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''j''' )
self.assertEqual(len(UpperCamelCase_ ) , 1000 )
def lowerCAmelCase__ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def lowerCAmelCase__ ( self ):
if not self.test_rust_tokenizer:
return
__magic_name__ = self.get_tokenizer()
__magic_name__ = self.get_rust_tokenizer()
__magic_name__ = '''I was born in 92000, and this is falsé.'''
__magic_name__ = tokenizer.tokenize(UpperCamelCase_ )
__magic_name__ = rust_tokenizer.tokenize(UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
__magic_name__ = tokenizer.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ )
__magic_name__ = rust_tokenizer.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
__magic_name__ = self.get_rust_tokenizer()
__magic_name__ = tokenizer.encode(UpperCamelCase_ )
__magic_name__ = rust_tokenizer.encode(UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
def lowerCAmelCase__ ( self , UpperCamelCase_=15 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__magic_name__ = self.rust_tokenizer_class.from_pretrained(UpperCamelCase_ , **UpperCamelCase_ )
# Simple input
__magic_name__ = '''This is a simple input'''
__magic_name__ = ['''This is a simple input 1''', '''This is a simple input 2''']
__magic_name__ = ('''This is a simple input''', '''This is a pair''')
__magic_name__ = [
('''This is a simple input 1''', '''This is a simple input 2'''),
('''This is a simple pair 1''', '''This is a simple pair 2'''),
]
# Simple input tests
self.assertRaises(UpperCamelCase_ , tokenizer_r.encode , UpperCamelCase_ , max_length=UpperCamelCase_ , padding='''max_length''' )
# Simple input
self.assertRaises(UpperCamelCase_ , tokenizer_r.encode_plus , UpperCamelCase_ , max_length=UpperCamelCase_ , padding='''max_length''' )
# Simple input
self.assertRaises(
UpperCamelCase_ , tokenizer_r.batch_encode_plus , UpperCamelCase_ , max_length=UpperCamelCase_ , padding='''max_length''' , )
# Pair input
self.assertRaises(UpperCamelCase_ , tokenizer_r.encode , UpperCamelCase_ , max_length=UpperCamelCase_ , padding='''max_length''' )
# Pair input
self.assertRaises(UpperCamelCase_ , tokenizer_r.encode_plus , UpperCamelCase_ , max_length=UpperCamelCase_ , padding='''max_length''' )
# Pair input
self.assertRaises(
UpperCamelCase_ , tokenizer_r.batch_encode_plus , UpperCamelCase_ , max_length=UpperCamelCase_ , padding='''max_length''' , )
def lowerCAmelCase__ ( self ):
pass
def lowerCAmelCase__ ( self ):
__magic_name__ = ReformerTokenizer(UpperCamelCase_ , keep_accents=UpperCamelCase_ )
__magic_name__ = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(UpperCamelCase_ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCamelCase_ ) , [285, 46, 10, 170, 382] , )
__magic_name__ = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
UpperCamelCase_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
__magic_name__ = tokenizer.convert_tokens_to_ids(UpperCamelCase_ )
self.assertListEqual(
UpperCamelCase_ , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
__magic_name__ = tokenizer.convert_ids_to_tokens(UpperCamelCase_ )
self.assertListEqual(
UpperCamelCase_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def lowerCAmelCase__ ( self ):
return ReformerTokenizer.from_pretrained('''google/reformer-crime-and-punishment''' )
@slow
def lowerCAmelCase__ ( self ):
__magic_name__ = '''Hello World!'''
__magic_name__ = [126, 32, 262, 152, 38, 72, 287]
self.assertListEqual(UpperCamelCase_ , self.big_tokenizer.encode(UpperCamelCase_ ) )
@slow
def lowerCAmelCase__ ( self ):
__magic_name__ = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
__magic_name__ = [
108,
265,
24,
111,
4,
258,
156,
35,
28,
275,
3,
259,
297,
260,
84,
4,
35,
110,
44,
8,
259,
91,
268,
21,
11,
209,
274,
109,
266,
277,
117,
86,
93,
315,
258,
278,
258,
277,
258,
0,
258,
288,
258,
319,
258,
0,
258,
0,
258,
0,
258,
0,
258,
287,
258,
315,
258,
289,
258,
278,
99,
269,
266,
262,
8,
259,
241,
4,
217,
230,
268,
266,
55,
168,
106,
75,
193,
266,
223,
27,
49,
26,
282,
25,
264,
299,
19,
26,
0,
258,
277,
117,
86,
93,
176,
183,
270,
11,
262,
42,
61,
265,
]
self.assertListEqual(UpperCamelCase_ , self.big_tokenizer.encode(UpperCamelCase_ ) )
@require_torch
@slow
def lowerCAmelCase__ ( self ):
import torch
from transformers import ReformerConfig, ReformerModel
# Build sequence
__magic_name__ = list(self.big_tokenizer.get_vocab().keys() )[:10]
__magic_name__ = ''' '''.join(UpperCamelCase_ )
__magic_name__ = self.big_tokenizer.encode_plus(UpperCamelCase_ , return_tensors='''pt''' )
__magic_name__ = self.big_tokenizer.batch_encode_plus([sequence, sequence] , return_tensors='''pt''' )
__magic_name__ = ReformerConfig()
# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
__magic_name__ = encoded_sequence['''input_ids'''].shape
__magic_name__ = ReformerModel(UpperCamelCase_ )
# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**UpperCamelCase_ )
model(**UpperCamelCase_ )
@slow
def lowerCAmelCase__ ( self ):
# fmt: off
__magic_name__ = {'''input_ids''': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# This tokenizer does not know some characters like ")".
# That is the reason why we use very simple texts here.
# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
__magic_name__ = [
'''This is a very simple sentence.''',
'''The quick brown fox jumps over the lazy dog.''',
]
self.tokenizer_integration_test_util(
expected_encoding=UpperCamelCase_ , model_name='''google/reformer-crime-and-punishment''' , revision='''0e6c3decb8211d49bf881013425dc8b0448b3f5a''' , padding=UpperCamelCase_ , sequences=UpperCamelCase_ , )
| 190
| 1
|
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging
_UpperCamelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowercase ( _UpperCamelCase ):
'''simple docstring'''
def __init__(self , __a , __a , __a , __a , __a , __a , __a , __a , __a , ) -> int:
"""simple docstring"""
super().__init__()
if safety_checker is None:
logger.warning(
F"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
' results in services or applications open to the public. Both the diffusers team and Hugging Face'
' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .' )
self.register_modules(
speech_model=__a , speech_processor=__a , vae=__a , text_encoder=__a , tokenizer=__a , unet=__a , scheduler=__a , feature_extractor=__a , )
def UpperCamelCase__ (self , __a = "auto" ) -> Optional[int]:
"""simple docstring"""
if slice_size == "auto":
UpperCAmelCase__ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__a )
def UpperCamelCase__ (self ) -> Optional[int]:
"""simple docstring"""
self.enable_attention_slicing(__a )
@torch.no_grad()
def __call__(self , __a , __a=16000 , __a = 512 , __a = 512 , __a = 50 , __a = 7.5 , __a = None , __a = 1 , __a = 0.0 , __a = None , __a = None , __a = "pil" , __a = True , __a = None , __a = 1 , **__a , ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = self.speech_processor.feature_extractor(
__a , return_tensors='pt' , sampling_rate=__a ).input_features.to(self.device )
UpperCAmelCase__ = self.speech_model.generate(__a , max_length=480000 )
UpperCAmelCase__ = self.speech_processor.tokenizer.batch_decode(__a , skip_special_tokens=__a , normalize=__a )[
0
]
if isinstance(__a , __a ):
UpperCAmelCase__ = 1
elif isinstance(__a , __a ):
UpperCAmelCase__ = len(__a )
else:
raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(__a )}" )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F"`height` and `width` have to be divisible by 8 but are {height} and {width}." )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__a , __a ) or callback_steps <= 0)
):
raise ValueError(
F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
F" {type(__a )}." )
# get prompt text embeddings
UpperCAmelCase__ = self.tokenizer(
__a , padding='max_length' , max_length=self.tokenizer.model_max_length , return_tensors='pt' , )
UpperCAmelCase__ = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
UpperCAmelCase__ = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'The following part of your input was truncated because CLIP can only handle sequences up to'
F" {self.tokenizer.model_max_length} tokens: {removed_text}" )
UpperCAmelCase__ = text_input_ids[:, : self.tokenizer.model_max_length]
UpperCAmelCase__ = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = text_embeddings.shape
UpperCAmelCase__ = text_embeddings.repeat(1 , __a , 1 )
UpperCAmelCase__ = text_embeddings.view(bs_embed * num_images_per_prompt , __a , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
UpperCAmelCase__ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
UpperCAmelCase__ = 42
if negative_prompt is None:
UpperCAmelCase__ = [''] * batch_size
elif type(__a ) is not type(__a ):
raise TypeError(
F"`negative_prompt` should be the same type to `prompt`, but got {type(__a )} !="
F" {type(__a )}." )
elif isinstance(__a , __a ):
UpperCAmelCase__ = [negative_prompt]
elif batch_size != len(__a ):
raise ValueError(
F"`negative_prompt`: {negative_prompt} has batch size {len(__a )}, but `prompt`:"
F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
' the batch size of `prompt`.' )
else:
UpperCAmelCase__ = negative_prompt
UpperCAmelCase__ = text_input_ids.shape[-1]
UpperCAmelCase__ = self.tokenizer(
__a , padding='max_length' , max_length=__a , truncation=__a , return_tensors='pt' , )
UpperCAmelCase__ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
UpperCAmelCase__ = uncond_embeddings.shape[1]
UpperCAmelCase__ = uncond_embeddings.repeat(1 , __a , 1 )
UpperCAmelCase__ = uncond_embeddings.view(batch_size * num_images_per_prompt , __a , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCAmelCase__ = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
UpperCAmelCase__ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
UpperCAmelCase__ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
UpperCAmelCase__ = torch.randn(__a , generator=__a , device='cpu' , dtype=__a ).to(
self.device )
else:
UpperCAmelCase__ = torch.randn(__a , generator=__a , device=self.device , dtype=__a )
else:
if latents.shape != latents_shape:
raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}" )
UpperCAmelCase__ = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(__a )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
UpperCAmelCase__ = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
UpperCAmelCase__ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
UpperCAmelCase__ = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
UpperCAmelCase__ = {}
if accepts_eta:
UpperCAmelCase__ = eta
for i, t in enumerate(self.progress_bar(__a ) ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase__ = self.scheduler.scale_model_input(__a , __a )
# predict the noise residual
UpperCAmelCase__ = self.unet(__a , __a , encoder_hidden_states=__a ).sample
# perform guidance
if do_classifier_free_guidance:
UpperCAmelCase__ , UpperCAmelCase__ = noise_pred.chunk(2 )
UpperCAmelCase__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase__ = self.scheduler.step(__a , __a , __a , **__a ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__a , __a , __a )
UpperCAmelCase__ = 1 / 0.1_82_15 * latents
UpperCAmelCase__ = self.vae.decode(__a ).sample
UpperCAmelCase__ = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
UpperCAmelCase__ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
UpperCAmelCase__ = self.numpy_to_pil(__a )
if not return_dict:
return image
return StableDiffusionPipelineOutput(images=__a , nsfw_content_detected=__a )
| 146
|
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowercase ( unittest.TestCase ):
'''simple docstring'''
@property
def UpperCamelCase__ (self ) -> int:
"""simple docstring"""
torch.manual_seed(0 )
UpperCAmelCase__ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
def UpperCamelCase__ (self ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = self.dummy_uncond_unet
UpperCAmelCase__ = KarrasVeScheduler()
UpperCAmelCase__ = KarrasVePipeline(unet=__a , scheduler=__a )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase__ = torch.manual_seed(0 )
UpperCAmelCase__ = pipe(num_inference_steps=2 , generator=__a , output_type='numpy' ).images
UpperCAmelCase__ = torch.manual_seed(0 )
UpperCAmelCase__ = pipe(num_inference_steps=2 , generator=__a , output_type='numpy' , return_dict=__a )[0]
UpperCAmelCase__ = image[0, -3:, -3:, -1]
UpperCAmelCase__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase__ = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class lowercase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ (self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = 'google/ncsnpp-celebahq-256'
UpperCAmelCase__ = UNetaDModel.from_pretrained(__a )
UpperCAmelCase__ = KarrasVeScheduler()
UpperCAmelCase__ = KarrasVePipeline(unet=__a , scheduler=__a )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase__ = torch.manual_seed(0 )
UpperCAmelCase__ = pipe(num_inference_steps=20 , generator=__a , output_type='numpy' ).images
UpperCAmelCase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
UpperCAmelCase__ = np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 146
| 1
|
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
UpperCAmelCase_ : Optional[Any] = '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'
UpperCAmelCase_ : List[Any] = '\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'
UpperCAmelCase_ : int = '\nArgs:\n predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n Estimated target values.\n references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n Ground truth (correct) target values.\n sample_weight: array-like of shape (n_samples,), default=None\n Sample weights.\n multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n "raw_values" : Returns a full set of errors in case of multioutput input.\n\n "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n squared : bool, default=True\n If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n mse : mean squared error.\nExamples:\n\n >>> mse_metric = datasets.load_metric("mse")\n >>> predictions = [2.5, 0.0, 2, 8]\n >>> references = [3, -0.5, 2, 7]\n >>> results = mse_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'mse\': 0.375}\n >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n >>> print(rmse_result)\n {\'mse\': 0.6123724356957945}\n\n If you\'re using multi-dimensional lists, then set the config as follows :\n\n >>> mse_metric = datasets.load_metric("mse", "multilist")\n >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n >>> references = [[0, 2], [-1, 2], [8, -5]]\n >>> results = mse_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'mse\': 0.7083333333333334}\n >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {\'mse\': array([0.41666667, 1. ])}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase__ ( datasets.Metric ):
def lowerCamelCase_ ( self : Optional[Any] ):
return datasets.MetricInfo(
description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(self._get_feature_types() ),reference_urls=[
"https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
],)
def lowerCamelCase_ ( self : Dict ):
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value("float" ) ),
"references": datasets.Sequence(datasets.Value("float" ) ),
}
else:
return {
"predictions": datasets.Value("float" ),
"references": datasets.Value("float" ),
}
def lowerCamelCase_ ( self : Optional[Any],__A : Tuple,__A : Any,__A : Optional[int]=None,__A : List[str]="uniform_average",__A : List[Any]=True ):
_lowerCamelCase : int = mean_squared_error(
__A,__A,sample_weight=__A,multioutput=__A,squared=__A )
return {"mse": mse}
| 701
|
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("'str' object cannot be interpreted as an integer" )
if num == 0:
return "0b0"
_lowerCamelCase : Tuple = False
if num < 0:
_lowerCamelCase : List[Any] = True
_lowerCamelCase : int = -num
_lowerCamelCase : list[int] = []
while num > 0:
binary.insert(0 , num % 2 )
num >>= 1
if negative:
return "-0b" + "".join(str(_lowerCAmelCase ) for e in binary )
return "0b" + "".join(str(_lowerCAmelCase ) for e in binary )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11
| 0
|
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
_A : List[str] = logging.getLogger()
@unittest.skip("Temporarily disable the doc tests." )
@require_torch
@require_tf
@slow
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCamelCase ( self : List[str] , A : Path , A : Union[str, None] = None , A : Union[List[str], None] = None , A : Union[str, List[str], None] = None , A : bool = True , ) ->str:
lowerCamelCase__ : Tuple = [file for file in os.listdir(__lowerCAmelCase ) if os.path.isfile(os.path.join(__lowerCAmelCase , __lowerCAmelCase ) )]
if identifier is not None:
lowerCamelCase__ : List[Any] = [file for file in files if identifier in file]
if n_identifier is not None:
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
for n_ in n_identifier:
lowerCamelCase__ : Optional[Any] = [file for file in files if n_ not in file]
else:
lowerCamelCase__ : Any = [file for file in files if n_identifier not in file]
lowerCamelCase__ : Optional[int] = ignore_files or []
ignore_files.append('''__init__.py''' )
lowerCamelCase__ : int = [file for file in files if file not in ignore_files]
for file in files:
# Open all files
print('''Testing''' , __lowerCAmelCase )
if only_modules:
lowerCamelCase__ : Optional[int] = file.split('''.''' )[0]
try:
lowerCamelCase__ : Optional[int] = getattr(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ : int = doctest.DocTestSuite(__lowerCAmelCase )
lowerCamelCase__ : Tuple = unittest.TextTestRunner().run(__lowerCAmelCase )
self.assertIs(len(result.failures ) , 0 )
except AttributeError:
logger.info(F"{module_identifier} is not a module." )
else:
lowerCamelCase__ : Union[str, Any] = doctest.testfile(str('''..''' / directory / file ) , optionflags=doctest.ELLIPSIS )
self.assertIs(result.failed , 0 )
def __lowerCamelCase ( self : List[Any] ) ->Any:
lowerCamelCase__ : Tuple = Path('''src/transformers''' )
lowerCamelCase__ : Dict = '''modeling'''
lowerCamelCase__ : Dict = [
'''modeling_ctrl.py''',
'''modeling_tf_ctrl.py''',
]
self.analyze_directory(__lowerCAmelCase , identifier=__lowerCAmelCase , ignore_files=__lowerCAmelCase )
def __lowerCamelCase ( self : Optional[int] ) ->Union[str, Any]:
lowerCamelCase__ : int = Path('''src/transformers''' )
lowerCamelCase__ : Optional[Any] = '''tokenization'''
self.analyze_directory(__lowerCAmelCase , identifier=__lowerCAmelCase )
def __lowerCamelCase ( self : List[str] ) ->Optional[int]:
lowerCamelCase__ : Any = Path('''src/transformers''' )
lowerCamelCase__ : Tuple = '''configuration'''
self.analyze_directory(__lowerCAmelCase , identifier=__lowerCAmelCase )
def __lowerCamelCase ( self : Optional[int] ) ->Tuple:
lowerCamelCase__ : str = Path('''src/transformers''' )
lowerCamelCase__ : Tuple = ['''configuration''', '''modeling''', '''tokenization''']
self.analyze_directory(__lowerCAmelCase , n_identifier=__lowerCAmelCase )
def __lowerCamelCase ( self : Any ) ->int:
lowerCamelCase__ : List[str] = Path('''docs/source''' )
lowerCamelCase__ : Tuple = ['''favicon.ico''']
self.analyze_directory(__lowerCAmelCase , ignore_files=__lowerCAmelCase , only_modules=__lowerCAmelCase )
| 315
|
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForMaskedImageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A : Optional[int] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')
A : List[str] = list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
A : Optional[int] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class A :
'''simple docstring'''
__lowerCamelCase : Optional[str] = field(
default='''cifar10''' , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
__lowerCamelCase : Optional[str] = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
__lowerCamelCase : Optional[str] = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''} , )
__lowerCamelCase : Optional[str] = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''A folder containing the training data.'''} )
__lowerCamelCase : Optional[str] = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''A folder containing the validation data.'''} )
__lowerCamelCase : Optional[float] = field(
default=0.15 , metadata={'''help''': '''Percent to split off of train for validation.'''} )
__lowerCamelCase : int = field(default=32 , metadata={'''help''': '''The size of the square patches to use for masking.'''} )
__lowerCamelCase : float = field(
default=0.6 , metadata={'''help''': '''Percentage of patches to mask.'''} , )
__lowerCamelCase : Optional[int] = field(
default=SCREAMING_SNAKE_CASE , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
__lowerCamelCase : Optional[int] = field(
default=SCREAMING_SNAKE_CASE , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def a_ ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
A__ = {}
if self.train_dir is not None:
A__ = self.train_dir
if self.validation_dir is not None:
A__ = self.validation_dir
A__ = data_files if data_files else None
@dataclass
class A :
'''simple docstring'''
__lowerCamelCase : str = field(
default=SCREAMING_SNAKE_CASE , metadata={
'''help''': (
'''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
'''checkpoint identifier on the hub. '''
'''Don\'t set if you want to train a model from scratch.'''
)
} , )
__lowerCamelCase : Optional[str] = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(SCREAMING_SNAKE_CASE )} , )
__lowerCamelCase : Optional[str] = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
__lowerCamelCase : Optional[str] = field(
default=SCREAMING_SNAKE_CASE , metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
} , )
__lowerCamelCase : Optional[str] = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''} , )
__lowerCamelCase : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
__lowerCamelCase : str = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Name or path of preprocessor config.'''} )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
__lowerCamelCase : Optional[int] = field(
default=SCREAMING_SNAKE_CASE , metadata={
'''help''': (
'''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
)
} , )
__lowerCamelCase : Optional[int] = field(
default=SCREAMING_SNAKE_CASE , metadata={
'''help''': (
'''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
)
} , )
__lowerCamelCase : Optional[int] = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Stride to use for the encoder.'''} , )
class A :
'''simple docstring'''
def __init__( self : str , __lowerCAmelCase : Optional[Any]=1_92 , __lowerCAmelCase : Tuple=32 , __lowerCAmelCase : str=4 , __lowerCAmelCase : Union[str, Any]=0.6 ) -> Union[str, Any]:
"""simple docstring"""
A__ = input_size
A__ = mask_patch_size
A__ = model_patch_size
A__ = mask_ratio
if self.input_size % self.mask_patch_size != 0:
raise ValueError("""Input size must be divisible by mask patch size""" )
if self.mask_patch_size % self.model_patch_size != 0:
raise ValueError("""Mask patch size must be divisible by model patch size""" )
A__ = self.input_size // self.mask_patch_size
A__ = self.mask_patch_size // self.model_patch_size
A__ = self.rand_size**2
A__ = int(np.ceil(self.token_count * self.mask_ratio ) )
def __call__( self : int ) -> Optional[Any]:
"""simple docstring"""
A__ = np.random.permutation(self.token_count )[: self.mask_count]
A__ = np.zeros(self.token_count , dtype=__lowerCAmelCase )
A__ = 1
A__ = mask.reshape((self.rand_size, self.rand_size) )
A__ = mask.repeat(self.scale , axis=0 ).repeat(self.scale , axis=1 )
return torch.tensor(mask.flatten() )
def __lowerCamelCase ( __a :Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
A__ = torch.stack([example["""pixel_values"""] for example in examples] )
A__ = torch.stack([example["""mask"""] for example in examples] )
return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def __lowerCamelCase ( ) -> Any:
"""simple docstring"""
A__ = 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.
A__ , A__ , A__ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
A__ , A__ , A__ = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_mim""" , __a , __a )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
A__ = training_args.get_process_log_level()
logger.setLevel(__a )
transformers.utils.logging.set_verbosity(__a )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ F'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(F'Training/evaluation parameters {training_args}' )
# Detecting last checkpoint.
A__ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
A__ = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. '
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Initialize our dataset.
A__ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
A__ = None if """validation""" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , __a ) and data_args.train_val_split > 0.0:
A__ = ds["""train"""].train_test_split(data_args.train_val_split )
A__ = split["""train"""]
A__ = split["""test"""]
# Create config
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
A__ = {
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name_or_path:
A__ = AutoConfig.from_pretrained(model_args.config_name_or_path , **__a )
elif model_args.model_name_or_path:
A__ = AutoConfig.from_pretrained(model_args.model_name_or_path , **__a )
else:
A__ = CONFIG_MAPPING[model_args.model_type]()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(F'Overriding config: {model_args.config_overrides}' )
config.update_from_string(model_args.config_overrides )
logger.info(F'New config: {config}' )
# make sure the decoder_type is "simmim" (only relevant for BEiT)
if hasattr(__a , """decoder_type""" ):
A__ = """simmim"""
# adapt config
A__ = model_args.image_size if model_args.image_size is not None else config.image_size
A__ = model_args.patch_size if model_args.patch_size is not None else config.patch_size
A__ = (
model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
)
config.update(
{
"""image_size""": model_args.image_size,
"""patch_size""": model_args.patch_size,
"""encoder_stride""": model_args.encoder_stride,
} )
# create image processor
if model_args.image_processor_name:
A__ = AutoImageProcessor.from_pretrained(model_args.image_processor_name , **__a )
elif model_args.model_name_or_path:
A__ = AutoImageProcessor.from_pretrained(model_args.model_name_or_path , **__a )
else:
A__ = {
conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
}
A__ = IMAGE_PROCESSOR_TYPES[model_args.model_type]()
# create model
if model_args.model_name_or_path:
A__ = AutoModelForMaskedImageModeling.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__a , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("""Training new model from scratch""" )
A__ = AutoModelForMaskedImageModeling.from_config(__a )
if training_args.do_train:
A__ = ds["""train"""].column_names
else:
A__ = ds["""validation"""].column_names
if data_args.image_column_name is not None:
A__ = data_args.image_column_name
elif "image" in column_names:
A__ = """image"""
elif "img" in column_names:
A__ = """img"""
else:
A__ = column_names[0]
# transformations as done in original SimMIM paper
# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
A__ = Compose(
[
Lambda(lambda __a : img.convert("""RGB""" ) if img.mode != "RGB" else img ),
RandomResizedCrop(model_args.image_size , scale=(0.67, 1.0) , ratio=(3.0 / 4.0, 4.0 / 3.0) ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
# create mask generator
A__ = MaskGenerator(
input_size=model_args.image_size , mask_patch_size=data_args.mask_patch_size , model_patch_size=model_args.patch_size , mask_ratio=data_args.mask_ratio , )
def preprocess_images(__a :Any ):
A__ = [transforms(__a ) for image in examples[image_column_name]]
A__ = [mask_generator() for i in range(len(examples[image_column_name] ) )]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError("""--do_train requires a train dataset""" )
if data_args.max_train_samples is not None:
A__ = ds["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(__a )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("""--do_eval requires a validation dataset""" )
if data_args.max_eval_samples is not None:
A__ = (
ds["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(__a )
# Initialize our trainer
A__ = Trainer(
model=__a , args=__a , train_dataset=ds["""train"""] if training_args.do_train else None , eval_dataset=ds["""validation"""] if training_args.do_eval else None , tokenizer=__a , data_collator=__a , )
# Training
if training_args.do_train:
A__ = None
if training_args.resume_from_checkpoint is not None:
A__ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
A__ = last_checkpoint
A__ = trainer.train(resume_from_checkpoint=__a )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
A__ = trainer.evaluate()
trainer.log_metrics("""eval""" , __a )
trainer.save_metrics("""eval""" , __a )
# Write model card and (optionally) push to hub
A__ = {
"""finetuned_from""": model_args.model_name_or_path,
"""tasks""": """masked-image-modeling""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""masked-image-modeling"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__a )
else:
trainer.create_model_card(**__a )
if __name__ == "__main__":
main()
| 176
| 0
|
'''simple docstring'''
import requests
def __snake_case (__UpperCAmelCase , __UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase_ : Dict = {'''Content-Type''': '''application/json'''}
lowerCamelCase_ : Optional[int] = requests.post(__UpperCAmelCase , json={'''text''': message_body} , headers=__UpperCAmelCase )
if response.status_code != 200:
lowerCamelCase_ : int = (
'''Request to slack returned an error '''
F"""{response.status_code}, the response is:\n{response.text}"""
)
raise ValueError(__UpperCAmelCase )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message("""<YOUR MESSAGE BODY>""", """<SLACK CHANNEL URL>""")
| 705
|
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase_ : Optional[Any] = create_tensor(__UpperCAmelCase )
lowerCamelCase_ : Union[str, Any] = gather(__UpperCAmelCase )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase_ : Dict = [state.process_index]
lowerCamelCase_ : str = gather_object(__UpperCAmelCase )
assert len(__UpperCAmelCase ) == state.num_processes, F"""{gathered_obj}, {len(__UpperCAmelCase )} != {state.num_processes}"""
assert gathered_obj == list(range(state.num_processes ) ), F"""{gathered_obj} != {list(range(state.num_processes ) )}"""
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase_ : Union[str, Any] = create_tensor(__UpperCAmelCase )
lowerCamelCase_ : List[Any] = broadcast(__UpperCAmelCase )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
# We need to pad the tensor with one more element if we are the main process
# to ensure that we can pad
if state.is_main_process:
lowerCamelCase_ : int = torch.arange(state.num_processes + 1 ).to(state.device )
else:
lowerCamelCase_ : Optional[Any] = torch.arange(state.num_processes ).to(state.device )
lowerCamelCase_ : Any = pad_across_processes(__UpperCAmelCase )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
# For now runs on only two processes
if state.num_processes != 2:
return
lowerCamelCase_ : Dict = create_tensor(__UpperCAmelCase )
lowerCamelCase_ : List[Any] = reduce(__UpperCAmelCase , '''sum''' )
lowerCamelCase_ : str = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(__UpperCAmelCase , __UpperCAmelCase ), F"""{reduced_tensor} != {truth_tensor}"""
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
# For now runs on only two processes
if state.num_processes != 2:
return
lowerCamelCase_ : Optional[int] = create_tensor(__UpperCAmelCase )
lowerCamelCase_ : Any = reduce(__UpperCAmelCase , '''mean''' )
lowerCamelCase_ : Any = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(__UpperCAmelCase , __UpperCAmelCase ), F"""{reduced_tensor} != {truth_tensor}"""
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
# For xla_spawn (TPUs)
main()
def __snake_case ():
"""simple docstring"""
lowerCamelCase_ : int = PartialState()
state.print(F"""State: {state}""" )
state.print('''testing gather''' )
test_gather(__UpperCAmelCase )
state.print('''testing gather_object''' )
test_gather_object(__UpperCAmelCase )
state.print('''testing broadcast''' )
test_broadcast(__UpperCAmelCase )
state.print('''testing pad_across_processes''' )
test_pad_across_processes(__UpperCAmelCase )
state.print('''testing reduce_sum''' )
test_reduce_sum(__UpperCAmelCase )
state.print('''testing reduce_mean''' )
test_reduce_mean(__UpperCAmelCase )
if __name__ == "__main__":
main()
| 418
| 0
|
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def UpperCAmelCase_ ( lowerCAmelCase_ ):
"""simple docstring"""
return 1 / (1 + np.exp(-z ))
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
return (-y * np.log(lowerCAmelCase_ ) - (1 - y) * np.log(1 - h )).mean()
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
lowercase = np.dot(lowerCAmelCase_ , lowerCAmelCase_ )
return np.sum(y * scores - np.log(1 + np.exp(lowerCAmelCase_ ) ) )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=7_0000 ):
"""simple docstring"""
lowercase = np.zeros(x.shape[1] )
for iterations in range(lowerCAmelCase_ ):
lowercase = np.dot(lowerCAmelCase_ , lowerCAmelCase_ )
lowercase = sigmoid_function(lowerCAmelCase_ )
lowercase = np.dot(x.T , h - y ) / y.size
lowercase = theta - alpha * gradient # updating the weights
lowercase = np.dot(lowerCAmelCase_ , lowerCAmelCase_ )
lowercase = sigmoid_function(lowerCAmelCase_ )
lowercase = cost_function(lowerCAmelCase_ , lowerCAmelCase_ )
if iterations % 100 == 0:
print(f'loss: {j} \t' ) # printing the loss after every 100 iterations
return theta
# In[68]:
if __name__ == "__main__":
__lowerCamelCase : int = datasets.load_iris()
__lowerCamelCase : Any = iris.data[:, :2]
__lowerCamelCase : List[Any] = (iris.target != 0) * 1
__lowerCamelCase : Tuple = 0.1
__lowerCamelCase : Dict = logistic_reg(alpha, x, y, max_iterations=7_0000)
print("theta: ", theta) # printing the theta i.e our weights vector
def UpperCAmelCase_ ( lowerCAmelCase_ ):
"""simple docstring"""
return sigmoid_function(
np.dot(lowerCAmelCase_ , lowerCAmelCase_ ) ) # predicting the value of probability from the logistic regression algorithm
plt.figure(figsize=(10, 6))
plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="b", label="0")
plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="r", label="1")
((__lowerCamelCase) , (__lowerCamelCase)) : int = (x[:, 0].min(), x[:, 0].max())
((__lowerCamelCase) , (__lowerCamelCase)) : int = (x[:, 1].min(), x[:, 1].max())
((__lowerCamelCase) , (__lowerCamelCase)) : Optional[int] = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
__lowerCamelCase : Any = np.c_[xxa.ravel(), xxa.ravel()]
__lowerCamelCase : Optional[int] = predict_prob(grid).reshape(xxa.shape)
plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="black")
plt.legend()
plt.show()
| 310
|
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__lowerCamelCase : Dict = pytest.mark.integration
@pytest.mark.parametrize("path" , ["paws", "csv"] )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
inspect_dataset(lowerCAmelCase_ , lowerCAmelCase_ )
lowercase = path + ".py"
assert script_name in os.listdir(lowerCAmelCase_ )
assert "__pycache__" not in os.listdir(lowerCAmelCase_ )
@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning" )
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning" )
@pytest.mark.parametrize("path" , ["accuracy"] )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
inspect_metric(lowerCAmelCase_ , lowerCAmelCase_ )
lowercase = path + ".py"
assert script_name in os.listdir(lowerCAmelCase_ )
assert "__pycache__" not in os.listdir(lowerCAmelCase_ )
@pytest.mark.parametrize(
"path, config_name, expected_splits" , [
("squad", "plain_text", ["train", "validation"]),
("dalle-mini/wit", "dalle-mini--wit", ["train"]),
("paws", "labeled_final", ["train", "test", "validation"]),
] , )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
lowercase = get_dataset_config_info(lowerCAmelCase_ , config_name=lowerCAmelCase_ )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"path, config_name, expected_exception" , [
("paws", None, ValueError),
] , )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
with pytest.raises(lowerCAmelCase_ ):
get_dataset_config_info(lowerCAmelCase_ , config_name=lowerCAmelCase_ )
@pytest.mark.parametrize(
"path, expected" , [
("squad", "plain_text"),
("acronym_identification", "default"),
("lhoestq/squad", "plain_text"),
("lhoestq/test", "default"),
("lhoestq/demo1", "lhoestq--demo1"),
("dalle-mini/wit", "dalle-mini--wit"),
] , )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
lowercase = get_dataset_config_names(lowerCAmelCase_ )
assert expected in config_names
@pytest.mark.parametrize(
"path, expected_configs, expected_splits_in_first_config" , [
("squad", ["plain_text"], ["train", "validation"]),
("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
] , )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
lowercase = get_dataset_infos(lowerCAmelCase_ )
assert list(infos.keys() ) == expected_configs
lowercase = expected_configs[0]
assert expected_config in infos
lowercase = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
"path, expected_config, expected_splits" , [
("squad", "plain_text", ["train", "validation"]),
("dalle-mini/wit", "dalle-mini--wit", ["train"]),
("paws", "labeled_final", ["train", "test", "validation"]),
] , )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
lowercase = get_dataset_infos(lowerCAmelCase_ )
assert expected_config in infos
lowercase = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"path, config_name, expected_exception" , [
("paws", None, ValueError),
] , )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
with pytest.raises(lowerCAmelCase_ ):
get_dataset_split_names(lowerCAmelCase_ , config_name=lowerCAmelCase_ )
| 310
| 1
|
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def UpperCAmelCase ( UpperCAmelCase__ : List[Any]):
lowerCamelCase , lowerCamelCase : List[str] = image.size
lowerCamelCase , lowerCamelCase : Optional[int] = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
lowerCamelCase : Tuple = image.resize((w, h) , resample=PIL_INTERPOLATION['lanczos'])
lowerCamelCase : List[Any] = np.array(UpperCAmelCase__).astype(np.floataa) / 2_5_5.0
lowerCamelCase : int = image[None].transpose(0 , 3 , 1 , 2)
lowerCamelCase : List[str] = torch.from_numpy(UpperCAmelCase__)
return 2.0 * image - 1.0
class __snake_case ( a__):
def __init__( self, A, A, A, ):
"""simple docstring"""
super().__init__()
self.register_modules(vqvae=A, unet=A, scheduler=A )
@torch.no_grad()
def __call__( self, A = None, A = 1, A = 100, A = 0.0, A = None, A = "pil", A = True, ):
"""simple docstring"""
if isinstance(A, PIL.Image.Image ):
lowerCamelCase : List[str] = 1
elif isinstance(A, torch.Tensor ):
lowerCamelCase : Any = image.shape[0]
else:
raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(A )}''' )
if isinstance(A, PIL.Image.Image ):
lowerCamelCase : Any = preprocess(A )
lowerCamelCase , lowerCamelCase : List[Any] = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
lowerCamelCase : Union[str, Any] = (batch_size, self.unet.config.in_channels // 2, height, width)
lowerCamelCase : Union[str, Any] = next(self.unet.parameters() ).dtype
lowerCamelCase : Dict = randn_tensor(A, generator=A, device=self.device, dtype=A )
lowerCamelCase : Tuple = image.to(device=self.device, dtype=A )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(A, device=self.device )
lowerCamelCase : int = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
lowerCamelCase : str = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
lowerCamelCase : Union[str, Any] = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowerCamelCase : Optional[Any] = {}
if accepts_eta:
lowerCamelCase : Optional[int] = eta
for t in self.progress_bar(A ):
# concat latents and low resolution image in the channel dimension.
lowerCamelCase : Union[str, Any] = torch.cat([latents, image], dim=1 )
lowerCamelCase : Tuple = self.scheduler.scale_model_input(A, A )
# predict the noise residual
lowerCamelCase : str = self.unet(A, A ).sample
# compute the previous noisy sample x_t -> x_t-1
lowerCamelCase : List[str] = self.scheduler.step(A, A, A, **A ).prev_sample
# decode the image latents with the VQVAE
lowerCamelCase : int = self.vqvae.decode(A ).sample
lowerCamelCase : str = torch.clamp(A, -1.0, 1.0 )
lowerCamelCase : Union[str, Any] = image / 2 + 0.5
lowerCamelCase : Dict = image.cpu().permute(0, 2, 3, 1 ).numpy()
if output_type == "pil":
lowerCamelCase : Union[str, Any] = self.numpy_to_pil(A )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=A )
| 449
|
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase ( UpperCAmelCase__ : int):
lowerCamelCase : List[Any] = str(UpperCAmelCase__)
return len(UpperCAmelCase__) == 9 and set(UpperCAmelCase__) == set('123456789')
def UpperCAmelCase ( ):
for base_num in range(99_99 , 49_99 , -1):
lowerCamelCase : Tuple = 10_00_02 * base_num
if is_9_pandigital(UpperCAmelCase__):
return candidate
for base_num in range(3_33 , 99 , -1):
lowerCamelCase : List[Any] = 1_00_20_03 * base_num
if is_9_pandigital(UpperCAmelCase__):
return candidate
return None
if __name__ == "__main__":
print(f"""{solution() = }""")
| 449
| 1
|
"""simple docstring"""
from __future__ import annotations
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self , A , A ) -> Optional[int]:
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = text, pattern
_UpperCAmelCase , _UpperCAmelCase : Tuple = len(A ), len(A )
def __lowerCAmelCase ( self , A ) -> int:
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def __lowerCAmelCase ( self , A ) -> int:
for i in range(self.patLen - 1 , -1 , -1 ):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def __lowerCAmelCase ( self ) -> list[int]:
# searches pattern in text and returns index positions
_UpperCAmelCase : Dict = []
for i in range(self.textLen - self.patLen + 1 ):
_UpperCAmelCase : Dict = self.mismatch_in_text(A )
if mismatch_index == -1:
positions.append(A )
else:
_UpperCAmelCase : List[Any] = self.match_in_pattern(self.text[mismatch_index] )
_UpperCAmelCase : int = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
_lowerCAmelCase :Tuple = 'ABAABA'
_lowerCAmelCase :Optional[int] = 'AB'
_lowerCAmelCase :Optional[int] = BoyerMooreSearch(text, pattern)
_lowerCAmelCase :Any = bms.bad_character_heuristic()
if len(positions) == 0:
print('No match found')
else:
print('Pattern found in following positions: ')
print(positions)
| 506
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase :Any = logging.get_logger(__name__)
_lowerCAmelCase :Union[str, Any] = {
'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}
class _UpperCAmelCase ( a ):
'''simple docstring'''
a__ ='''lxmert'''
a__ ={}
def __init__( self , A=3_0_5_2_2 , A=7_6_8 , A=1_2 , A=9_5_0_0 , A=1_6_0_0 , A=4_0_0 , A=3_0_7_2 , A="gelu" , A=0.1 , A=0.1 , A=5_1_2 , A=2 , A=0.02 , A=1E-12 , A=9 , A=5 , A=5 , A=2_0_4_8 , A=4 , A=6.67 , A=True , A=True , A=True , A=True , A=True , A=True , A=True , **A , ) -> List[Any]:
_UpperCAmelCase : int = vocab_size
_UpperCAmelCase : str = hidden_size
_UpperCAmelCase : str = num_attention_heads
_UpperCAmelCase : str = hidden_act
_UpperCAmelCase : Dict = intermediate_size
_UpperCAmelCase : Union[str, Any] = hidden_dropout_prob
_UpperCAmelCase : str = attention_probs_dropout_prob
_UpperCAmelCase : Dict = max_position_embeddings
_UpperCAmelCase : str = type_vocab_size
_UpperCAmelCase : List[str] = initializer_range
_UpperCAmelCase : List[str] = layer_norm_eps
_UpperCAmelCase : Optional[int] = num_qa_labels
_UpperCAmelCase : Tuple = num_object_labels
_UpperCAmelCase : Optional[int] = num_attr_labels
_UpperCAmelCase : List[str] = l_layers
_UpperCAmelCase : Any = x_layers
_UpperCAmelCase : Tuple = r_layers
_UpperCAmelCase : Optional[Any] = visual_feat_dim
_UpperCAmelCase : Optional[int] = visual_pos_dim
_UpperCAmelCase : Optional[Any] = visual_loss_normalizer
_UpperCAmelCase : int = task_matched
_UpperCAmelCase : Optional[Any] = task_mask_lm
_UpperCAmelCase : Union[str, Any] = task_obj_predict
_UpperCAmelCase : Optional[int] = task_qa
_UpperCAmelCase : Union[str, Any] = visual_obj_loss
_UpperCAmelCase : List[str] = visual_attr_loss
_UpperCAmelCase : Optional[int] = visual_feat_loss
_UpperCAmelCase : Tuple = {'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
super().__init__(**A )
| 506
| 1
|
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
a__ : Any = logging.get_logger(__name__)
a__ : List[Any] = OrderedDict(
[
("""align""", """EfficientNetImageProcessor"""),
("""beit""", """BeitImageProcessor"""),
("""bit""", """BitImageProcessor"""),
("""blip""", """BlipImageProcessor"""),
("""blip-2""", """BlipImageProcessor"""),
("""bridgetower""", """BridgeTowerImageProcessor"""),
("""chinese_clip""", """ChineseCLIPImageProcessor"""),
("""clip""", """CLIPImageProcessor"""),
("""clipseg""", """ViTImageProcessor"""),
("""conditional_detr""", """ConditionalDetrImageProcessor"""),
("""convnext""", """ConvNextImageProcessor"""),
("""convnextv2""", """ConvNextImageProcessor"""),
("""cvt""", """ConvNextImageProcessor"""),
("""data2vec-vision""", """BeitImageProcessor"""),
("""deformable_detr""", """DeformableDetrImageProcessor"""),
("""deit""", """DeiTImageProcessor"""),
("""deta""", """DetaImageProcessor"""),
("""detr""", """DetrImageProcessor"""),
("""dinat""", """ViTImageProcessor"""),
("""donut-swin""", """DonutImageProcessor"""),
("""dpt""", """DPTImageProcessor"""),
("""efficientformer""", """EfficientFormerImageProcessor"""),
("""efficientnet""", """EfficientNetImageProcessor"""),
("""flava""", """FlavaImageProcessor"""),
("""focalnet""", """BitImageProcessor"""),
("""git""", """CLIPImageProcessor"""),
("""glpn""", """GLPNImageProcessor"""),
("""groupvit""", """CLIPImageProcessor"""),
("""imagegpt""", """ImageGPTImageProcessor"""),
("""instructblip""", """BlipImageProcessor"""),
("""layoutlmv2""", """LayoutLMv2ImageProcessor"""),
("""layoutlmv3""", """LayoutLMv3ImageProcessor"""),
("""levit""", """LevitImageProcessor"""),
("""mask2former""", """Mask2FormerImageProcessor"""),
("""maskformer""", """MaskFormerImageProcessor"""),
("""mgp-str""", """ViTImageProcessor"""),
("""mobilenet_v1""", """MobileNetV1ImageProcessor"""),
("""mobilenet_v2""", """MobileNetV2ImageProcessor"""),
("""mobilevit""", """MobileViTImageProcessor"""),
("""mobilevit""", """MobileViTImageProcessor"""),
("""mobilevitv2""", """MobileViTImageProcessor"""),
("""nat""", """ViTImageProcessor"""),
("""oneformer""", """OneFormerImageProcessor"""),
("""owlvit""", """OwlViTImageProcessor"""),
("""perceiver""", """PerceiverImageProcessor"""),
("""pix2struct""", """Pix2StructImageProcessor"""),
("""poolformer""", """PoolFormerImageProcessor"""),
("""regnet""", """ConvNextImageProcessor"""),
("""resnet""", """ConvNextImageProcessor"""),
("""sam""", """SamImageProcessor"""),
("""segformer""", """SegformerImageProcessor"""),
("""swiftformer""", """ViTImageProcessor"""),
("""swin""", """ViTImageProcessor"""),
("""swin2sr""", """Swin2SRImageProcessor"""),
("""swinv2""", """ViTImageProcessor"""),
("""table-transformer""", """DetrImageProcessor"""),
("""timesformer""", """VideoMAEImageProcessor"""),
("""tvlt""", """TvltImageProcessor"""),
("""upernet""", """SegformerImageProcessor"""),
("""van""", """ConvNextImageProcessor"""),
("""videomae""", """VideoMAEImageProcessor"""),
("""vilt""", """ViltImageProcessor"""),
("""vit""", """ViTImageProcessor"""),
("""vit_hybrid""", """ViTHybridImageProcessor"""),
("""vit_mae""", """ViTImageProcessor"""),
("""vit_msn""", """ViTImageProcessor"""),
("""xclip""", """CLIPImageProcessor"""),
("""yolos""", """YolosImageProcessor"""),
]
)
a__ : str = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def A__ ( __lowerCamelCase ):
"""simple docstring"""
for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
if class_name in extractors:
_lowerCAmelCase = model_type_to_module_name(__lowerCamelCase )
_lowerCAmelCase = importlib.import_module(F'''.{module_name}''', 'transformers.models' )
try:
return getattr(__lowerCamelCase, __lowerCamelCase )
except AttributeError:
continue
for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
if getattr(__lowerCamelCase, '__name__', __lowerCamelCase ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
_lowerCAmelCase = importlib.import_module('transformers' )
if hasattr(__lowerCamelCase, __lowerCamelCase ):
return getattr(__lowerCamelCase, __lowerCamelCase )
return None
def A__ ( __lowerCamelCase, __lowerCamelCase = None, __lowerCamelCase = False, __lowerCamelCase = False, __lowerCamelCase = None, __lowerCamelCase = None, __lowerCamelCase = None, __lowerCamelCase = False, **__lowerCamelCase, ):
"""simple docstring"""
_lowerCAmelCase = get_file_from_repo(
__lowerCamelCase, __lowerCamelCase, cache_dir=__lowerCamelCase, force_download=__lowerCamelCase, resume_download=__lowerCamelCase, proxies=__lowerCamelCase, use_auth_token=__lowerCamelCase, revision=__lowerCamelCase, local_files_only=__lowerCamelCase, )
if resolved_config_file is None:
logger.info(
'Could not locate the image processor configuration file, will try to use the model config instead.' )
return {}
with open(__lowerCamelCase, encoding='utf-8' ) as reader:
return json.load(__lowerCamelCase )
class __magic_name__ :
def __init__( self ):
"""simple docstring"""
raise EnvironmentError(
'AutoImageProcessor is designed to be instantiated '
'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.' )
@classmethod
@replace_list_option_in_docstrings(__magic_name__ )
def _lowerCamelCase ( cls , __magic_name__ , **__magic_name__ ):
"""simple docstring"""
_lowerCAmelCase = kwargs.pop('config' , __magic_name__ )
_lowerCAmelCase = kwargs.pop('trust_remote_code' , __magic_name__ )
_lowerCAmelCase = True
_lowerCAmelCase , _lowerCAmelCase = ImageProcessingMixin.get_image_processor_dict(__magic_name__ , **__magic_name__ )
_lowerCAmelCase = config_dict.get('image_processor_type' , __magic_name__ )
_lowerCAmelCase = None
if "AutoImageProcessor" in config_dict.get('auto_map' , {} ):
_lowerCAmelCase = config_dict['auto_map']['AutoImageProcessor']
# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
# and if so, infer the image processor class from there.
if image_processor_class is None and image_processor_auto_map is None:
_lowerCAmelCase = config_dict.pop('feature_extractor_type' , __magic_name__ )
if feature_extractor_class is not None:
logger.warning(
'Could not find image processor class in the image processor config or the model config. Loading'
' based on pattern matching with the model\'s feature extractor configuration.' )
_lowerCAmelCase = feature_extractor_class.replace('FeatureExtractor' , 'ImageProcessor' )
if "AutoFeatureExtractor" in config_dict.get('auto_map' , {} ):
_lowerCAmelCase = config_dict['auto_map']['AutoFeatureExtractor']
_lowerCAmelCase = feature_extractor_auto_map.replace('FeatureExtractor' , 'ImageProcessor' )
logger.warning(
'Could not find image processor auto map in the image processor config or the model config.'
' Loading based on pattern matching with the model\'s feature extractor configuration.' )
# If we don't find the image processor class in the image processor config, let's try the model config.
if image_processor_class is None and image_processor_auto_map is None:
if not isinstance(__magic_name__ , __magic_name__ ):
_lowerCAmelCase = AutoConfig.from_pretrained(__magic_name__ , **__magic_name__ )
# It could be in `config.image_processor_type``
_lowerCAmelCase = getattr(__magic_name__ , 'image_processor_type' , __magic_name__ )
if hasattr(__magic_name__ , 'auto_map' ) and "AutoImageProcessor" in config.auto_map:
_lowerCAmelCase = config.auto_map['AutoImageProcessor']
if image_processor_class is not None:
_lowerCAmelCase = image_processor_class_from_name(__magic_name__ )
_lowerCAmelCase = image_processor_auto_map is not None
_lowerCAmelCase = image_processor_class is not None or type(__magic_name__ ) in IMAGE_PROCESSOR_MAPPING
_lowerCAmelCase = resolve_trust_remote_code(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
if has_remote_code and trust_remote_code:
_lowerCAmelCase = get_class_from_dynamic_module(
__magic_name__ , __magic_name__ , **__magic_name__ )
_lowerCAmelCase = kwargs.pop('code_revision' , __magic_name__ )
if os.path.isdir(__magic_name__ ):
image_processor_class.register_for_auto_class()
return image_processor_class.from_dict(__magic_name__ , **__magic_name__ )
elif image_processor_class is not None:
return image_processor_class.from_dict(__magic_name__ , **__magic_name__ )
# Last try: we use the IMAGE_PROCESSOR_MAPPING.
elif type(__magic_name__ ) in IMAGE_PROCESSOR_MAPPING:
_lowerCAmelCase = IMAGE_PROCESSOR_MAPPING[type(__magic_name__ )]
return image_processor_class.from_dict(__magic_name__ , **__magic_name__ )
raise ValueError(
F'''Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '''
F'''`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '''
F'''`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys() )}''' )
@staticmethod
def _lowerCamelCase ( __magic_name__ , __magic_name__ ):
"""simple docstring"""
IMAGE_PROCESSOR_MAPPING.register(__magic_name__ , __magic_name__ )
| 708
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : List[Any] = {
"""configuration_jukebox""": [
"""JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""JukeboxConfig""",
"""JukeboxPriorConfig""",
"""JukeboxVQVAEConfig""",
],
"""tokenization_jukebox""": ["""JukeboxTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Union[str, Any] = [
"""JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""JukeboxModel""",
"""JukeboxPreTrainedModel""",
"""JukeboxVQVAE""",
"""JukeboxPrior""",
]
if TYPE_CHECKING:
from .configuration_jukebox import (
JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
JukeboxConfig,
JukeboxPriorConfig,
JukeboxVQVAEConfig,
)
from .tokenization_jukebox import JukeboxTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_jukebox import (
JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
JukeboxModel,
JukeboxPreTrainedModel,
JukeboxPrior,
JukeboxVQVAE,
)
else:
import sys
a__ : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 309
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A_: List[Any] = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_: Tuple = [
'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'AdaptiveEmbedding',
'TransfoXLForSequenceClassification',
'TransfoXLLMHeadModel',
'TransfoXLModel',
'TransfoXLPreTrainedModel',
'load_tf_weights_in_transfo_xl',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_: int = [
'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFAdaptiveEmbedding',
'TFTransfoXLForSequenceClassification',
'TFTransfoXLLMHeadModel',
'TFTransfoXLMainLayer',
'TFTransfoXLModel',
'TFTransfoXLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
else:
import sys
A_: Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 398
|
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
A_: Tuple = logging.get_logger(__name__)
A_: Optional[Any] = {
'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class _lowercase ( _UpperCAmelCase ):
"""simple docstring"""
lowerCAmelCase__ = 'gpt_neo'
lowerCAmelCase__ = ['past_key_values']
lowerCAmelCase__ = {'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
def __init__( self , UpperCAmelCase=50257 , UpperCAmelCase=2048 , UpperCAmelCase=2048 , UpperCAmelCase=24 , UpperCAmelCase=[[["global", "local"], 12]] , UpperCAmelCase=16 , UpperCAmelCase=None , UpperCAmelCase=256 , UpperCAmelCase="gelu_new" , UpperCAmelCase=0.0 , UpperCAmelCase=0.0 , UpperCAmelCase=0.0 , UpperCAmelCase=0.1 , UpperCAmelCase=1e-5 , UpperCAmelCase=0.02 , UpperCAmelCase=True , UpperCAmelCase=50256 , UpperCAmelCase=50256 , **UpperCAmelCase , ):
'''simple docstring'''
_lowercase = vocab_size
_lowercase = max_position_embeddings
_lowercase = hidden_size
_lowercase = num_layers
_lowercase = num_heads
_lowercase = intermediate_size
_lowercase = window_size
_lowercase = activation_function
_lowercase = resid_dropout
_lowercase = embed_dropout
_lowercase = attention_dropout
_lowercase = classifier_dropout
_lowercase = layer_norm_epsilon
_lowercase = initializer_range
_lowercase = use_cache
_lowercase = bos_token_id
_lowercase = eos_token_id
_lowercase = attention_types
_lowercase = self.expand_attention_types_params(UpperCAmelCase )
if len(self.attention_layers ) != self.num_layers:
raise ValueError(
"""Configuration for convolutional module is incorrect. """
"""It is required that `len(config.attention_layers)` == `config.num_layers` """
F'''but is `len(config.attention_layers) = {len(self.attention_layers )}`, '''
F'''`config.num_layers = {self.num_layers}`. '''
"""`config.attention_layers` is prepared using `config.attention_types`. """
"""Please verify the value of `config.attention_types` argument.""" )
super().__init__(bos_token_id=UpperCAmelCase , eos_token_id=UpperCAmelCase , **UpperCAmelCase )
@staticmethod
def _UpperCAmelCase ( UpperCAmelCase ):
'''simple docstring'''
_lowercase = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def __lowerCAmelCase ( _A ,_A ,_A ,_A ):
"""simple docstring"""
import torch
_lowercase = input.size()
_lowercase = len(_A )
_lowercase = shape[dimension]
_lowercase = torch.arange(0 ,_A ,_A )
_lowercase = torch.div(sizedim - size ,_A ,rounding_mode="""floor""" ) + 1
_lowercase = torch.arange(_A ) + low_indices[:min_length][:, None]
_lowercase = [slice(_A )] * rank
_lowercase = indices
_lowercase = input[s]
_lowercase = list(range(0 ,rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(_A )
def __lowerCAmelCase ( _A ,_A ):
"""simple docstring"""
import torch
_lowercase = torch.arange(1 ,_A )
_lowercase = torch.remainder(_A ,_A )
_lowercase = remainders == 0
_lowercase = candidates[divisor_indices]
_lowercase = torch.max(_A )
return largest_divisor, torch.div(_A ,_A ,rounding_mode="""floor""" )
class _lowercase ( _UpperCAmelCase ):
"""simple docstring"""
@property
def _UpperCAmelCase ( self ):
'''simple docstring'''
_lowercase = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(UpperCAmelCase , direction="""inputs""" )
_lowercase = {0: """batch""", 1: """past_sequence + sequence"""}
else:
_lowercase = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def _UpperCAmelCase ( self ):
'''simple docstring'''
return self._config.num_heads
def _UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase = -1 , UpperCAmelCase = -1 , UpperCAmelCase = False , UpperCAmelCase = None , ):
'''simple docstring'''
_lowercase = super(UpperCAmelCase , self ).generate_dummy_inputs(
UpperCAmelCase , batch_size=UpperCAmelCase , seq_length=UpperCAmelCase , is_pair=UpperCAmelCase , framework=UpperCAmelCase )
# We need to order the input in the way they appears in the forward()
_lowercase = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
_lowercase , _lowercase = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
_lowercase = seqlen + 2
_lowercase = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_lowercase = [
(torch.zeros(UpperCAmelCase ), torch.zeros(UpperCAmelCase )) for _ in range(self.num_layers )
]
_lowercase = common_inputs["""attention_mask"""]
if self.use_past:
_lowercase = ordered_inputs["""attention_mask"""].dtype
_lowercase = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(UpperCAmelCase , UpperCAmelCase , dtype=UpperCAmelCase )] , dim=1 )
return ordered_inputs
@property
def _UpperCAmelCase ( self ):
'''simple docstring'''
return 13
| 398
| 1
|
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class A_ ( SCREAMING_SNAKE_CASE ):
_UpperCAmelCase : Any = ''''''
_UpperCAmelCase : Tuple = '''hf-legacy''' # "hf://"" is reserved for hffs
def __init__( self : int ,SCREAMING_SNAKE_CASE__ : Optional[DatasetInfo] = None ,SCREAMING_SNAKE_CASE__ : Optional[str] = None ,**SCREAMING_SNAKE_CASE__ : Tuple ,):
super().__init__(self ,**SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[str] = repo_info
__lowerCamelCase : int = token
__lowerCamelCase : Optional[Any] = None
def lowerCAmelCase ( self : Any):
if self.dir_cache is None:
__lowerCamelCase : Dict = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
__lowerCamelCase : List[Any] = {
'name': hf_file.rfilename,
'size': None,
'type': 'file',
}
self.dir_cache.update(
{
str(SCREAMING_SNAKE_CASE__): {'name': str(SCREAMING_SNAKE_CASE__), 'size': None, 'type': 'directory'}
for d in list(PurePosixPath(hf_file.rfilename).parents)[:-1]
})
def lowerCAmelCase ( self : List[Any] ,SCREAMING_SNAKE_CASE__ : str ,SCREAMING_SNAKE_CASE__ : str = "rb" ,**SCREAMING_SNAKE_CASE__ : Union[str, Any] ,):
if not isinstance(self.repo_info ,SCREAMING_SNAKE_CASE__):
raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}")
__lowerCamelCase : Tuple = hf_hub_url(self.repo_info.id ,SCREAMING_SNAKE_CASE__ ,revision=self.repo_info.sha)
return fsspec.open(
SCREAMING_SNAKE_CASE__ ,mode=SCREAMING_SNAKE_CASE__ ,headers=get_authentication_headers_for_url(SCREAMING_SNAKE_CASE__ ,use_auth_token=self.token) ,client_kwargs={'trust_env': True} ,).open()
def lowerCAmelCase ( self : List[str] ,SCREAMING_SNAKE_CASE__ : str ,**SCREAMING_SNAKE_CASE__ : int):
self._get_dirs()
__lowerCamelCase : List[str] = self._strip_protocol(SCREAMING_SNAKE_CASE__)
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(SCREAMING_SNAKE_CASE__)
def lowerCAmelCase ( self : List[Any] ,SCREAMING_SNAKE_CASE__ : List[Any] ,SCREAMING_SNAKE_CASE__ : Any=False ,**SCREAMING_SNAKE_CASE__ : Optional[int]):
self._get_dirs()
__lowerCamelCase : List[str] = PurePosixPath(path.strip('/'))
__lowerCamelCase : Tuple = {}
for p, f in self.dir_cache.items():
__lowerCamelCase : int = PurePosixPath(p.strip('/'))
__lowerCamelCase : int = p.parent
if root == path:
__lowerCamelCase : Union[str, Any] = f
__lowerCamelCase : int = list(paths.values())
if detail:
return out
else:
return sorted(f['name'] for f in out)
| 337
|
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> list[int]:
if length <= 0 or not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(lowerCamelCase__ )]
if __name__ == "__main__":
print(hexagonal_numbers(length=5))
print(hexagonal_numbers(length=10))
| 337
| 1
|
from collections import deque
from math import floor
from random import random
from time import time
class __lowerCAmelCase :
def __init__( self ):
'''simple docstring'''
__lowerCamelCase = {}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=1 ):
'''simple docstring'''
if self.graph.get(lowerCamelCase__ ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
__lowerCamelCase = [[w, v]]
if not self.graph.get(lowerCamelCase__ ):
__lowerCamelCase = []
def lowerCamelCase ( self ):
'''simple docstring'''
return list(self.graph )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
if self.graph.get(lowerCamelCase__ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCamelCase__ )
def lowerCamelCase ( self , __UpperCAmelCase=-2 , __UpperCAmelCase=-1 ):
'''simple docstring'''
if s == d:
return []
__lowerCamelCase = []
__lowerCamelCase = []
if s == -2:
__lowerCamelCase = list(self.graph )[0]
stack.append(lowerCamelCase__ )
visited.append(lowerCamelCase__ )
__lowerCamelCase = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__lowerCamelCase = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCamelCase__ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
__lowerCamelCase = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCamelCase__ ) != 0:
__lowerCamelCase = stack[len(lowerCamelCase__ ) - 1]
else:
__lowerCamelCase = ss
# check if se have reached the starting point
if len(lowerCamelCase__ ) == 0:
return visited
def lowerCamelCase ( self , __UpperCAmelCase=-1 ):
'''simple docstring'''
if c == -1:
__lowerCamelCase = floor(random() * 10000 ) + 10
for i in range(lowerCamelCase__ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
__lowerCamelCase = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCamelCase__ , lowerCamelCase__ , 1 )
def lowerCamelCase ( self , __UpperCAmelCase=-2 ):
'''simple docstring'''
__lowerCamelCase = deque()
__lowerCamelCase = []
if s == -2:
__lowerCamelCase = list(self.graph )[0]
d.append(lowerCamelCase__ )
visited.append(lowerCamelCase__ )
while d:
__lowerCamelCase = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
return len(self.graph[u] )
def lowerCamelCase ( self , __UpperCAmelCase=-2 ):
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = []
if s == -2:
__lowerCamelCase = list(self.graph )[0]
stack.append(lowerCamelCase__ )
visited.append(lowerCamelCase__ )
__lowerCamelCase = s
__lowerCamelCase = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__lowerCamelCase = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__lowerCamelCase = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(lowerCamelCase__ ) != 0:
__lowerCamelCase = stack[len(lowerCamelCase__ ) - 1]
else:
__lowerCamelCase = ss
# check if se have reached the starting point
if len(lowerCamelCase__ ) == 0:
return sorted_nodes
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = list(self.graph )[0]
stack.append(lowerCamelCase__ )
visited.append(lowerCamelCase__ )
__lowerCamelCase = -2
__lowerCamelCase = []
__lowerCamelCase = s
__lowerCamelCase = False
__lowerCamelCase = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__lowerCamelCase = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
__lowerCamelCase = len(lowerCamelCase__ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__lowerCamelCase = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
__lowerCamelCase = True
if len(lowerCamelCase__ ) != 0:
__lowerCamelCase = stack[len(lowerCamelCase__ ) - 1]
else:
__lowerCamelCase = False
indirect_parents.append(lowerCamelCase__ )
__lowerCamelCase = s
__lowerCamelCase = ss
# check if se have reached the starting point
if len(lowerCamelCase__ ) == 0:
return list(lowerCamelCase__ )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = list(self.graph )[0]
stack.append(lowerCamelCase__ )
visited.append(lowerCamelCase__ )
__lowerCamelCase = -2
__lowerCamelCase = []
__lowerCamelCase = s
__lowerCamelCase = False
__lowerCamelCase = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__lowerCamelCase = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
__lowerCamelCase = len(lowerCamelCase__ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__lowerCamelCase = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
__lowerCamelCase = True
if len(lowerCamelCase__ ) != 0:
__lowerCamelCase = stack[len(lowerCamelCase__ ) - 1]
else:
__lowerCamelCase = False
indirect_parents.append(lowerCamelCase__ )
__lowerCamelCase = s
__lowerCamelCase = ss
# check if se have reached the starting point
if len(lowerCamelCase__ ) == 0:
return False
def lowerCamelCase ( self , __UpperCAmelCase=-2 , __UpperCAmelCase=-1 ):
'''simple docstring'''
__lowerCamelCase = time()
self.dfs(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = time()
return end - begin
def lowerCamelCase ( self , __UpperCAmelCase=-2 ):
'''simple docstring'''
__lowerCamelCase = time()
self.bfs(lowerCamelCase__ )
__lowerCamelCase = time()
return end - begin
class __lowerCAmelCase :
def __init__( self ):
'''simple docstring'''
__lowerCamelCase = {}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=1 ):
'''simple docstring'''
if self.graph.get(lowerCamelCase__ ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
__lowerCamelCase = [[w, v]]
# add the other way
if self.graph.get(lowerCamelCase__ ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
__lowerCamelCase = [[w, u]]
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
if self.graph.get(lowerCamelCase__ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCamelCase__ )
# the other way round
if self.graph.get(lowerCamelCase__ ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(lowerCamelCase__ )
def lowerCamelCase ( self , __UpperCAmelCase=-2 , __UpperCAmelCase=-1 ):
'''simple docstring'''
if s == d:
return []
__lowerCamelCase = []
__lowerCamelCase = []
if s == -2:
__lowerCamelCase = list(self.graph )[0]
stack.append(lowerCamelCase__ )
visited.append(lowerCamelCase__ )
__lowerCamelCase = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__lowerCamelCase = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCamelCase__ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
__lowerCamelCase = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCamelCase__ ) != 0:
__lowerCamelCase = stack[len(lowerCamelCase__ ) - 1]
else:
__lowerCamelCase = ss
# check if se have reached the starting point
if len(lowerCamelCase__ ) == 0:
return visited
def lowerCamelCase ( self , __UpperCAmelCase=-1 ):
'''simple docstring'''
if c == -1:
__lowerCamelCase = floor(random() * 10000 ) + 10
for i in range(lowerCamelCase__ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
__lowerCamelCase = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCamelCase__ , lowerCamelCase__ , 1 )
def lowerCamelCase ( self , __UpperCAmelCase=-2 ):
'''simple docstring'''
__lowerCamelCase = deque()
__lowerCamelCase = []
if s == -2:
__lowerCamelCase = list(self.graph )[0]
d.append(lowerCamelCase__ )
visited.append(lowerCamelCase__ )
while d:
__lowerCamelCase = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
return len(self.graph[u] )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = list(self.graph )[0]
stack.append(lowerCamelCase__ )
visited.append(lowerCamelCase__ )
__lowerCamelCase = -2
__lowerCamelCase = []
__lowerCamelCase = s
__lowerCamelCase = False
__lowerCamelCase = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__lowerCamelCase = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
__lowerCamelCase = len(lowerCamelCase__ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__lowerCamelCase = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
__lowerCamelCase = True
if len(lowerCamelCase__ ) != 0:
__lowerCamelCase = stack[len(lowerCamelCase__ ) - 1]
else:
__lowerCamelCase = False
indirect_parents.append(lowerCamelCase__ )
__lowerCamelCase = s
__lowerCamelCase = ss
# check if se have reached the starting point
if len(lowerCamelCase__ ) == 0:
return list(lowerCamelCase__ )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = list(self.graph )[0]
stack.append(lowerCamelCase__ )
visited.append(lowerCamelCase__ )
__lowerCamelCase = -2
__lowerCamelCase = []
__lowerCamelCase = s
__lowerCamelCase = False
__lowerCamelCase = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__lowerCamelCase = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
__lowerCamelCase = len(lowerCamelCase__ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__lowerCamelCase = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
__lowerCamelCase = True
if len(lowerCamelCase__ ) != 0:
__lowerCamelCase = stack[len(lowerCamelCase__ ) - 1]
else:
__lowerCamelCase = False
indirect_parents.append(lowerCamelCase__ )
__lowerCamelCase = s
__lowerCamelCase = ss
# check if se have reached the starting point
if len(lowerCamelCase__ ) == 0:
return False
def lowerCamelCase ( self ):
'''simple docstring'''
return list(self.graph )
def lowerCamelCase ( self , __UpperCAmelCase=-2 , __UpperCAmelCase=-1 ):
'''simple docstring'''
__lowerCamelCase = time()
self.dfs(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = time()
return end - begin
def lowerCamelCase ( self , __UpperCAmelCase=-2 ):
'''simple docstring'''
__lowerCamelCase = time()
self.bfs(lowerCamelCase__ )
__lowerCamelCase = time()
return end - begin
| 175
|
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
snake_case_ : Optional[Any] = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class lowercase__ ( snake_case_, unittest.TestCase ):
'''simple docstring'''
_snake_case = XLMRobertaTokenizer
_snake_case = XLMRobertaTokenizerFast
_snake_case = True
_snake_case = True
def UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
UpperCamelCase = XLMRobertaTokenizer(lowerCamelCase__ , keep_accents=lowerCamelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = '''<pad>'''
UpperCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__ ) , lowerCamelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__ ) , lowerCamelCase__ )
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(lowerCamelCase__ ) , 1_0_0_2 )
def UpperCAmelCase ( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_2 )
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = XLMRobertaTokenizer(lowerCamelCase__ , keep_accents=lowerCamelCase__ )
UpperCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCamelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
UpperCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCamelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
UpperCamelCase = tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
UpperCamelCase = tokenizer.convert_ids_to_tokens(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def UpperCAmelCase ( self ):
'''simple docstring'''
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
UpperCamelCase = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
UpperCamelCase = self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
UpperCamelCase = self.tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
UpperCamelCase = tempfile.mkdtemp()
UpperCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ )
UpperCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
UpperCamelCase = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(lowerCamelCase__ , lowerCamelCase__ )
# Checks everything loads correctly in the same way
UpperCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
UpperCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowerCamelCase__ )
# Save tokenizer rust, legacy_format=True
UpperCamelCase = tempfile.mkdtemp()
UpperCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ , legacy_format=lowerCamelCase__ )
UpperCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it save with the same files
self.assertSequenceEqual(lowerCamelCase__ , lowerCamelCase__ )
# Checks everything loads correctly in the same way
UpperCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
UpperCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
shutil.rmtree(lowerCamelCase__ )
# Save tokenizer rust, legacy_format=False
UpperCamelCase = tempfile.mkdtemp()
UpperCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ , legacy_format=lowerCamelCase__ )
UpperCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
UpperCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
UpperCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
shutil.rmtree(lowerCamelCase__ )
@cached_property
def UpperCAmelCase ( self ):
'''simple docstring'''
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def UpperCAmelCase ( self ):
'''simple docstring'''
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowerCamelCase__ , f.name )
UpperCamelCase = XLMRobertaTokenizer(f.name , keep_accents=lowerCamelCase__ )
UpperCamelCase = pickle.dumps(lowerCamelCase__ )
pickle.loads(lowerCamelCase__ )
def UpperCAmelCase ( self ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = self.get_rust_tokenizer()
UpperCamelCase = '''I was born in 92000, and this is falsé.'''
UpperCamelCase = tokenizer.tokenize(lowerCamelCase__ )
UpperCamelCase = rust_tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
UpperCamelCase = tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
UpperCamelCase = rust_tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
UpperCamelCase = self.get_rust_tokenizer()
UpperCamelCase = tokenizer.encode(lowerCamelCase__ )
UpperCamelCase = rust_tokenizer.encode(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
@slow
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = '''Hello World!'''
UpperCamelCase = [0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCamelCase__ , self.big_tokenizer.encode(lowerCamelCase__ ) )
@slow
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
UpperCamelCase = [
0,
3_2_9_3,
8_3,
1_0,
4_5_5_2,
4_9_8_9,
7_9_8_6,
6_7_8,
1_0,
5_9_1_5,
1_1_1,
1_7_9_4_5_9,
1_2_4_8_5_0,
4,
6_0_4_4,
2_3_7,
1_2,
6,
5,
6,
4,
6_7_8_0,
7_0_5,
1_5,
1_3_8_8,
4_4,
3_7_8,
1_0_1_1_4,
7_1_1,
1_5_2,
2_0,
6,
5,
2_2_3_7_6,
6_4_2,
1_2_2_1,
1_5_1_9_0,
3_4_1_5_3,
4_5_0,
5_6_0_8,
9_5_9,
1_1_1_9,
5_7_7_0_2,
1_3_6,
1_8_6,
4_7,
1_0_9_8,
2_9_3_6_7,
4_7,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6_0_4_4,
2_3_7,
6_2_8_4,
5_0_9_0_1,
5_2_8,
3_1,
9_0,
3_4,
9_2_7,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCamelCase__ , self.big_tokenizer.encode(lowerCamelCase__ ) )
@slow
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCamelCase__ , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 212
| 0
|
"""simple docstring"""
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available
if is_torch_available() and is_datasets_available() and is_faiss_available():
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.tokenization_rag import RagTokenizer
@require_faiss
@require_torch
class UpperCamelCase (__snake_case ):
def __snake_case ( self :str ) ->Optional[Any]:
lowercase : int = tempfile.mkdtemp()
lowercase : str = 8
# DPR tok
lowercase : List[Any] = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowercase : List[str] = os.path.join(self.tmpdirname , """dpr_tokenizer""" )
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
lowercase : Optional[int] = os.path.join(__magic_name__ , DPR_VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
# BART tok
lowercase : Dict = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
lowercase : str = dict(zip(__magic_name__ , range(len(__magic_name__ ) ) ) )
lowercase : Union[str, Any] = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
lowercase : Union[str, Any] = {"""unk_token""": """<unk>"""}
lowercase : Dict = os.path.join(self.tmpdirname , """bart_tokenizer""" )
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
lowercase : Optional[int] = os.path.join(__magic_name__ , BART_VOCAB_FILES_NAMES["""vocab_file"""] )
lowercase : Any = os.path.join(__magic_name__ , BART_VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(__magic_name__ ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(__magic_name__ ) )
def __snake_case ( self :List[str] ) ->DPRQuestionEncoderTokenizer:
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , """dpr_tokenizer""" ) )
def __snake_case ( self :Union[str, Any] ) ->BartTokenizer:
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , """bart_tokenizer""" ) )
def __snake_case ( self :List[str] ) ->Optional[int]:
shutil.rmtree(self.tmpdirname )
@require_tokenizers
def __snake_case ( self :Any ) ->str:
lowercase : List[str] = os.path.join(self.tmpdirname , """rag_tokenizer""" )
lowercase : Tuple = RagConfig(question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() )
lowercase : str = RagTokenizer(question_encoder=self.get_dpr_tokenizer() , generator=self.get_bart_tokenizer() )
rag_config.save_pretrained(__magic_name__ )
rag_tokenizer.save_pretrained(__magic_name__ )
lowercase : Union[str, Any] = RagTokenizer.from_pretrained(__magic_name__ , config=__magic_name__ )
self.assertIsInstance(new_rag_tokenizer.question_encoder , __magic_name__ )
self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab() , rag_tokenizer.question_encoder.get_vocab() )
self.assertIsInstance(new_rag_tokenizer.generator , __magic_name__ )
self.assertEqual(new_rag_tokenizer.generator.get_vocab() , rag_tokenizer.generator.get_vocab() )
@slow
def __snake_case ( self :Optional[Any] ) ->Any:
lowercase : Optional[int] = RagTokenizer.from_pretrained("""facebook/rag-token-nq""" )
lowercase : int = [
"""who got the first nobel prize in physics""",
"""when is the next deadpool movie being released""",
"""which mode is used for short wave broadcast service""",
"""who is the owner of reading football club""",
"""when is the next scandal episode coming out""",
"""when is the last time the philadelphia won the superbowl""",
"""what is the most current adobe flash player version""",
"""how many episodes are there in dragon ball z""",
"""what is the first step in the evolution of the eye""",
"""where is gall bladder situated in human body""",
"""what is the main mineral in lithium batteries""",
"""who is the president of usa right now""",
"""where do the greasers live in the outsiders""",
"""panda is a national animal of which country""",
"""what is the name of manchester united stadium""",
]
lowercase : Optional[Any] = tokenizer(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
@slow
def __snake_case ( self :str ) ->Any:
lowercase : Tuple = RagTokenizer.from_pretrained("""facebook/rag-sequence-nq""" )
lowercase : Union[str, Any] = [
"""who got the first nobel prize in physics""",
"""when is the next deadpool movie being released""",
"""which mode is used for short wave broadcast service""",
"""who is the owner of reading football club""",
"""when is the next scandal episode coming out""",
"""when is the last time the philadelphia won the superbowl""",
"""what is the most current adobe flash player version""",
"""how many episodes are there in dragon ball z""",
"""what is the first step in the evolution of the eye""",
"""where is gall bladder situated in human body""",
"""what is the main mineral in lithium batteries""",
"""who is the president of usa right now""",
"""where do the greasers live in the outsiders""",
"""panda is a national animal of which country""",
"""what is the name of manchester united stadium""",
]
lowercase : Union[str, Any] = tokenizer(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
| 348
|
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase (metaclass=__snake_case ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = ["""flax""", """transformers"""]
def __init__( self :List[str] , *__magic_name__ :int , **__magic_name__ :Tuple ) ->Dict:
requires_backends(self , ["""flax""", """transformers"""] )
@classmethod
def __snake_case ( cls :List[Any] , *__magic_name__ :Any , **__magic_name__ :Union[str, Any] ) ->Dict:
requires_backends(cls , ["""flax""", """transformers"""] )
@classmethod
def __snake_case ( cls :str , *__magic_name__ :Optional[Any] , **__magic_name__ :Union[str, Any] ) ->Tuple:
requires_backends(cls , ["""flax""", """transformers"""] )
class UpperCamelCase (metaclass=__snake_case ):
_SCREAMING_SNAKE_CASE : List[str] = ["""flax""", """transformers"""]
def __init__( self :str , *__magic_name__ :int , **__magic_name__ :List[str] ) ->str:
requires_backends(self , ["""flax""", """transformers"""] )
@classmethod
def __snake_case ( cls :Optional[int] , *__magic_name__ :Tuple , **__magic_name__ :Dict ) ->Dict:
requires_backends(cls , ["""flax""", """transformers"""] )
@classmethod
def __snake_case ( cls :Tuple , *__magic_name__ :Tuple , **__magic_name__ :Optional[int] ) ->Optional[Any]:
requires_backends(cls , ["""flax""", """transformers"""] )
class UpperCamelCase (metaclass=__snake_case ):
_SCREAMING_SNAKE_CASE : Tuple = ["""flax""", """transformers"""]
def __init__( self :Tuple , *__magic_name__ :Dict , **__magic_name__ :Optional[int] ) ->Dict:
requires_backends(self , ["""flax""", """transformers"""] )
@classmethod
def __snake_case ( cls :List[str] , *__magic_name__ :Any , **__magic_name__ :Tuple ) ->Dict:
requires_backends(cls , ["""flax""", """transformers"""] )
@classmethod
def __snake_case ( cls :Any , *__magic_name__ :List[Any] , **__magic_name__ :Optional[Any] ) ->Optional[int]:
requires_backends(cls , ["""flax""", """transformers"""] )
class UpperCamelCase (metaclass=__snake_case ):
_SCREAMING_SNAKE_CASE : Optional[Any] = ["""flax""", """transformers"""]
def __init__( self :List[str] , *__magic_name__ :int , **__magic_name__ :Dict ) ->Optional[int]:
requires_backends(self , ["""flax""", """transformers"""] )
@classmethod
def __snake_case ( cls :str , *__magic_name__ :Any , **__magic_name__ :Any ) ->Optional[int]:
requires_backends(cls , ["""flax""", """transformers"""] )
@classmethod
def __snake_case ( cls :int , *__magic_name__ :List[str] , **__magic_name__ :Any ) ->Any:
requires_backends(cls , ["""flax""", """transformers"""] )
| 348
| 1
|
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class __snake_case :
# setable values
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None # sigma(t_i)
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls ):
"""simple docstring"""
return cls()
@dataclass
class __snake_case ( SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = 42
class __snake_case ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
@property
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
return True
@register_to_config
def __init__( self ,a_ = 0.02 ,a_ = 100 ,a_ = 1.007 ,a_ = 80 ,a_ = 0.05 ,a_ = 50 ,):
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
return KarrasVeSchedulerState.create()
def SCREAMING_SNAKE_CASE_ ( self ,a_ ,a_ ,a_ = () ):
"""simple docstring"""
lowerCAmelCase__ = jnp.arange(0 ,a_ )[::-1].copy()
lowerCAmelCase__ = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in timesteps
]
return state.replace(
num_inference_steps=a_ ,schedule=jnp.array(a_ ,dtype=jnp.floataa ) ,timesteps=a_ ,)
def SCREAMING_SNAKE_CASE_ ( self ,a_ ,a_ ,a_ ,a_ ,):
"""simple docstring"""
if self.config.s_min <= sigma <= self.config.s_max:
lowerCAmelCase__ = min(self.config.s_churn / state.num_inference_steps ,2**0.5 - 1 )
else:
lowerCAmelCase__ = 0
# sample eps ~ N(0, S_noise^2 * I)
lowerCAmelCase__ = random.split(a_ ,num=1 )
lowerCAmelCase__ = self.config.s_noise * random.normal(key=a_ ,shape=sample.shape )
lowerCAmelCase__ = sigma + gamma * sigma
lowerCAmelCase__ = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def SCREAMING_SNAKE_CASE_ ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ = True ,):
"""simple docstring"""
lowerCAmelCase__ = sample_hat + sigma_hat * model_output
lowerCAmelCase__ = (sample_hat - pred_original_sample) / sigma_hat
lowerCAmelCase__ = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=a_ ,derivative=a_ ,state=a_ )
def SCREAMING_SNAKE_CASE_ ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ = True ,):
"""simple docstring"""
lowerCAmelCase__ = sample_prev + sigma_prev * model_output
lowerCAmelCase__ = (sample_prev - pred_original_sample) / sigma_prev
lowerCAmelCase__ = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=a_ ,derivative=a_ ,state=a_ )
def SCREAMING_SNAKE_CASE_ ( self ,a_ ,a_ ,a_ ,a_ ):
"""simple docstring"""
raise NotImplementedError()
| 193
|
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
_lowerCAmelCase : str = logging.get_logger(__name__)
# General docstring
_lowerCAmelCase : Optional[int] = "MobileNetV1Config"
# Base docstring
_lowerCAmelCase : Tuple = "google/mobilenet_v1_1.0_224"
_lowerCAmelCase : Optional[int] = [1, 1_0_2_4, 7, 7]
# Image classification docstring
_lowerCAmelCase : Tuple = "google/mobilenet_v1_1.0_224"
_lowerCAmelCase : List[Any] = "tabby, tabby cat"
_lowerCAmelCase : Tuple = [
"google/mobilenet_v1_1.0_224",
"google/mobilenet_v1_0.75_192",
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def UpperCAmelCase_ ( snake_case__ , snake_case__ , snake_case__=None ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase__ = {}
if isinstance(snake_case__ , snake_case__ ):
lowerCAmelCase__ = model.mobilenet_va
else:
lowerCAmelCase__ = model
lowerCAmelCase__ = 'MobilenetV1/Conv2d_0/'
lowerCAmelCase__ = backbone.conv_stem.convolution.weight
lowerCAmelCase__ = backbone.conv_stem.normalization.bias
lowerCAmelCase__ = backbone.conv_stem.normalization.weight
lowerCAmelCase__ = backbone.conv_stem.normalization.running_mean
lowerCAmelCase__ = backbone.conv_stem.normalization.running_var
for i in range(13 ):
lowerCAmelCase__ = i + 1
lowerCAmelCase__ = i * 2
lowerCAmelCase__ = backbone.layer[pt_index]
lowerCAmelCase__ = f'MobilenetV1/Conv2d_{tf_index}_depthwise/'
lowerCAmelCase__ = pointer.convolution.weight
lowerCAmelCase__ = pointer.normalization.bias
lowerCAmelCase__ = pointer.normalization.weight
lowerCAmelCase__ = pointer.normalization.running_mean
lowerCAmelCase__ = pointer.normalization.running_var
lowerCAmelCase__ = backbone.layer[pt_index + 1]
lowerCAmelCase__ = f'MobilenetV1/Conv2d_{tf_index}_pointwise/'
lowerCAmelCase__ = pointer.convolution.weight
lowerCAmelCase__ = pointer.normalization.bias
lowerCAmelCase__ = pointer.normalization.weight
lowerCAmelCase__ = pointer.normalization.running_mean
lowerCAmelCase__ = pointer.normalization.running_var
if isinstance(snake_case__ , snake_case__ ):
lowerCAmelCase__ = 'MobilenetV1/Logits/Conv2d_1c_1x1/'
lowerCAmelCase__ = model.classifier.weight
lowerCAmelCase__ = model.classifier.bias
return tf_to_pt_map
def UpperCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ ) -> List[str]:
"""simple docstring"""
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '
'https://www.tensorflow.org/install/ for installation instructions.' )
raise
# Load weights from TF model
lowerCAmelCase__ = tf.train.list_variables(snake_case__ )
lowerCAmelCase__ = {}
for name, shape in init_vars:
logger.info(f'Loading TF weight {name} with shape {shape}' )
lowerCAmelCase__ = tf.train.load_variable(snake_case__ , snake_case__ )
lowerCAmelCase__ = array
# Build TF to PyTorch weights loading map
lowerCAmelCase__ = _build_tf_to_pytorch_map(snake_case__ , snake_case__ , snake_case__ )
for name, pointer in tf_to_pt_map.items():
logger.info(f'Importing {name}' )
if name not in tf_weights:
logger.info(f'{name} not in tf pre-trained weights, skipping' )
continue
lowerCAmelCase__ = tf_weights[name]
if "depthwise_weights" in name:
logger.info('Transposing depthwise' )
lowerCAmelCase__ = np.transpose(snake_case__ , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('Transposing' )
if len(pointer.shape ) == 2: # copying into linear layer
lowerCAmelCase__ = array.squeeze().transpose()
else:
lowerCAmelCase__ = np.transpose(snake_case__ , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(f'Pointer shape {pointer.shape} and array shape {array.shape} mismatched' )
logger.info(f'Initialize PyTorch weight {name} {array.shape}' )
lowerCAmelCase__ = torch.from_numpy(snake_case__ )
tf_weights.pop(snake_case__ , snake_case__ )
tf_weights.pop(name + '/RMSProp' , snake_case__ )
tf_weights.pop(name + '/RMSProp_1' , snake_case__ )
tf_weights.pop(name + '/ExponentialMovingAverage' , snake_case__ )
logger.info(f'Weights not copied to PyTorch model: {", ".join(tf_weights.keys() )}' )
return model
def UpperCAmelCase_ ( snake_case__ , snake_case__ ) -> torch.Tensor:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ = features.shape[-2:]
lowerCAmelCase__ , lowerCAmelCase__ = conv_layer.stride
lowerCAmelCase__ , lowerCAmelCase__ = conv_layer.kernel_size
if in_height % stride_height == 0:
lowerCAmelCase__ = max(kernel_height - stride_height , 0 )
else:
lowerCAmelCase__ = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
lowerCAmelCase__ = max(kernel_width - stride_width , 0 )
else:
lowerCAmelCase__ = max(kernel_width - (in_width % stride_width) , 0 )
lowerCAmelCase__ = pad_along_width // 2
lowerCAmelCase__ = pad_along_width - pad_left
lowerCAmelCase__ = pad_along_height // 2
lowerCAmelCase__ = pad_along_height - pad_top
lowerCAmelCase__ = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(snake_case__ , snake_case__ , 'constant' , 0.0 )
class __snake_case ( nn.Module ):
def __init__( self ,a_ ,a_ ,a_ ,a_ ,a_ = 1 ,a_ = 1 ,a_ = False ,a_ = True ,a_ = True ,):
"""simple docstring"""
super().__init__()
lowerCAmelCase__ = config
if in_channels % groups != 0:
raise ValueError(f'Input channels ({in_channels}) are not divisible by {groups} groups.' )
if out_channels % groups != 0:
raise ValueError(f'Output channels ({out_channels}) are not divisible by {groups} groups.' )
lowerCAmelCase__ = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
lowerCAmelCase__ = nn.Convad(
in_channels=a_ ,out_channels=a_ ,kernel_size=a_ ,stride=a_ ,padding=a_ ,groups=a_ ,bias=a_ ,padding_mode='zeros' ,)
if use_normalization:
lowerCAmelCase__ = nn.BatchNormad(
num_features=a_ ,eps=config.layer_norm_eps ,momentum=0.9997 ,affine=a_ ,track_running_stats=a_ ,)
else:
lowerCAmelCase__ = None
if use_activation:
if isinstance(a_ ,a_ ):
lowerCAmelCase__ = ACTaFN[use_activation]
elif isinstance(config.hidden_act ,a_ ):
lowerCAmelCase__ = ACTaFN[config.hidden_act]
else:
lowerCAmelCase__ = config.hidden_act
else:
lowerCAmelCase__ = None
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
if self.config.tf_padding:
lowerCAmelCase__ = apply_tf_padding(a_ ,self.convolution )
lowerCAmelCase__ = self.convolution(a_ )
if self.normalization is not None:
lowerCAmelCase__ = self.normalization(a_ )
if self.activation is not None:
lowerCAmelCase__ = self.activation(a_ )
return features
class __snake_case ( SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ = MobileNetVaConfig
SCREAMING_SNAKE_CASE__ = load_tf_weights_in_mobilenet_va
SCREAMING_SNAKE_CASE__ = 'mobilenet_v1'
SCREAMING_SNAKE_CASE__ = 'pixel_values'
SCREAMING_SNAKE_CASE__ = False
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
if isinstance(a_ ,(nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 ,std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(a_ ,nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
_lowerCAmelCase : Tuple = R"\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"
_lowerCAmelCase : Dict = R"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`MobileNetV1ImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"
@add_start_docstrings(
'The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.' , SCREAMING_SNAKE_CASE , )
class __snake_case ( SCREAMING_SNAKE_CASE ):
def __init__( self ,a_ ,a_ = True ):
"""simple docstring"""
super().__init__(a_ )
lowerCAmelCase__ = config
lowerCAmelCase__ = 32
lowerCAmelCase__ = max(int(depth * config.depth_multiplier ) ,config.min_depth )
lowerCAmelCase__ = MobileNetVaConvLayer(
a_ ,in_channels=config.num_channels ,out_channels=a_ ,kernel_size=3 ,stride=2 ,)
lowerCAmelCase__ = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
lowerCAmelCase__ = nn.ModuleList()
for i in range(13 ):
lowerCAmelCase__ = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
lowerCAmelCase__ = max(int(depth * config.depth_multiplier ) ,config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
a_ ,in_channels=a_ ,out_channels=a_ ,kernel_size=3 ,stride=strides[i] ,groups=a_ ,) )
self.layer.append(
MobileNetVaConvLayer(
a_ ,in_channels=a_ ,out_channels=a_ ,kernel_size=1 ,) )
lowerCAmelCase__ = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
raise NotImplementedError
@add_start_docstrings_to_model_forward(a_ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC ,output_type=a_ ,config_class=_CONFIG_FOR_DOC ,modality='vision' ,expected_output=_EXPECTED_OUTPUT_SHAPE ,)
def SCREAMING_SNAKE_CASE_ ( self ,a_ = None ,a_ = None ,a_ = None ,):
"""simple docstring"""
lowerCAmelCase__ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowerCAmelCase__ = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
lowerCAmelCase__ = self.conv_stem(a_ )
lowerCAmelCase__ = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
lowerCAmelCase__ = layer_module(a_ )
if output_hidden_states:
lowerCAmelCase__ = all_hidden_states + (hidden_states,)
lowerCAmelCase__ = hidden_states
if self.pooler is not None:
lowerCAmelCase__ = torch.flatten(self.pooler(a_ ) ,start_dim=1 )
else:
lowerCAmelCase__ = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=a_ ,pooler_output=a_ ,hidden_states=a_ ,)
@add_start_docstrings(
'\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , SCREAMING_SNAKE_CASE , )
class __snake_case ( SCREAMING_SNAKE_CASE ):
def __init__( self ,a_ ):
"""simple docstring"""
super().__init__(a_ )
lowerCAmelCase__ = config.num_labels
lowerCAmelCase__ = MobileNetVaModel(a_ )
lowerCAmelCase__ = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
lowerCAmelCase__ = nn.Dropout(config.classifier_dropout_prob ,inplace=a_ )
lowerCAmelCase__ = nn.Linear(a_ ,config.num_labels ) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(a_ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=a_ ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,)
def SCREAMING_SNAKE_CASE_ ( self ,a_ = None ,a_ = None ,a_ = None ,a_ = None ,):
"""simple docstring"""
lowerCAmelCase__ = return_dict if return_dict is not None else self.config.use_return_dict
lowerCAmelCase__ = self.mobilenet_va(a_ ,output_hidden_states=a_ ,return_dict=a_ )
lowerCAmelCase__ = outputs.pooler_output if return_dict else outputs[1]
lowerCAmelCase__ = self.classifier(self.dropout(a_ ) )
lowerCAmelCase__ = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowerCAmelCase__ = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowerCAmelCase__ = 'single_label_classification'
else:
lowerCAmelCase__ = 'multi_label_classification'
if self.config.problem_type == "regression":
lowerCAmelCase__ = MSELoss()
if self.num_labels == 1:
lowerCAmelCase__ = loss_fct(logits.squeeze() ,labels.squeeze() )
else:
lowerCAmelCase__ = loss_fct(a_ ,a_ )
elif self.config.problem_type == "single_label_classification":
lowerCAmelCase__ = CrossEntropyLoss()
lowerCAmelCase__ = loss_fct(logits.view(-1 ,self.num_labels ) ,labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowerCAmelCase__ = BCEWithLogitsLoss()
lowerCAmelCase__ = loss_fct(a_ ,a_ )
if not return_dict:
lowerCAmelCase__ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=a_ ,logits=a_ ,hidden_states=outputs.hidden_states ,)
| 193
| 1
|
"""simple docstring"""
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__snake_case = logging.get_logger(__name__)
def __lowerCAmelCase ( lowercase : int ) -> Any:
"""simple docstring"""
snake_case : List[str] = R"\w+[.]\d+"
snake_case : List[str] = re.findall(lowercase , lowercase )
for pat in pats:
snake_case : List[str] = key.replace(lowercase , "_".join(pat.split("." ) ) )
return key
def __lowerCAmelCase ( lowercase : Optional[int] , lowercase : Union[str, Any] , lowercase : int ) -> str:
"""simple docstring"""
snake_case : Tuple = pt_tuple_key[:-1] + ("scale",)
if (
any("norm" in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
snake_case : str = pt_tuple_key[:-1] + ("scale",)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
snake_case : Any = pt_tuple_key[:-1] + ("scale",)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
snake_case : Optional[Any] = pt_tuple_key[:-1] + ("embedding",)
return renamed_pt_tuple_key, pt_tensor
# conv layer
snake_case : List[Any] = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
snake_case : Optional[int] = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
snake_case : Tuple = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight":
snake_case : Optional[Any] = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
snake_case : int = pt_tuple_key[:-1] + ("weight",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
snake_case : int = pt_tuple_key[:-1] + ("bias",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def __lowerCAmelCase ( lowercase : Optional[Any] , lowercase : List[str] , lowercase : List[Any]=42 ) -> Dict:
"""simple docstring"""
snake_case : List[Any] = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
snake_case : List[Any] = flax_model.init_weights(PRNGKey(lowercase ) )
snake_case : Tuple = flatten_dict(lowercase )
snake_case : Any = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
snake_case : List[str] = rename_key(lowercase )
snake_case : List[str] = tuple(renamed_pt_key.split("." ) )
# Correctly rename weight parameters
snake_case ,snake_case : str = rename_key_and_reshape_tensor(lowercase , lowercase , lowercase )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
# also add unexpected weight so that warning is thrown
snake_case : List[Any] = jnp.asarray(lowercase )
return unflatten_dict(lowercase )
| 709
|
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__snake_case = """src/diffusers"""
__snake_case = """."""
# This is to make sure the diffusers module imported is the one in the repo.
__snake_case = importlib.util.spec_from_file_location(
"""diffusers""",
os.path.join(DIFFUSERS_PATH, """__init__.py"""),
submodule_search_locations=[DIFFUSERS_PATH],
)
__snake_case = spec.loader.load_module()
def __lowerCAmelCase ( lowercase : List[str] , lowercase : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
return line.startswith(lowercase ) or len(lowercase ) <= 1 or re.search(R"^\s*\)(\s*->.*:|:)\s*$" , lowercase ) is not None
def __lowerCAmelCase ( lowercase : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
snake_case : Any = object_name.split("." )
snake_case : Dict = 0
# First let's find the module where our object lives.
snake_case : Optional[Any] = parts[i]
while i < len(lowercase ) and not os.path.isfile(os.path.join(lowercase , F'{module}.py' ) ):
i += 1
if i < len(lowercase ):
snake_case : List[Any] = os.path.join(lowercase , parts[i] )
if i >= len(lowercase ):
raise ValueError(F'`object_name` should begin with the name of a module of diffusers but got {object_name}.' )
with open(os.path.join(lowercase , F'{module}.py' ) , "r" , encoding="utf-8" , newline="\n" ) as f:
snake_case : str = f.readlines()
# Now let's find the class / func in the code!
snake_case : List[str] = ""
snake_case : Optional[Any] = 0
for name in parts[i + 1 :]:
while (
line_index < len(lowercase ) and re.search(RF'^{indent}(class|def)\s+{name}(\(|\:)' , lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(lowercase ):
raise ValueError(F' {object_name} does not match any function or class in {module}.' )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
snake_case : Union[str, Any] = line_index
while line_index < len(lowercase ) and _should_continue(lines[line_index] , lowercase ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
snake_case : Tuple = lines[start_index:line_index]
return "".join(lowercase )
__snake_case = re.compile(R"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
__snake_case = re.compile(R"""^\s*(\S+)->(\S+)(\s+.*|$)""")
__snake_case = re.compile(R"""<FILL\s+[^>]*>""")
def __lowerCAmelCase ( lowercase : Optional[int] ) -> Dict:
"""simple docstring"""
snake_case : List[str] = code.split("\n" )
snake_case : Tuple = 0
while idx < len(lowercase ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(lowercase ):
return re.search(R"^(\s*)\S" , lines[idx] ).groups()[0]
return ""
def __lowerCAmelCase ( lowercase : Any ) -> Optional[int]:
"""simple docstring"""
snake_case : Optional[int] = len(get_indent(lowercase ) ) > 0
if has_indent:
snake_case : List[Any] = F'class Bla:\n{code}'
snake_case : List[Any] = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 , preview=lowercase )
snake_case : str = black.format_str(lowercase , mode=lowercase )
snake_case ,snake_case : int = style_docstrings_in_code(lowercase )
return result[len("class Bla:\n" ) :] if has_indent else result
def __lowerCAmelCase ( lowercase : Dict , lowercase : List[Any]=False ) -> Dict:
"""simple docstring"""
with open(lowercase , "r" , encoding="utf-8" , newline="\n" ) as f:
snake_case : str = f.readlines()
snake_case : Optional[int] = []
snake_case : List[Any] = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(lowercase ):
snake_case : List[Any] = _re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
snake_case ,snake_case ,snake_case : List[str] = search.groups()
snake_case : List[Any] = find_code_in_diffusers(lowercase )
snake_case : Dict = get_indent(lowercase )
snake_case : Union[str, Any] = line_index + 1 if indent == theoretical_indent else line_index + 2
snake_case : Any = theoretical_indent
snake_case : Optional[int] = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
snake_case : Dict = True
while line_index < len(lowercase ) and should_continue:
line_index += 1
if line_index >= len(lowercase ):
break
snake_case : int = lines[line_index]
snake_case : Tuple = _should_continue(lowercase , lowercase ) and re.search(F'^{indent}# End copy' , lowercase ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
snake_case : Optional[int] = lines[start_index:line_index]
snake_case : List[Any] = "".join(lowercase )
# Remove any nested `Copied from` comments to avoid circular copies
snake_case : List[str] = [line for line in theoretical_code.split("\n" ) if _re_copy_warning.search(lowercase ) is None]
snake_case : Optional[int] = "\n".join(lowercase )
# Before comparing, use the `replace_pattern` on the original code.
if len(lowercase ) > 0:
snake_case : List[str] = replace_pattern.replace("with" , "" ).split("," )
snake_case : Union[str, Any] = [_re_replace_pattern.search(lowercase ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
snake_case ,snake_case ,snake_case : str = pattern.groups()
snake_case : Any = re.sub(lowercase , lowercase , lowercase )
if option.strip() == "all-casing":
snake_case : int = re.sub(obja.lower() , obja.lower() , lowercase )
snake_case : Optional[int] = re.sub(obja.upper() , obja.upper() , lowercase )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
snake_case : Tuple = blackify(lines[start_index - 1] + theoretical_code )
snake_case : Any = theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
snake_case : Any = lines[:start_index] + [theoretical_code] + lines[line_index:]
snake_case : Tuple = start_index + 1
if overwrite and len(lowercase ) > 0:
# Warn the user a file has been modified.
print(F'Detected changes, rewriting {filename}.' )
with open(lowercase , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(lowercase )
return diffs
def __lowerCAmelCase ( lowercase : bool = False ) -> Optional[Any]:
"""simple docstring"""
snake_case : str = glob.glob(os.path.join(lowercase , "**/*.py" ) , recursive=lowercase )
snake_case : List[str] = []
for filename in all_files:
snake_case : List[str] = is_copy_consistent(lowercase , lowercase )
diffs += [F'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
if not overwrite and len(lowercase ) > 0:
snake_case : List[str] = "\n".join(lowercase )
raise Exception(
"Found the following copy inconsistencies:\n"
+ diff
+ "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them." )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
__snake_case = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 117
| 0
|
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _UpperCAmelCase ( unittest.TestCase ):
def __init__( self : Any , _lowercase : Optional[int] , _lowercase : Any=3 , _lowercase : int=32 , _lowercase : int=3 , _lowercase : Optional[Any]=10 , _lowercase : List[Any]=[10, 20, 30, 40] , _lowercase : Union[str, Any]=[1, 1, 2, 1] , _lowercase : List[str]=True , _lowercase : str=True , _lowercase : Dict="relu" , _lowercase : Optional[Any]=3 , _lowercase : int=None , ):
__UpperCAmelCase = parent
__UpperCAmelCase = batch_size
__UpperCAmelCase = image_size
__UpperCAmelCase = num_channels
__UpperCAmelCase = embeddings_size
__UpperCAmelCase = hidden_sizes
__UpperCAmelCase = depths
__UpperCAmelCase = is_training
__UpperCAmelCase = use_labels
__UpperCAmelCase = hidden_act
__UpperCAmelCase = num_labels
__UpperCAmelCase = scope
__UpperCAmelCase = len(_lowerCamelCase )
def a ( self : Optional[Any] ):
__UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__UpperCAmelCase = self.get_config()
return config, pixel_values
def a ( self : Tuple ):
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def a ( self : Dict , _lowercase : List[Any] , _lowercase : Any ):
__UpperCAmelCase = FlaxRegNetModel(config=_lowerCamelCase )
__UpperCAmelCase = model(_lowerCamelCase )
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def a ( self : List[Any] , _lowercase : Dict , _lowercase : Dict ):
__UpperCAmelCase = self.num_labels
__UpperCAmelCase = FlaxRegNetForImageClassification(config=_lowerCamelCase )
__UpperCAmelCase = model(_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a ( self : Union[str, Any] ):
__UpperCAmelCase = self.prepare_config_and_inputs()
__UpperCAmelCase , __UpperCAmelCase = config_and_inputs
__UpperCAmelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_flax
class _UpperCAmelCase ( __UpperCAmelCase , unittest.TestCase ):
a__ : str = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
a__ : Optional[int] = False
a__ : str = False
a__ : Optional[Any] = False
def a ( self : Tuple ):
__UpperCAmelCase = FlaxRegNetModelTester(self )
__UpperCAmelCase = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
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 : Dict ):
return
def a ( self : Any ):
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def a ( self : Tuple ):
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def a ( self : List[str] ):
pass
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def a ( self : Union[str, Any] ):
pass
def a ( self : str ):
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase = model_class(_lowerCamelCase )
__UpperCAmelCase = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCAmelCase = [*signature.parameters.keys()]
__UpperCAmelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
def a ( self : Optional[int] ):
def check_hidden_states_output(_lowercase : List[str] , _lowercase : Optional[int] , _lowercase : int ):
__UpperCAmelCase = model_class(_lowerCamelCase )
__UpperCAmelCase = model(**self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
__UpperCAmelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__UpperCAmelCase = self.model_tester.num_stages
self.assertEqual(len(_lowerCamelCase ) , expected_num_stages + 1 )
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def a ( self : Union[str, Any] ):
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__UpperCAmelCase = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
__UpperCAmelCase = model_class(_lowerCamelCase )
@jax.jit
def model_jitted(_lowercase : int , **_lowercase : Optional[Any] ):
return model(pixel_values=_lowerCamelCase , **_lowerCamelCase )
with self.subTest('''JIT Enabled''' ):
__UpperCAmelCase = model_jitted(**_lowerCamelCase ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
__UpperCAmelCase = model_jitted(**_lowerCamelCase ).to_tuple()
self.assertEqual(len(_lowerCamelCase ) , len(_lowerCamelCase ) )
for jitted_output, output in zip(_lowerCamelCase , _lowerCamelCase ):
self.assertEqual(jitted_output.shape , output.shape )
def lowercase__ ( ):
__UpperCAmelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_flax
class _UpperCAmelCase ( unittest.TestCase ):
@cached_property
def a ( self : int ):
return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040''' ) if is_vision_available() else None
@slow
def a ( self : Any ):
__UpperCAmelCase = FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040''' )
__UpperCAmelCase = self.default_image_processor
__UpperCAmelCase = prepare_img()
__UpperCAmelCase = image_processor(images=_lowerCamelCase , return_tensors='''np''' )
__UpperCAmelCase = model(**_lowerCamelCase )
# verify the logits
__UpperCAmelCase = (1, 10_00)
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
__UpperCAmelCase = jnp.array([-0.4_180, -1.5_051, -3.4_836] )
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , _lowerCamelCase , atol=1E-4 ) )
| 49
|
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__SCREAMING_SNAKE_CASE ={
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
class __magic_name__ ( __UpperCAmelCase):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = "mask2former"
SCREAMING_SNAKE_CASE__ : Any = ["swin"]
SCREAMING_SNAKE_CASE__ : int = {"hidden_size": "hidden_dim"}
def __init__( self: str , _lowerCamelCase: Optional[Dict] = None , _lowerCamelCase: int = 2_56 , _lowerCamelCase: int = 2_56 , _lowerCamelCase: int = 2_56 , _lowerCamelCase: int = 10_24 , _lowerCamelCase: str = "relu" , _lowerCamelCase: int = 6 , _lowerCamelCase: int = 10 , _lowerCamelCase: int = 8 , _lowerCamelCase: float = 0.0 , _lowerCamelCase: int = 20_48 , _lowerCamelCase: bool = False , _lowerCamelCase: bool = False , _lowerCamelCase: int = 4 , _lowerCamelCase: int = 2_55 , _lowerCamelCase: int = 1_00 , _lowerCamelCase: float = 0.1 , _lowerCamelCase: float = 2.0 , _lowerCamelCase: float = 5.0 , _lowerCamelCase: float = 5.0 , _lowerCamelCase: int = 1_25_44 , _lowerCamelCase: float = 3.0 , _lowerCamelCase: float = 0.75 , _lowerCamelCase: float = 0.02 , _lowerCamelCase: float = 1.0 , _lowerCamelCase: bool = True , _lowerCamelCase: List[int] = [4, 8, 16, 32] , _lowerCamelCase: bool = None , **_lowerCamelCase: Optional[int] , ):
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.''' )
SCREAMING_SNAKE_CASE_ = CONFIG_MAPPING['''swin'''](
image_size=2_24 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=_lowerCamelCase , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] , )
if isinstance(_lowerCamelCase , _lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = backbone_config.pop('''model_type''' )
SCREAMING_SNAKE_CASE_ = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE_ = config_class.from_dict(_lowerCamelCase )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. "
f"Supported model types: {','.join(self.backbones_supported )}" )
SCREAMING_SNAKE_CASE_ = backbone_config
SCREAMING_SNAKE_CASE_ = feature_size
SCREAMING_SNAKE_CASE_ = mask_feature_size
SCREAMING_SNAKE_CASE_ = hidden_dim
SCREAMING_SNAKE_CASE_ = encoder_feedforward_dim
SCREAMING_SNAKE_CASE_ = activation_function
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = decoder_layers
SCREAMING_SNAKE_CASE_ = num_attention_heads
SCREAMING_SNAKE_CASE_ = dropout
SCREAMING_SNAKE_CASE_ = dim_feedforward
SCREAMING_SNAKE_CASE_ = pre_norm
SCREAMING_SNAKE_CASE_ = enforce_input_projection
SCREAMING_SNAKE_CASE_ = common_stride
SCREAMING_SNAKE_CASE_ = ignore_value
SCREAMING_SNAKE_CASE_ = num_queries
SCREAMING_SNAKE_CASE_ = no_object_weight
SCREAMING_SNAKE_CASE_ = class_weight
SCREAMING_SNAKE_CASE_ = mask_weight
SCREAMING_SNAKE_CASE_ = dice_weight
SCREAMING_SNAKE_CASE_ = train_num_points
SCREAMING_SNAKE_CASE_ = oversample_ratio
SCREAMING_SNAKE_CASE_ = importance_sample_ratio
SCREAMING_SNAKE_CASE_ = init_std
SCREAMING_SNAKE_CASE_ = init_xavier_std
SCREAMING_SNAKE_CASE_ = use_auxiliary_loss
SCREAMING_SNAKE_CASE_ = feature_strides
SCREAMING_SNAKE_CASE_ = output_auxiliary_logits
SCREAMING_SNAKE_CASE_ = decoder_layers
super().__init__(**_lowerCamelCase )
@classmethod
def _A ( cls: int , _lowerCamelCase: PretrainedConfig , **_lowerCamelCase: Tuple ):
return cls(
backbone_config=_lowerCamelCase , **_lowerCamelCase , )
def _A ( self: int ):
SCREAMING_SNAKE_CASE_ = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE_ = self.backbone_config.to_dict()
SCREAMING_SNAKE_CASE_ = self.__class__.model_type
return output
| 234
| 0
|
def _UpperCamelCase ( lowerCAmelCase__: float ,lowerCAmelCase__: int ) -> Tuple:
if digit_amount > 0:
return round(number - int(_lowerCamelCase ) ,_lowerCamelCase )
return number - int(_lowerCamelCase )
if __name__ == "__main__":
print(decimal_isolate(1.5_3, 0))
print(decimal_isolate(3_5.3_4_5, 1))
print(decimal_isolate(3_5.3_4_5, 2))
print(decimal_isolate(3_5.3_4_5, 3))
print(decimal_isolate(-1_4.7_8_9, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-1_4.1_2_3, 1))
print(decimal_isolate(-1_4.1_2_3, 2))
print(decimal_isolate(-1_4.1_2_3, 3))
| 719
|
'''simple docstring'''
from math import ceil
def _UpperCamelCase ( lowerCAmelCase__: int = 1001 ) -> int:
SCREAMING_SNAKE_CASE_ = 1
for i in range(1 ,int(ceil(n / 2.0 ) ) ):
SCREAMING_SNAKE_CASE_ = 2 * i + 1
SCREAMING_SNAKE_CASE_ = 2 * i
SCREAMING_SNAKE_CASE_ = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
SCREAMING_SNAKE_CASE : List[str] = int(sys.argv[1])
print(solution(n))
except ValueError:
print("Invalid entry - please enter a number")
| 238
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__a: str = {
"""configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
"""configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a: List[Any] = ["""MaskFormerFeatureExtractor"""]
__a: Tuple = ["""MaskFormerImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a: Any = [
"""MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MaskFormerForInstanceSegmentation""",
"""MaskFormerModel""",
"""MaskFormerPreTrainedModel""",
]
__a: str = [
"""MaskFormerSwinBackbone""",
"""MaskFormerSwinModel""",
"""MaskFormerSwinPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
from .configuration_maskformer_swin import MaskFormerSwinConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_maskformer import MaskFormerFeatureExtractor
from .image_processing_maskformer import MaskFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskformer import (
MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskFormerForInstanceSegmentation,
MaskFormerModel,
MaskFormerPreTrainedModel,
)
from .modeling_maskformer_swin import (
MaskFormerSwinBackbone,
MaskFormerSwinModel,
MaskFormerSwinPreTrainedModel,
)
else:
import sys
__a: List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 152
|
'''simple docstring'''
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class UpperCAmelCase ( a__ ):
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=True , __lowerCAmelCase=None , **__lowerCAmelCase ) -> Dict:
lowercase__ : Any = parent
lowercase__ : List[Any] = config_class
lowercase__ : Dict = has_text_modality
lowercase__ : List[str] = kwargs
lowercase__ : List[Any] = common_properties
def _lowerCAmelCase( self ) -> Any:
lowercase__ : int = self.config_class(**self.inputs_dict )
lowercase__ : Any = (
['''hidden_size''', '''num_attention_heads''', '''num_hidden_layers''']
if self.common_properties is None
else self.common_properties
)
# Add common fields for text models
if self.has_text_modality:
common_properties.extend(['''vocab_size'''] )
# Test that config has the common properties as getters
for prop in common_properties:
self.parent.assertTrue(hasattr(__lowerCAmelCase , __lowerCAmelCase ) , msg=F"""`{prop}` does not exist""" )
# Test that config has the common properties as setter
for idx, name in enumerate(__lowerCAmelCase ):
try:
setattr(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
self.parent.assertEqual(
getattr(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase , msg=F"""`{name} value {idx} expected, but was {getattr(__lowerCAmelCase , __lowerCAmelCase )}""" )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
# Test if config class can be called with Config(prop_name=..)
for idx, name in enumerate(__lowerCAmelCase ):
try:
lowercase__ : Tuple = self.config_class(**{name: idx} )
self.parent.assertEqual(
getattr(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase , msg=F"""`{name} value {idx} expected, but was {getattr(__lowerCAmelCase , __lowerCAmelCase )}""" )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
def _lowerCAmelCase( self ) -> Tuple:
lowercase__ : Union[str, Any] = self.config_class(**self.inputs_dict )
lowercase__ : int = json.loads(config.to_json_string() )
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key] , __lowerCAmelCase )
def _lowerCAmelCase( self ) -> int:
lowercase__ : List[str] = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase__ : Tuple = os.path.join(__lowerCAmelCase , '''config.json''' )
config_first.to_json_file(__lowerCAmelCase )
lowercase__ : Tuple = self.config_class.from_json_file(__lowerCAmelCase )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowerCAmelCase( self ) -> Optional[Any]:
lowercase__ : Optional[int] = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
config_first.save_pretrained(__lowerCAmelCase )
lowercase__ : Dict = self.config_class.from_pretrained(__lowerCAmelCase )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowerCAmelCase( self ) -> Optional[int]:
lowercase__ : Union[str, Any] = self.config_class(**self.inputs_dict )
lowercase__ : str = '''test'''
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase__ : Any = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
config_first.save_pretrained(__lowerCAmelCase )
lowercase__ : Tuple = self.config_class.from_pretrained(__lowerCAmelCase , subfolder=__lowerCAmelCase )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowerCAmelCase( self ) -> List[Any]:
lowercase__ : List[Any] = self.config_class(**self.inputs_dict , num_labels=5 )
self.parent.assertEqual(len(config.idalabel ) , 5 )
self.parent.assertEqual(len(config.labelaid ) , 5 )
lowercase__ : List[Any] = 3
self.parent.assertEqual(len(config.idalabel ) , 3 )
self.parent.assertEqual(len(config.labelaid ) , 3 )
def _lowerCAmelCase( self ) -> Any:
if self.config_class.is_composition:
return
lowercase__ : Tuple = self.config_class()
self.parent.assertIsNotNone(__lowerCAmelCase )
def _lowerCAmelCase( self ) -> Any:
lowercase__ : str = copy.deepcopy(__lowerCAmelCase )
lowercase__ : Dict = self.config_class(**__lowerCAmelCase )
lowercase__ : Dict = []
for key, value in config_common_kwargs.items():
if key == "torch_dtype":
if not is_torch_available():
continue
else:
import torch
if config.torch_dtype != torch.floataa:
wrong_values.append(('''torch_dtype''', config.torch_dtype, torch.floataa) )
elif getattr(__lowerCAmelCase , __lowerCAmelCase ) != value:
wrong_values.append((key, getattr(__lowerCAmelCase , __lowerCAmelCase ), value) )
if len(__lowerCAmelCase ) > 0:
lowercase__ : Any = '''\n'''.join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values] )
raise ValueError(F"""The following keys were not properly set in the config:\n{errors}""" )
def _lowerCAmelCase( self ) -> Any:
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
self.create_and_test_config_from_and_save_pretrained()
self.create_and_test_config_from_and_save_pretrained_subfolder()
self.create_and_test_config_with_num_labels()
self.check_config_can_be_init_without_params()
self.check_config_arguments_init()
| 152
| 1
|
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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 (
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
TFDebertaVaModel,
)
class lowercase :
'''simple docstring'''
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=2 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=512 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=False , _snake_case=True , _snake_case="None" , _snake_case=3 , _snake_case=4 , _snake_case=None , ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = seq_length
UpperCAmelCase = is_training
UpperCAmelCase = use_input_mask
UpperCAmelCase = use_token_type_ids
UpperCAmelCase = use_labels
UpperCAmelCase = vocab_size
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 = max_position_embeddings
UpperCAmelCase = type_vocab_size
UpperCAmelCase = type_sequence_label_size
UpperCAmelCase = initializer_range
UpperCAmelCase = num_labels
UpperCAmelCase = num_choices
UpperCAmelCase = relative_attention
UpperCAmelCase = position_biased_input
UpperCAmelCase = pos_att_type
UpperCAmelCase = scope
def snake_case_ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase = None
if self.use_input_mask:
UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase = None
if self.use_token_type_ids:
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase = DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , initializer_range=self.initializer_range , return_dict=__UpperCamelCase , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ) -> Tuple:
"""simple docstring"""
UpperCAmelCase = TFDebertaVaModel(config=__UpperCamelCase )
UpperCAmelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
UpperCAmelCase = [input_ids, input_mask]
UpperCAmelCase = model(__UpperCamelCase )
UpperCAmelCase = model(__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ) -> Any:
"""simple docstring"""
UpperCAmelCase = TFDebertaVaForMaskedLM(config=__UpperCamelCase )
UpperCAmelCase = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCAmelCase = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = self.num_labels
UpperCAmelCase = TFDebertaVaForSequenceClassification(config=__UpperCamelCase )
UpperCAmelCase = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCAmelCase = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = self.num_labels
UpperCAmelCase = TFDebertaVaForTokenClassification(config=__UpperCamelCase )
UpperCAmelCase = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCAmelCase = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def snake_case_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ) -> Tuple:
"""simple docstring"""
UpperCAmelCase = TFDebertaVaForQuestionAnswering(config=__UpperCamelCase )
UpperCAmelCase = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCAmelCase = 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 snake_case_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase = self.prepare_config_and_inputs()
(
(
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) ,
) = config_and_inputs
UpperCAmelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class lowercase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (
(
TFDebertaVaModel,
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
)
if is_tf_available()
else ()
)
__SCREAMING_SNAKE_CASE = (
{
'feature-extraction': TFDebertaVaModel,
'fill-mask': TFDebertaVaForMaskedLM,
'question-answering': TFDebertaVaForQuestionAnswering,
'text-classification': TFDebertaVaForSequenceClassification,
'token-classification': TFDebertaVaForTokenClassification,
'zero-shot': TFDebertaVaForSequenceClassification,
}
if is_tf_available()
else {}
)
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
def snake_case_ ( self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase = TFDebertaVaModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=__UpperCamelCase , hidden_size=37 )
def snake_case_ ( self ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def snake_case_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def snake_case_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase )
def snake_case_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase )
def snake_case_ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase )
def snake_case_ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase )
@slow
def snake_case_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase = TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge''' )
self.assertIsNotNone(__UpperCamelCase )
@require_tf
class lowercase ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip(reason='''Model not available yet''' )
def snake_case_ ( self ) -> Dict:
"""simple docstring"""
pass
@slow
def snake_case_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase = TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge''' )
UpperCAmelCase = tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] )
UpperCAmelCase = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
UpperCAmelCase = model(__UpperCamelCase , attention_mask=__UpperCamelCase )[0]
UpperCAmelCase = tf.constant(
[[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] )
tf.debugging.assert_near(output[:, 1:4, 1:4] , __UpperCamelCase , atol=1e-4 )
| 719
|
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
__magic_name__ = logging.getLogger(__name__)
class lowercase ( A__ ):
'''simple docstring'''
def snake_case_ ( self , _snake_case , _snake_case , _snake_case=None , _snake_case=None ) -> Any:
"""simple docstring"""
UpperCAmelCase = self.layer[current_layer](_snake_case , _snake_case , head_mask[current_layer] )
UpperCAmelCase = layer_outputs[0]
return hidden_states
@add_start_docstrings(
"""The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.""" , A__ , )
class lowercase ( A__ ):
'''simple docstring'''
def __init__( self , _snake_case ) -> Optional[Any]:
"""simple docstring"""
super().__init__(_snake_case )
UpperCAmelCase = BertEncoderWithPabee(_snake_case )
self.init_weights()
UpperCAmelCase = 0
UpperCAmelCase = 0
UpperCAmelCase = 0
UpperCAmelCase = 0
def snake_case_ ( self , _snake_case ) -> int:
"""simple docstring"""
UpperCAmelCase = threshold
def snake_case_ ( self , _snake_case ) -> str:
"""simple docstring"""
UpperCAmelCase = patience
def snake_case_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase = 0
UpperCAmelCase = 0
def snake_case_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase = self.inference_layers_num / self.inference_instances_num
UpperCAmelCase = (
f"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
f""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
)
print(_snake_case )
@add_start_docstrings_to_model_forward(_snake_case )
def snake_case_ ( self , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=False , ) -> List[Any]:
"""simple docstring"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time''' )
elif input_ids is not None:
UpperCAmelCase = input_ids.size()
elif inputs_embeds is not None:
UpperCAmelCase = inputs_embeds.size()[:-1]
else:
raise ValueError('''You have to specify either input_ids or inputs_embeds''' )
UpperCAmelCase = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
UpperCAmelCase = torch.ones(_snake_case , device=_snake_case )
if token_type_ids is None:
UpperCAmelCase = torch.zeros(_snake_case , dtype=torch.long , device=_snake_case )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
UpperCAmelCase = self.get_extended_attention_mask(_snake_case , _snake_case , _snake_case )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = encoder_hidden_states.size()
UpperCAmelCase = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
UpperCAmelCase = torch.ones(_snake_case , device=_snake_case )
UpperCAmelCase = self.invert_attention_mask(_snake_case )
else:
UpperCAmelCase = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
UpperCAmelCase = self.get_head_mask(_snake_case , self.config.num_hidden_layers )
UpperCAmelCase = self.embeddings(
input_ids=_snake_case , position_ids=_snake_case , token_type_ids=_snake_case , inputs_embeds=_snake_case )
UpperCAmelCase = embedding_output
if self.training:
UpperCAmelCase = []
for i in range(self.config.num_hidden_layers ):
UpperCAmelCase = self.encoder.adaptive_forward(
_snake_case , current_layer=_snake_case , attention_mask=_snake_case , head_mask=_snake_case )
UpperCAmelCase = self.pooler(_snake_case )
UpperCAmelCase = output_layers[i](output_dropout(_snake_case ) )
res.append(_snake_case )
elif self.patience == 0: # Use all layers for inference
UpperCAmelCase = self.encoder(
_snake_case , attention_mask=_snake_case , head_mask=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , )
UpperCAmelCase = self.pooler(encoder_outputs[0] )
UpperCAmelCase = [output_layers[self.config.num_hidden_layers - 1](_snake_case )]
else:
UpperCAmelCase = 0
UpperCAmelCase = None
UpperCAmelCase = 0
for i in range(self.config.num_hidden_layers ):
calculated_layer_num += 1
UpperCAmelCase = self.encoder.adaptive_forward(
_snake_case , current_layer=_snake_case , attention_mask=_snake_case , head_mask=_snake_case )
UpperCAmelCase = self.pooler(_snake_case )
UpperCAmelCase = output_layers[i](_snake_case )
if regression:
UpperCAmelCase = logits.detach()
if patient_result is not None:
UpperCAmelCase = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels ) < self.regression_threshold:
patient_counter += 1
else:
UpperCAmelCase = 0
else:
UpperCAmelCase = logits.detach().argmax(dim=1 )
if patient_result is not None:
UpperCAmelCase = patient_result.detach().argmax(dim=1 )
if (patient_result is not None) and torch.all(labels.eq(_snake_case ) ):
patient_counter += 1
else:
UpperCAmelCase = 0
UpperCAmelCase = logits
if patient_counter == self.patience:
break
UpperCAmelCase = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
"""Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """ , A__ , )
class lowercase ( A__ ):
'''simple docstring'''
def __init__( self , _snake_case ) -> Optional[int]:
"""simple docstring"""
super().__init__(_snake_case )
UpperCAmelCase = config.num_labels
UpperCAmelCase = BertModelWithPabee(_snake_case )
UpperCAmelCase = nn.Dropout(config.hidden_dropout_prob )
UpperCAmelCase = nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels ) for _ in range(config.num_hidden_layers )] )
self.init_weights()
@add_start_docstrings_to_model_forward(_snake_case )
def snake_case_ ( self , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , ) -> Tuple:
"""simple docstring"""
UpperCAmelCase = self.bert(
input_ids=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , position_ids=_snake_case , head_mask=_snake_case , inputs_embeds=_snake_case , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
UpperCAmelCase = (logits[-1],)
if labels is not None:
UpperCAmelCase = None
UpperCAmelCase = 0
for ix, logits_item in enumerate(_snake_case ):
if self.num_labels == 1:
# We are doing regression
UpperCAmelCase = MSELoss()
UpperCAmelCase = loss_fct(logits_item.view(-1 ) , labels.view(-1 ) )
else:
UpperCAmelCase = CrossEntropyLoss()
UpperCAmelCase = loss_fct(logits_item.view(-1 , self.num_labels ) , labels.view(-1 ) )
if total_loss is None:
UpperCAmelCase = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
UpperCAmelCase = (total_loss / total_weights,) + outputs
return outputs
| 391
| 0
|
_UpperCamelCase = "Tobias Carryer"
from time import time
class __lowercase :
def __init__( self , A_ , A_ , A_ , A_=int(time() ) ) ->Optional[int]: # noqa: B008
'''simple docstring'''
__lowerCAmelCase : Tuple = multiplier
__lowerCAmelCase : Tuple = increment
__lowerCAmelCase : int = modulo
__lowerCAmelCase : Dict = seed
def UpperCamelCase__ ( self ) ->str:
'''simple docstring'''
__lowerCAmelCase : List[str] = (self.multiplier * self.seed + self.increment) % self.modulo
return self.seed
if __name__ == "__main__":
# Show the LCG in action.
_UpperCamelCase = LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
while True:
print(lcg.next_number())
| 492
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = [
"LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
"LukeForEntityClassification",
"LukeForEntityPairClassification",
"LukeForEntitySpanClassification",
"LukeForMultipleChoice",
"LukeForQuestionAnswering",
"LukeForSequenceClassification",
"LukeForTokenClassification",
"LukeForMaskedLM",
"LukeModel",
"LukePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
from .tokenization_luke import LukeTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_luke import (
LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
LukeForEntityClassification,
LukeForEntityPairClassification,
LukeForEntitySpanClassification,
LukeForMaskedLM,
LukeForMultipleChoice,
LukeForQuestionAnswering,
LukeForSequenceClassification,
LukeForTokenClassification,
LukeModel,
LukePreTrainedModel,
)
else:
import sys
_UpperCamelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 492
| 1
|
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowerCamelCase : str = '''src/transformers'''
# This is to make sure the transformers module imported is the one in the repo.
_lowerCamelCase : List[str] = importlib.util.spec_from_file_location(
'''transformers''',
os.path.join(PATH_TO_TRANSFORMERS, '''__init__.py'''),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
_lowerCamelCase : int = spec.loader.load_module()
_lowerCamelCase : Dict = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_lowerCamelCase : List[str] = re.compile('''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')
_lowerCamelCase : Optional[int] = {
'''CLIPConfigMixin''',
'''DecisionTransformerConfigMixin''',
'''EncoderDecoderConfigMixin''',
'''RagConfigMixin''',
'''SpeechEncoderDecoderConfigMixin''',
'''VisionEncoderDecoderConfigMixin''',
'''VisionTextDualEncoderConfigMixin''',
}
def _a ( ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = []
for config_class in list(CONFIG_MAPPING.values() ):
SCREAMING_SNAKE_CASE__ : str = False
# source code of `config_class`
SCREAMING_SNAKE_CASE__ : Dict = inspect.getsource(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : int = _re_checkpoint.findall(SCREAMING_SNAKE_CASE__ )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : Tuple = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
SCREAMING_SNAKE_CASE__ : Union[str, Any] = f'''https://huggingface.co/{ckpt_name}'''
if ckpt_link == ckpt_link_from_name:
SCREAMING_SNAKE_CASE__ : int = True
break
SCREAMING_SNAKE_CASE__ : Optional[int] = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(SCREAMING_SNAKE_CASE__ )
if len(SCREAMING_SNAKE_CASE__ ) > 0:
SCREAMING_SNAKE_CASE__ : List[Any] = "\n".join(sorted(SCREAMING_SNAKE_CASE__ ) )
raise ValueError(f'''The following configurations don\'t contain any valid checkpoint:\n{message}''' )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 157
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Union[str, Any] = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextConfig''',
'''XCLIPVisionConfig''',
],
'''processing_x_clip''': ['''XCLIPProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : str = [
'''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XCLIPModel''',
'''XCLIPPreTrainedModel''',
'''XCLIPTextModel''',
'''XCLIPVisionModel''',
]
if TYPE_CHECKING:
from .configuration_x_clip import (
XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
XCLIPConfig,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .processing_x_clip import XCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_x_clip import (
XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
else:
import sys
_lowerCamelCase : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 157
| 1
|
'''simple docstring'''
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def UpperCAmelCase ( *UpperCAmelCase__ : Optional[Any]):
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__):
lowerCamelCase : str = list(UpperCAmelCase__)
for i in range(len(UpperCAmelCase__)):
lowerCamelCase : Tuple = 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 UpperCAmelCase ( UpperCAmelCase__ : Exception):
lowerCamelCase : Dict = [
'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 UpperCAmelCase ( UpperCAmelCase__ : callable = None , UpperCAmelCase__ : int = 1_28):
if function is None:
return functools.partial(UpperCAmelCase__ , starting_batch_size=UpperCAmelCase__)
lowerCamelCase : Optional[int] = starting_batch_size
def decorator(*UpperCAmelCase__ : Optional[int] , **UpperCAmelCase__ : int):
nonlocal batch_size
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
lowerCamelCase : Optional[Any] = list(inspect.signature(UpperCAmelCase__).parameters.keys())
# Guard against user error
if len(UpperCAmelCase__) < (len(UpperCAmelCase__) + 1):
lowerCamelCase : Any = ', '.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
| 320
|
'''simple docstring'''
A = [
'Audio',
'Array2D',
'Array3D',
'Array4D',
'Array5D',
'ClassLabel',
'Features',
'Sequence',
'Value',
'Image',
'Translation',
'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
| 320
| 1
|
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a (_lowerCAmelCase ):
return (data["data"], data["target"])
def a (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = XGBRegressor(verbosity=0 , random_state=4_2 )
xgb.fit(_lowerCAmelCase , _lowerCAmelCase )
# Predict target for test data
SCREAMING_SNAKE_CASE_ = xgb.predict(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = predictions.reshape(len(_lowerCAmelCase ) , 1 )
return predictions
def a ():
SCREAMING_SNAKE_CASE_ = fetch_california_housing()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = data_handling(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = train_test_split(
_lowerCAmelCase , _lowerCAmelCase , test_size=0.25 , random_state=1 )
SCREAMING_SNAKE_CASE_ = xgboost(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# Error printing
print(F"Mean Absolute Error : {mean_absolute_error(_lowerCAmelCase , _lowerCAmelCase )}" )
print(F"Mean Square Error : {mean_squared_error(_lowerCAmelCase , _lowerCAmelCase )}" )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 89
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ={
"""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 __magic_name__ ( __UpperCAmelCase):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = "realm"
def __init__( self: Tuple , _lowerCamelCase: Union[str, Any]=3_05_22 , _lowerCamelCase: Tuple=7_68 , _lowerCamelCase: str=1_28 , _lowerCamelCase: str=12 , _lowerCamelCase: int=12 , _lowerCamelCase: Union[str, Any]=8 , _lowerCamelCase: Optional[Any]=30_72 , _lowerCamelCase: str="gelu_new" , _lowerCamelCase: str=0.1 , _lowerCamelCase: Union[str, Any]=0.1 , _lowerCamelCase: Optional[int]=5_12 , _lowerCamelCase: Union[str, Any]=2 , _lowerCamelCase: int=0.02 , _lowerCamelCase: Tuple=1E-12 , _lowerCamelCase: List[Any]=2_56 , _lowerCamelCase: Any=10 , _lowerCamelCase: Optional[Any]=1E-3 , _lowerCamelCase: Any=5 , _lowerCamelCase: List[str]=3_20 , _lowerCamelCase: List[str]=13_35_37_18 , _lowerCamelCase: str=50_00 , _lowerCamelCase: str=1 , _lowerCamelCase: str=0 , _lowerCamelCase: Dict=2 , **_lowerCamelCase: Tuple , ):
super().__init__(pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , **_lowerCamelCase )
# Common config
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = hidden_size
SCREAMING_SNAKE_CASE_ = retriever_proj_size
SCREAMING_SNAKE_CASE_ = num_hidden_layers
SCREAMING_SNAKE_CASE_ = num_attention_heads
SCREAMING_SNAKE_CASE_ = num_candidates
SCREAMING_SNAKE_CASE_ = intermediate_size
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = type_vocab_size
SCREAMING_SNAKE_CASE_ = layer_norm_eps
# Reader config
SCREAMING_SNAKE_CASE_ = span_hidden_size
SCREAMING_SNAKE_CASE_ = max_span_width
SCREAMING_SNAKE_CASE_ = reader_layer_norm_eps
SCREAMING_SNAKE_CASE_ = reader_beam_size
SCREAMING_SNAKE_CASE_ = reader_seq_len
# Retrieval config
SCREAMING_SNAKE_CASE_ = num_block_records
SCREAMING_SNAKE_CASE_ = searcher_beam_size
| 89
| 1
|
"""simple docstring"""
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : List[Any] ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_lowerCamelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : str ):
"""simple docstring"""
snake_case_ : Dict = str(_lowerCamelCase )
snake_case_ : Any = [n]
for i in range(1 , len(_lowerCamelCase ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
"""simple docstring"""
if len(str(_lowerCamelCase ) ) > 3:
if not is_prime(int(str(_lowerCamelCase )[-3:] ) ) or not is_prime(int(str(_lowerCamelCase )[:3] ) ):
return False
return True
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : List[Any] = 1_1 ):
"""simple docstring"""
snake_case_ : list[int] = []
snake_case_ : Any = 1_3
while len(_lowerCamelCase ) != count:
if validate(_lowerCamelCase ):
snake_case_ : List[str] = list_truncated_nums(_lowerCamelCase )
if all(is_prime(_lowerCamelCase ) for i in list_nums ):
list_truncated_primes.append(_lowerCamelCase )
num += 2
return list_truncated_primes
def SCREAMING_SNAKE_CASE__ ( ):
"""simple docstring"""
return sum(compute_truncated_primes(1_1 ) )
if __name__ == "__main__":
print(F'''{sum(compute_truncated_primes(11)) = }''')
| 480
|
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 42
lowerCamelCase__ = 42
def __init__( self, __a, __a):
'''simple docstring'''
super().__init__()
self.register_modules(unet=__a, scheduler=__a)
@torch.no_grad()
def __call__( self, __a = 1, __a = 50, __a = None, __a = "pil", __a = True, **__a, ):
'''simple docstring'''
_lowerCAmelCase : List[str] = self.unet.config.sample_size
_lowerCAmelCase : Optional[Any] = (batch_size, 3, img_size, img_size)
_lowerCAmelCase : Any = self.unet
# sample x_0 ~ N(0, sigma_0^2 * I)
_lowerCAmelCase : Union[str, Any] = randn_tensor(__a, generator=__a, device=self.device) * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(__a)
for t in self.progress_bar(self.scheduler.timesteps):
# here sigma_t == t_i from the paper
_lowerCAmelCase : Optional[Any] = self.scheduler.schedule[t]
_lowerCAmelCase : int = self.scheduler.schedule[t - 1] if t > 0 else 0
# 1. Select temporarily increased noise level sigma_hat
# 2. Add new noise to move from sample_i to sample_hat
_lowerCAmelCase , _lowerCAmelCase : Dict = self.scheduler.add_noise_to_input(__a, __a, generator=__a)
# 3. Predict the noise residual given the noise magnitude `sigma_hat`
# The model inputs and output are adjusted by following eq. (213) in [1].
_lowerCAmelCase : Optional[int] = (sigma_hat / 2) * model((sample_hat + 1) / 2, sigma_hat / 2).sample
# 4. Evaluate dx/dt at sigma_hat
# 5. Take Euler step from sigma to sigma_prev
_lowerCAmelCase : Optional[int] = self.scheduler.step(__a, __a, __a, __a)
if sigma_prev != 0:
# 6. Apply 2nd order correction
# The model inputs and output are adjusted by following eq. (213) in [1].
_lowerCAmelCase : List[str] = (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2, sigma_prev / 2).sample
_lowerCAmelCase : List[str] = self.scheduler.step_correct(
__a, __a, __a, __a, step_output.prev_sample, step_output["derivative"], )
_lowerCAmelCase : Optional[int] = step_output.prev_sample
_lowerCAmelCase : Tuple = (sample / 2 + 0.5).clamp(0, 1)
_lowerCAmelCase : int = sample.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
_lowerCAmelCase : int = self.numpy_to_pil(__a)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__a)
| 500
| 0
|
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase = logging.get_logger(__name__)
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> Any:
print('Loading config file...' )
def flatten_yaml_as_dict(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE="" , SCREAMING_SNAKE_CASE="." ):
_lowercase : Optional[int] = []
for k, v in d.items():
_lowercase : List[Any] = parent_key + sep + k if parent_key else k
if isinstance(SCREAMING_SNAKE_CASE , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , sep=SCREAMING_SNAKE_CASE ).items() )
else:
items.append((new_key, v) )
return dict(SCREAMING_SNAKE_CASE )
_lowercase : Optional[int] = argparse.Namespace()
with open(SCREAMING_SNAKE_CASE , 'r' ) as yaml_file:
try:
_lowercase : List[str] = yaml.load(SCREAMING_SNAKE_CASE , Loader=yaml.FullLoader )
_lowercase : str = flatten_yaml_as_dict(SCREAMING_SNAKE_CASE )
for k, v in flat_cfg.items():
setattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
except yaml.YAMLError as exc:
logger.error('Error while loading config file: {}. Error message: {}'.format(SCREAMING_SNAKE_CASE , str(SCREAMING_SNAKE_CASE ) ) )
return config
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[Any]:
_lowercase : List[Any] = MobileViTVaConfig()
_lowercase : Dict = False
# dataset
if task_name.startswith('imagenet1k_' ):
_lowercase : Any = 1_000
if int(task_name.strip().split('_' )[-1] ) == 384:
_lowercase : Any = 384
else:
_lowercase : List[str] = 256
_lowercase : str = 'imagenet-1k-id2label.json'
elif task_name.startswith('imagenet21k_to_1k_' ):
_lowercase : Optional[Any] = 21_000
if int(task_name.strip().split('_' )[-1] ) == 384:
_lowercase : Dict = 384
else:
_lowercase : Optional[Any] = 256
_lowercase : str = 'imagenet-22k-id2label.json'
elif task_name.startswith('ade20k_' ):
_lowercase : int = 151
_lowercase : List[str] = 512
_lowercase : Optional[Any] = 'ade20k-id2label.json'
_lowercase : Any = True
elif task_name.startswith('voc_' ):
_lowercase : Union[str, Any] = 21
_lowercase : List[Any] = 512
_lowercase : List[Any] = 'pascal-voc-id2label.json'
_lowercase : Dict = True
# orig_config
_lowercase : List[str] = load_orig_config_file(SCREAMING_SNAKE_CASE )
assert getattr(SCREAMING_SNAKE_CASE , 'model.classification.name' , -1 ) == "mobilevit_v2", "Invalid model"
_lowercase : List[Any] = getattr(SCREAMING_SNAKE_CASE , 'model.classification.mitv2.width_multiplier' , 1.0 )
assert (
getattr(SCREAMING_SNAKE_CASE , 'model.classification.mitv2.attn_norm_layer' , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
_lowercase : Optional[Any] = getattr(SCREAMING_SNAKE_CASE , 'model.classification.activation.name' , 'swish' )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
_lowercase : Union[str, Any] = getattr(SCREAMING_SNAKE_CASE , 'model.segmentation.output_stride' , 16 )
if "_deeplabv3" in task_name:
_lowercase : List[Any] = getattr(SCREAMING_SNAKE_CASE , 'model.segmentation.deeplabv3.aspp_rates' , [12, 24, 36] )
_lowercase : str = getattr(SCREAMING_SNAKE_CASE , 'model.segmentation.deeplabv3.aspp_out_channels' , 512 )
_lowercase : str = getattr(SCREAMING_SNAKE_CASE , 'model.segmentation.deeplabv3.aspp_dropout' , 0.1 )
# id2label
_lowercase : Tuple = 'huggingface/label-files'
_lowercase : Union[str, Any] = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , repo_type='dataset' ) , 'r' ) )
_lowercase : str = {int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
_lowercase : Dict = idalabel
_lowercase : Dict = {v: k for k, v in idalabel.items()}
return config
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[int]:
_lowercase : List[str] = dct.pop(SCREAMING_SNAKE_CASE )
_lowercase : Optional[int] = val
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False ) -> Any:
if base_model:
_lowercase : Optional[int] = ''
else:
_lowercase : int = 'mobilevitv2.'
_lowercase : Optional[Any] = []
for k in state_dict.keys():
if k[:8] == "encoder.":
_lowercase : str = k[8:]
else:
_lowercase : Union[str, Any] = k
if ".block." in k:
_lowercase : Tuple = k_new.replace('.block.' , '.' )
if ".conv." in k:
_lowercase : Dict = k_new.replace('.conv.' , '.convolution.' )
if ".norm." in k:
_lowercase : List[str] = k_new.replace('.norm.' , '.normalization.' )
if "conv_1." in k:
_lowercase : Optional[Any] = k_new.replace('conv_1.' , F"""{model_prefix}conv_stem.""" )
for i in [1, 2]:
if F"""layer_{i}.""" in k:
_lowercase : Tuple = k_new.replace(F"""layer_{i}.""" , F"""{model_prefix}encoder.layer.{i-1}.layer.""" )
if ".exp_1x1." in k:
_lowercase : List[Any] = k_new.replace('.exp_1x1.' , '.expand_1x1.' )
if ".red_1x1." in k:
_lowercase : Optional[int] = k_new.replace('.red_1x1.' , '.reduce_1x1.' )
for i in [3, 4, 5]:
if F"""layer_{i}.0.""" in k:
_lowercase : Any = k_new.replace(F"""layer_{i}.0.""" , F"""{model_prefix}encoder.layer.{i-1}.downsampling_layer.""" )
if F"""layer_{i}.1.local_rep.0.""" in k:
_lowercase : Tuple = k_new.replace(F"""layer_{i}.1.local_rep.0.""" , F"""{model_prefix}encoder.layer.{i-1}.conv_kxk.""" )
if F"""layer_{i}.1.local_rep.1.""" in k:
_lowercase : str = k_new.replace(F"""layer_{i}.1.local_rep.1.""" , F"""{model_prefix}encoder.layer.{i-1}.conv_1x1.""" )
for i in [3, 4, 5]:
if i == 3:
_lowercase : str = [0, 1]
elif i == 4:
_lowercase : int = [0, 1, 2, 3]
elif i == 5:
_lowercase : Union[str, Any] = [0, 1, 2]
for j in j_in:
if F"""layer_{i}.1.global_rep.{j}.""" in k:
_lowercase : int = k_new.replace(
F"""layer_{i}.1.global_rep.{j}.""" , F"""{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.""" )
if F"""layer_{i}.1.global_rep.{j+1}.""" in k:
_lowercase : int = k_new.replace(
F"""layer_{i}.1.global_rep.{j+1}.""" , F"""{model_prefix}encoder.layer.{i-1}.layernorm.""" )
if F"""layer_{i}.1.conv_proj.""" in k:
_lowercase : Any = k_new.replace(F"""layer_{i}.1.conv_proj.""" , F"""{model_prefix}encoder.layer.{i-1}.conv_projection.""" )
if "pre_norm_attn.0." in k:
_lowercase : Dict = k_new.replace('pre_norm_attn.0.' , 'layernorm_before.' )
if "pre_norm_attn.1." in k:
_lowercase : Tuple = k_new.replace('pre_norm_attn.1.' , 'attention.' )
if "pre_norm_ffn.0." in k:
_lowercase : str = k_new.replace('pre_norm_ffn.0.' , 'layernorm_after.' )
if "pre_norm_ffn.1." in k:
_lowercase : Optional[Any] = k_new.replace('pre_norm_ffn.1.' , 'ffn.conv1.' )
if "pre_norm_ffn.3." in k:
_lowercase : List[Any] = k_new.replace('pre_norm_ffn.3.' , 'ffn.conv2.' )
if "classifier.1." in k:
_lowercase : Optional[Any] = k_new.replace('classifier.1.' , 'classifier.' )
if "seg_head." in k:
_lowercase : Optional[Any] = k_new.replace('seg_head.' , 'segmentation_head.' )
if ".aspp_layer." in k:
_lowercase : Any = k_new.replace('.aspp_layer.' , '.' )
if ".aspp_pool." in k:
_lowercase : Union[str, Any] = k_new.replace('.aspp_pool.' , '.' )
rename_keys.append((k, k_new) )
return rename_keys
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> List[Any]:
_lowercase : Union[str, Any] = []
for k in state_dict.keys():
if k.startswith('seg_head.aux_head.' ):
keys_to_ignore.append(SCREAMING_SNAKE_CASE )
for k in keys_to_ignore:
state_dict.pop(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __magic_name__ ( ) -> Union[str, Any]:
_lowercase : Optional[Any] = 'http://images.cocodataset.org/val2017/000000039769.jpg'
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
_lowercase : List[Any] = Image.open(requests.get(SCREAMING_SNAKE_CASE , stream=SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Dict:
_lowercase : Optional[int] = get_mobilevitva_config(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# load original state_dict
_lowercase : Optional[Any] = torch.load(SCREAMING_SNAKE_CASE , map_location='cpu' )
# load huggingface model
if task_name.startswith('ade20k_' ) or task_name.startswith('voc_' ):
_lowercase : Tuple = MobileViTVaForSemanticSegmentation(SCREAMING_SNAKE_CASE ).eval()
_lowercase : List[str] = False
else:
_lowercase : Optional[int] = MobileViTVaForImageClassification(SCREAMING_SNAKE_CASE ).eval()
_lowercase : Any = False
# remove and rename some keys of load the original model
_lowercase : Optional[Any] = checkpoint
remove_unused_keys(SCREAMING_SNAKE_CASE )
_lowercase : Tuple = create_rename_keys(SCREAMING_SNAKE_CASE , base_model=SCREAMING_SNAKE_CASE )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# load modified state_dict
model.load_state_dict(SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by MobileViTImageProcessor
_lowercase : List[str] = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
_lowercase : Any = image_processor(images=prepare_img() , return_tensors='pt' )
_lowercase : int = model(**SCREAMING_SNAKE_CASE )
# verify classification model
if task_name.startswith('imagenet' ):
_lowercase : Union[str, Any] = outputs.logits
_lowercase : str = logits.argmax(-1 ).item()
print('Predicted class:' , model.config.idalabel[predicted_class_idx] )
if task_name.startswith('imagenet1k_256' ) and config.width_multiplier == 1.0:
# expected_logits for base variant
_lowercase : Any = torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01] )
assert torch.allclose(logits[0, :3] , SCREAMING_SNAKE_CASE , atol=1E-4 )
Path(SCREAMING_SNAKE_CASE ).mkdir(exist_ok=SCREAMING_SNAKE_CASE )
print(F"""Saving model {task_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task",
default="imagenet1k_256",
type=str,
help=(
"Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
"\n Classification (ImageNet-1k)\n - MobileViTV2 (256x256) : imagenet1k_256\n - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n imagenet21k_to_1k_256\n - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n ImageNet-1k 384x384) : imagenet21k_to_1k_384\n Segmentation\n - ADE20K Dataset : ade20k_deeplabv3\n - Pascal VOC 2012 Dataset: voc_deeplabv3\n "
),
choices=[
"imagenet1k_256",
"imagenet1k_384",
"imagenet21k_to_1k_256",
"imagenet21k_to_1k_384",
"ade20k_deeplabv3",
"voc_deeplabv3",
],
)
parser.add_argument(
"--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
)
parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
UpperCamelCase = parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 677
|
from __future__ import annotations
class lowerCAmelCase_ :
def __init__( self , _lowerCAmelCase=None ):
_lowercase : int = data
_lowercase : Union[str, Any] = None
def __repr__( self ):
_lowercase : Dict = []
_lowercase : Tuple = self
while temp:
string_rep.append(F"""{temp.data}""" )
_lowercase : Optional[Any] = temp.next
return "->".join(_lowerCAmelCase )
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> Any:
if not elements_list:
raise Exception('The Elements List is empty' )
_lowercase : Union[str, Any] = Node(elements_list[0] )
for i in range(1 , len(SCREAMING_SNAKE_CASE ) ):
_lowercase : Optional[int] = Node(elements_list[i] )
_lowercase : List[Any] = current.next
return head
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> None:
if head_node is not None and isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
print_reverse(head_node.next )
print(head_node.data )
def __magic_name__ ( ) -> List[str]:
from doctest import testmod
testmod()
_lowercase : int = make_linked_list([14, 52, 14, 12, 43] )
print('Linked List:' )
print(SCREAMING_SNAKE_CASE )
print('Elements in Reverse:' )
print_reverse(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 677
| 1
|
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class __magic_name__ (unittest.TestCase ):
def __a ( self ) -> Dict:
lowerCAmelCase_ = tempfile.mkdtemp()
# fmt: off
lowerCAmelCase_ = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
# fmt: on
lowerCAmelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
lowerCAmelCase_ = {
"do_resize": True,
"size": {"height": 18, "width": 18},
"do_normalize": True,
"image_mean": [0.5, 0.5, 0.5],
"image_std": [0.5, 0.5, 0.5],
}
lowerCAmelCase_ = os.path.join(self.tmpdirname , _a )
with open(self.image_processor_file , "w" , encoding="utf-8" ) as fp:
json.dump(_a , _a )
def __a ( self , **_a ) -> Dict:
return BertTokenizer.from_pretrained(self.tmpdirname , **_a )
def __a ( self , **_a ) -> List[Any]:
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_a )
def __a ( self ) -> int:
shutil.rmtree(self.tmpdirname )
def __a ( self ) -> Optional[int]:
lowerCAmelCase_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowerCAmelCase_ = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __a ( self ) -> Dict:
lowerCAmelCase_ = self.get_tokenizer()
lowerCAmelCase_ = self.get_image_processor()
lowerCAmelCase_ = VisionTextDualEncoderProcessor(tokenizer=_a , image_processor=_a )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase_ = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def __a ( self ) -> Dict:
lowerCAmelCase_ = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase_ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
lowerCAmelCase_ = self.get_image_processor(do_normalize=_a , padding_value=1.0 )
lowerCAmelCase_ = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=_a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def __a ( self ) -> Union[str, Any]:
lowerCAmelCase_ = self.get_image_processor()
lowerCAmelCase_ = self.get_tokenizer()
lowerCAmelCase_ = VisionTextDualEncoderProcessor(tokenizer=_a , image_processor=_a )
lowerCAmelCase_ = self.prepare_image_inputs()
lowerCAmelCase_ = image_processor(_a , return_tensors="np" )
lowerCAmelCase_ = processor(images=_a , return_tensors="np" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __a ( self ) -> Optional[Any]:
lowerCAmelCase_ = self.get_image_processor()
lowerCAmelCase_ = self.get_tokenizer()
lowerCAmelCase_ = VisionTextDualEncoderProcessor(tokenizer=_a , image_processor=_a )
lowerCAmelCase_ = "lower newer"
lowerCAmelCase_ = processor(text=_a )
lowerCAmelCase_ = tokenizer(_a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __a ( self ) -> List[str]:
lowerCAmelCase_ = self.get_image_processor()
lowerCAmelCase_ = self.get_tokenizer()
lowerCAmelCase_ = VisionTextDualEncoderProcessor(tokenizer=_a , image_processor=_a )
lowerCAmelCase_ = "lower newer"
lowerCAmelCase_ = self.prepare_image_inputs()
lowerCAmelCase_ = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "token_type_ids", "attention_mask", "pixel_values"] )
# test if it raises when no input is passed
with self.assertRaises(_a ):
processor()
def __a ( self ) -> str:
lowerCAmelCase_ = self.get_image_processor()
lowerCAmelCase_ = self.get_tokenizer()
lowerCAmelCase_ = VisionTextDualEncoderProcessor(tokenizer=_a , image_processor=_a )
lowerCAmelCase_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCAmelCase_ = processor.batch_decode(_a )
lowerCAmelCase_ = tokenizer.batch_decode(_a )
self.assertListEqual(_a , _a )
def __a ( self ) -> int:
lowerCAmelCase_ = self.get_image_processor()
lowerCAmelCase_ = self.get_tokenizer()
lowerCAmelCase_ = VisionTextDualEncoderProcessor(tokenizer=_a , image_processor=_a )
lowerCAmelCase_ = "lower newer"
lowerCAmelCase_ = self.prepare_image_inputs()
lowerCAmelCase_ = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 122
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
'''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTMSNModel''',
'''ViTMSNForImageClassification''',
'''ViTMSNPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 122
| 1
|
"""simple docstring"""
from collections import namedtuple
import requests
from lxml import html # type: ignore
UpperCAmelCase__ : Union[str, Any] = namedtuple('covid_data', 'cases deaths recovered')
def lowercase_ ( _snake_case = "https://www.worldometers.info/coronavirus/" ):
SCREAMING_SNAKE_CASE__ : str = '''//div[@class = "maincounter-number"]/span/text()'''
return covid_data(*html.fromstring(requests.get(__UpperCamelCase ).content ).xpath(__UpperCamelCase ) )
UpperCAmelCase__ : Tuple = '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()))
| 711
|
"""simple docstring"""
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader
from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
ROUGE_KEYS,
LegacySeqaSeqDataset,
SeqaSeqDataset,
assert_all_frozen,
calculate_bleu,
calculate_rouge,
check_output_dir,
flatten_list,
freeze_embeds,
freeze_params,
get_git_info,
label_smoothed_nll_loss,
lmap,
pickle_save,
save_git_info,
save_json,
use_task_specific_params,
)
# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train # noqa
UpperCAmelCase__ : Optional[int] = logging.getLogger(__name__)
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
__UpperCamelCase : Optional[int] = '''summarization'''
__UpperCamelCase : int = ['''loss''']
__UpperCamelCase : Dict = ROUGE_KEYS
__UpperCamelCase : Any = '''rouge2'''
def __init__(self , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) -> int:
"""simple docstring"""
if hparams.sortish_sampler and hparams.gpus > 1:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = False
elif hparams.max_tokens_per_batch is not None:
if hparams.gpus > 1:
raise NotImplementedError("""Dynamic Batch size does not work for multi-gpu training""" )
if hparams.sortish_sampler:
raise ValueError("""--sortish_sampler and --max_tokens_per_batch may not be used simultaneously""" )
super().__init__(SCREAMING_SNAKE_CASE__ , num_labels=SCREAMING_SNAKE_CASE__ , mode=self.mode , **SCREAMING_SNAKE_CASE__ )
use_task_specific_params(self.model , """summarization""" )
save_git_info(self.hparams.output_dir )
SCREAMING_SNAKE_CASE__ : str = Path(self.output_dir ) / """metrics.json"""
SCREAMING_SNAKE_CASE__ : Any = Path(self.output_dir ) / """hparams.pkl"""
pickle_save(self.hparams , self.hparams_save_path )
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Union[str, Any] = defaultdict(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Any = self.config.model_type
SCREAMING_SNAKE_CASE__ : Dict = self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size
SCREAMING_SNAKE_CASE__ : dict = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""train""": self.hparams.n_train,
"""val""": self.hparams.n_val,
"""test""": self.hparams.n_test,
}
SCREAMING_SNAKE_CASE__ : Optional[Any] = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
SCREAMING_SNAKE_CASE__ : Any = {
"""train""": self.hparams.max_target_length,
"""val""": self.hparams.val_max_target_length,
"""test""": self.hparams.test_max_target_length,
}
assert self.target_lens["train"] <= self.target_lens["val"], F'''target_lens: {self.target_lens}'''
assert self.target_lens["train"] <= self.target_lens["test"], F'''target_lens: {self.target_lens}'''
if self.hparams.freeze_embeds:
freeze_embeds(self.model )
if self.hparams.freeze_encoder:
freeze_params(self.model.get_encoder() )
assert_all_frozen(self.model.get_encoder() )
SCREAMING_SNAKE_CASE__ : Tuple = get_git_info()["""repo_sha"""]
SCREAMING_SNAKE_CASE__ : List[str] = hparams.num_workers
SCREAMING_SNAKE_CASE__ : Optional[int] = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer , SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE__ : List[Any] = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
SCREAMING_SNAKE_CASE__ : List[Any] = self.decoder_start_token_id
SCREAMING_SNAKE_CASE__ : List[str] = (
SeqaSeqDataset if hasattr(self.tokenizer , """prepare_seq2seq_batch""" ) else LegacySeqaSeqDataset
)
SCREAMING_SNAKE_CASE__ : Optional[int] = False
SCREAMING_SNAKE_CASE__ : int = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
if self.hparams.eval_max_gen_length is not None:
SCREAMING_SNAKE_CASE__ : Optional[int] = self.hparams.eval_max_gen_length
else:
SCREAMING_SNAKE_CASE__ : List[str] = self.model.config.max_length
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> Dict[str, List[str]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
k: self.tokenizer.batch_decode(v.tolist() ) if """mask""" not in k else v.shape for k, v in batch.items()
}
save_json(SCREAMING_SNAKE_CASE__ , Path(self.output_dir ) / """text_batch.json""" )
save_json({k: v.tolist() for k, v in batch.items()} , Path(self.output_dir ) / """tok_batch.json""" )
SCREAMING_SNAKE_CASE__ : str = True
return readable_batch
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) -> int:
"""simple docstring"""
return self.model(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.tokenizer.batch_decode(
SCREAMING_SNAKE_CASE__ , skip_special_tokens=SCREAMING_SNAKE_CASE__ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
return lmap(str.strip , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.tokenizer.pad_token_id
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch["""input_ids"""], batch["""attention_mask"""]
SCREAMING_SNAKE_CASE__ : Dict = batch["""labels"""]
if isinstance(self.model , SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE__ : int = self.model._shift_right(SCREAMING_SNAKE_CASE__ )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = shift_tokens_right(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
SCREAMING_SNAKE_CASE__ : List[Any] = decoder_input_ids
self.save_readable_batch(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Dict = self(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , decoder_input_ids=SCREAMING_SNAKE_CASE__ , use_cache=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = outputs["""logits"""]
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
SCREAMING_SNAKE_CASE__ : Dict = nn.CrossEntropyLoss(ignore_index=SCREAMING_SNAKE_CASE__ )
assert lm_logits.shape[-1] == self.vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] = ce_loss_fct(lm_logits.view(-1 , lm_logits.shape[-1] ) , tgt_ids.view(-1 ) )
else:
SCREAMING_SNAKE_CASE__ : int = nn.functional.log_softmax(SCREAMING_SNAKE_CASE__ , dim=-1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = label_smoothed_nll_loss(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , self.hparams.label_smoothing , ignore_index=SCREAMING_SNAKE_CASE__ )
return (loss,)
@property
def __magic_name__ (self ) -> int:
"""simple docstring"""
return self.tokenizer.pad_token_id
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self._step(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Dict = dict(zip(self.loss_names , SCREAMING_SNAKE_CASE__ ) )
# tokens per batch
SCREAMING_SNAKE_CASE__ : List[Any] = batch["""input_ids"""].ne(self.pad ).sum() + batch["""labels"""].ne(self.pad ).sum()
SCREAMING_SNAKE_CASE__ : int = batch["""input_ids"""].shape[0]
SCREAMING_SNAKE_CASE__ : int = batch["""input_ids"""].eq(self.pad ).sum()
SCREAMING_SNAKE_CASE__ : int = batch["""input_ids"""].eq(self.pad ).float().mean()
# TODO(SS): make a wandb summary metric for this
return {"loss": loss_tensors[0], "log": logs}
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Dict:
"""simple docstring"""
return self._generative_step(SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__="val" ) -> Dict:
"""simple docstring"""
self.step_count += 1
SCREAMING_SNAKE_CASE__ : Optional[int] = {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
SCREAMING_SNAKE_CASE__ : Any = losses["""loss"""]
SCREAMING_SNAKE_CASE__ : Dict = {
k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
}
SCREAMING_SNAKE_CASE__ : str = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
SCREAMING_SNAKE_CASE__ : torch.FloatTensor = torch.tensor(SCREAMING_SNAKE_CASE__ ).type_as(SCREAMING_SNAKE_CASE__ )
generative_metrics.update({k: v.item() for k, v in losses.items()} )
losses.update(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = {F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
SCREAMING_SNAKE_CASE__ : List[Any] = self.step_count
self.metrics[prefix].append(SCREAMING_SNAKE_CASE__ ) # callback writes this to self.metrics_save_path
SCREAMING_SNAKE_CASE__ : Optional[Any] = flatten_list([x["""preds"""] for x in outputs] )
return {
"log": all_metrics,
"preds": preds,
F'''{prefix}_loss''': loss,
F'''{prefix}_{self.val_metric}''': metric_tensor,
}
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Dict:
"""simple docstring"""
return calculate_rouge(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
SCREAMING_SNAKE_CASE__ : int = self.model.generate(
batch["""input_ids"""] , attention_mask=batch["""attention_mask"""] , use_cache=SCREAMING_SNAKE_CASE__ , decoder_start_token_id=self.decoder_start_token_id , num_beams=self.eval_beams , max_length=self.eval_max_length , )
SCREAMING_SNAKE_CASE__ : Optional[int] = (time.time() - ta) / batch["""input_ids"""].shape[0]
SCREAMING_SNAKE_CASE__ : List[str] = self.ids_to_clean_text(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[str] = self.ids_to_clean_text(batch["""labels"""] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self._step(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = dict(zip(self.loss_names , SCREAMING_SNAKE_CASE__ ) )
SCREAMING_SNAKE_CASE__ : Dict = self.calc_generative_metrics(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : int = np.mean(lmap(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
base_metrics.update(gen_time=SCREAMING_SNAKE_CASE__ , gen_len=SCREAMING_SNAKE_CASE__ , preds=SCREAMING_SNAKE_CASE__ , target=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
return base_metrics
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
"""simple docstring"""
return self._generative_step(SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> List[str]:
"""simple docstring"""
return self.validation_epoch_end(SCREAMING_SNAKE_CASE__ , prefix="""test""" )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> SeqaSeqDataset:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.n_obs[type_path]
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.target_lens[type_path]
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.dataset_class(
self.tokenizer , type_path=SCREAMING_SNAKE_CASE__ , n_obs=SCREAMING_SNAKE_CASE__ , max_target_length=SCREAMING_SNAKE_CASE__ , **self.dataset_kwargs , )
return dataset
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = False ) -> DataLoader:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_dataset(SCREAMING_SNAKE_CASE__ )
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
SCREAMING_SNAKE_CASE__ : Tuple = dataset.make_sortish_sampler(SCREAMING_SNAKE_CASE__ , distributed=self.hparams.gpus > 1 )
return DataLoader(
SCREAMING_SNAKE_CASE__ , batch_size=SCREAMING_SNAKE_CASE__ , collate_fn=dataset.collate_fn , shuffle=SCREAMING_SNAKE_CASE__ , num_workers=self.num_workers , sampler=SCREAMING_SNAKE_CASE__ , )
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
SCREAMING_SNAKE_CASE__ : int = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch , distributed=self.hparams.gpus > 1 )
return DataLoader(
SCREAMING_SNAKE_CASE__ , batch_sampler=SCREAMING_SNAKE_CASE__ , collate_fn=dataset.collate_fn , num_workers=self.num_workers , )
else:
return DataLoader(
SCREAMING_SNAKE_CASE__ , batch_size=SCREAMING_SNAKE_CASE__ , collate_fn=dataset.collate_fn , shuffle=SCREAMING_SNAKE_CASE__ , num_workers=self.num_workers , sampler=SCREAMING_SNAKE_CASE__ , )
def __magic_name__ (self ) -> DataLoader:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.get_dataloader("""train""" , batch_size=self.hparams.train_batch_size , shuffle=SCREAMING_SNAKE_CASE__ )
return dataloader
def __magic_name__ (self ) -> DataLoader:
"""simple docstring"""
return self.get_dataloader("""val""" , batch_size=self.hparams.eval_batch_size )
def __magic_name__ (self ) -> DataLoader:
"""simple docstring"""
return self.get_dataloader("""test""" , batch_size=self.hparams.eval_batch_size )
@staticmethod
def __magic_name__ (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
"""simple docstring"""
BaseTransformer.add_model_specific_args(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
add_generic_args(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
parser.add_argument(
"""--max_source_length""" , default=10_24 , type=SCREAMING_SNAKE_CASE__ , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--max_target_length""" , default=56 , type=SCREAMING_SNAKE_CASE__ , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--val_max_target_length""" , default=1_42 , type=SCREAMING_SNAKE_CASE__ , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--test_max_target_length""" , default=1_42 , type=SCREAMING_SNAKE_CASE__ , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument("""--freeze_encoder""" , action="""store_true""" )
parser.add_argument("""--freeze_embeds""" , action="""store_true""" )
parser.add_argument("""--sortish_sampler""" , action="""store_true""" , default=SCREAMING_SNAKE_CASE__ )
parser.add_argument("""--overwrite_output_dir""" , action="""store_true""" , default=SCREAMING_SNAKE_CASE__ )
parser.add_argument("""--max_tokens_per_batch""" , type=SCREAMING_SNAKE_CASE__ , default=SCREAMING_SNAKE_CASE__ )
parser.add_argument("""--logger_name""" , type=SCREAMING_SNAKE_CASE__ , choices=["""default""", """wandb""", """wandb_shared"""] , default="""default""" )
parser.add_argument("""--n_train""" , type=SCREAMING_SNAKE_CASE__ , default=-1 , required=SCREAMING_SNAKE_CASE__ , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_val""" , type=SCREAMING_SNAKE_CASE__ , default=5_00 , required=SCREAMING_SNAKE_CASE__ , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_test""" , type=SCREAMING_SNAKE_CASE__ , default=-1 , required=SCREAMING_SNAKE_CASE__ , help="""# examples. -1 means use all.""" )
parser.add_argument(
"""--task""" , type=SCREAMING_SNAKE_CASE__ , default="""summarization""" , required=SCREAMING_SNAKE_CASE__ , help="""# examples. -1 means use all.""" )
parser.add_argument("""--label_smoothing""" , type=SCREAMING_SNAKE_CASE__ , default=0.0 , required=SCREAMING_SNAKE_CASE__ )
parser.add_argument("""--src_lang""" , type=SCREAMING_SNAKE_CASE__ , default="""""" , required=SCREAMING_SNAKE_CASE__ )
parser.add_argument("""--tgt_lang""" , type=SCREAMING_SNAKE_CASE__ , default="""""" , required=SCREAMING_SNAKE_CASE__ )
parser.add_argument("""--eval_beams""" , type=SCREAMING_SNAKE_CASE__ , default=SCREAMING_SNAKE_CASE__ , required=SCREAMING_SNAKE_CASE__ )
parser.add_argument(
"""--val_metric""" , type=SCREAMING_SNAKE_CASE__ , default=SCREAMING_SNAKE_CASE__ , required=SCREAMING_SNAKE_CASE__ , choices=["""bleu""", """rouge2""", """loss""", None] )
parser.add_argument("""--eval_max_gen_length""" , type=SCREAMING_SNAKE_CASE__ , default=SCREAMING_SNAKE_CASE__ , help="""never generate more than n tokens""" )
parser.add_argument("""--save_top_k""" , type=SCREAMING_SNAKE_CASE__ , default=1 , required=SCREAMING_SNAKE_CASE__ , help="""How many checkpoints to save""" )
parser.add_argument(
"""--early_stopping_patience""" , type=SCREAMING_SNAKE_CASE__ , default=-1 , required=SCREAMING_SNAKE_CASE__ , help=(
"""-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
""" val_check_interval will effect it."""
) , )
return parser
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
__UpperCamelCase : Union[str, Any] = '''translation'''
__UpperCamelCase : Optional[Any] = ['''loss''']
__UpperCamelCase : Optional[int] = ['''bleu''']
__UpperCamelCase : Tuple = '''bleu'''
def __init__(self , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) -> List[str]:
"""simple docstring"""
super().__init__(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = hparams.src_lang
SCREAMING_SNAKE_CASE__ : int = hparams.tgt_lang
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> dict:
"""simple docstring"""
return calculate_bleu(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def lowercase_ ( _snake_case ,_snake_case=None ):
Path(args.output_dir ).mkdir(exist_ok=_snake_case )
check_output_dir(_snake_case ,expected_items=3 )
if model is None:
if "summarization" in args.task:
SCREAMING_SNAKE_CASE__ : SummarizationModule = SummarizationModule(_snake_case )
else:
SCREAMING_SNAKE_CASE__ : SummarizationModule = TranslationModule(_snake_case )
SCREAMING_SNAKE_CASE__ : List[str] = Path(args.data_dir ).name
if (
args.logger_name == "default"
or args.fast_dev_run
or str(args.output_dir ).startswith("""/tmp""" )
or str(args.output_dir ).startswith("""/var""" )
):
SCREAMING_SNAKE_CASE__ : List[Any] = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
SCREAMING_SNAKE_CASE__ : Optional[int] = os.environ.get("""WANDB_PROJECT""" ,_snake_case )
SCREAMING_SNAKE_CASE__ : Optional[Any] = WandbLogger(name=model.output_dir.name ,project=_snake_case )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
SCREAMING_SNAKE_CASE__ : Tuple = WandbLogger(name=model.output_dir.name ,project=f'''hf_{dataset}''' )
if args.early_stopping_patience >= 0:
SCREAMING_SNAKE_CASE__ : List[str] = get_early_stopping_callback(model.val_metric ,args.early_stopping_patience )
else:
SCREAMING_SNAKE_CASE__ : Tuple = False
SCREAMING_SNAKE_CASE__ : Any = args.val_metric == """loss"""
SCREAMING_SNAKE_CASE__ : pl.Trainer = generic_train(
_snake_case ,_snake_case ,logging_callback=SeqaSeqLoggingCallback() ,checkpoint_callback=get_checkpoint_callback(
args.output_dir ,model.val_metric ,args.save_top_k ,_snake_case ) ,early_stopping_callback=_snake_case ,logger=_snake_case ,)
pickle_save(model.hparams ,model.output_dir / """hparams.pkl""" )
if not args.do_predict:
return model
SCREAMING_SNAKE_CASE__ : List[str] = """"""
SCREAMING_SNAKE_CASE__ : List[Any] = sorted(glob.glob(os.path.join(args.output_dir ,"""*.ckpt""" ) ,recursive=_snake_case ) )
if checkpoints:
SCREAMING_SNAKE_CASE__ : Optional[int] = checkpoints[-1]
SCREAMING_SNAKE_CASE__ : Any = checkpoints[-1]
trainer.logger.log_hyperparams(model.hparams )
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
if __name__ == "__main__":
UpperCAmelCase__ : Optional[int] = argparse.ArgumentParser()
UpperCAmelCase__ : Optional[int] = pl.Trainer.add_argparse_args(parser)
UpperCAmelCase__ : List[Any] = SummarizationModule.add_model_specific_args(parser, os.getcwd())
UpperCAmelCase__ : Any = parser.parse_args()
main(args)
| 545
| 0
|
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCamelCase_ ( _UpperCamelCase ) -> List[str]:
"""simple docstring"""
for param in module.parameters():
snake_case_ : Dict = False
def lowerCamelCase_ ( ) -> Dict:
"""simple docstring"""
snake_case_ : Union[str, Any] = '''cuda''' if torch.cuda.is_available() else '''cpu'''
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
snake_case_ : Tuple = '''mps'''
if device == "mps":
print(
'''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
''' with generations.''' )
return device
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : List[str] = plt.imshow(_UpperCamelCase )
fig.axes.get_xaxis().set_visible(_UpperCamelCase )
fig.axes.get_yaxis().set_visible(_UpperCamelCase )
plt.show()
def lowerCamelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : List[str] = datetime.now()
snake_case_ : Optional[Any] = current_time.strftime('''%H:%M:%S''' )
return timestamp
| 60
|
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def a ():
SCREAMING_SNAKE_CASE_ = ArgumentParser('''Transformers CLI tool''' , usage='''transformers-cli <command> [<args>]''' )
SCREAMING_SNAKE_CASE_ = parser.add_subparsers(help='''transformers-cli command helpers''' )
# Register commands
ConvertCommand.register_subcommand(_lowerCAmelCase )
DownloadCommand.register_subcommand(_lowerCAmelCase )
EnvironmentCommand.register_subcommand(_lowerCAmelCase )
RunCommand.register_subcommand(_lowerCAmelCase )
ServeCommand.register_subcommand(_lowerCAmelCase )
UserCommands.register_subcommand(_lowerCAmelCase )
AddNewModelCommand.register_subcommand(_lowerCAmelCase )
AddNewModelLikeCommand.register_subcommand(_lowerCAmelCase )
LfsCommands.register_subcommand(_lowerCAmelCase )
PTtoTFCommand.register_subcommand(_lowerCAmelCase )
# Let's go
SCREAMING_SNAKE_CASE_ = parser.parse_args()
if not hasattr(_lowerCAmelCase , '''func''' ):
parser.print_help()
exit(1 )
# Run
SCREAMING_SNAKE_CASE_ = args.func(_lowerCAmelCase )
service.run()
if __name__ == "__main__":
main()
| 234
| 0
|
def UpperCAmelCase_ ( __lowerCAmelCase ) -> set:
__lowercase : List[Any] = set()
# edges = list of graph's edges
__lowercase : Dict = get_edges(__lowerCAmelCase )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
__lowercase : List[Any] = edges.pop()
chosen_vertices.add(__lowerCAmelCase )
chosen_vertices.add(__lowerCAmelCase )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(__lowerCAmelCase )
return chosen_vertices
def UpperCAmelCase_ ( __lowerCAmelCase ) -> set:
__lowercase : Optional[int] = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 713
|
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers import AutoModelForCausalLM
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case_ ( self : Union[str, Any] ):
__lowercase : Optional[Any] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
__lowercase : Any = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_snake_case )
__lowercase : List[str] = -1
__lowercase : Optional[Any] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_snake_case )
__lowercase : Tuple = model.generate(_snake_case , max_new_tokens=10 , do_sample=_snake_case )
__lowercase : Optional[int] = tokenizer.decode(greedy_ids[0] )
with CaptureStdout() as cs:
__lowercase : Optional[Any] = TextStreamer(_snake_case )
model.generate(_snake_case , max_new_tokens=10 , do_sample=_snake_case , streamer=_snake_case )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
__lowercase : List[Any] = cs.out[:-1]
self.assertEqual(_snake_case , _snake_case )
def snake_case_ ( self : Optional[int] ):
__lowercase : Optional[int] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
__lowercase : Optional[Any] = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_snake_case )
__lowercase : List[str] = -1
__lowercase : Tuple = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_snake_case )
__lowercase : List[Any] = model.generate(_snake_case , max_new_tokens=10 , do_sample=_snake_case )
__lowercase : Optional[Any] = tokenizer.decode(greedy_ids[0] )
__lowercase : int = TextIteratorStreamer(_snake_case )
__lowercase : int = {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
__lowercase : Optional[Any] = Thread(target=model.generate , kwargs=_snake_case )
thread.start()
__lowercase : Optional[int] = ''''''
for new_text in streamer:
streamer_text += new_text
self.assertEqual(_snake_case , _snake_case )
def snake_case_ ( self : List[str] ):
__lowercase : Dict = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
__lowercase : int = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_snake_case )
__lowercase : List[Any] = -1
__lowercase : List[Any] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_snake_case )
__lowercase : str = model.generate(_snake_case , max_new_tokens=10 , do_sample=_snake_case )
__lowercase : List[Any] = greedy_ids[:, input_ids.shape[1] :]
__lowercase : Optional[Any] = tokenizer.decode(new_greedy_ids[0] )
with CaptureStdout() as cs:
__lowercase : int = TextStreamer(_snake_case , skip_prompt=_snake_case )
model.generate(_snake_case , max_new_tokens=10 , do_sample=_snake_case , streamer=_snake_case )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
__lowercase : int = cs.out[:-1]
self.assertEqual(_snake_case , _snake_case )
def snake_case_ ( self : Any ):
# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
# with actual models -- the dummy models' tokenizers are not aligned with their models, and
# `skip_special_tokens=True` has no effect on them
__lowercase : Dict = AutoTokenizer.from_pretrained('''distilgpt2''' )
__lowercase : List[str] = AutoModelForCausalLM.from_pretrained('''distilgpt2''' ).to(_snake_case )
__lowercase : str = -1
__lowercase : Dict = torch.ones((1, 5) , device=_snake_case ).long() * model.config.bos_token_id
with CaptureStdout() as cs:
__lowercase : Optional[Any] = TextStreamer(_snake_case , skip_special_tokens=_snake_case )
model.generate(_snake_case , max_new_tokens=1 , do_sample=_snake_case , streamer=_snake_case )
# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
# re-tokenized, must only contain one token
__lowercase : Dict = cs.out[:-1] # Remove the final "\n"
__lowercase : Any = tokenizer(_snake_case , return_tensors='''pt''' )
self.assertEqual(streamer_text_tokenized.input_ids.shape , (1, 1) )
def snake_case_ ( self : Any ):
__lowercase : List[str] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
__lowercase : Union[str, Any] = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_snake_case )
__lowercase : Dict = -1
__lowercase : List[Any] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_snake_case )
__lowercase : Tuple = TextIteratorStreamer(_snake_case , timeout=0.0_01 )
__lowercase : Tuple = {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
__lowercase : Optional[int] = Thread(target=model.generate , kwargs=_snake_case )
thread.start()
# The streamer will timeout after 0.001 seconds, so an exception will be raised
with self.assertRaises(_snake_case ):
__lowercase : List[Any] = ''''''
for new_text in streamer:
streamer_text += new_text
| 284
| 0
|
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ : List[str] = logging.get_logger(__name__)
lowercase__ : Dict = {
"""b0""": efficientnet.EfficientNetBa,
"""b1""": efficientnet.EfficientNetBa,
"""b2""": efficientnet.EfficientNetBa,
"""b3""": efficientnet.EfficientNetBa,
"""b4""": efficientnet.EfficientNetBa,
"""b5""": efficientnet.EfficientNetBa,
"""b6""": efficientnet.EfficientNetBa,
"""b7""": efficientnet.EfficientNetBa,
}
lowercase__ : List[str] = {
"""b0""": {
"""hidden_dim""": 1280,
"""width_coef""": 1.0,
"""depth_coef""": 1.0,
"""image_size""": 224,
"""dropout_rate""": 0.2,
"""dw_padding""": [],
},
"""b1""": {
"""hidden_dim""": 1280,
"""width_coef""": 1.0,
"""depth_coef""": 1.1,
"""image_size""": 240,
"""dropout_rate""": 0.2,
"""dw_padding""": [16],
},
"""b2""": {
"""hidden_dim""": 1408,
"""width_coef""": 1.1,
"""depth_coef""": 1.2,
"""image_size""": 260,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 8, 16],
},
"""b3""": {
"""hidden_dim""": 1536,
"""width_coef""": 1.2,
"""depth_coef""": 1.4,
"""image_size""": 300,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 18],
},
"""b4""": {
"""hidden_dim""": 1792,
"""width_coef""": 1.4,
"""depth_coef""": 1.8,
"""image_size""": 380,
"""dropout_rate""": 0.4,
"""dw_padding""": [6],
},
"""b5""": {
"""hidden_dim""": 2048,
"""width_coef""": 1.6,
"""depth_coef""": 2.2,
"""image_size""": 456,
"""dropout_rate""": 0.4,
"""dw_padding""": [13, 27],
},
"""b6""": {
"""hidden_dim""": 2304,
"""width_coef""": 1.8,
"""depth_coef""": 2.6,
"""image_size""": 528,
"""dropout_rate""": 0.5,
"""dw_padding""": [31],
},
"""b7""": {
"""hidden_dim""": 2560,
"""width_coef""": 2.0,
"""depth_coef""": 3.1,
"""image_size""": 600,
"""dropout_rate""": 0.5,
"""dw_padding""": [18],
},
}
def lowerCamelCase__ ( _A ):
'''simple docstring'''
snake_case_ = EfficientNetConfig()
snake_case_ = CONFIG_MAP[model_name]["hidden_dim"]
snake_case_ = CONFIG_MAP[model_name]["width_coef"]
snake_case_ = CONFIG_MAP[model_name]["depth_coef"]
snake_case_ = CONFIG_MAP[model_name]["image_size"]
snake_case_ = CONFIG_MAP[model_name]["dropout_rate"]
snake_case_ = CONFIG_MAP[model_name]["dw_padding"]
snake_case_ = "huggingface/label-files"
snake_case_ = "imagenet-1k-id2label.json"
snake_case_ = 1000
snake_case_ = json.load(open(hf_hub_download(_A , _A , repo_type="dataset" ) , "r" ) )
snake_case_ = {int(_A ): v for k, v in idalabel.items()}
snake_case_ = idalabel
snake_case_ = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase__ ( ):
'''simple docstring'''
snake_case_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
snake_case_ = Image.open(requests.get(_A , stream=_A ).raw )
return im
def lowerCamelCase__ ( _A ):
'''simple docstring'''
snake_case_ = CONFIG_MAP[model_name]["image_size"]
snake_case_ = EfficientNetImageProcessor(
size={"height": size, "width": size} , image_mean=[0.4_85, 0.4_56, 0.4_06] , image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63] , do_center_crop=_A , )
return preprocessor
def lowerCamelCase__ ( _A ):
'''simple docstring'''
snake_case_ = [v.split("_" )[0].split("block" )[1] for v in original_param_names if v.startswith("block" )]
snake_case_ = sorted(set(_A ) )
snake_case_ = len(_A )
snake_case_ = {b: str(_A ) for b, i in zip(_A , range(_A ) )}
snake_case_ = []
rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight") )
rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight") )
rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias") )
rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean") )
rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var") )
for b in block_names:
snake_case_ = block_name_mapping[b]
rename_keys.append((f"block{b}_expand_conv/kernel:0", f"encoder.blocks.{hf_b}.expansion.expand_conv.weight") )
rename_keys.append((f"block{b}_expand_bn/gamma:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.weight") )
rename_keys.append((f"block{b}_expand_bn/beta:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.bias") )
rename_keys.append(
(f"block{b}_expand_bn/moving_mean:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean") )
rename_keys.append(
(f"block{b}_expand_bn/moving_variance:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_var") )
rename_keys.append(
(f"block{b}_dwconv/depthwise_kernel:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight") )
rename_keys.append((f"block{b}_bn/gamma:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight") )
rename_keys.append((f"block{b}_bn/beta:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias") )
rename_keys.append(
(f"block{b}_bn/moving_mean:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean") )
rename_keys.append(
(f"block{b}_bn/moving_variance:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var") )
rename_keys.append((f"block{b}_se_reduce/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight") )
rename_keys.append((f"block{b}_se_reduce/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias") )
rename_keys.append((f"block{b}_se_expand/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.weight") )
rename_keys.append((f"block{b}_se_expand/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.bias") )
rename_keys.append(
(f"block{b}_project_conv/kernel:0", f"encoder.blocks.{hf_b}.projection.project_conv.weight") )
rename_keys.append((f"block{b}_project_bn/gamma:0", f"encoder.blocks.{hf_b}.projection.project_bn.weight") )
rename_keys.append((f"block{b}_project_bn/beta:0", f"encoder.blocks.{hf_b}.projection.project_bn.bias") )
rename_keys.append(
(f"block{b}_project_bn/moving_mean:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_mean") )
rename_keys.append(
(f"block{b}_project_bn/moving_variance:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_var") )
rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight") )
rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight") )
rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias") )
rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean") )
rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var") )
snake_case_ = {}
for item in rename_keys:
if item[0] in original_param_names:
snake_case_ = "efficientnet." + item[1]
snake_case_ = "classifier.weight"
snake_case_ = "classifier.bias"
return key_mapping
def lowerCamelCase__ ( _A , _A , _A ):
'''simple docstring'''
for key, value in tf_params.items():
if "normalization" in key:
continue
snake_case_ = key_mapping[key]
if "_conv" in key and "kernel" in key:
snake_case_ = torch.from_numpy(_A ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
snake_case_ = torch.from_numpy(_A ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
snake_case_ = torch.from_numpy(np.transpose(_A ) )
else:
snake_case_ = torch.from_numpy(_A )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(_A )
@torch.no_grad()
def lowerCamelCase__ ( _A , _A , _A , _A ):
'''simple docstring'''
snake_case_ = model_classes[model_name](
include_top=_A , weights="imagenet" , input_tensor=_A , input_shape=_A , pooling=_A , classes=1000 , classifier_activation="softmax" , )
snake_case_ = original_model.trainable_variables
snake_case_ = original_model.non_trainable_variables
snake_case_ = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
snake_case_ = param.numpy()
snake_case_ = list(tf_params.keys() )
# Load HuggingFace model
snake_case_ = get_efficientnet_config(_A )
snake_case_ = EfficientNetForImageClassification(_A ).eval()
snake_case_ = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("Converting parameters..." )
snake_case_ = rename_keys(_A )
replace_params(_A , _A , _A )
# Initialize preprocessor and preprocess input image
snake_case_ = convert_image_processor(_A )
snake_case_ = preprocessor(images=prepare_img() , return_tensors="pt" )
# HF model inference
hf_model.eval()
with torch.no_grad():
snake_case_ = hf_model(**_A )
snake_case_ = outputs.logits.detach().numpy()
# Original model inference
snake_case_ = False
snake_case_ = CONFIG_MAP[model_name]["image_size"]
snake_case_ = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
snake_case_ = image.img_to_array(_A )
snake_case_ = np.expand_dims(_A , axis=0 )
snake_case_ = original_model.predict(_A )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(_A , _A , atol=1E-3 ), "The predicted logits are not the same."
print("Model outputs match!" )
if save_model:
# Create folder to save model
if not os.path.isdir(_A ):
os.mkdir(_A )
# Save converted model and image processor
hf_model.save_pretrained(_A )
preprocessor.save_pretrained(_A )
if push_to_hub:
# Push model and image processor to hub
print(f"Pushing converted {model_name} to the hub..." )
snake_case_ = f"efficientnet-{model_name}"
preprocessor.push_to_hub(_A )
hf_model.push_to_hub(_A )
if __name__ == "__main__":
lowercase__ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="b0",
type=str,
help="Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="hf_model",
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument("--save_model", action="store_true", help="Save model to local")
parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
lowercase__ : List[str] = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 376
|
'''simple docstring'''
from __future__ import annotations
import math
_lowercase : Dict = """2020.9.26"""
_lowercase : Any = """xcodz-dot, cclaus, dhruvmanila"""
def lowerCamelCase__ ( A : float , A : float , A : float , A : float , A : float ):
'''simple docstring'''
if not all(isinstance(A , (float, int) ) for val in locals().values() ):
UpperCAmelCase = f"""Input values must either be float or int: {list(locals().values() )}"""
raise TypeError(A )
UpperCAmelCase = ((x * distance) / (z + distance)) * scale
UpperCAmelCase = ((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def lowerCamelCase__ ( A : float , A : float , A : float , A : str , A : float ):
'''simple docstring'''
if not isinstance(A , A ):
raise TypeError('''Axis must be a str''' )
UpperCAmelCase = locals()
del input_variables["axis"]
if not all(isinstance(A , (float, int) ) for val in input_variables.values() ):
UpperCAmelCase = (
'''Input values except axis must either be float or int: '''
f"""{list(input_variables.values() )}"""
)
raise TypeError(A )
UpperCAmelCase = (angle % 3_60) / 4_50 * 1_80 / math.pi
if axis == "z":
UpperCAmelCase = x * math.cos(A ) - y * math.sin(A )
UpperCAmelCase = y * math.cos(A ) + x * math.sin(A )
UpperCAmelCase = z
elif axis == "x":
UpperCAmelCase = y * math.cos(A ) - z * math.sin(A )
UpperCAmelCase = z * math.cos(A ) + y * math.sin(A )
UpperCAmelCase = x
elif axis == "y":
UpperCAmelCase = x * math.cos(A ) - z * math.sin(A )
UpperCAmelCase = z * math.cos(A ) + x * math.sin(A )
UpperCAmelCase = y
else:
raise ValueError('''not a valid axis, choose one of \'x\', \'y\', \'z\'''' )
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
print(F"""{rotate(1.0, 2.0, 3.0, "y", 90.0) = }""")
| 210
| 0
|
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"""google/pix2struct-textcaps-base""": (
"""https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
),
}
class __a ( _lowerCAmelCase ):
UpperCamelCase_ : Optional[Any] = '''pix2struct_text_model'''
UpperCamelCase_ : int = ['''past_key_values''']
UpperCamelCase_ : Tuple = {
'''hidden_size''': '''hidden_size''',
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self : Dict , UpperCAmelCase_ : int=50_244 , UpperCAmelCase_ : Optional[int]=768 , UpperCAmelCase_ : Any=64 , UpperCAmelCase_ : Optional[Any]=2_048 , UpperCAmelCase_ : List[str]=12 , UpperCAmelCase_ : Dict=12 , UpperCAmelCase_ : Optional[Any]=32 , UpperCAmelCase_ : Tuple=128 , UpperCAmelCase_ : Dict=0.1 , UpperCAmelCase_ : Tuple=1e-6 , UpperCAmelCase_ : Any=1.0 , UpperCAmelCase_ : List[str]="gelu_new" , UpperCAmelCase_ : List[Any]=0 , UpperCAmelCase_ : Any=False , UpperCAmelCase_ : Any=0 , UpperCAmelCase_ : Dict=1 , UpperCAmelCase_ : List[Any]=False , UpperCAmelCase_ : Optional[int]=True , **UpperCAmelCase_ : List[Any] , )-> Tuple:
"""simple docstring"""
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = d_kv
UpperCamelCase = d_ff
UpperCamelCase = num_layers
UpperCamelCase = num_heads
UpperCamelCase = relative_attention_num_buckets
UpperCamelCase = relative_attention_max_distance
UpperCamelCase = dropout_rate
UpperCamelCase = layer_norm_epsilon
UpperCamelCase = initializer_factor
UpperCamelCase = use_cache
UpperCamelCase = eos_token_id
UpperCamelCase = decoder_start_token_id
# for backwards compatibility
UpperCamelCase = dense_act_fn
super().__init__(
pad_token_id=UpperCAmelCase_ , eos_token_id=UpperCAmelCase_ , decoder_start_token_id=UpperCAmelCase_ , tie_word_embeddings=UpperCAmelCase_ , is_decoder=UpperCAmelCase_ , **UpperCAmelCase_ , )
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : str , UpperCAmelCase_ : Union[str, os.PathLike] , **UpperCAmelCase_ : int )-> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(UpperCAmelCase_ )
UpperCamelCase , UpperCamelCase = cls.get_config_dict(UpperCAmelCase_ , **UpperCAmelCase_ )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get("model_type" ) == "pix2struct":
UpperCamelCase = config_dict["text_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(UpperCAmelCase_ , **UpperCAmelCase_ )
class __a ( _lowerCAmelCase ):
UpperCamelCase_ : Union[str, Any] = '''pix2struct_vision_model'''
def __init__( self : Tuple , UpperCAmelCase_ : str=768 , UpperCAmelCase_ : Optional[Any]=768 , UpperCAmelCase_ : List[str]=2_048 , UpperCAmelCase_ : Union[str, Any]=64 , UpperCAmelCase_ : Dict=12 , UpperCAmelCase_ : Tuple=12 , UpperCAmelCase_ : str="gelu_new" , UpperCAmelCase_ : List[Any]=1e-6 , UpperCAmelCase_ : Tuple=0.0 , UpperCAmelCase_ : Union[str, Any]=0.0 , UpperCAmelCase_ : List[str]=1e-10 , UpperCAmelCase_ : Dict=1.0 , UpperCAmelCase_ : int=4_096 , UpperCAmelCase_ : Any=32 , UpperCAmelCase_ : List[Any]=128 , **UpperCAmelCase_ : List[Any] , )-> Optional[Any]:
"""simple docstring"""
super().__init__(**UpperCAmelCase_ )
UpperCamelCase = hidden_size
UpperCamelCase = patch_embed_hidden_size
UpperCamelCase = d_ff
UpperCamelCase = dropout_rate
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = initializer_range
UpperCamelCase = initializer_factor
UpperCamelCase = attention_dropout
UpperCamelCase = layer_norm_eps
UpperCamelCase = dense_act_fn
UpperCamelCase = seq_len
UpperCamelCase = relative_attention_num_buckets
UpperCamelCase = relative_attention_max_distance
UpperCamelCase = d_kv
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Optional[Any] , UpperCAmelCase_ : Union[str, os.PathLike] , **UpperCAmelCase_ : Optional[int] )-> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(UpperCAmelCase_ )
UpperCamelCase , UpperCamelCase = cls.get_config_dict(UpperCAmelCase_ , **UpperCAmelCase_ )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get("model_type" ) == "pix2struct":
UpperCamelCase = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(UpperCAmelCase_ , **UpperCAmelCase_ )
class __a ( _lowerCAmelCase ):
UpperCamelCase_ : Dict = '''pix2struct'''
UpperCamelCase_ : int = True
def __init__( self : Optional[Any] , UpperCAmelCase_ : Union[str, Any]=None , UpperCAmelCase_ : Optional[Any]=None , UpperCAmelCase_ : int=1.0 , UpperCAmelCase_ : List[str]=0.02 , UpperCAmelCase_ : List[str]=False , UpperCAmelCase_ : Tuple=False , UpperCAmelCase_ : List[Any]=True , **UpperCAmelCase_ : Optional[Any] , )-> int:
"""simple docstring"""
super().__init__(tie_word_embeddings=UpperCAmelCase_ , is_encoder_decoder=UpperCAmelCase_ , **UpperCAmelCase_ )
if text_config is None:
UpperCamelCase = {}
logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values." )
if vision_config is None:
UpperCamelCase = {}
logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values." )
UpperCamelCase = PixaStructTextConfig(**UpperCAmelCase_ )
UpperCamelCase = PixaStructVisionConfig(**UpperCAmelCase_ )
UpperCamelCase = self.text_config.decoder_start_token_id
UpperCamelCase = self.text_config.pad_token_id
UpperCamelCase = self.text_config.eos_token_id
UpperCamelCase = initializer_factor
UpperCamelCase = initializer_range
UpperCamelCase = self.initializer_range
UpperCamelCase = self.initializer_range
UpperCamelCase = is_vqa
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Optional[Any] , UpperCAmelCase_ : PixaStructTextConfig , UpperCAmelCase_ : PixaStructVisionConfig , **UpperCAmelCase_ : int )-> int:
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **UpperCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Tuple )-> str:
"""simple docstring"""
UpperCamelCase = copy.deepcopy(self.__dict__ )
UpperCamelCase = self.text_config.to_dict()
UpperCamelCase = self.vision_config.to_dict()
UpperCamelCase = self.__class__.model_type
return output
| 556
|
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class __a ( _lowerCAmelCase , unittest.TestCase ):
UpperCamelCase_ : Any = BarthezTokenizer
UpperCamelCase_ : Tuple = BarthezTokenizerFast
UpperCamelCase_ : str = True
UpperCamelCase_ : Dict = True
def _SCREAMING_SNAKE_CASE ( self : List[Any] )-> int:
"""simple docstring"""
super().setUp()
UpperCamelCase = BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez" )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=UpperCAmelCase_ )
UpperCamelCase = tokenizer
def _SCREAMING_SNAKE_CASE ( self : Dict )-> Optional[Any]:
"""simple docstring"""
UpperCamelCase = "<pad>"
UpperCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_ ) , UpperCAmelCase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_ ) , UpperCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] )-> int:
"""simple docstring"""
UpperCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "<mask>" )
self.assertEqual(len(UpperCAmelCase_ ) , 101_122 )
def _SCREAMING_SNAKE_CASE ( self : List[Any] )-> List[str]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 101_122 )
@require_torch
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] )-> int:
"""simple docstring"""
UpperCamelCase = ["A long paragraph for summarization.", "Another paragraph for summarization."]
UpperCamelCase = [0, 57, 3_018, 70_307, 91, 2]
UpperCamelCase = self.tokenizer(
UpperCAmelCase_ , max_length=len(UpperCAmelCase_ ) , padding=UpperCAmelCase_ , truncation=UpperCAmelCase_ , return_tensors="pt" )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
UpperCamelCase = batch.input_ids.tolist()[0]
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] )-> Optional[int]:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = self.get_rust_tokenizer()
UpperCamelCase = "I was born in 92000, and this is falsé."
UpperCamelCase = tokenizer.tokenize(UpperCAmelCase_ )
UpperCamelCase = rust_tokenizer.tokenize(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
UpperCamelCase = tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
UpperCamelCase = rust_tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
UpperCamelCase = self.get_rust_tokenizer()
UpperCamelCase = tokenizer.encode(UpperCAmelCase_ )
UpperCamelCase = rust_tokenizer.encode(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _SCREAMING_SNAKE_CASE ( self : int )-> Union[str, Any]:
"""simple docstring"""
# fmt: off
UpperCamelCase = {"input_ids": [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
UpperCamelCase = [
"Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
"utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
"À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
"pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
"telles que la traduction et la synthèse de texte.",
]
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase_ , model_name="moussaKam/mbarthez" , revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6" , sequences=UpperCAmelCase_ , )
| 556
| 1
|
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {'''tokenizer_file''': '''tokenizer.json'''}
__lowerCAmelCase = {
'''tokenizer_file''': {
'''bigscience/tokenizer''': '''https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json''',
'''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json''',
'''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json''',
'''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json''',
'''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json''',
'''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json''',
'''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json''',
},
}
class UpperCAmelCase__ ( _lowerCamelCase ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = VOCAB_FILES_NAMES
__UpperCAmelCase : Any = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase : Optional[Any] = ["""input_ids""", """attention_mask"""]
__UpperCAmelCase : Any = None
def __init__( self : List[str] ,_a : Any=None ,_a : Optional[Any]=None ,_a : Optional[Any]=None ,_a : List[str]="<unk>" ,_a : List[str]="<s>" ,_a : Dict="</s>" ,_a : Tuple="<pad>" ,_a : str=False ,_a : str=False ,**_a : Any ,):
'''simple docstring'''
super().__init__(
__a ,__a ,tokenizer_file=__a ,unk_token=__a ,bos_token=__a ,eos_token=__a ,pad_token=__a ,add_prefix_space=__a ,clean_up_tokenization_spaces=__a ,**__a ,)
_a : int = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' ,__a ) != add_prefix_space:
_a : List[str] = getattr(__a ,pre_tok_state.pop('type' ) )
_a : List[str] = add_prefix_space
_a : List[str] = pre_tok_class(**__a )
_a : Any = add_prefix_space
def __lowercase ( self : Optional[int] ,*_a : Dict ,**_a : List[str] ):
'''simple docstring'''
_a : int = kwargs.get('is_split_into_words' ,__a )
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
' pretokenized inputs.' )
return super()._batch_encode_plus(*__a ,**__a )
def __lowercase ( self : Tuple ,*_a : Union[str, Any] ,**_a : List[Any] ):
'''simple docstring'''
_a : int = kwargs.get('is_split_into_words' ,__a )
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
' pretokenized inputs.' )
return super()._encode_plus(*__a ,**__a )
def __lowercase ( self : Any ,_a : List[Any] ,_a : List[str] = None ):
'''simple docstring'''
_a : Optional[int] = self._tokenizer.model.save(__a ,name=__a )
return tuple(__a )
def __lowercase ( self : List[str] ,_a : Optional[int] ):
'''simple docstring'''
_a : List[Any] = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(__a ,add_special_tokens=__a ) + [self.eos_token_id] )
if len(__a ) > self.model_max_length:
_a : Tuple = input_ids[-self.model_max_length :]
return input_ids
| 229
|
"""simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snake_case_ ( _lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_: List[str] = CodeGenTokenizer
SCREAMING_SNAKE_CASE_: Optional[Any] = CodeGenTokenizerFast
SCREAMING_SNAKE_CASE_: Any = True
SCREAMING_SNAKE_CASE_: str = {"""add_prefix_space""": True}
SCREAMING_SNAKE_CASE_: Any = False
def _UpperCAmelCase ( self ):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
A__ = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
'<|endoftext|>',
]
A__ = dict(zip(__a , range(len(__a ) ) ) )
A__ = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
A__ = {'unk_token': '<unk>'}
A__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
A__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__a ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__a ) )
def _UpperCAmelCase ( self , **__a ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **__a )
def _UpperCAmelCase ( self , **__a ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **__a )
def _UpperCAmelCase ( self , __a ):
"""simple docstring"""
A__ = 'lower newer'
A__ = 'lower newer'
return input_text, output_text
def _UpperCAmelCase ( self ):
"""simple docstring"""
A__ = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
A__ = 'lower newer'
A__ = ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
A__ = tokenizer.tokenize(__a , add_prefix_space=__a )
self.assertListEqual(__a , __a )
A__ = tokens + [tokenizer.unk_token]
A__ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , __a )
def _UpperCAmelCase ( self ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
A__ = self.get_tokenizer()
A__ = self.get_rust_tokenizer(add_prefix_space=__a )
A__ = 'lower newer'
# Testing tokenization
A__ = tokenizer.tokenize(__a , add_prefix_space=__a )
A__ = rust_tokenizer.tokenize(__a )
self.assertListEqual(__a , __a )
# Testing conversion to ids without special tokens
A__ = tokenizer.encode(__a , add_special_tokens=__a , add_prefix_space=__a )
A__ = rust_tokenizer.encode(__a , add_special_tokens=__a )
self.assertListEqual(__a , __a )
# Testing conversion to ids with special tokens
A__ = self.get_rust_tokenizer(add_prefix_space=__a )
A__ = tokenizer.encode(__a , add_prefix_space=__a )
A__ = rust_tokenizer.encode(__a )
self.assertListEqual(__a , __a )
# Testing the unknown token
A__ = tokens + [rust_tokenizer.unk_token]
A__ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__a ) , __a )
def _UpperCAmelCase ( self , *__a , **__a ):
"""simple docstring"""
pass
def _UpperCAmelCase ( self , __a=15 ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
A__ = self.rust_tokenizer_class.from_pretrained(__a , **__a )
# Simple input
A__ = 'This is a simple input'
A__ = ['This is a simple input 1', 'This is a simple input 2']
A__ = ('This is a simple input', 'This is a pair')
A__ = [
('This is a simple input 1', 'This is a simple input 2'),
('This is a simple pair 1', 'This is a simple pair 2'),
]
# Simple input tests
self.assertRaises(__a , tokenizer_r.encode , __a , max_length=__a , padding='max_length' )
# Simple input
self.assertRaises(__a , tokenizer_r.encode_plus , __a , max_length=__a , padding='max_length' )
# Simple input
self.assertRaises(
__a , tokenizer_r.batch_encode_plus , __a , max_length=__a , padding='max_length' , )
# Pair input
self.assertRaises(__a , tokenizer_r.encode , __a , max_length=__a , padding='max_length' )
# Pair input
self.assertRaises(__a , tokenizer_r.encode_plus , __a , max_length=__a , padding='max_length' )
# Pair input
self.assertRaises(
__a , tokenizer_r.batch_encode_plus , __a , max_length=__a , padding='max_length' , )
def _UpperCAmelCase ( self ):
"""simple docstring"""
A__ = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token='<pad>' )
# Simple input
A__ = 'This is a simple input'
A__ = ['This is a simple input looooooooong', 'This is a simple input']
A__ = ('This is a simple input', 'This is a pair')
A__ = [
('This is a simple input loooooong', 'This is a simple input'),
('This is a simple pair loooooong', 'This is a simple pair'),
]
A__ = tokenizer.pad_token_id
A__ = tokenizer(__a , padding='max_length' , max_length=30 , return_tensors='np' )
A__ = tokenizer(__a , padding=__a , truncate=__a , return_tensors='np' )
A__ = tokenizer(*__a , padding='max_length' , max_length=60 , return_tensors='np' )
A__ = tokenizer(__a , padding=__a , truncate=__a , return_tensors='np' )
# s
# test single string max_length padding
self.assertEqual(out_s['input_ids'].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['input_ids'] )
self.assertTrue(0 in out_s['attention_mask'] )
# s2
# test automatic padding
self.assertEqual(out_sa['input_ids'].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['input_ids'][0] )
self.assertFalse(0 in out_sa['attention_mask'][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['input_ids'][1] )
self.assertTrue(0 in out_sa['attention_mask'][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['input_ids'].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['input_ids'] )
self.assertTrue(0 in out_p['attention_mask'] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['input_ids'].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['input_ids'][0] )
self.assertFalse(0 in out_pa['attention_mask'][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['input_ids'][1] )
self.assertTrue(0 in out_pa['attention_mask'][1] )
def _UpperCAmelCase ( self ):
"""simple docstring"""
A__ = '$$$'
A__ = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=__a , add_bos_token=__a )
A__ = 'This is a simple input'
A__ = ['This is a simple input 1', 'This is a simple input 2']
A__ = tokenizer.bos_token_id
A__ = tokenizer(__a )
A__ = tokenizer(__a )
self.assertEqual(out_s.input_ids[0] , __a )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
A__ = tokenizer.decode(out_s.input_ids )
A__ = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , __a )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def _UpperCAmelCase ( self ):
"""simple docstring"""
A__ = CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono' )
A__ = '\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#'
A__ = '\nif len_a > len_b: result = a\nelse: result = b'
A__ = tokenizer.encode(__a )
A__ = ['^#', re.escape('<|endoftext|>' ), '^\'\'\'', '^"""', '\n\n\n']
A__ = tokenizer.decode(__a , truncate_before_pattern=__a )
self.assertEqual(__a , __a )
def _UpperCAmelCase ( self ):
"""simple docstring"""
pass
| 260
| 0
|
def __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def __UpperCamelCase ( _A , _A , _A ):
lowerCAmelCase_ = 0
while b > 0:
if b & 1:
lowerCAmelCase_ = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res
| 704
|
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 = logging.get_logger(__name__)
@add_end_docstrings(__UpperCAmelCase )
class A ( __UpperCAmelCase ):
def __init__( self, *UpperCamelCase__, **UpperCamelCase__ ):
"""simple docstring"""
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 SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__=None, UpperCamelCase__=None, UpperCamelCase__=None ):
"""simple docstring"""
lowerCAmelCase_ = {}
lowerCAmelCase_ = {}
if prompt is not None:
lowerCAmelCase_ = prompt
if generate_kwargs is not None:
lowerCAmelCase_ = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
lowerCAmelCase_ = {}
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''' )
lowerCAmelCase_ = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self, UpperCamelCase__, **UpperCamelCase__ ):
"""simple docstring"""
return super().__call__(UpperCamelCase__, **UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__=None ):
"""simple docstring"""
lowerCAmelCase_ = 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.''' )
lowerCAmelCase_ = self.model.config.model_type
if model_type == "git":
lowerCAmelCase_ = self.image_processor(images=UpperCamelCase__, return_tensors=self.framework )
lowerCAmelCase_ = self.tokenizer(text=UpperCamelCase__, add_special_tokens=UpperCamelCase__ ).input_ids
lowerCAmelCase_ = [self.tokenizer.cls_token_id] + input_ids
lowerCAmelCase_ = torch.tensor(UpperCamelCase__ ).unsqueeze(0 )
model_inputs.update({'''input_ids''': input_ids} )
elif model_type == "pix2struct":
lowerCAmelCase_ = 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
lowerCAmelCase_ = self.image_processor(images=UpperCamelCase__, return_tensors=self.framework )
lowerCAmelCase_ = 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:
lowerCAmelCase_ = self.image_processor(images=UpperCamelCase__, return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
lowerCAmelCase_ = None
return model_inputs
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__=None ):
"""simple docstring"""
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'''] )
):
lowerCAmelCase_ = None
if generate_kwargs is None:
lowerCAmelCase_ = {}
# 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.
lowerCAmelCase_ = model_inputs.pop(self.model.main_input_name )
lowerCAmelCase_ = self.model.generate(UpperCamelCase__, **UpperCamelCase__, **UpperCamelCase__ )
return model_outputs
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = []
for output_ids in model_outputs:
lowerCAmelCase_ = {
'''generated_text''': self.tokenizer.decode(
UpperCamelCase__, skip_special_tokens=UpperCamelCase__, )
}
records.append(UpperCamelCase__ )
return records
| 325
| 0
|
import itertools
import math
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowerCamelCase__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def SCREAMING_SNAKE_CASE__ ( ) -> Union[str, Any]:
__lowerCamelCase : Union[str, Any] = 2
while True:
if is_prime(lowerCamelCase__ ):
yield num
num += 1
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ = 1_0_0_0_1 ) -> int:
return next(itertools.islice(prime_generator() , nth - 1 , lowerCamelCase__ ) )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 652
|
import math
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> int:
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
__lowerCamelCase : List[str] = F"Input value of [number={number}] must be an integer"
raise TypeError(lowerCamelCase__ )
if number < 1:
__lowerCamelCase : int = F"Input value of [number={number}] must be > 0"
raise ValueError(lowerCamelCase__ )
elif number == 1:
return 3
elif number == 2:
return 5
else:
__lowerCamelCase : Any = int(math.log(number // 3 , 2 ) ) + 2
__lowerCamelCase : List[Any] = [3, 5]
__lowerCamelCase : Union[str, Any] = 2
__lowerCamelCase : List[str] = 3
for block in range(1 , lowerCamelCase__ ):
for _ in range(lowerCamelCase__ ):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(11):
a =0
try:
a =proth(number)
except ValueError:
print(F"""ValueError: there is no {number}th Proth number""")
continue
print(F"""The {number}th Proth number: {value}""")
| 652
| 1
|
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _UpperCAmelCase ( unittest.TestCase):
__lowercase : int = JukeboxTokenizer
__lowercase : Any = {
"""artist""": """Zac Brown Band""",
"""genres""": """Country""",
"""lyrics""": """I met a traveller from an antique land,
Who said \"Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Half sunk a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them, and the heart that fed;
And on the pedestal, these words appear:
My name is Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!
Nothing beside remains. Round the decay
Of that colossal Wreck, boundless and bare
The lone and level sands stretch far away
""",
}
@require_torch
def lowerCamelCase__ ( self ):
import torch
_snake_case : List[str] = JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics" )
_snake_case : Dict = tokenizer(**self.metas )["input_ids"]
# fmt: off
_snake_case : Optional[int] = [
torch.tensor([[
0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
76, 76]] ),
torch.tensor([[0, 0, 0, 10_69, 11]] ),
torch.tensor([[0, 0, 0, 10_69, 11]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
@require_torch
def lowerCamelCase__ ( self ):
import torch
_snake_case : List[str] = JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics" )
_snake_case : Dict = tokenizer(**self.metas )["input_ids"]
# fmt: off
_snake_case : Tuple = [
torch.tensor([[
0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
77, 77, 77, 77, 77, 77]] ),
torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
| 87
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Optional[int] = logging.get_logger(__name__)
_a : str = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class _UpperCAmelCase ( _snake_case):
__lowercase : Optional[Any] = """openai-gpt"""
__lowercase : Dict = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , snake_case_=4_04_78 , snake_case_=5_12 , snake_case_=7_68 , snake_case_=12 , snake_case_=12 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=0.1 , snake_case_=1E-5 , snake_case_=0.02 , snake_case_="cls_index" , snake_case_=True , snake_case_=None , snake_case_=True , snake_case_=0.1 , **snake_case_ , ):
_snake_case : Tuple = vocab_size
_snake_case : Dict = n_positions
_snake_case : Any = n_embd
_snake_case : Any = n_layer
_snake_case : Optional[int] = n_head
_snake_case : Union[str, Any] = afn
_snake_case : Dict = resid_pdrop
_snake_case : str = embd_pdrop
_snake_case : Union[str, Any] = attn_pdrop
_snake_case : str = layer_norm_epsilon
_snake_case : Union[str, Any] = initializer_range
_snake_case : Any = summary_type
_snake_case : List[str] = summary_use_proj
_snake_case : Optional[int] = summary_activation
_snake_case : Union[str, Any] = summary_first_dropout
_snake_case : Optional[int] = summary_proj_to_labels
super().__init__(**snake_case_ )
| 87
| 1
|
'''simple docstring'''
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def lowercase (_A , _A=() , _A=None , _A="no" , _A="29500" ):
"""simple docstring"""
_lowerCAmelCase : Dict = False
_lowerCAmelCase : int = False
if any(key.startswith('KAGGLE' ) for key in os.environ.keys() ):
_lowerCAmelCase : str = True
elif "IPython" in sys.modules:
_lowerCAmelCase : Tuple = 'google.colab' in str(sys.modules['IPython'].get_ipython() )
try:
_lowerCAmelCase : str = PrecisionType(mixed_precision.lower() )
except ValueError:
raise ValueError(
f'Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.' )
if (in_colab or in_kaggle) and (os.environ.get('TPU_NAME' , __snake_case ) is not None):
# TPU launch
import torch_xla.distributed.xla_multiprocessing as xmp
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
'To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '
'your training function. Restart your notebook and make sure no cells initializes an '
'`Accelerator`.' )
if num_processes is None:
_lowerCAmelCase : Dict = 8
_lowerCAmelCase : Union[str, Any] = PrepareForLaunch(__snake_case , distributed_type='TPU' )
print(f'Launching a training on {num_processes} TPU cores.' )
xmp.spawn(__snake_case , args=__snake_case , nprocs=__snake_case , start_method='fork' )
elif in_colab:
# No need for a distributed launch otherwise as it's either CPU or one GPU.
if torch.cuda.is_available():
print('Launching training on one GPU.' )
else:
print('Launching training on one CPU.' )
function(*__snake_case )
else:
if num_processes is None:
raise ValueError(
'You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.' )
if num_processes > 1:
# Multi-GPU launch
from torch.multiprocessing import start_processes
from torch.multiprocessing.spawn import ProcessRaisedException
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
'To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '
'inside your training function. Restart your notebook and make sure no cells initializes an '
'`Accelerator`.' )
if torch.cuda.is_initialized():
raise ValueError(
'To launch a multi-GPU training from your notebook, you need to avoid running any instruction '
'using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '
'function.' )
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=__snake_case , master_addr='127.0.01' , master_port=__snake_case , mixed_precision=__snake_case ):
_lowerCAmelCase : Optional[Any] = PrepareForLaunch(__snake_case , distributed_type='MULTI_GPU' )
print(f'Launching training on {num_processes} GPUs.' )
try:
start_processes(__snake_case , args=__snake_case , nprocs=__snake_case , start_method='fork' )
except ProcessRaisedException as e:
if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
raise RuntimeError(
'CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '
'This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '
'Please review your imports and test them when running the `notebook_launcher()` to identify '
'which one is problematic.' ) from e
else:
# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
if is_mps_available():
_lowerCAmelCase : Union[str, Any] = '1'
print('Launching training on MPS.' )
elif torch.cuda.is_available():
print('Launching training on one GPU.' )
else:
print('Launching training on CPU.' )
function(*__snake_case )
def lowercase (_A , _A=() , _A=2 ):
"""simple docstring"""
from torch.multiprocessing import start_processes
with tempfile.NamedTemporaryFile() as tmp_file:
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=__snake_case , master_addr='127.0.01' , master_port='29500' , accelerate_mixed_precision='no' , accelerate_debug_rdv_file=tmp_file.name , accelerate_use_cpu='yes' , ):
_lowerCAmelCase : List[str] = PrepareForLaunch(__snake_case , debug=__snake_case )
start_processes(__snake_case , args=__snake_case , nprocs=__snake_case , start_method='fork' )
| 444
|
'''simple docstring'''
import math
import sys
def snake_case_ ( __snake_case : str) -> str:
lowerCAmelCase_ = ''''''
try:
with open(__snake_case , '''rb''') as binary_file:
lowerCAmelCase_ = binary_file.read()
for dat in data:
lowerCAmelCase_ = F'''{dat:08b}'''
result += curr_byte
return result
except OSError:
print('''File not accessible''')
sys.exit()
def snake_case_ ( __snake_case : str) -> str:
lowerCAmelCase_ = {'''0''': '''0''', '''1''': '''1'''}
lowerCAmelCase_ ,lowerCAmelCase_ = '''''', ''''''
lowerCAmelCase_ = len(__snake_case)
for i in range(len(__snake_case)):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
lowerCAmelCase_ = lexicon[curr_string]
result += last_match_id
lowerCAmelCase_ = last_match_id + '''0'''
if math.loga(__snake_case).is_integer():
lowerCAmelCase_ = {}
for curr_key in list(__snake_case):
lowerCAmelCase_ = lexicon.pop(__snake_case)
lowerCAmelCase_ = new_lex
lowerCAmelCase_ = last_match_id + '''1'''
index += 1
lowerCAmelCase_ = ''''''
return result
def snake_case_ ( __snake_case : str , __snake_case : str) -> None:
lowerCAmelCase_ = 8
try:
with open(__snake_case , '''wb''') as opened_file:
lowerCAmelCase_ = [
to_write[i : i + byte_length]
for i in range(0 , len(__snake_case) , __snake_case)
]
if len(result_byte_array[-1]) % byte_length == 0:
result_byte_array.append('''10000000''')
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1]) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(__snake_case , 2).to_bytes(1 , byteorder='''big'''))
except OSError:
print('''File not accessible''')
sys.exit()
def snake_case_ ( __snake_case : str) -> str:
lowerCAmelCase_ = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
lowerCAmelCase_ = data_bits[counter:]
lowerCAmelCase_ = data_bits[counter + 1 :]
return data_bits
def snake_case_ ( __snake_case : str , __snake_case : str) -> None:
lowerCAmelCase_ = read_file_binary(__snake_case)
lowerCAmelCase_ = remove_prefix(__snake_case)
lowerCAmelCase_ = decompress_data(__snake_case)
write_file_binary(__snake_case , __snake_case)
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 274
| 0
|
"""simple docstring"""
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 (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__A : str = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
__A : Any = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append(
(
f'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
)
)
rename_keys.append(
(
f'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
)
)
rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
)
rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))
# q, k, v projections in self/cross-attention in decoder for conditional DETR
rename_keys.append(
(f'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', f'''decoder.layers.{i}.sa_qcontent_proj.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', f'''decoder.layers.{i}.sa_kcontent_proj.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', f'''decoder.layers.{i}.sa_qpos_proj.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', f'''decoder.layers.{i}.sa_kpos_proj.weight''')
)
rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.weight''', f'''decoder.layers.{i}.sa_v_proj.weight'''))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', f'''decoder.layers.{i}.ca_qcontent_proj.weight''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', f'''decoder.layers.{i}.ca_kcontent_proj.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', f'''decoder.layers.{i}.ca_kpos_proj.weight''')
)
rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.weight''', f'''decoder.layers.{i}.ca_v_proj.weight'''))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', f'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', f'''decoder.layers.{i}.sa_qcontent_proj.bias''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', f'''decoder.layers.{i}.sa_kcontent_proj.bias''')
)
rename_keys.append((f'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', f'''decoder.layers.{i}.sa_qpos_proj.bias'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', f'''decoder.layers.{i}.sa_kpos_proj.bias'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.bias''', f'''decoder.layers.{i}.sa_v_proj.bias'''))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', f'''decoder.layers.{i}.ca_qcontent_proj.bias''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', f'''decoder.layers.{i}.ca_kcontent_proj.bias''')
)
rename_keys.append((f'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', f'''decoder.layers.{i}.ca_kpos_proj.bias'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.bias''', f'''decoder.layers.{i}.ca_v_proj.bias'''))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', f'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
)
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
[
("input_proj.weight", "input_projection.weight"),
("input_proj.bias", "input_projection.bias"),
("query_embed.weight", "query_position_embeddings.weight"),
("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
("class_embed.weight", "class_labels_classifier.weight"),
("class_embed.bias", "class_labels_classifier.bias"),
("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
]
)
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
_UpperCAmelCase = state_dict.pop(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = val
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
_UpperCAmelCase = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
_UpperCAmelCase = key.replace('''backbone.0.body''' , '''backbone.conv_encoder.model''' )
_UpperCAmelCase = value
else:
_UpperCAmelCase = value
return new_state_dict
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : List[Any]=False ):
'''simple docstring'''
_UpperCAmelCase = ''''''
if is_panoptic:
_UpperCAmelCase = '''conditional_detr.'''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
_UpperCAmelCase = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
_UpperCAmelCase = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase = in_proj_weight[:256, :]
_UpperCAmelCase = in_proj_bias[:256]
_UpperCAmelCase = in_proj_weight[256:512, :]
_UpperCAmelCase = in_proj_bias[256:512]
_UpperCAmelCase = in_proj_weight[-256:, :]
_UpperCAmelCase = in_proj_bias[-256:]
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_UpperCAmelCase = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = ConditionalDetrConfig()
# set backbone and dilation attributes
if "resnet101" in model_name:
_UpperCAmelCase = '''resnet101'''
if "dc5" in model_name:
_UpperCAmelCase = True
_UpperCAmelCase = '''panoptic''' in model_name
if is_panoptic:
_UpperCAmelCase = 250
else:
_UpperCAmelCase = 91
_UpperCAmelCase = '''huggingface/label-files'''
_UpperCAmelCase = '''coco-detection-id2label.json'''
_UpperCAmelCase = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type='''dataset''' ) , '''r''' ) )
_UpperCAmelCase = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
_UpperCAmelCase = idalabel
_UpperCAmelCase = {v: k for k, v in idalabel.items()}
# load image processor
_UpperCAmelCase = '''coco_panoptic''' if is_panoptic else '''coco_detection'''
_UpperCAmelCase = ConditionalDetrImageProcessor(format=_SCREAMING_SNAKE_CASE )
# prepare image
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors='''pt''' )
_UpperCAmelCase = encoding['''pixel_values''']
logger.info(f'Converting model {model_name}...' )
# load original model from torch hub
_UpperCAmelCase = torch.hub.load('''DeppMeng/ConditionalDETR''' , _SCREAMING_SNAKE_CASE , pretrained=_SCREAMING_SNAKE_CASE ).eval()
_UpperCAmelCase = conditional_detr.state_dict()
# rename keys
for src, dest in rename_keys:
if is_panoptic:
_UpperCAmelCase = '''conditional_detr.''' + src
rename_key(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = rename_backbone_keys(_SCREAMING_SNAKE_CASE )
# query, key and value matrices need special treatment
read_in_q_k_v(_SCREAMING_SNAKE_CASE , is_panoptic=_SCREAMING_SNAKE_CASE )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
_UpperCAmelCase = '''conditional_detr.model.''' if is_panoptic else '''model.'''
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith('''conditional_detr''' )
and not key.startswith('''class_labels_classifier''' )
and not key.startswith('''bbox_predictor''' )
):
_UpperCAmelCase = state_dict.pop(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
_UpperCAmelCase = state_dict.pop(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = val
elif key.startswith('''bbox_attention''' ) or key.startswith('''mask_head''' ):
continue
else:
_UpperCAmelCase = state_dict.pop(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = val
else:
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
_UpperCAmelCase = state_dict.pop(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = val
# finally, create HuggingFace model and load state dict
_UpperCAmelCase = ConditionalDetrForSegmentation(_SCREAMING_SNAKE_CASE ) if is_panoptic else ConditionalDetrForObjectDetection(_SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
model.eval()
model.push_to_hub(repo_id=_SCREAMING_SNAKE_CASE , organization='''DepuMeng''' , commit_message='''Add model''' )
# verify our conversion
_UpperCAmelCase = conditional_detr(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE )
assert torch.allclose(outputs.logits , original_outputs['''pred_logits'''] , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes , original_outputs['''pred_boxes'''] , atol=1E-4 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs['''pred_masks'''] , atol=1E-4 )
# Save model and image processor
logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__A : Tuple = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="conditional_detr_resnet50",
type=str,
help="Name of the CONDITIONAL_DETR model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
__A : str = parser.parse_args()
convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 95
|
"""simple docstring"""
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
_UpperCAmelCase = filter(lambda _SCREAMING_SNAKE_CASE : p.requires_grad , model.parameters() )
_UpperCAmelCase = sum([np.prod(p.size() ) for p in model_parameters] )
return params
__A : Dict = logging.getLogger(__name__)
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
if metric == "rouge2":
_UpperCAmelCase = '''{val_avg_rouge2:.4f}-{step_count}'''
elif metric == "bleu":
_UpperCAmelCase = '''{val_avg_bleu:.4f}-{step_count}'''
elif metric == "em":
_UpperCAmelCase = '''{val_avg_em:.4f}-{step_count}'''
else:
raise NotImplementedError(
f'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
''' function.''' )
_UpperCAmelCase = ModelCheckpoint(
dirpath=_SCREAMING_SNAKE_CASE , filename=_SCREAMING_SNAKE_CASE , monitor=f'val_{metric}' , mode='''max''' , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
return EarlyStopping(
monitor=f'val_{metric}' , mode='''min''' if '''loss''' in metric else '''max''' , patience=_SCREAMING_SNAKE_CASE , verbose=_SCREAMING_SNAKE_CASE , )
class _a ( pl.Callback):
"""simple docstring"""
def lowercase__ ( self : List[str] , __UpperCamelCase : str , __UpperCamelCase : str )->Tuple:
_UpperCAmelCase = {F'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(__UpperCamelCase )
@rank_zero_only
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : pl.Trainer , __UpperCamelCase : pl.LightningModule , __UpperCamelCase : str , __UpperCamelCase : Tuple=True )->None:
logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****' )
_UpperCAmelCase = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']} )
# Log results
_UpperCAmelCase = Path(pl_module.hparams.output_dir )
if type_path == "test":
_UpperCAmelCase = od / '''test_results.txt'''
_UpperCAmelCase = od / '''test_generations.txt'''
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
_UpperCAmelCase = od / F'{type_path}_results/{trainer.global_step:05d}.txt'
_UpperCAmelCase = od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
results_file.parent.mkdir(exist_ok=__UpperCamelCase )
generations_file.parent.mkdir(exist_ok=__UpperCamelCase )
with open(__UpperCamelCase , '''a+''' ) as writer:
for key in sorted(__UpperCamelCase ):
if key in ["log", "progress_bar", "preds"]:
continue
_UpperCAmelCase = metrics[key]
if isinstance(__UpperCamelCase , torch.Tensor ):
_UpperCAmelCase = val.item()
_UpperCAmelCase = F'{key}: {val:.6f}\n'
writer.write(__UpperCamelCase )
if not save_generations:
return
if "preds" in metrics:
_UpperCAmelCase = '''\n'''.join(metrics['''preds'''] )
generations_file.open('''w+''' ).write(__UpperCamelCase )
@rank_zero_only
def lowercase__ ( self : Optional[int] , __UpperCamelCase : int , __UpperCamelCase : Dict )->Union[str, Any]:
try:
_UpperCAmelCase = pl_module.model.model.num_parameters()
except AttributeError:
_UpperCAmelCase = pl_module.model.num_parameters()
_UpperCAmelCase = count_trainable_parameters(__UpperCamelCase )
# mp stands for million parameters
trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1e6, '''grad_mp''': n_trainable_pars / 1e6} )
@rank_zero_only
def lowercase__ ( self : str , __UpperCamelCase : pl.Trainer , __UpperCamelCase : pl.LightningModule )->List[Any]:
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(__UpperCamelCase , __UpperCamelCase , '''test''' )
@rank_zero_only
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : pl.Trainer , __UpperCamelCase : List[str] )->Optional[Any]:
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 95
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def lowercase_ ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
snake_case__ : int =0
snake_case__ : int =len(SCREAMING_SNAKE_CASE ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
snake_case__ : Optional[int] =left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(SCREAMING_SNAKE_CASE ):
return None
snake_case__ : List[Any] =sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
snake_case__ : Any =left
snake_case__ : Tuple =point
elif point > right:
snake_case__ : List[str] =right
snake_case__ : Tuple =point
else:
if item < current_item:
snake_case__ : List[str] =point - 1
else:
snake_case__ : Optional[int] =point + 1
return None
def lowercase_ ( SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[Any] ):
"""simple docstring"""
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
snake_case__ : Tuple =left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(SCREAMING_SNAKE_CASE ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
elif point > right:
return interpolation_search_by_recursion(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , point - 1 )
else:
return interpolation_search_by_recursion(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , point + 1 , SCREAMING_SNAKE_CASE )
def lowercase_ ( SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
if collection != sorted(SCREAMING_SNAKE_CASE ):
raise ValueError('''Collection must be ascending sorted''' )
return True
if __name__ == "__main__":
import sys
lowerCamelCase__ = 0
if debug == 1:
lowerCamelCase__ = [10, 30, 40, 45, 50, 66, 77, 93]
try:
__assert_sorted(collection)
except ValueError:
sys.exit('''Sequence must be ascending sorted to apply interpolation search''')
lowerCamelCase__ = 67
lowerCamelCase__ = interpolation_search(collection, target)
if result is not None:
print(F"""{target} found at positions: {result}""")
else:
print('''Not found''')
| 381
|
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCamelCase__ = '''src/diffusers'''
# Matches is_xxx_available()
lowerCamelCase__ = re.compile(r'''is\_([a-z_]*)_available\(\)''')
# Matches from xxx import bla
lowerCamelCase__ = re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
lowerCamelCase__ = '''
{0} = None
'''
lowerCamelCase__ = '''
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, {1})
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, {1})
'''
lowerCamelCase__ = '''
def {0}(*args, **kwargs):
requires_backends({0}, {1})
'''
def lowercase_ ( SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
snake_case__ : Tuple =_re_backend.findall(SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) == 0:
return None
return "_and_".join(SCREAMING_SNAKE_CASE )
def lowercase_ ( ):
"""simple docstring"""
with open(os.path.join(SCREAMING_SNAKE_CASE , '''__init__.py''' ) , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
snake_case__ : int =f.readlines()
# Get to the point we do the actual imports for type checking
snake_case__ : Optional[Any] =0
snake_case__ : Any ={}
# Go through the end of the file
while line_index < len(SCREAMING_SNAKE_CASE ):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
snake_case__ : List[str] =find_backend(lines[line_index] )
if backend is not None:
while not lines[line_index].startswith('''else:''' ):
line_index += 1
line_index += 1
snake_case__ : List[Any] =[]
# Until we unindent, add backend objects to the list
while line_index < len(SCREAMING_SNAKE_CASE ) and len(lines[line_index] ) > 1:
snake_case__ : List[str] =lines[line_index]
snake_case__ : Any =_re_single_line_import.search(SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 8 ):
objects.append(line[8:-2] )
line_index += 1
if len(SCREAMING_SNAKE_CASE ) > 0:
snake_case__ : List[Any] =objects
else:
line_index += 1
return backend_specific_objects
def lowercase_ ( SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
if name.isupper():
return DUMMY_CONSTANT.format(SCREAMING_SNAKE_CASE )
elif name.islower():
return DUMMY_FUNCTION.format(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
else:
return DUMMY_CLASS.format(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def lowercase_ ( SCREAMING_SNAKE_CASE : str=None ):
"""simple docstring"""
if backend_specific_objects is None:
snake_case__ : int =read_init()
# For special correspondence backend to module name as used in the function requires_modulename
snake_case__ : Dict ={}
for backend, objects in backend_specific_objects.items():
snake_case__ : str ='''[''' + ''', '''.join(F'''"{b}"''' for b in backend.split('''_and_''' ) ) + ''']'''
snake_case__ : List[Any] ='''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) for o in objects] )
snake_case__ : int =dummy_file
return dummy_files
def lowercase_ ( SCREAMING_SNAKE_CASE : Optional[int]=False ):
"""simple docstring"""
snake_case__ : Dict =create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
snake_case__ : int ={'''torch''': '''pt'''}
# Locate actual dummy modules and read their content.
snake_case__ : List[Any] =os.path.join(SCREAMING_SNAKE_CASE , '''utils''' )
snake_case__ : str ={
backend: os.path.join(SCREAMING_SNAKE_CASE , F'''dummy_{short_names.get(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )}_objects.py''' )
for backend in dummy_files.keys()
}
snake_case__ : Tuple ={}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(SCREAMING_SNAKE_CASE ):
with open(SCREAMING_SNAKE_CASE , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
snake_case__ : Optional[int] =f.read()
else:
snake_case__ : Union[str, Any] =''''''
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
F'''Updating diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )}_objects.py as the main '''
'''__init__ has new objects.''' )
with open(dummy_file_paths[backend] , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.write(dummy_files[backend] )
else:
raise ValueError(
'''The main __init__ has objects that are not present in '''
F'''diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )}_objects.py. Run `make fix-copies` '''
'''to fix this.''' )
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
lowerCamelCase__ = parser.parse_args()
check_dummies(args.fix_and_overwrite)
| 381
| 1
|
'''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
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
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ImageGPTImageProcessor
class UpperCamelCase__ ( unittest.TestCase ):
def __init__( self : Tuple , lowerCamelCase : Union[str, Any] , lowerCamelCase : List[str]=7 , lowerCamelCase : int=3 , lowerCamelCase : int=1_8 , lowerCamelCase : Tuple=3_0 , lowerCamelCase : str=4_0_0 , lowerCamelCase : Any=True , lowerCamelCase : int=None , lowerCamelCase : Union[str, Any]=True , ):
'''simple docstring'''
a__ = size if size is not None else {"height": 1_8, "width": 1_8}
a__ = parent
a__ = batch_size
a__ = num_channels
a__ = image_size
a__ = min_resolution
a__ = max_resolution
a__ = do_resize
a__ = size
a__ = do_normalize
def __a ( self : Optional[Any] ):
'''simple docstring'''
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
[-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
] ),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class UpperCamelCase__ ( __lowerCAmelCase ,unittest.TestCase ):
lowerCAmelCase__ : Dict = ImageGPTImageProcessor if is_vision_available() else None
def __a ( self : List[Any] ):
'''simple docstring'''
a__ = ImageGPTImageProcessingTester(self )
@property
def __a ( self : Any ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def __a ( self : Optional[int] ):
'''simple docstring'''
a__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase , "clusters" ) )
self.assertTrue(hasattr(lowerCamelCase , "do_resize" ) )
self.assertTrue(hasattr(lowerCamelCase , "size" ) )
self.assertTrue(hasattr(lowerCamelCase , "do_normalize" ) )
def __a ( self : List[str] ):
'''simple docstring'''
a__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"height": 1_8, "width": 1_8} )
a__ = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 )
self.assertEqual(image_processor.size , {"height": 4_2, "width": 4_2} )
def __a ( self : int ):
'''simple docstring'''
a__ = self.image_processing_class(**self.image_processor_dict )
a__ = json.loads(image_processor.to_json_string() )
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowerCamelCase , obj[key] ) )
else:
self.assertEqual(obj[key] , lowerCamelCase )
def __a ( self : List[Any] ):
'''simple docstring'''
a__ = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
a__ = os.path.join(lowerCamelCase , "image_processor.json" )
image_processor_first.to_json_file(lowerCamelCase )
a__ = self.image_processing_class.from_json_file(lowerCamelCase ).to_dict()
a__ = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowerCamelCase , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , lowerCamelCase )
def __a ( self : str ):
'''simple docstring'''
a__ = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(lowerCamelCase )
a__ = self.image_processing_class.from_pretrained(lowerCamelCase ).to_dict()
a__ = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowerCamelCase , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , lowerCamelCase )
@unittest.skip("ImageGPT requires clusters at initialization" )
def __a ( self : Optional[int] ):
'''simple docstring'''
pass
def _lowerCamelCase () -> Optional[Any]:
a__ = load_dataset("hf-internal-testing/fixtures_image_utils" , split="test" )
a__ = Image.open(dataset[4]["file"] )
a__ = Image.open(dataset[5]["file"] )
a__ = [imagea, imagea]
return images
@require_vision
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
@slow
def __a ( self : Optional[int] ):
'''simple docstring'''
a__ = ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small" )
a__ = prepare_images()
# test non-batched
a__ = image_processing(images[0] , return_tensors="pt" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (1, 1_0_2_4) )
a__ = [3_0_6, 1_9_1, 1_9_1]
self.assertEqual(encoding.input_ids[0, :3].tolist() , lowerCamelCase )
# test batched
a__ = image_processing(lowerCamelCase , return_tensors="pt" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (2, 1_0_2_4) )
a__ = [3_0_3, 1_3, 1_3]
self.assertEqual(encoding.input_ids[1, -3:].tolist() , lowerCamelCase )
| 289
|
'''simple docstring'''
import argparse
import json
from tqdm import tqdm
def _lowerCamelCase () -> Any:
a__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=__lowerCamelCase , default="biencoder-nq-dev.json" , help="Path to raw DPR training data" , )
parser.add_argument(
"--evaluation_set" , type=__lowerCamelCase , help="where to store parsed evaluation_set file" , )
parser.add_argument(
"--gold_data_path" , type=__lowerCamelCase , help="where to store parsed gold_data_path file" , )
a__ = 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:
a__ = json.load(__lowerCamelCase )
for dpr_record in tqdm(__lowerCamelCase ):
a__ = dpr_record["question"]
a__ = [context["title"] for context in dpr_record["positive_ctxs"]]
eval_file.write(question + "\n" )
gold_file.write("\t".join(__lowerCamelCase ) + "\n" )
if __name__ == "__main__":
main()
| 289
| 1
|
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .scheduling_consistency_models import CMStochasticIterativeScheduler
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddim_inverse import DDIMInverseScheduler
from .scheduling_ddim_parallel import DDIMParallelScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_ddpm_parallel import DDPMParallelScheduler
from .scheduling_deis_multistep import DEISMultistepScheduler
from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from .scheduling_euler_discrete import EulerDiscreteScheduler
from .scheduling_heun_discrete import HeunDiscreteScheduler
from .scheduling_ipndm import IPNDMScheduler
from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_repaint import RePaintScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_unclip import UnCLIPScheduler
from .scheduling_unipc_multistep import UniPCMultistepScheduler
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
from .scheduling_vq_diffusion import VQDiffusionScheduler
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_flax_objects import * # noqa F403
else:
from .scheduling_ddim_flax import FlaxDDIMScheduler
from .scheduling_ddpm_flax import FlaxDDPMScheduler
from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
from .scheduling_pndm_flax import FlaxPNDMScheduler
from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
from .scheduling_utils_flax import (
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
broadcast_to_shape_from_left,
)
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .scheduling_lms_discrete import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
| 81
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __lowercase( lowercase__ , lowercase__ , unittest.TestCase ):
'''simple docstring'''
__a : int = StableDiffusionXLImgaImgPipeline
__a : Optional[int] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
__a : Optional[Any] = PipelineTesterMixin.required_optional_params - {'latents'}
__a : List[str] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
__a : List[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
__a : Union[str, Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
def snake_case_ ( self ):
torch.manual_seed(0 )
__lowerCamelCase : str = 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') , attention_head_dim=(2, 4) , use_linear_projection=__a , addition_embed_type='text_time' , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=80 , cross_attention_dim=64 , )
__lowerCamelCase : Union[str, Any] = EulerDiscreteScheduler(
beta_start=0.00_085 , beta_end=0.012 , steps_offset=1 , beta_schedule='scaled_linear' , timestep_spacing='leading' , )
torch.manual_seed(0 )
__lowerCamelCase : Dict = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
__lowerCamelCase : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='gelu' , projection_dim=32 , )
__lowerCamelCase : List[Any] = CLIPTextModel(__a )
__lowerCamelCase : Any = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' , local_files_only=__a )
__lowerCamelCase : List[Any] = CLIPTextModelWithProjection(__a )
__lowerCamelCase : Optional[int] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' , local_files_only=__a )
__lowerCamelCase : List[str] = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'text_encoder_2': text_encoder_a,
'tokenizer_2': tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def snake_case_ ( self , __a , __a=0 ):
__lowerCamelCase : List[str] = floats_tensor((1, 3, 32, 32) , rng=random.Random(__a ) ).to(__a )
__lowerCamelCase : Any = image / 2 + 0.5
if str(__a ).startswith('mps' ):
__lowerCamelCase : Dict = torch.manual_seed(__a )
else:
__lowerCamelCase : Optional[int] = torch.Generator(device=__a ).manual_seed(__a )
__lowerCamelCase : Union[str, Any] = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 5.0,
'output_type': 'numpy',
'strength': 0.75,
}
return inputs
def snake_case_ ( self ):
__lowerCamelCase : List[str] = 'cpu' # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase : Tuple = self.get_dummy_components()
__lowerCamelCase : Union[str, Any] = StableDiffusionXLImgaImgPipeline(**__a )
__lowerCamelCase : Dict = sd_pipe.to(__a )
sd_pipe.set_progress_bar_config(disable=__a )
__lowerCamelCase : Any = self.get_dummy_inputs(__a )
__lowerCamelCase : Tuple = sd_pipe(**__a ).images
__lowerCamelCase : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowerCamelCase : List[str] = np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def snake_case_ ( self ):
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def snake_case_ ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def snake_case_ ( self ):
pass
def snake_case_ ( self ):
__lowerCamelCase : int = self.get_dummy_components()
__lowerCamelCase : Union[str, Any] = StableDiffusionXLImgaImgPipeline(**__a )
__lowerCamelCase : List[Any] = sd_pipe.to(__a )
__lowerCamelCase : Optional[int] = sd_pipe.to(__a )
sd_pipe.set_progress_bar_config(disable=__a )
# forward without prompt embeds
__lowerCamelCase : Tuple = self.get_dummy_inputs(__a )
__lowerCamelCase : Dict = 3 * ['this is a negative prompt']
__lowerCamelCase : Optional[int] = negative_prompt
__lowerCamelCase : List[str] = 3 * [inputs['prompt']]
__lowerCamelCase : Any = sd_pipe(**__a )
__lowerCamelCase : Any = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
__lowerCamelCase : Dict = self.get_dummy_inputs(__a )
__lowerCamelCase : str = 3 * ['this is a negative prompt']
__lowerCamelCase : List[str] = 3 * [inputs.pop('prompt' )]
(
(
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) ,
) : List[str] = sd_pipe.encode_prompt(__a , negative_prompt=__a )
__lowerCamelCase : Union[str, Any] = sd_pipe(
**__a , prompt_embeds=__a , negative_prompt_embeds=__a , pooled_prompt_embeds=__a , negative_pooled_prompt_embeds=__a , )
__lowerCamelCase : Any = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class __lowercase( unittest.TestCase ):
'''simple docstring'''
def snake_case_ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ ( self , __a , __a="cpu" , __a=torch.floataa , __a=0 ):
__lowerCamelCase : List[Any] = torch.Generator(device=__a ).manual_seed(__a )
__lowerCamelCase : List[str] = np.random.RandomState(__a ).standard_normal((1, 4, 64, 64) )
__lowerCamelCase : Tuple = torch.from_numpy(__a ).to(device=__a , dtype=__a )
__lowerCamelCase : Optional[int] = {
'prompt': 'a photograph of an astronaut riding a horse',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def snake_case_ ( self ):
__lowerCamelCase : Optional[Any] = DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base' )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__lowerCamelCase : Dict = self.get_inputs(__a )
__lowerCamelCase : Optional[Any] = pipe(**__a ).images
__lowerCamelCase : int = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase : str = np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506] )
assert np.abs(image_slice - expected_slice ).max() < 7E-3
| 594
| 0
|
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase : int = 5_0 ):
lowerCamelCase_ = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 66
|
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_tensorflow_text_available():
from transformers.models.bert import TFBertTokenizer
__lowercase : List[str] = ["""bert-base-uncased""", """bert-base-cased"""]
__lowercase : Tuple = """hf-internal-testing/tiny-bert-tf-only"""
if is_tf_available():
class lowerCAmelCase ( tf.keras.Model ):
"""simple docstring"""
def __init__( self , UpperCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
lowerCamelCase_ = tokenizer
lowerCamelCase_ = AutoConfig.from_pretrained(UpperCamelCase__ )
lowerCamelCase_ = TFAutoModel.from_config(UpperCamelCase__ )
def _lowerCAmelCase ( self , UpperCamelCase__ ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(UpperCamelCase__ )
lowerCamelCase_ = self.bert(**UpperCamelCase__ )
return out["pooler_output"]
@require_tf
@require_tensorflow_text
class lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self ) -> int:
'''simple docstring'''
super().setUp()
lowerCamelCase_ = [
BertTokenizer.from_pretrained(UpperCamelCase__ ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
] # repeat for when fast_bert_tokenizer=false
lowerCamelCase_ = [TFBertTokenizer.from_pretrained(UpperCamelCase__ ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
TFBertTokenizer.from_pretrained(UpperCamelCase__ , use_fast_bert_tokenizer=UpperCamelCase__ )
for checkpoint in TOKENIZER_CHECKPOINTS
]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
lowerCamelCase_ = [
'''This is a straightforward English test sentence.''',
'''This one has some weird characters\rto\nsee\r\nif those\u00E9break things.''',
'''Now we\'re going to add some Chinese: 一 二 三 一二三''',
'''And some much more rare Chinese: 齉 堃 齉堃''',
'''Je vais aussi écrire en français pour tester les accents''',
'''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
]
lowerCamelCase_ = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def _lowerCAmelCase ( self ) -> List[str]:
'''simple docstring'''
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in (self.test_sentences, self.paired_sentences):
lowerCamelCase_ = tokenizer(UpperCamelCase__ , return_tensors='''tf''' , padding='''longest''' )
lowerCamelCase_ = tf_tokenizer(UpperCamelCase__ )
for key in python_outputs.keys():
self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape ) )
self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] , tf.intaa ) == tf_outputs[key] ) )
@slow
def _lowerCAmelCase ( self ) -> Tuple:
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
lowerCamelCase_ = tf_tokenizer(self.paired_sentences )
lowerCamelCase_ = tf_tokenizer(
text=[sentence[0] for sentence in self.paired_sentences] , text_pair=[sentence[1] for sentence in self.paired_sentences] , )
for key in merged_outputs.keys():
self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] , tf.intaa ) == separated_outputs[key] ) )
@slow
def _lowerCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
lowerCamelCase_ = tf.function(UpperCamelCase__ )
for test_inputs in (self.test_sentences, self.paired_sentences):
lowerCamelCase_ = tf.constant(UpperCamelCase__ )
lowerCamelCase_ = compiled_tokenizer(UpperCamelCase__ )
lowerCamelCase_ = tf_tokenizer(UpperCamelCase__ )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def _lowerCAmelCase ( self ) -> int:
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
lowerCamelCase_ = ModelToSave(tokenizer=UpperCamelCase__ )
lowerCamelCase_ = tf.convert_to_tensor(self.test_sentences )
lowerCamelCase_ = model(UpperCamelCase__ ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
lowerCamelCase_ = Path(UpperCamelCase__ ) / '''saved.model'''
model.save(UpperCamelCase__ )
lowerCamelCase_ = tf.keras.models.load_model(UpperCamelCase__ )
lowerCamelCase_ = loaded_model(UpperCamelCase__ )
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output ) ) , 1e-5 )
| 66
| 1
|
import numpy as np
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = int(np.ceil((x_end - xa) / h ) )
snake_case_ = np.zeros((n + 1,) )
snake_case_ = ya
snake_case_ = xa
for k in range(lowercase__ ):
snake_case_ = f(lowercase__ , y[k] )
snake_case_ = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
snake_case_ = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
snake_case_ = f(x + h , y[k] + h * ka )
snake_case_ = y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
x += h
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 187
|
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = len(lowercase__ )
for _ in range(lowercase__ ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
snake_case_ , snake_case_ = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
A = list(range(10, 0, -1))
print(f"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")
| 187
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase : List[Any] = {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Dict = [
'SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
'SwinForImageClassification',
'SwinForMaskedImageModeling',
'SwinModel',
'SwinPreTrainedModel',
'SwinBackbone',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Optional[int] = [
'TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFSwinForImageClassification',
'TFSwinForMaskedImageModeling',
'TFSwinModel',
'TFSwinPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swin import (
SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinBackbone,
SwinForImageClassification,
SwinForMaskedImageModeling,
SwinModel,
SwinPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_swin import (
TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSwinForImageClassification,
TFSwinForMaskedImageModeling,
TFSwinModel,
TFSwinPreTrainedModel,
)
else:
import sys
lowercase : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 705
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Optional[Any] = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
'Blip2VisionConfig',
],
'processing_blip_2': ['Blip2Processor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Tuple = [
'BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST',
'Blip2Model',
'Blip2QFormerModel',
'Blip2PreTrainedModel',
'Blip2ForConditionalGeneration',
'Blip2VisionModel',
]
if TYPE_CHECKING:
from .configuration_blip_a import (
BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlipaConfig,
BlipaQFormerConfig,
BlipaVisionConfig,
)
from .processing_blip_a import BlipaProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip_a import (
BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipaForConditionalGeneration,
BlipaModel,
BlipaPreTrainedModel,
BlipaQFormerModel,
BlipaVisionModel,
)
else:
import sys
lowercase : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 94
| 0
|
from __future__ import annotations
from collections.abc import Iterator
class _lowercase :
def __init__( self , a ):
snake_case__ : List[str] =value
snake_case__ : Node | None =None
snake_case__ : Node | None =None
class _lowercase :
def __init__( self , a ):
snake_case__ : List[str] =tree
def lowercase__ ( self , a ):
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self ):
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 385
|
import numpy as np
def A__ ( _a : np.array ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 385
| 1
|
"""simple docstring"""
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
A__ : str = logging.get_logger(__name__)
A__ : Any = {"""vocab_file""": """vocab.txt""", """emoji_file""": """emoji.json"""}
A__ : int = {
"""vocab_file""": {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt""",
},
"""emoji_file""": {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json""",
},
}
A__ : Dict = {
"""abeja/gpt-neox-japanese-2.7b""": 2_048,
}
def a__ ( lowerCAmelCase : Optional[int] , lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
with open(lowerCAmelCase , "r" , encoding="utf-8" ) as f:
UpperCAmelCase__ : Optional[Any] = json.loads(f.read() )
UpperCAmelCase__ : Optional[int] = collections.OrderedDict()
UpperCAmelCase__ : List[str] = collections.OrderedDict()
UpperCAmelCase__ : int = collections.OrderedDict()
with open(lowerCAmelCase , "r" , encoding="utf-8" ) as f:
UpperCAmelCase__ : Dict = f.readlines()
UpperCAmelCase__ : List[str] = [[t.rstrip("\n" )] if (t == "," or "," not in t) else t.rstrip("\n" ).split("," ) for t in token]
for idx, b in enumerate(lowerCAmelCase ):
UpperCAmelCase__ : List[Any] = b
UpperCAmelCase__ : str = idx
for wd in b:
UpperCAmelCase__ : str = idx
return vocab, raw_vocab, ids_to_tokens, emoji
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = VOCAB_FILES_NAMES
_A = PRETRAINED_VOCAB_FILES_MAP
_A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A = ['input_ids', 'attention_mask']
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase="<|endoftext|>" , __UpperCamelCase="<|endoftext|>" , __UpperCamelCase="<|startoftext|>" , __UpperCamelCase="<|endoftext|>" , __UpperCamelCase=False , **__UpperCamelCase , )-> Dict:
super().__init__(
unk_token=__UpperCamelCase , pad_token=__UpperCamelCase , bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , do_clean_text=__UpperCamelCase , **__UpperCamelCase , )
if not os.path.isfile(__UpperCamelCase ):
raise ValueError(
F"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
" model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" )
if not os.path.isfile(__UpperCamelCase ):
raise ValueError(
F"Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"
" pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" )
UpperCAmelCase__ : List[Any] = do_clean_text
UpperCAmelCase__ : int = load_vocab_and_emoji(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase__ : Tuple = SubWordJapaneseTokenizer(
vocab=self.vocab , ids_to_tokens=self.ids_to_tokens , emoji=self.emoji )
@property
def lowerCAmelCase__ ( self )-> Union[str, Any]:
# self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
return len(self.raw_vocab )
def lowerCAmelCase__ ( self )-> Optional[Any]:
return dict(self.raw_vocab , **self.added_tokens_encoder )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> int:
return self.subword_tokenizer.tokenize(__UpperCamelCase , clean=self.do_clean_text )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Optional[int]:
return self.vocab.get(__UpperCamelCase , self.vocab.get(self.unk_token ) )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Union[str, Any]:
return self.subword_tokenizer.convert_id_to_token(__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Optional[Any]:
UpperCAmelCase__ : str = "".join(__UpperCamelCase ).strip()
return out_string
def lowerCAmelCase__ ( self , __UpperCamelCase )-> List[int]:
UpperCAmelCase__ : Dict = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase ) + [self.eos_token_id] )
if len(__UpperCamelCase ) > self.model_max_length:
UpperCAmelCase__ : List[str] = input_ids[-self.model_max_length :]
return input_ids
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = None )-> Tuple[str]:
UpperCAmelCase__ : Any = 0
if os.path.isdir(__UpperCamelCase ):
UpperCAmelCase__ : Optional[int] = os.path.join(
__UpperCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase__ : Any = os.path.join(
__UpperCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"] )
else:
UpperCAmelCase__ : Tuple = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
)
UpperCAmelCase__ : List[str] = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
)
with open(__UpperCamelCase , "w" , encoding="utf-8" ) as writer:
for token_index, token in self.ids_to_tokens.items():
if index != token_index:
logger.warning(
F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
" Please check that the vocabulary is not corrupted!" )
UpperCAmelCase__ : str = token_index
writer.write(",".join(__UpperCamelCase ) + "\n" )
index += 1
with open(__UpperCamelCase , "w" , encoding="utf-8" ) as writer:
json.dump(self.emoji , __UpperCamelCase )
return vocab_file, emoji_file
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Optional[int]:
UpperCAmelCase__ : Dict = vocab # same as swe
UpperCAmelCase__ : Optional[int] = ids_to_tokens # same as bpe
UpperCAmelCase__ : str = emoji
UpperCAmelCase__ : List[Any] = np.max([len(__UpperCamelCase ) for w in self.vocab.keys()] )
UpperCAmelCase__ : Tuple = re.compile(r"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)" )
UpperCAmelCase__ : Optional[int] = re.compile(r"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*" )
UpperCAmelCase__ : List[str] = re.compile(r"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}" )
UpperCAmelCase__ : Any = re.compile(
r"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" )
UpperCAmelCase__ : List[str] = re.compile(
r"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" )
UpperCAmelCase__ : int = re.compile(
r"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*" )
UpperCAmelCase__ : List[str] = "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
UpperCAmelCase__ : Tuple = "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
UpperCAmelCase__ : Optional[int] = str.maketrans({k: "<BLOCK>" for k in keisen + blocks} )
def __len__( self )-> List[Any]:
return len(self.ids_to_tokens )
def lowerCAmelCase__ ( self , __UpperCamelCase )-> Dict:
UpperCAmelCase__ : Optional[int] = self.content_repattera.sub("<URL>" , __UpperCamelCase )
UpperCAmelCase__ : Any = self.content_repattera.sub("<EMAIL>" , __UpperCamelCase )
UpperCAmelCase__ : str = self.content_repattera.sub("<TEL>" , __UpperCamelCase )
UpperCAmelCase__ : Optional[int] = self.content_repattera.sub("<DATE>" , __UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = self.content_repattera.sub("<DATE>" , __UpperCamelCase )
UpperCAmelCase__ : Optional[int] = self.content_repattera.sub("<PRICE>" , __UpperCamelCase )
UpperCAmelCase__ : List[str] = content.translate(self.content_transa )
while "<BLOCK><BLOCK>" in content:
UpperCAmelCase__ : Union[str, Any] = content.replace("<BLOCK><BLOCK>" , "<BLOCK>" )
return content
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase=False )-> List[Any]:
UpperCAmelCase__ : Optional[int] = text.replace(" " , "<SP>" )
UpperCAmelCase__ : Any = text.replace(" " , "<SP>" )
UpperCAmelCase__ : Union[str, Any] = text.replace("\r\n" , "<BR>" )
UpperCAmelCase__ : List[str] = text.replace("\n" , "<BR>" )
UpperCAmelCase__ : Any = text.replace("\r" , "<BR>" )
UpperCAmelCase__ : int = text.replace("\t" , "<TAB>" )
UpperCAmelCase__ : str = text.replace("—" , "ー" )
UpperCAmelCase__ : Optional[Any] = text.replace("−" , "ー" )
for k, v in self.emoji["emoji"].items():
if k in text:
UpperCAmelCase__ : Any = text.replace(__UpperCamelCase , __UpperCamelCase )
if clean:
UpperCAmelCase__ : Any = self.clean_text(__UpperCamelCase )
def check_simbol(__UpperCamelCase ):
UpperCAmelCase__ : Dict = x.encode()
if len(__UpperCamelCase ) == 1 and len(__UpperCamelCase ) == 2:
UpperCAmelCase__ : Optional[Any] = (int(e[0] ) << 8) + int(e[1] )
if (
(c >= 0Xc2a1 and c <= 0Xc2bf)
or (c >= 0Xc780 and c <= 0Xc783)
or (c >= 0Xcab9 and c <= 0Xcbbf)
or (c >= 0Xcc80 and c <= 0Xcda2)
):
return True
return False
def checkuae(__UpperCamelCase ):
UpperCAmelCase__ : Any = x.encode()
if len(__UpperCamelCase ) == 1 and len(__UpperCamelCase ) == 3:
UpperCAmelCase__ : Optional[int] = (int(e[0] ) << 16) + (int(e[1] ) << 8) + int(e[2] )
if c >= 0Xe2_8080 and c <= 0Xe2_b07f:
return True
return False
UpperCAmelCase__ : Any = 0
UpperCAmelCase__ : str = []
while pos < len(__UpperCamelCase ):
UpperCAmelCase__ : Any = min(len(__UpperCamelCase ) , pos + self.maxlen + 1 ) if text[pos] == "<" else pos + 3
UpperCAmelCase__ : Any = [] # (token_id, token, pos)
for e in range(__UpperCamelCase , __UpperCamelCase , -1 ):
UpperCAmelCase__ : str = text[pos:e]
if wd in self.vocab:
if wd[0] == "<" and len(__UpperCamelCase ) > 2:
UpperCAmelCase__ : Optional[int] = [(self.vocab[wd], wd, e)]
break
else:
candidates.append((self.vocab[wd], wd, e) )
if len(__UpperCamelCase ) > 0:
# the smallest token_id is adopted
UpperCAmelCase__ : List[str] = sorted(__UpperCamelCase , key=lambda __UpperCamelCase : x[0] )[0]
result.append(__UpperCamelCase )
UpperCAmelCase__ : List[Any] = e
else:
UpperCAmelCase__ : Union[str, Any] = pos + 1
UpperCAmelCase__ : Optional[int] = text[pos:end]
if check_simbol(__UpperCamelCase ):
result.append("<KIGOU>" )
elif checkuae(__UpperCamelCase ):
result.append("<U2000U2BFF>" )
else:
for i in wd.encode("utf-8" ):
result.append("<|byte%d|>" % i )
UpperCAmelCase__ : List[Any] = end
return result
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase="\n" )-> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = []
UpperCAmelCase__ : str = []
UpperCAmelCase__ : List[Any] = self.ids_to_tokens[index][0]
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2] ) )
else:
if len(__UpperCamelCase ) > 0:
words.append(bytearray(__UpperCamelCase ).decode("utf-8" , errors="replace" ) )
UpperCAmelCase__ : Dict = []
if word[:7] == "<|emoji" and word[-2:] == "|>":
words.append(self.emoji["emoji_inv"][word] )
elif word == "<SP>":
words.append(" " )
elif word == "<BR>":
words.append(__UpperCamelCase )
elif word == "<TAB>":
words.append("\t" )
elif word == "<BLOCK>":
words.append("▀" )
elif word == "<KIGOU>":
words.append("ǀ" )
elif word == "<U2000U2BFF>":
words.append("‖" )
else:
words.append(__UpperCamelCase )
if len(__UpperCamelCase ) > 0:
words.append(bytearray(__UpperCamelCase ).decode("utf-8" , errors="replace" ) )
UpperCAmelCase__ : str = "".join(__UpperCamelCase )
return text
| 716
|
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def a__ ( lowerCAmelCase : List[str] ):
'''simple docstring'''
def wrapper(*lowerCAmelCase : Any , **lowerCAmelCase : Tuple ):
UpperCAmelCase__ : Optional[int] = timeit.default_timer()
UpperCAmelCase__ : int = func(*lowerCAmelCase , **lowerCAmelCase )
UpperCAmelCase__ : List[Any] = timeit.default_timer() - starttime
return delta
UpperCAmelCase__ : int = func.__name__
return wrapper
def a__ ( lowerCAmelCase : dict , lowerCAmelCase : Optional[int]=100 , lowerCAmelCase : List[str]=None ):
'''simple docstring'''
UpperCAmelCase__ : str = []
UpperCAmelCase__ : Optional[Any] = seq_shapes or {}
for i in range(lowerCAmelCase ):
UpperCAmelCase__ : int = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(lowerCAmelCase , _ArrayXD ):
UpperCAmelCase__ : List[str] = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(lowerCAmelCase , datasets.Value ):
if v.dtype == "string":
UpperCAmelCase__ : Dict = "The small grey turtle was surprisingly fast when challenged."
else:
UpperCAmelCase__ : str = np.random.randint(10 , size=1 ).astype(v.dtype ).item()
elif isinstance(lowerCAmelCase , datasets.Sequence ):
while isinstance(lowerCAmelCase , datasets.Sequence ):
UpperCAmelCase__ : List[str] = v.feature
UpperCAmelCase__ : Optional[int] = seq_shapes[k]
UpperCAmelCase__ : Optional[int] = np.random.rand(*lowerCAmelCase ).astype(v.dtype )
UpperCAmelCase__ : Union[str, Any] = data
dummy_data.append((i, example) )
return dummy_data
def a__ ( lowerCAmelCase : List[str] , lowerCAmelCase : Tuple , lowerCAmelCase : List[str]=100 , lowerCAmelCase : Optional[int]=None ):
'''simple docstring'''
UpperCAmelCase__ : int = generate_examples(lowerCAmelCase , num_examples=lowerCAmelCase , seq_shapes=lowerCAmelCase )
with ArrowWriter(features=lowerCAmelCase , path=lowerCAmelCase ) as writer:
for key, record in dummy_data:
UpperCAmelCase__ : List[Any] = features.encode_example(lowerCAmelCase )
writer.write(lowerCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
F"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}." )
UpperCAmelCase__ : Optional[int] = datasets.Dataset.from_file(filename=lowerCAmelCase , info=datasets.DatasetInfo(features=lowerCAmelCase ) )
return dataset
| 660
| 0
|
"""simple docstring"""
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__UpperCAmelCase =logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class lowerCAmelCase__ ( UpperCAmelCase_ ):
def __init__( self , *UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , **UpperCamelCase__ ):
'''simple docstring'''
super().__init__(*UpperCamelCase__ , **UpperCamelCase__ )
A__ = eval_examples
A__ = post_process_function
A__ = quant_trainer_args
A__ = 1_28 # default number of calibration samples
def lowercase_ ( self , UpperCamelCase__=None ):
'''simple docstring'''
if calib_dataset is None and self.calib_dataset is None:
raise ValueError("Trainer: calibration requires an calib_dataset." )
A__ = calib_dataset if calib_dataset is not None else self.calib_dataset
A__ = self._remove_unused_columns(UpperCamelCase__ , description="Calibration" )
return DataLoader(
UpperCamelCase__ , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=UpperCamelCase__ , )
def lowercase_ ( self , UpperCamelCase__=None ):
'''simple docstring'''
A__ = self.train_dataset if calib_dataset is None else calib_dataset
A__ = self.get_calib_dataloader(UpperCamelCase__ )
A__ = self.model
quant_trainer.configure_model(UpperCamelCase__ , self.quant_trainer_args , calib=UpperCamelCase__ )
model.eval()
quant_trainer.enable_calibration(UpperCamelCase__ )
logger.info("***** Running calibration *****" )
logger.info(f""" Num examples = {self.calib_num}""" )
logger.info(f""" Batch size = {calib_dataloader.batch_size}""" )
for step, inputs in enumerate(UpperCamelCase__ ):
# Prediction step
A__ , A__ , A__ = self.prediction_step(UpperCamelCase__ , UpperCamelCase__ , prediction_loss_only=UpperCamelCase__ )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(UpperCamelCase__ , self.quant_trainer_args )
A__ = model
def lowercase_ ( self , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__ = "eval" ):
'''simple docstring'''
A__ = self.eval_dataset if eval_dataset is None else eval_dataset
A__ = self.get_eval_dataloader(UpperCamelCase__ )
A__ = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
A__ = self.compute_metrics
A__ = None
A__ = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
A__ = eval_loop(
UpperCamelCase__ , description="Evaluation" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=UpperCamelCase__ , )
finally:
A__ = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
A__ = self.post_process_function(UpperCamelCase__ , UpperCamelCase__ , output.predictions )
A__ = self.compute_metrics(UpperCamelCase__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
A__ = metrics.pop(UpperCamelCase__ )
self.log(UpperCamelCase__ )
else:
A__ = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
A__ = self.callback_handler.on_evaluate(self.args , self.state , self.control , UpperCamelCase__ )
return metrics
def lowercase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__ = "test" ):
'''simple docstring'''
A__ = self.get_test_dataloader(UpperCamelCase__ )
# Temporarily disable metric computation, we will do it in the loop here.
A__ = self.compute_metrics
A__ = None
A__ = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
A__ = eval_loop(
UpperCamelCase__ , description="Prediction" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=UpperCamelCase__ , )
finally:
A__ = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
A__ = self.post_process_function(UpperCamelCase__ , UpperCamelCase__ , output.predictions , "predict" )
A__ = self.compute_metrics(UpperCamelCase__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
A__ = metrics.pop(UpperCamelCase__ )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=UpperCamelCase__ )
def lowercase_ ( self , UpperCamelCase__="./" ):
'''simple docstring'''
A__ = self.eval_dataset
A__ = self.get_eval_dataloader(UpperCamelCase__ )
A__ = next(iter(UpperCamelCase__ ) )
# saving device - to make it consistent
A__ = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
# convert to tuple
A__ = tuple(v.to(UpperCamelCase__ ) for k, v in batch.items() )
logger.info("Converting model to be onnx compatible" )
from pytorch_quantization.nn import TensorQuantizer
A__ = True
A__ = self.model.to(UpperCamelCase__ )
model.eval()
model.float()
A__ = model.module if hasattr(UpperCamelCase__ , "module" ) else model
quant_trainer.configure_model(UpperCamelCase__ , self.quant_trainer_args )
A__ = os.path.join(UpperCamelCase__ , "model.onnx" )
logger.info(f"""exporting model to {output_model_file}""" )
A__ = {0: "batch_size", 1: "seq_len"}
torch.onnx.export(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , export_params=UpperCamelCase__ , opset_version=13 , do_constant_folding=UpperCamelCase__ , input_names=["input_ids", "attention_mask", "token_type_ids"] , output_names=["output_start_logits", "output_end_logits"] , dynamic_axes={
"input_ids": axes,
"attention_mask": axes,
"token_type_ids": axes,
"output_start_logits": axes,
"output_end_logits": axes,
} , verbose=UpperCamelCase__ , )
logger.info("onnx export finished" )
| 337
|
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __a ( A ) -> Union[str, Any]:
'''simple docstring'''
A__ = []
embed.append(
(
f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
f"""stage{idx}.patch_embed.proj.weight""",
) )
embed.append(
(
f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
f"""stage{idx}.patch_embed.proj.bias""",
) )
embed.append(
(
f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
f"""stage{idx}.patch_embed.norm.weight""",
) )
embed.append(
(
f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
f"""stage{idx}.patch_embed.norm.bias""",
) )
return embed
def __a ( A , A ) -> str:
'''simple docstring'''
A__ = []
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
f"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
f"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
f"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
f"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
f"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
f"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
f"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
f"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
) )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", f"""stage{idx}.blocks.{cnt}.norm1.weight""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", f"""stage{idx}.blocks.{cnt}.norm1.bias""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", f"""stage{idx}.blocks.{cnt}.norm2.weight""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", f"""stage{idx}.blocks.{cnt}.norm2.bias""") )
return attention_weights
def __a ( A ) -> List[str]:
'''simple docstring'''
A__ = []
token.append((f"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token") )
return token
def __a ( ) -> Dict:
'''simple docstring'''
A__ = []
head.append(("layernorm.weight", "norm.weight") )
head.append(("layernorm.bias", "norm.bias") )
head.append(("classifier.weight", "head.weight") )
head.append(("classifier.bias", "head.bias") )
return head
def __a ( A , A , A , A ) -> int:
'''simple docstring'''
A__ = "imagenet-1k-id2label.json"
A__ = 1_000
A__ = "huggingface/label-files"
A__ = num_labels
A__ = json.load(open(cached_download(hf_hub_url(A , A , repo_type="dataset" ) ) , "r" ) )
A__ = {int(A ): v for k, v in idalabel.items()}
A__ = idalabel
A__ = {v: k for k, v in idalabel.items()}
A__ = A__ = CvtConfig(num_labels=A , idalabel=A , labelaid=A )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit("/" , 1 )[-1][4:6] == "13":
A__ = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit("/" , 1 )[-1][4:6] == "21":
A__ = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
A__ = [2, 2, 20]
A__ = [3, 12, 16]
A__ = [192, 768, 1_024]
A__ = CvtForImageClassification(A )
A__ = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
A__ = image_size
A__ = torch.load(A , map_location=torch.device("cpu" ) )
A__ = OrderedDict()
A__ = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
A__ = list_of_state_dict + cls_token(A )
A__ = list_of_state_dict + embeddings(A )
for cnt in range(config.depth[idx] ):
A__ = list_of_state_dict + attention(A , A )
A__ = list_of_state_dict + final()
for gg in list_of_state_dict:
print(A )
for i in range(len(A ) ):
A__ = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(A )
model.save_pretrained(A )
image_processor.save_pretrained(A )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
__UpperCAmelCase =argparse.ArgumentParser()
parser.add_argument(
"""--cvt_model""",
default="""cvt-w24""",
type=str,
help="""Name of the cvt model you'd like to convert.""",
)
parser.add_argument(
"""--image_size""",
default=384,
type=int,
help="""Input Image Size""",
)
parser.add_argument(
"""--cvt_file_name""",
default=r"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
type=str,
help="""Input Image Size""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
__UpperCAmelCase =parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 337
| 1
|
def __lowerCAmelCase ( __magic_name__ , __magic_name__ = " " ):
_lowercase: Dict = []
_lowercase: Dict = 0
for index, char in enumerate(__magic_name__ ):
if char == separator:
split_words.append(string[last_index:index] )
_lowercase: Dict = index + 1
elif index + 1 == len(__magic_name__ ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 721
|
def __lowerCAmelCase ( __magic_name__ ):
_lowercase: Any = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def __lowerCAmelCase ( __magic_name__ = 1_0_0 ):
_lowercase: Any = 1
_lowercase: Optional[int] = 2
for i in range(2 , max_n + 1 ):
_lowercase: Any = pre_numerator
_lowercase: Optional[Any] = 2 * i // 3 if i % 3 == 0 else 1
_lowercase: Dict = cur_numerator
_lowercase: Dict = e_cont * pre_numerator + temp
return sum_digits(__magic_name__ )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 206
| 0
|
'''simple docstring'''
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowerCAmelCase = "src/transformers"
# This is to make sure the transformers module imported is the one in the repo.
__lowerCAmelCase = importlib.util.spec_from_file_location(
"transformers",
os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
__lowerCAmelCase = spec.loader.load_module()
__lowerCAmelCase = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
__lowerCAmelCase = re.compile("\[(.+?)\]\((https://huggingface\.co/.+?)\)")
__lowerCAmelCase = {
"CLIPConfigMixin",
"DecisionTransformerConfigMixin",
"EncoderDecoderConfigMixin",
"RagConfigMixin",
"SpeechEncoderDecoderConfigMixin",
"VisionEncoderDecoderConfigMixin",
"VisionTextDualEncoderConfigMixin",
}
def __UpperCamelCase ( ):
"""simple docstring"""
a_ = []
for config_class in list(CONFIG_MAPPING.values() ):
a_ = False
# source code of `config_class`
a_ = inspect.getsource(a__ )
a_ = _re_checkpoint.findall(a__ )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
a_ , a_ = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
a_ = F'https://huggingface.co/{ckpt_name}'
if ckpt_link == ckpt_link_from_name:
a_ = True
break
a_ = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(a__ )
if len(a__ ) > 0:
a_ = '\n'.join(sorted(a__ ) )
raise ValueError(F'The following configurations don\'t contain any valid checkpoint:\n{message}' )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 536
|
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 SPIECE_UNDERLINE, logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {'''vocab_file''': '''spiece.model'''}
lowerCamelCase__ = {
'''vocab_file''': {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
'''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
}
}
lowerCamelCase__ = {
'''xlnet-base-cased''': None,
'''xlnet-large-cased''': None,
}
# Segments (not really needed)
lowerCamelCase__ = 0
lowerCamelCase__ = 1
lowerCamelCase__ = 2
lowerCamelCase__ = 3
lowerCamelCase__ = 4
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
__A = VOCAB_FILES_NAMES
__A = PRETRAINED_VOCAB_FILES_MAP
__A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = '''left'''
def __init__( self : int , lowercase_ : Optional[int] , lowercase_ : Dict=False , lowercase_ : Any=True , lowercase_ : Any=False , lowercase_ : Any="<s>" , lowercase_ : Dict="</s>" , lowercase_ : Optional[Any]="<unk>" , lowercase_ : Optional[int]="<sep>" , lowercase_ : Optional[Any]="<pad>" , lowercase_ : Tuple="<cls>" , lowercase_ : Union[str, Any]="<mask>" , lowercase_ : Dict=["<eop>", "<eod>"] , lowercase_ : Optional[Dict[str, Any]] = None , **lowercase_ : int , ) -> None:
"""simple docstring"""
_UpperCamelCase = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_) if isinstance(lowercase_ , lowercase_) else mask_token
_UpperCamelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=lowercase_ , remove_space=lowercase_ , keep_accents=lowercase_ , bos_token=lowercase_ , eos_token=lowercase_ , unk_token=lowercase_ , sep_token=lowercase_ , pad_token=lowercase_ , cls_token=lowercase_ , mask_token=lowercase_ , additional_special_tokens=lowercase_ , sp_model_kwargs=self.sp_model_kwargs , **lowercase_ , )
_UpperCamelCase = 3
_UpperCamelCase = do_lower_case
_UpperCamelCase = remove_space
_UpperCamelCase = keep_accents
_UpperCamelCase = vocab_file
_UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(lowercase_)
@property
def __UpperCAmelCase ( self : List[str]) -> Dict:
"""simple docstring"""
return len(self.sp_model)
def __UpperCAmelCase ( self : List[str]) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = {self.convert_ids_to_tokens(lowercase_): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__( self : Optional[int]) -> Any:
"""simple docstring"""
_UpperCamelCase = self.__dict__.copy()
_UpperCamelCase = None
return state
def __setstate__( self : Tuple , lowercase_ : List[str]) -> Dict:
"""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 __UpperCAmelCase ( self : str , lowercase_ : Optional[Any]) -> Tuple:
"""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" , lowercase_)
_UpperCamelCase = "".join([c for c in outputs if not unicodedata.combining(lowercase_)])
if self.do_lower_case:
_UpperCamelCase = outputs.lower()
return outputs
def __UpperCAmelCase ( self : str , lowercase_ : str) -> List[str]:
"""simple docstring"""
_UpperCamelCase = self.preprocess_text(lowercase_)
_UpperCamelCase = self.sp_model.encode(lowercase_ , out_type=lowercase_)
_UpperCamelCase = []
for piece in pieces:
if len(lowercase_) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
_UpperCamelCase = self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase_ , ""))
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(lowercase_)
else:
new_pieces.append(lowercase_)
return new_pieces
def __UpperCAmelCase ( self : Optional[Any] , lowercase_ : Tuple) -> List[str]:
"""simple docstring"""
return self.sp_model.PieceToId(lowercase_)
def __UpperCAmelCase ( self : Optional[Any] , lowercase_ : int) -> Any:
"""simple docstring"""
return self.sp_model.IdToPiece(lowercase_)
def __UpperCAmelCase ( self : Optional[int] , lowercase_ : Union[str, Any]) -> Tuple:
"""simple docstring"""
_UpperCamelCase = "".join(lowercase_).replace(lowercase_ , " ").strip()
return out_string
def __UpperCAmelCase ( self : Optional[Any] , lowercase_ : List[int] , lowercase_ : bool = False , lowercase_ : bool = None , lowercase_ : bool = True , **lowercase_ : int , ) -> str:
"""simple docstring"""
_UpperCamelCase = kwargs.pop("use_source_tokenizer" , lowercase_)
_UpperCamelCase = self.convert_ids_to_tokens(lowercase_ , skip_special_tokens=lowercase_)
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
_UpperCamelCase = []
_UpperCamelCase = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(lowercase_))
_UpperCamelCase = []
sub_texts.append(lowercase_)
else:
current_sub_text.append(lowercase_)
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(lowercase_))
# Mimic the behavior of the Rust tokenizer:
# By default, there are no spaces between special tokens
_UpperCamelCase = "".join(lowercase_)
_UpperCamelCase = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
_UpperCamelCase = self.clean_up_tokenization(lowercase_)
return clean_text
else:
return text
def __UpperCAmelCase ( self : str , lowercase_ : List[int] , lowercase_ : 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 token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def __UpperCAmelCase ( self : Tuple , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None , lowercase_ : bool = False) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase_ , token_ids_a=lowercase_ , already_has_special_tokens=lowercase_)
if token_ids_a is not None:
return ([0] * len(lowercase_)) + [1] + ([0] * len(lowercase_)) + [1, 1]
return ([0] * len(lowercase_)) + [1, 1]
def __UpperCAmelCase ( self : str , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None) -> List[int]:
"""simple docstring"""
_UpperCamelCase = [self.sep_token_id]
_UpperCamelCase = [2]
if token_ids_a is None:
return len(token_ids_a + sep) * [0] + cls_segment_id
return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id
def __UpperCAmelCase ( self : Tuple , lowercase_ : str , lowercase_ : Optional[str] = None) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(lowercase_):
logger.error(f'Vocabulary path ({save_directory}) should be a directory')
return
_UpperCamelCase = os.path.join(
lowercase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase_) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , lowercase_)
elif not os.path.isfile(self.vocab_file):
with open(lowercase_ , "wb") as fi:
_UpperCamelCase = self.sp_model.serialized_model_proto()
fi.write(lowercase_)
return (out_vocab_file,)
| 547
| 0
|
"""simple docstring"""
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
_UpperCamelCase : Any = {
"""facebook/maskformer-swin-base-ade""": (
"""https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
_UpperCamelCase : Optional[int] = logging.get_logger(__name__)
class UpperCAmelCase_ ( snake_case_):
lowerCamelCase__ : Dict = """maskformer"""
lowerCamelCase__ : int = {"""hidden_size""": """mask_feature_size"""}
lowerCamelCase__ : str = ["""resnet""", """swin"""]
lowerCamelCase__ : List[str] = ["""detr"""]
def __init__( self , a = 2_5_6 , a = 2_5_6 , a = 0.1 , a = False , a = None , a = None , a = 0.02 , a = 1.0 , a = 1.0 , a = 1.0 , a = 20.0 , a = None , **a , ) -> Optional[int]:
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
lowercase__ : Optional[int] = SwinConfig(
image_size=3_8_4 , in_channels=3 , patch_size=4 , embed_dim=1_2_8 , depths=[2, 2, 1_8, 2] , num_heads=[4, 8, 1_6, 3_2] , window_size=1_2 , drop_path_rate=0.3 , out_features=['stage1', 'stage2', 'stage3', 'stage4'] , )
if isinstance(a , a ):
lowercase__ : str = backbone_config.pop('model_type' )
lowercase__ : int = CONFIG_MAPPING[backbone_model_type]
lowercase__ : str = config_class.from_dict(a )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
f"""Supported model types: {",".join(self.backbones_supported )}""" )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
lowercase__ : Optional[int] = DetrConfig()
else:
# verify that the decoder is supported
lowercase__ : Optional[int] = (
decoder_config.pop('model_type' ) if isinstance(a , a ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
f"""Transformer Decoder {decoder_type} not supported, please use one of"""
f""" {",".join(self.decoders_supported )}""" )
if isinstance(a , a ):
lowercase__ : str = CONFIG_MAPPING[decoder_type]
lowercase__ : List[Any] = config_class.from_dict(a )
lowercase__ : Tuple = backbone_config
lowercase__ : int = decoder_config
# main feature dimension for the model
lowercase__ : List[str] = fpn_feature_size
lowercase__ : int = mask_feature_size
# initializer
lowercase__ : int = init_std
lowercase__ : Dict = init_xavier_std
# Hungarian matcher && loss
lowercase__ : int = cross_entropy_weight
lowercase__ : Tuple = dice_weight
lowercase__ : Dict = mask_weight
lowercase__ : int = use_auxiliary_loss
lowercase__ : Tuple = no_object_weight
lowercase__ : List[Any] = output_auxiliary_logits
lowercase__ : Optional[int] = self.decoder_config.encoder_attention_heads
lowercase__ : Tuple = self.decoder_config.num_hidden_layers
super().__init__(**a )
@classmethod
def _UpperCAmelCase ( cls , a , a , **a ) -> List[Any]:
return cls(
backbone_config=a , decoder_config=a , **a , )
def _UpperCAmelCase ( self ) -> Any:
lowercase__ : int = copy.deepcopy(self.__dict__ )
lowercase__ : Optional[int] = self.backbone_config.to_dict()
lowercase__ : List[Any] = self.decoder_config.to_dict()
lowercase__ : str = self.__class__.model_type
return output
| 701
|
"""simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
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 (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEModel,
)
from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=1_0 , a=3 , a=2 , a=2 , a=2 , a=True , a=True , a=3_2 , a=5 , a=4 , a=3_7 , a="gelu" , a=0.1 , a=0.1 , a=1_0 , a=0.02 , a=0.9 , a=None , ) -> Optional[Any]:
lowercase__ : str = parent
lowercase__ : int = batch_size
lowercase__ : Union[str, Any] = image_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : Dict = patch_size
lowercase__ : Tuple = tubelet_size
lowercase__ : Optional[int] = num_frames
lowercase__ : Optional[int] = is_training
lowercase__ : int = use_labels
lowercase__ : Optional[int] = hidden_size
lowercase__ : Union[str, Any] = num_hidden_layers
lowercase__ : Optional[int] = num_attention_heads
lowercase__ : Any = intermediate_size
lowercase__ : str = hidden_act
lowercase__ : List[Any] = hidden_dropout_prob
lowercase__ : str = attention_probs_dropout_prob
lowercase__ : Union[str, Any] = type_sequence_label_size
lowercase__ : List[Any] = initializer_range
lowercase__ : str = mask_ratio
lowercase__ : Optional[Any] = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
lowercase__ : Optional[Any] = (image_size // patch_size) ** 2
lowercase__ : str = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
lowercase__ : str = int(mask_ratio * self.seq_length )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : int = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
lowercase__ : int = None
if self.use_labels:
lowercase__ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : Dict = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ) -> Tuple:
return VideoMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , tubelet_size=self.tubelet_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 , is_decoder=a , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self , a , a , a ) -> Optional[int]:
lowercase__ : Dict = VideoMAEModel(config=a )
model.to(a )
model.eval()
lowercase__ : Tuple = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCAmelCase ( self , a , a , a ) -> Union[str, Any]:
lowercase__ : str = VideoMAEForPreTraining(a )
model.to(a )
model.eval()
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
lowercase__ : Any = torch.ones((self.num_masks,) )
lowercase__ : str = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0 ) )] )
lowercase__ : Optional[int] = mask.expand(self.batch_size , -1 ).bool()
lowercase__ : str = model(a , a )
# model only returns predictions for masked patches
lowercase__ : str = mask.sum().item()
lowercase__ : int = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_masked_patches, decoder_num_labels) )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Dict = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = config_and_inputs
lowercase__ : List[str] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _a , _a , unittest.TestCase):
lowerCamelCase__ : Tuple = (
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
lowerCamelCase__ : Optional[int] = (
{"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
lowerCamelCase__ : Any = False
lowerCamelCase__ : Any = False
lowerCamelCase__ : Union[str, Any] = False
lowerCamelCase__ : str = False
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Optional[Any] = VideoMAEModelTester(self )
lowercase__ : Optional[Any] = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=3_7 )
def _UpperCAmelCase ( self , a , a , a=False ) -> Optional[int]:
lowercase__ : Union[str, Any] = copy.deepcopy(a )
if model_class == VideoMAEForPreTraining:
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
lowercase__ : Optional[Any] = torch.ones((self.model_tester.num_masks,) )
lowercase__ : Any = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0 ) )] )
lowercase__ : Any = mask.expand(self.model_tester.batch_size , -1 ).bool()
lowercase__ : Union[str, Any] = bool_masked_pos.to(a )
if return_labels:
if model_class in [
*get_values(a ),
]:
lowercase__ : Dict = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=a )
return inputs_dict
def _UpperCAmelCase ( self ) -> Tuple:
self.config_tester.run_common_tests()
@unittest.skip(reason='VideoMAE does not use inputs_embeds' )
def _UpperCAmelCase ( self ) -> Dict:
pass
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ , lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : int = model_class(a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear ) )
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = model_class(a )
lowercase__ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Optional[Any] = [*signature.parameters.keys()]
lowercase__ : int = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*a )
@slow
def _UpperCAmelCase ( self ) -> str:
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : List[Any] = VideoMAEModel.from_pretrained(a )
self.assertIsNotNone(a )
def _UpperCAmelCase ( self ) -> Optional[Any]:
if not self.has_attentions:
pass
else:
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : str = True
for model_class in self.all_model_classes:
lowercase__ : Union[str, Any] = self.model_tester.seq_length - self.model_tester.num_masks
lowercase__ : Any = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
lowercase__ : Optional[Any] = True
lowercase__ : int = False
lowercase__ : Any = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Optional[int] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Dict = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__ : str = True
lowercase__ : List[str] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : List[Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[Any] = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
lowercase__ : List[str] = len(a )
# Check attention is always last and order is fine
lowercase__ : Optional[int] = True
lowercase__ : List[str] = True
lowercase__ : int = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : List[str] = model(**self._prepare_for_class(a , a ) )
self.assertEqual(out_len + 1 , len(a ) )
lowercase__ : int = outputs.attentions
self.assertEqual(len(a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def _UpperCAmelCase ( self ) -> Optional[int]:
def check_hidden_states_output(a , a , a ):
lowercase__ : Optional[int] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowercase__ : Optional[Any] = model(**self._prepare_for_class(a , a ) )
lowercase__ : Optional[int] = outputs.hidden_states
lowercase__ : List[Any] = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(a ) , a )
lowercase__ : Optional[Any] = self.model_tester.seq_length - self.model_tester.num_masks
lowercase__ : Union[str, Any] = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
lowercase__ , lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Tuple = True
check_hidden_states_output(a , a , a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : Union[str, Any] = True
check_hidden_states_output(a , a , a )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def _UpperCAmelCase ( self ) -> List[Any]:
pass
def a_ ( ):
'''simple docstring'''
lowercase__ : int = hf_hub_download(
repo_id='hf-internal-testing/spaghetti-video' , filename='eating_spaghetti.npy' , repo_type='dataset' )
lowercase__ : str = np.load(_lowerCAmelCase )
return list(_lowerCAmelCase )
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
@cached_property
def _UpperCAmelCase ( self ) -> Optional[Any]:
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Dict = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics' ).to(
a )
lowercase__ : str = self.default_image_processor
lowercase__ : List[str] = prepare_video()
lowercase__ : int = image_processor(a , return_tensors='pt' ).to(a )
# forward pass
with torch.no_grad():
lowercase__ : Union[str, Any] = model(**a )
# verify the logits
lowercase__ : str = torch.Size((1, 4_0_0) )
self.assertEqual(outputs.logits.shape , a )
lowercase__ : List[Any] = torch.tensor([0.3_669, -0.0_688, -0.2_421] ).to(a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , a , atol=1e-4 ) )
@slow
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Optional[int] = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short' ).to(a )
lowercase__ : Optional[Any] = self.default_image_processor
lowercase__ : List[str] = prepare_video()
lowercase__ : str = image_processor(a , return_tensors='pt' ).to(a )
# add boolean mask, indicating which patches to mask
lowercase__ : Union[str, Any] = hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos' , filename='bool_masked_pos.pt' )
lowercase__ : str = torch.load(a )
# forward pass
with torch.no_grad():
lowercase__ : List[Any] = model(**a )
# verify the logits
lowercase__ : Dict = torch.Size([1, 1_4_0_8, 1_5_3_6] )
lowercase__ : List[str] = torch.tensor(
[[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]] , device=a )
self.assertEqual(outputs.logits.shape , a )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , a , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `True`)
lowercase__ : List[Any] = torch.tensor([0.5_142] , device=a )
self.assertTrue(torch.allclose(outputs.loss , a , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `False`)
lowercase__ : Tuple = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short' , norm_pix_loss=a ).to(
a )
with torch.no_grad():
lowercase__ : Any = model(**a )
lowercase__ : List[Any] = torch.tensor(torch.tensor([0.6_469] ) , device=a )
self.assertTrue(torch.allclose(outputs.loss , a , atol=1e-4 ) )
| 645
| 0
|
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase : Dict = logging.get_logger(__name__)
def UpperCamelCase_ ( __a ) -> Union[str, Any]:
a__ : Tuple = R"\w+[.]\d+"
a__ : List[Any] = re.findall(__a , __a )
for pat in pats:
a__ : Union[str, Any] = key.replace(__a , "_".join(pat.split("." ) ) )
return key
def UpperCamelCase_ ( __a , __a , __a ) -> List[str]:
a__ : List[str] = pt_tuple_key[:-1] + ("scale",)
if (
any("norm" in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
a__ : Any = pt_tuple_key[:-1] + ("scale",)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
a__ : Optional[Any] = pt_tuple_key[:-1] + ("scale",)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
a__ : Union[str, Any] = pt_tuple_key[:-1] + ("embedding",)
return renamed_pt_tuple_key, pt_tensor
# conv layer
a__ : List[str] = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
a__ : str = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
a__ : Tuple = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight":
a__ : Tuple = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
a__ : Optional[Any] = pt_tuple_key[:-1] + ("weight",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
a__ : Union[str, Any] = pt_tuple_key[:-1] + ("bias",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def UpperCamelCase_ ( __a , __a , __a=42 ) -> str:
# Step 1: Convert pytorch tensor to numpy
a__ : Optional[int] = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
a__ : Tuple = flax_model.init_weights(PRNGKey(__a ) )
a__ : Optional[Any] = flatten_dict(__a )
a__ : Union[str, Any] = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
a__ : Optional[int] = rename_key(__a )
a__ : Optional[int] = tuple(renamed_pt_key.split("." ) )
# Correctly rename weight parameters
a__, a__ : Union[str, Any] = rename_key_and_reshape_tensor(__a , __a , __a )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.''' )
# also add unexpected weight so that warning is thrown
a__ : str = jnp.asarray(__a )
return unflatten_dict(__a )
| 37
|
'''simple docstring'''
a : Dict = range(2, 20 + 1)
a : Optional[int] = [10**k for k in range(ks[-1] + 1)]
a : dict[int, dict[int, list[list[int]]]] = {}
def __UpperCAmelCase ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[Any] ) -> int:
__snake_case = sum(a_i[j] for j in range(_UpperCAmelCase , len(_UpperCAmelCase ) ) )
__snake_case = sum(a_i[j] * base[j] for j in range(min(len(_UpperCAmelCase ) , _UpperCAmelCase ) ) )
__snake_case , __snake_case = 0, 0
__snake_case = n - i
__snake_case = memo.get(_UpperCAmelCase )
if sub_memo is not None:
__snake_case = sub_memo.get(_UpperCAmelCase )
if jumps is not None and len(_UpperCAmelCase ) > 0:
# find and make the largest jump without going over
__snake_case = -1
for _k in range(len(_UpperCAmelCase ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
__snake_case = _k
break
if max_jump >= 0:
__snake_case , __snake_case , __snake_case = jumps[max_jump]
# since the difference between jumps is cached, add c
__snake_case = diff + c
for j in range(min(_UpperCAmelCase , len(_UpperCAmelCase ) ) ):
__snake_case , __snake_case = divmod(_UpperCAmelCase , 10 )
if new_c > 0:
add(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
else:
__snake_case = []
else:
__snake_case = {c: []}
__snake_case = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
__snake_case , __snake_case = next_term(_UpperCAmelCase , k - 1 , i + dn , _UpperCAmelCase )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
__snake_case , __snake_case = compute(_UpperCAmelCase , _UpperCAmelCase , i + dn , _UpperCAmelCase )
diff += _diff
dn += terms_jumped
__snake_case = sub_memo[c]
# keep jumps sorted by # of terms skipped
__snake_case = 0
while j < len(_UpperCAmelCase ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(_UpperCAmelCase , (diff, dn, k) )
return (diff, dn)
def __UpperCAmelCase ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[int] ) -> Optional[int]:
if i >= n:
return 0, i
if k > len(_UpperCAmelCase ):
a_i.extend([0 for _ in range(k - len(_UpperCAmelCase ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
__snake_case = i
__snake_case , __snake_case , __snake_case = 0, 0, 0
for j in range(len(_UpperCAmelCase ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
__snake_case = ds_c + ds_b
diff += addend
__snake_case = 0
for j in range(_UpperCAmelCase ):
__snake_case = a_i[j] + addend
__snake_case , __snake_case = divmod(_UpperCAmelCase , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
return diff, i - start_i
def __UpperCAmelCase ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : str ) -> Tuple:
for j in range(_UpperCAmelCase , len(_UpperCAmelCase ) ):
__snake_case = digits[j] + addend
if s >= 10:
__snake_case , __snake_case = divmod(_UpperCAmelCase , 10 )
__snake_case = addend // 10 + quotient
else:
__snake_case = s
__snake_case = addend // 10
if addend == 0:
break
while addend > 0:
__snake_case , __snake_case = divmod(_UpperCAmelCase , 10 )
digits.append(_UpperCAmelCase )
def __UpperCAmelCase ( _UpperCAmelCase : int = 10**15 ) -> int:
__snake_case = [1]
__snake_case = 1
__snake_case = 0
while True:
__snake_case , __snake_case = next_term(_UpperCAmelCase , 20 , i + dn , _UpperCAmelCase )
dn += terms_jumped
if dn == n - i:
break
__snake_case = 0
for j in range(len(_UpperCAmelCase ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(F'''{solution() = }''')
| 69
| 0
|
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase__ ( snake_case_, unittest.TestCase ):
'''simple docstring'''
_snake_case = PhobertTokenizer
_snake_case = False
def UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCamelCase = ['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
UpperCamelCase = dict(zip(__lowerCAmelCase , range(len(__lowerCAmelCase ) ) ) )
UpperCamelCase = ['''#version: 0.2''', '''l à</w>''']
UpperCamelCase = {'''unk_token''': '''<unk>'''}
UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
for token in vocab_tokens:
fp.write(f'{token} {vocab_tokens[token]}\n' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__lowerCAmelCase ) )
def UpperCAmelCase ( self , **lowerCamelCase__ ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return PhobertTokenizer.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def UpperCAmelCase ( self , lowerCamelCase__ ):
'''simple docstring'''
UpperCamelCase = '''Tôi là VinAI Research'''
UpperCamelCase = '''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
return input_text, output_text
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = PhobertTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
UpperCamelCase = '''Tôi là VinAI Research'''
UpperCamelCase = '''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
UpperCamelCase = tokenizer.tokenize(__lowerCAmelCase )
print(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
UpperCamelCase = tokens + [tokenizer.unk_token]
UpperCamelCase = [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase ) , __lowerCAmelCase )
| 707
|
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
snake_case_ : Tuple = [
'word_embeddings_layernorm.weight',
'word_embeddings_layernorm.bias',
'input_layernorm.weight',
'input_layernorm.bias',
'post_attention_layernorm.weight',
'post_attention_layernorm.bias',
'self_attention.dense.bias',
'mlp.dense_4h_to_h.bias',
'ln_f.weight',
'ln_f.bias',
]
snake_case_ : Union[str, Any] = [
'mlp.dense_4h_to_h.weight',
'self_attention.dense.weight',
]
def __snake_case ( _UpperCAmelCase : List[str], _UpperCAmelCase : List[str]):
UpperCamelCase = {
'''word_embeddings.weight''': '''word_embeddings.weight''',
'''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
'''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
'''weight''': '''ln_f.weight''',
'''bias''': '''ln_f.bias''',
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
UpperCamelCase = int(re.match(R'''.*layer_(\d*).*''', _UpperCAmelCase)[1])
layer_number -= 3
return f'h.{layer_number}.' + key
def __snake_case ( _UpperCAmelCase : str):
if dtype == torch.bool:
return 1 / 8
UpperCamelCase = re.search(R'''[^\d](\d+)$''', str(_UpperCAmelCase))
if bit_search is None:
raise ValueError(f'`dtype` is not a valid dtype: {dtype}.')
UpperCamelCase = int(bit_search.groups()[0])
return bit_size // 8
def __snake_case ( _UpperCAmelCase : int, _UpperCAmelCase : Optional[Any], _UpperCAmelCase : str, _UpperCAmelCase : List[Any], _UpperCAmelCase : Optional[int]):
# Construct model
if bloom_config_file == "":
UpperCamelCase = BloomConfig()
else:
UpperCamelCase = BloomConfig.from_json_file(_UpperCAmelCase)
if shard_model:
UpperCamelCase = os.listdir(_UpperCAmelCase)
UpperCamelCase = sorted(filter(lambda _UpperCAmelCase: s.startswith('''layer''') and "model_00" in s, _UpperCAmelCase))
UpperCamelCase = {'''weight_map''': {}, '''metadata''': {}}
UpperCamelCase = 0
UpperCamelCase = None
UpperCamelCase = BloomConfig()
for j, file in enumerate(_UpperCAmelCase):
print('''Processing file: {}'''.format(_UpperCAmelCase))
UpperCamelCase = None
for i in range(_UpperCAmelCase):
# load all TP files
UpperCamelCase = file.replace('''model_00''', f'model_0{i}')
UpperCamelCase = torch.load(os.path.join(_UpperCAmelCase, _UpperCAmelCase), map_location='''cpu''')
# Rename keys in the transformers names
UpperCamelCase = list(temp.keys())
for key in keys:
UpperCamelCase = temp.pop(_UpperCAmelCase)
if tensors is None:
UpperCamelCase = temp
else:
for key in tensors.keys():
if any(key.endswith(_UpperCAmelCase) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
UpperCamelCase = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN) else 0
# We concatenate these weights accross TP ranks
UpperCamelCase = torch.cat([tensors[key], temp[key]], dim=_UpperCAmelCase)
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(_UpperCAmelCase) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
UpperCamelCase = tensors[key] / pretraining_tp
torch.save(
_UpperCAmelCase, os.path.join(
_UpperCAmelCase, '''pytorch_model_{}-of-{}.bin'''.format(str(j + 1).zfill(5), str(len(_UpperCAmelCase)).zfill(5)), ), )
for key in tensors.keys():
UpperCamelCase = tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype)
if key not in index_dict["weight_map"]:
UpperCamelCase = '''pytorch_model_{}-of-{}.bin'''.format(
str(j + 1).zfill(5), str(len(_UpperCAmelCase)).zfill(5))
UpperCamelCase = BloomConfig()
UpperCamelCase = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
UpperCamelCase = total_size
with open(_UpperCAmelCase, '''w''', encoding='''utf-8''') as f:
f.write(config.to_json_string())
with open(os.path.join(_UpperCAmelCase, WEIGHTS_NAME + '''.index.json'''), '''w''', encoding='''utf-8''') as f:
UpperCamelCase = json.dumps(_UpperCAmelCase, indent=2, sort_keys=_UpperCAmelCase) + '''\n'''
f.write(_UpperCAmelCase)
else:
UpperCamelCase = BloomModel(_UpperCAmelCase)
UpperCamelCase = os.listdir(_UpperCAmelCase)
UpperCamelCase = sorted(filter(lambda _UpperCAmelCase: s.startswith('''layer''') and "model_00" in s, _UpperCAmelCase))
UpperCamelCase = None
for i, file in enumerate(_UpperCAmelCase):
UpperCamelCase = None
for i in range(_UpperCAmelCase):
# load all TP files
UpperCamelCase = file.replace('''model_00''', f'model_0{i}')
UpperCamelCase = torch.load(os.path.join(_UpperCAmelCase, _UpperCAmelCase), map_location='''cpu''')
# Rename keys in the transformers names
UpperCamelCase = list(temp.keys())
for key in keys:
UpperCamelCase = temp.pop(_UpperCAmelCase)
if tensors is None:
UpperCamelCase = temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(_UpperCAmelCase) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
UpperCamelCase = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN) else 0
# We concatenate these weights accross TP ranks
UpperCamelCase = torch.cat([tensors[key], temp[key]], dim=_UpperCAmelCase)
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(_UpperCAmelCase) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
UpperCamelCase = tensors[key] / pretraining_tp
UpperCamelCase = model.load_state_dict(_UpperCAmelCase, strict=_UpperCAmelCase)
assert not other_keys.unexpected_keys, f'The keys {other_keys.unexpected_keys} are unexpected'
if missing_keys is None:
UpperCamelCase = set(other_keys.missing_keys)
else:
UpperCamelCase = missing_keys.intersection(set(other_keys.missing_keys))
assert not missing_keys, f'The keys {missing_keys} are missing'
# Save pytorch-model
os.makedirs(_UpperCAmelCase, exist_ok=_UpperCAmelCase)
UpperCamelCase = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
UpperCamelCase = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
print(f'Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}')
if config.torch_dtype is not None:
UpperCamelCase = model.to(config.torch_dtype)
torch.save(model.state_dict(), _UpperCAmelCase)
print(f'Save configuration file to {pytorch_config_dump_path}')
with open(_UpperCAmelCase, '''w''', encoding='''utf-8''') as f:
f.write(config.to_json_string())
if __name__ == "__main__":
snake_case_ : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--bloom_checkpoint_path',
default=None,
type=str,
required=True,
help='Path to the Megatron-LM checkpoint path.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--bloom_config_file',
default='',
type=str,
help=(
'An optional config json file corresponding to the pre-trained model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--shard_model',
action='store_true',
help='An optional setting to shard the output model \nThis enables sharding the converted checkpoint',
)
parser.add_argument(
'--pretraining_tp',
default=4,
type=int,
help='Pretraining TP rank that has been used when training the model in Megatron-LM \n',
)
snake_case_ : List[str] = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| 350
| 0
|
import random
def lowerCAmelCase_ ( __a , __a , __a ) -> int:
"""simple docstring"""
lowerCamelCase__: List[Any] =a[left_index]
lowerCamelCase__: Optional[int] =left_index + 1
for j in range(left_index + 1 , __a ):
if a[j] < pivot:
lowerCamelCase__ , lowerCamelCase__: Tuple =a[i], a[j]
i += 1
lowerCamelCase__ , lowerCamelCase__: Union[str, Any] =a[i - 1], a[left_index]
return i - 1
def lowerCAmelCase_ ( __a , __a , __a ) -> int:
"""simple docstring"""
if left < right:
lowerCamelCase__: Dict =random.randint(__a , right - 1 )
lowerCamelCase__ , lowerCamelCase__: Any =(
a[left],
a[pivot],
) # switches the pivot with the left most bound
lowerCamelCase__: Any =partition(__a , __a , __a )
quick_sort_random(
__a , __a , __a ) # recursive quicksort to the left of the pivot point
quick_sort_random(
__a , pivot_index + 1 , __a ) # recursive quicksort to the right of the pivot point
def lowerCAmelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase__: List[Any] =input("Enter numbers separated by a comma:\n" ).strip()
lowerCamelCase__: Optional[Any] =[int(__a ) for item in user_input.split("," )]
quick_sort_random(__a , 0 , len(__a ) )
print(__a )
if __name__ == "__main__":
main()
| 59
|
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowerCAmelCase :
"""simple docstring"""
def __init__( self , UpperCamelCase__ , UpperCamelCase__=3 , UpperCamelCase__=32 , UpperCamelCase__=3 , UpperCamelCase__=10 , UpperCamelCase__=[10, 20, 30, 40] , UpperCamelCase__=[1, 1, 2, 1] , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__="relu" , UpperCamelCase__=3 , UpperCamelCase__=None , ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = image_size
lowerCamelCase_ = num_channels
lowerCamelCase_ = embeddings_size
lowerCamelCase_ = hidden_sizes
lowerCamelCase_ = depths
lowerCamelCase_ = is_training
lowerCamelCase_ = use_labels
lowerCamelCase_ = hidden_act
lowerCamelCase_ = num_labels
lowerCamelCase_ = scope
lowerCamelCase_ = len(UpperCamelCase__ )
def _lowerCAmelCase ( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase_ = None
if self.use_labels:
lowerCamelCase_ = ids_tensor([self.batch_size] , self.num_labels )
lowerCamelCase_ = self.get_config()
return config, pixel_values, labels
def _lowerCAmelCase ( self ) -> Dict:
'''simple docstring'''
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def _lowerCAmelCase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = TFRegNetModel(config=UpperCamelCase__ )
lowerCamelCase_ = model(UpperCamelCase__ , training=UpperCamelCase__ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _lowerCAmelCase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = self.num_labels
lowerCamelCase_ = TFRegNetForImageClassification(UpperCamelCase__ )
lowerCamelCase_ = model(UpperCamelCase__ , labels=UpperCamelCase__ , training=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowerCAmelCase ( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.prepare_config_and_inputs()
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = config_and_inputs
lowerCamelCase_ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class lowerCAmelCase ( a , a , unittest.TestCase ):
"""simple docstring"""
__lowercase :Union[str, Any] = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
__lowercase :Optional[int] = (
{"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
if is_tf_available()
else {}
)
__lowercase :List[str] = False
__lowercase :List[str] = False
__lowercase :List[Any] = False
__lowercase :Dict = False
__lowercase :List[str] = False
def _lowerCAmelCase ( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = TFRegNetModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=UpperCamelCase__ , has_text_modality=UpperCamelCase__ )
def _lowerCAmelCase ( self ) -> Optional[int]:
'''simple docstring'''
return
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def _lowerCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0 , reason='''TF does not support backprop for grouped convolutions on CPU.''' , )
@slow
def _lowerCAmelCase ( self ) -> Tuple:
'''simple docstring'''
super().test_keras_fit()
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def _lowerCAmelCase ( self ) -> Any:
'''simple docstring'''
pass
def _lowerCAmelCase ( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ , lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ = model_class(UpperCamelCase__ )
lowerCamelCase_ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase_ = [*signature.parameters.keys()]
lowerCamelCase_ = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCamelCase__ )
def _lowerCAmelCase ( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def _lowerCAmelCase ( self ) -> Tuple:
'''simple docstring'''
def check_hidden_states_output(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
lowerCamelCase_ = model_class(UpperCamelCase__ )
lowerCamelCase_ = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) , training=UpperCamelCase__ )
lowerCamelCase_ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCamelCase_ = self.model_tester.num_stages
self.assertEqual(len(UpperCamelCase__ ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
lowerCamelCase_ , lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase_ = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
lowerCamelCase_ = layer_type
lowerCamelCase_ = True
check_hidden_states_output(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCamelCase_ = True
check_hidden_states_output(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def _lowerCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ , lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__={} ):
lowerCamelCase_ = model(UpperCamelCase__ , return_dict=UpperCamelCase__ , **UpperCamelCase__ )
lowerCamelCase_ = model(UpperCamelCase__ , return_dict=UpperCamelCase__ , **UpperCamelCase__ ).to_tuple()
def recursive_check(UpperCamelCase__ , UpperCamelCase__ ):
if isinstance(UpperCamelCase__ , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(UpperCamelCase__ , UpperCamelCase__ ):
recursive_check(UpperCamelCase__ , UpperCamelCase__ )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(UpperCamelCase__ , UpperCamelCase__ ) ) , msg=(
'''Tuple and dict output are not equal. Difference:'''
F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"""
) , )
recursive_check(UpperCamelCase__ , UpperCamelCase__ )
for model_class in self.all_model_classes:
lowerCamelCase_ = model_class(UpperCamelCase__ )
lowerCamelCase_ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase_ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ )
check_equivalence(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase_ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ , return_labels=UpperCamelCase__ )
lowerCamelCase_ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ , return_labels=UpperCamelCase__ )
check_equivalence(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase_ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase_ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ )
check_equivalence(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , {'''output_hidden_states''': True} )
lowerCamelCase_ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ , return_labels=UpperCamelCase__ )
lowerCamelCase_ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ , return_labels=UpperCamelCase__ )
check_equivalence(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , {'''output_hidden_states''': True} )
def _lowerCAmelCase ( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )
@slow
def _lowerCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = TFRegNetModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
def lowerCamelCase_ ( ):
lowerCamelCase_ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _lowerCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def _lowerCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
lowerCamelCase_ = self.default_image_processor
lowerCamelCase_ = prepare_img()
lowerCamelCase_ = image_processor(images=UpperCamelCase__ , return_tensors='''tf''' )
# forward pass
lowerCamelCase_ = model(**UpperCamelCase__ , training=UpperCamelCase__ )
# verify the logits
lowerCamelCase_ = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , UpperCamelCase__ )
lowerCamelCase_ = tf.constant([-0.4_180, -1.5_051, -3.4_836] )
tf.debugging.assert_near(outputs.logits[0, :3] , UpperCamelCase__ , atol=1e-4 )
| 142
| 0
|
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 UpperCAmelCase :
@staticmethod
def lowerCamelCase_ ( *__magic_name__ : int , **__magic_name__ : int ):
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
class UpperCAmelCase ( unittest.TestCase ):
@require_torch
def lowerCamelCase_ ( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = pipeline(
model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification""" , )
UpperCamelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
UpperCamelCase = image_classifier(__magic_name__ , 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(__magic_name__ ) , [
[{"""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"""}],
] , )
UpperCamelCase = image_classifier([image] * 5 , candidate_labels=["""A""", """B""", """C"""] , batch_size=2 )
self.assertEqual(
nested_simplify(__magic_name__ ) , [
[
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
],
[
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
],
[
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
],
[
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
],
[
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
],
] , )
@require_tf
def lowerCamelCase_ ( self : List[str] ):
"""simple docstring"""
UpperCamelCase = pipeline(
model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification""" , framework="""tf""" )
UpperCamelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
UpperCamelCase = image_classifier(__magic_name__ , candidate_labels=["""a""", """b""", """c"""] )
self.assertEqual(
nested_simplify(__magic_name__ ) , [{"""score""": 0.333, """label""": """a"""}, {"""score""": 0.333, """label""": """b"""}, {"""score""": 0.333, """label""": """c"""}] , )
UpperCamelCase = image_classifier([image] * 5 , candidate_labels=["""A""", """B""", """C"""] , batch_size=2 )
self.assertEqual(
nested_simplify(__magic_name__ ) , [
[
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
],
[
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
],
[
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
],
[
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
],
[
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
{"""score""": 0.333, """label""": ANY(__magic_name__ )},
],
] , )
@slow
@require_torch
def lowerCamelCase_ ( self : str ):
"""simple docstring"""
UpperCamelCase = pipeline(
task="""zero-shot-image-classification""" , model="""openai/clip-vit-base-patch32""" , )
# This is an image of 2 cats with remotes and no planes
UpperCamelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
UpperCamelCase = image_classifier(__magic_name__ , candidate_labels=["""cat""", """plane""", """remote"""] )
self.assertEqual(
nested_simplify(__magic_name__ ) , [
{"""score""": 0.511, """label""": """remote"""},
{"""score""": 0.485, """label""": """cat"""},
{"""score""": 0.004, """label""": """plane"""},
] , )
UpperCamelCase = image_classifier([image] * 5 , candidate_labels=["""cat""", """plane""", """remote"""] , batch_size=2 )
self.assertEqual(
nested_simplify(__magic_name__ ) , [
[
{"""score""": 0.511, """label""": """remote"""},
{"""score""": 0.485, """label""": """cat"""},
{"""score""": 0.004, """label""": """plane"""},
],
]
* 5 , )
@slow
@require_tf
def lowerCamelCase_ ( self : List[str] ):
"""simple docstring"""
UpperCamelCase = 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
UpperCamelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
UpperCamelCase = image_classifier(__magic_name__ , candidate_labels=["""cat""", """plane""", """remote"""] )
self.assertEqual(
nested_simplify(__magic_name__ ) , [
{"""score""": 0.511, """label""": """remote"""},
{"""score""": 0.485, """label""": """cat"""},
{"""score""": 0.004, """label""": """plane"""},
] , )
UpperCamelCase = image_classifier([image] * 5 , candidate_labels=["""cat""", """plane""", """remote"""] , batch_size=2 )
self.assertEqual(
nested_simplify(__magic_name__ ) , [
[
{"""score""": 0.511, """label""": """remote"""},
{"""score""": 0.485, """label""": """cat"""},
{"""score""": 0.004, """label""": """plane"""},
],
]
* 5 , )
| 181
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case = logging.get_logger(__name__)
def _lowercase ( SCREAMING_SNAKE_CASE_ : Optional[int] ):
"""simple docstring"""
UpperCamelCase = DPTConfig()
if "large" in checkpoint_url:
UpperCamelCase = 1_024
UpperCamelCase = 4_096
UpperCamelCase = 24
UpperCamelCase = 16
UpperCamelCase = [5, 11, 17, 23]
UpperCamelCase = [256, 512, 1_024, 1_024]
UpperCamelCase = (1, 384, 384)
if "ade" in checkpoint_url:
UpperCamelCase = True
UpperCamelCase = 150
UpperCamelCase = """huggingface/label-files"""
UpperCamelCase = """ade20k-id2label.json"""
UpperCamelCase = json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , repo_type="""dataset""" ) ) , """r""" ) )
UpperCamelCase = {int(SCREAMING_SNAKE_CASE_ ): v for k, v in idalabel.items()}
UpperCamelCase = idalabel
UpperCamelCase = {v: k for k, v in idalabel.items()}
UpperCamelCase = [1, 150, 480, 480]
return config, expected_shape
def _lowercase ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _lowercase ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
UpperCamelCase = name.replace("""pretrained.model""" , """dpt.encoder""" )
if "pretrained.model" in name:
UpperCamelCase = name.replace("""pretrained.model""" , """dpt.embeddings""" )
if "patch_embed" in name:
UpperCamelCase = name.replace("""patch_embed""" , """patch_embeddings""" )
if "pos_embed" in name:
UpperCamelCase = name.replace("""pos_embed""" , """position_embeddings""" )
if "attn.proj" in name:
UpperCamelCase = name.replace("""attn.proj""" , """attention.output.dense""" )
if "proj" in name and "project" not in name:
UpperCamelCase = name.replace("""proj""" , """projection""" )
if "blocks" in name:
UpperCamelCase = name.replace("""blocks""" , """layer""" )
if "mlp.fc1" in name:
UpperCamelCase = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
UpperCamelCase = name.replace("""mlp.fc2""" , """output.dense""" )
if "norm1" in name:
UpperCamelCase = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
UpperCamelCase = name.replace("""norm2""" , """layernorm_after""" )
if "scratch.output_conv" in name:
UpperCamelCase = name.replace("""scratch.output_conv""" , """head""" )
if "scratch" in name:
UpperCamelCase = name.replace("""scratch""" , """neck""" )
if "layer1_rn" in name:
UpperCamelCase = name.replace("""layer1_rn""" , """convs.0""" )
if "layer2_rn" in name:
UpperCamelCase = name.replace("""layer2_rn""" , """convs.1""" )
if "layer3_rn" in name:
UpperCamelCase = name.replace("""layer3_rn""" , """convs.2""" )
if "layer4_rn" in name:
UpperCamelCase = name.replace("""layer4_rn""" , """convs.3""" )
if "refinenet" in name:
UpperCamelCase = int(name[len("""neck.refinenet""" ) : len("""neck.refinenet""" ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
UpperCamelCase = name.replace(f'refinenet{layer_idx}' , f'fusion_stage.layers.{abs(layer_idx-4 )}' )
if "out_conv" in name:
UpperCamelCase = name.replace("""out_conv""" , """projection""" )
if "resConfUnit1" in name:
UpperCamelCase = name.replace("""resConfUnit1""" , """residual_layer1""" )
if "resConfUnit2" in name:
UpperCamelCase = name.replace("""resConfUnit2""" , """residual_layer2""" )
if "conv1" in name:
UpperCamelCase = name.replace("""conv1""" , """convolution1""" )
if "conv2" in name:
UpperCamelCase = name.replace("""conv2""" , """convolution2""" )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess1.0.project.0""" , """neck.reassemble_stage.readout_projects.0.0""" )
if "pretrained.act_postprocess2.0.project.0" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess2.0.project.0""" , """neck.reassemble_stage.readout_projects.1.0""" )
if "pretrained.act_postprocess3.0.project.0" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess3.0.project.0""" , """neck.reassemble_stage.readout_projects.2.0""" )
if "pretrained.act_postprocess4.0.project.0" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess4.0.project.0""" , """neck.reassemble_stage.readout_projects.3.0""" )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess1.3""" , """neck.reassemble_stage.layers.0.projection""" )
if "pretrained.act_postprocess1.4" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess1.4""" , """neck.reassemble_stage.layers.0.resize""" )
if "pretrained.act_postprocess2.3" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess2.3""" , """neck.reassemble_stage.layers.1.projection""" )
if "pretrained.act_postprocess2.4" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess2.4""" , """neck.reassemble_stage.layers.1.resize""" )
if "pretrained.act_postprocess3.3" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess3.3""" , """neck.reassemble_stage.layers.2.projection""" )
if "pretrained.act_postprocess4.3" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess4.3""" , """neck.reassemble_stage.layers.3.projection""" )
if "pretrained.act_postprocess4.4" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess4.4""" , """neck.reassemble_stage.layers.3.resize""" )
if "pretrained" in name:
UpperCamelCase = name.replace("""pretrained""" , """dpt""" )
if "bn" in name:
UpperCamelCase = name.replace("""bn""" , """batch_norm""" )
if "head" in name:
UpperCamelCase = name.replace("""head""" , """head.head""" )
if "encoder.norm" in name:
UpperCamelCase = name.replace("""encoder.norm""" , """layernorm""" )
if "auxlayer" in name:
UpperCamelCase = name.replace("""auxlayer""" , """auxiliary_head.head""" )
return name
def _lowercase ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Tuple ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
UpperCamelCase = state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight' )
UpperCamelCase = state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
UpperCamelCase = in_proj_weight[: config.hidden_size, :]
UpperCamelCase = in_proj_bias[: config.hidden_size]
UpperCamelCase = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCamelCase = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
UpperCamelCase = in_proj_weight[
-config.hidden_size :, :
]
UpperCamelCase = in_proj_bias[-config.hidden_size :]
def _lowercase ( ):
"""simple docstring"""
UpperCamelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
UpperCamelCase = Image.open(requests.get(SCREAMING_SNAKE_CASE_ , stream=SCREAMING_SNAKE_CASE_ ).raw )
return im
@torch.no_grad()
def _lowercase ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[str] ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = get_dpt_config(SCREAMING_SNAKE_CASE_ )
# load original state_dict from URL
UpperCamelCase = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ , map_location="""cpu""" )
# remove certain keys
remove_ignore_keys_(SCREAMING_SNAKE_CASE_ )
# rename keys
for key in state_dict.copy().keys():
UpperCamelCase = state_dict.pop(SCREAMING_SNAKE_CASE_ )
UpperCamelCase = val
# read in qkv matrices
read_in_q_k_v(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# load HuggingFace model
UpperCamelCase = DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE_ ) if """ade""" in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE_ )
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
model.eval()
# Check outputs on an image
UpperCamelCase = 480 if """ade""" in checkpoint_url else 384
UpperCamelCase = DPTImageProcessor(size=SCREAMING_SNAKE_CASE_ )
UpperCamelCase = prepare_img()
UpperCamelCase = image_processor(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" )
# forward pass
UpperCamelCase = model(**SCREAMING_SNAKE_CASE_ ).logits if """ade""" in checkpoint_url else model(**SCREAMING_SNAKE_CASE_ ).predicted_depth
# Assert logits
UpperCamelCase = torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]] )
if "ade" in checkpoint_url:
UpperCamelCase = torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]] )
assert outputs.shape == torch.Size(SCREAMING_SNAKE_CASE_ )
assert (
torch.allclose(outputs[0, 0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3] , SCREAMING_SNAKE_CASE_ )
)
Path(SCREAMING_SNAKE_CASE_ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
print(f'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
print(f'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(SCREAMING_SNAKE_CASE_ )
if push_to_hub:
print("""Pushing model to hub...""" )
model.push_to_hub(
repo_path_or_name=Path(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=SCREAMING_SNAKE_CASE_ , )
image_processor.push_to_hub(
repo_path_or_name=Path(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=SCREAMING_SNAKE_CASE_ , )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
type=str,
help="URL of the original DPT checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
)
parser.add_argument(
"--model_name",
default="dpt-large",
type=str,
help="Name of the model, in case you're pushing to the hub.",
)
__snake_case = parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 181
| 1
|
import qiskit
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
__snake_case : List[Any] = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
__snake_case : Optional[Any] = qiskit.QuantumCircuit(__lowerCamelCase , __lowerCamelCase )
# Map the quantum measurement to the classical bits
circuit.measure([0] , [0] )
# Execute the circuit on the simulator
__snake_case : List[Any] = qiskit.execute(__lowerCamelCase , __lowerCamelCase , shots=1_0_0_0 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(__lowerCamelCase )
if __name__ == "__main__":
print(f'''Total count for various states are: {single_qubit_measure(1, 1)}''')
| 81
|
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case : Any = {
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InformerConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case : int = [
"INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"InformerForPrediction",
"InformerModel",
"InformerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
_snake_case : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 81
| 1
|
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
a_ : Optional[int] = 16
a_ : Union[str, Any] = 32
def a__ ( __lowercase , __lowercase = 16 ) -> Dict:
_A = AutoTokenizer.from_pretrained("bert-base-cased" )
_A = load_dataset("glue" , "mrpc" )
def tokenize_function(__lowercase ):
# max_length=None => use the model max length (it's actually the default)
_A = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=__UpperCamelCase , max_length=__UpperCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_A = datasets.map(
__UpperCamelCase , batched=__UpperCamelCase , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_A = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(__lowercase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_A = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_A = 16
elif accelerator.mixed_precision != "no":
_A = 8
else:
_A = None
return tokenizer.pad(
__UpperCamelCase , padding="longest" , max_length=__UpperCamelCase , pad_to_multiple_of=__UpperCamelCase , return_tensors="pt" , )
# Instantiate dataloaders.
_A = DataLoader(
tokenized_datasets["train"] , shuffle=__UpperCamelCase , collate_fn=__UpperCamelCase , batch_size=__UpperCamelCase )
_A = DataLoader(
tokenized_datasets["validation"] , shuffle=__UpperCamelCase , collate_fn=__UpperCamelCase , batch_size=__UpperCamelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
a_ : Any = mocked_dataloaders # noqa: F811
def a__ ( __lowercase , __lowercase ) -> List[Any]:
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS" , __UpperCamelCase ) == "1":
_A = 2
# New Code #
_A = int(args.gradient_accumulation_steps )
_A = int(args.local_sgd_steps )
# Initialize accelerator
_A = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=__UpperCamelCase )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_A = config["lr"]
_A = int(config["num_epochs"] )
_A = int(config["seed"] )
_A = int(config["batch_size"] )
_A = evaluate.load("glue" , "mrpc" )
set_seed(__UpperCamelCase )
_A , _A = get_dataloaders(__UpperCamelCase , __UpperCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_A = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=__UpperCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_A = model.to(accelerator.device )
# Instantiate optimizer
_A = AdamW(params=model.parameters() , lr=__UpperCamelCase )
# Instantiate scheduler
_A = get_linear_schedule_with_warmup(
optimizer=__UpperCamelCase , num_warmup_steps=100 , num_training_steps=(len(__UpperCamelCase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_A , _A , _A , _A , _A = accelerator.prepare(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Now we train the model
for epoch in range(__UpperCamelCase ):
model.train()
with LocalSGD(
accelerator=__UpperCamelCase , model=__UpperCamelCase , local_sgd_steps=__UpperCamelCase , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(__UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(__UpperCamelCase ):
_A = model(**__UpperCamelCase )
_A = output.loss
accelerator.backward(__UpperCamelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(__UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_A = model(**__UpperCamelCase )
_A = outputs.logits.argmax(dim=-1 )
_A , _A = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=__UpperCamelCase , references=__UpperCamelCase , )
_A = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , __UpperCamelCase )
def a__ ( ) -> List[Any]:
_A = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=__UpperCamelCase , default=__UpperCamelCase , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
# New Code #
parser.add_argument(
"--gradient_accumulation_steps" , type=__UpperCamelCase , default=1 , help="The number of minibatches to be ran before gradients are accumulated." , )
parser.add_argument(
"--local_sgd_steps" , type=__UpperCamelCase , default=8 , help="Number of local SGD steps or None to disable local SGD" )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
_A = parser.parse_args()
_A = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(__UpperCamelCase , __UpperCamelCase )
if __name__ == "__main__":
main()
| 713
|
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from typing import List
from unittest.mock import Mock
import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset
from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType
class snake_case ( _UpperCamelCase):
def __init__( self : List[Any] , a__ : Any ) -> Any:
'''simple docstring'''
_A = data
def __iter__( self : List[str] ) -> str:
'''simple docstring'''
for element in self.data:
yield element
def a__ ( __lowercase=True ) -> Tuple:
_A = Accelerator(even_batches=__lowercase )
assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
return accelerator
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase = False ) -> Union[str, Any]:
if iterable:
_A = DummyIterableDataset(torch.as_tensor(range(__lowercase ) ) )
else:
_A = TensorDataset(torch.as_tensor(range(__lowercase ) ) )
_A = DataLoader(__lowercase , batch_size=__lowercase )
_A = accelerator.prepare(__lowercase )
return dl
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , ) -> Dict:
_A = create_dataloader(accelerator=__lowercase , dataset_size=__lowercase , batch_size=__lowercase )
_A = [len(batch[0] ) for batch in dl]
if accelerator.process_index == 0:
assert batch_sizes == process_0_expected_batch_sizes
elif accelerator.process_index == 1:
assert batch_sizes == process_1_expected_batch_sizes
def a__ ( ) -> List[str]:
_A = create_accelerator()
# without padding, we would expect a different number of batches
verify_dataloader_batch_sizes(
__lowercase , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1, 1] , )
# without padding, we would expect the same number of batches, but different sizes
verify_dataloader_batch_sizes(
__lowercase , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 2] , )
def a__ ( ) -> List[Any]:
_A = create_accelerator(even_batches=__lowercase )
verify_dataloader_batch_sizes(
__lowercase , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1] , )
verify_dataloader_batch_sizes(
__lowercase , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 1] , )
def a__ ( ) -> int:
_A = create_accelerator(even_batches=__lowercase )
_A = torch.nn.Linear(1 , 1 )
_A = accelerator.prepare(__lowercase )
_A = create_dataloader(__lowercase , dataset_size=3 , batch_size=1 )
_A = []
with accelerator.join_uneven_inputs([ddp_model] ):
for batch_idx, batch in enumerate(__lowercase ):
_A = ddp_model(batch[0].float() )
_A = output.sum()
loss.backward()
batch_idxs.append(__lowercase )
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
assert batch_idxs == [0, 1]
elif accelerator.process_index == 1:
assert batch_idxs == [0]
def a__ ( __lowercase ) -> List[str]:
with warnings.catch_warnings(record=__lowercase ) as w:
with accelerator.join_uneven_inputs([Mock()] ):
pass
assert issubclass(w[-1].category , __lowercase )
assert "only supported for multi-GPU" in str(w[-1].message )
def a__ ( ) -> Tuple:
_A = True
_A = False
_A = create_accelerator(even_batches=__lowercase )
_A = torch.nn.Linear(1 , 1 )
_A = accelerator.prepare(__lowercase )
_A = create_dataloader(__lowercase , dataset_size=3 , batch_size=1 )
_A = create_dataloader(__lowercase , dataset_size=3 , batch_size=1 )
with accelerator.join_uneven_inputs([ddp_model] , even_batches=__lowercase ):
_A = train_dl.batch_sampler.even_batches
_A = valid_dl.batch_sampler.even_batches
assert train_dl_overridden_value == overridden_even_batches
assert valid_dl_overridden_value == overridden_even_batches
assert train_dl.batch_sampler.even_batches == default_even_batches
assert valid_dl.batch_sampler.even_batches == default_even_batches
def a__ ( ) -> int:
_A = True
_A = False
_A = create_accelerator(even_batches=__lowercase )
_A = torch.nn.Linear(1 , 1 )
_A = accelerator.prepare(__lowercase )
create_dataloader(__lowercase , dataset_size=3 , batch_size=1 , iterable=__lowercase )
_A = create_dataloader(__lowercase , dataset_size=3 , batch_size=1 )
with warnings.catch_warnings():
warnings.filterwarnings("ignore" )
try:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=__lowercase ):
_A = batch_dl.batch_sampler.even_batches
except AttributeError:
# ensure attribute error is not raised when processing iterable dl
raise AssertionError
assert batch_dl_overridden_value == overridden_even_batches
assert batch_dl.batch_sampler.even_batches == default_even_batches
def a__ ( ) -> Optional[Any]:
_A = create_accelerator()
_A = torch.nn.Linear(1 , 1 )
_A = accelerator.prepare(__lowercase )
create_dataloader(__lowercase , dataset_size=3 , batch_size=1 , iterable=__lowercase )
with warnings.catch_warnings(record=__lowercase ) as w:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=__lowercase ):
pass
assert issubclass(w[-1].category , __lowercase )
assert "only supported for map-style datasets" in str(w[-1].message )
def a__ ( ) -> Optional[Any]:
_A = create_accelerator()
accelerator.print("Test that even_batches variable ensures uniform batches across processes" )
test_default_ensures_even_batch_sizes()
accelerator.print("Run tests with even_batches disabled" )
test_can_disable_even_batches()
accelerator.print("Test joining uneven inputs" )
test_can_join_uneven_inputs()
accelerator.print("Test overriding even_batches when joining uneven inputs" )
test_join_can_override_even_batches()
accelerator.print("Test overriding even_batches for mixed dataloader types" )
test_join_can_override_for_mixed_type_dataloaders()
accelerator.print("Test overriding even_batches raises a warning for iterable dataloaders" )
test_join_raises_warning_for_iterable_when_overriding_even_batches()
accelerator.print("Test join with non DDP distributed raises warning" )
_A = accelerator.state.distributed_type
_A = DistributedType.FSDP
test_join_raises_warning_for_non_ddp_distributed(__lowercase )
_A = original_state
if __name__ == "__main__":
main()
| 621
| 0
|
class SCREAMING_SNAKE_CASE__ :
def __init__( self,__lowerCamelCase ):
A__ = size
A__ = [0] * size
A__ = [0] * size
@staticmethod
def UpperCamelCase ( __lowerCamelCase ):
return index | (index + 1)
@staticmethod
def UpperCamelCase ( __lowerCamelCase ):
return (index & (index + 1)) - 1
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase ):
A__ = value
while index < self.size:
A__ = self.get_prev(__lowerCamelCase ) + 1
if current_left_border == index:
A__ = value
else:
A__ = max(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )
A__ = self.get_next(__lowerCamelCase )
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase ):
right -= 1 # Because of right is exclusive
A__ = 0
while left <= right:
A__ = self.get_prev(__lowerCamelCase )
if left <= current_left:
A__ = max(__lowerCamelCase,self.tree[right] )
A__ = current_left
else:
A__ = max(__lowerCamelCase,self.arr[right] )
right -= 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 190
|
from __future__ import annotations
import math
from collections.abc import Callable
def UpperCamelCase__( UpperCamelCase__ : Callable[[int | float], int | float] , UpperCamelCase__ : int | float , UpperCamelCase__ : int | float , UpperCamelCase__ : int = 1_00 , )->float:
A__ = x_start
A__ = fnc(UpperCamelCase__ )
A__ = 0.0
for _ in range(UpperCamelCase__ ):
# Approximates curve as a sequence of linear lines and sums their length
A__ = (x_end - x_start) / steps + xa
A__ = fnc(UpperCamelCase__ )
length += math.hypot(xa - xa , fxa - fxa )
# Increment step
A__ = xa
A__ = fxa
return length
if __name__ == "__main__":
def UpperCamelCase__( UpperCamelCase__ : Union[str, Any] )->Dict:
return math.sin(10 * x )
print('f(x) = sin(10 * x)')
print('The length of the curve from x = -10 to x = 10 is:')
a__: str = 10
while i <= 100_000:
print(F"With {i} steps: {line_length(f, -10, 10, i)}")
i *= 10
| 190
| 1
|
'''simple docstring'''
from __future__ import annotations
def snake_case (UpperCamelCase : str ):
'''simple docstring'''
lowerCamelCase__ = 2
lowerCamelCase__ = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__snake_case )
if n > 1:
factors.append(__snake_case )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 705
|
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 (UpperCamelCase : float = 0.1 ):
'''simple docstring'''
lowerCamelCase__ = 3
lowerCamelCase__ = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(UpperCamelCase )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 235
| 0
|
import logging
import os
from .state import PartialState
class lowerCAmelCase_ ( logging.LoggerAdapter ):
@staticmethod
def UpperCamelCase_ ( _A : Any ):
_UpperCamelCase = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def UpperCamelCase_ ( self : Union[str, Any] , _A : Optional[Any] , _A : str , *_A : int , **_A : List[Any] ):
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
_UpperCamelCase = kwargs.pop('''main_process_only''' , _A )
_UpperCamelCase = kwargs.pop('''in_order''' , _A )
if self.isEnabledFor(_A ):
if self._should_log(_A ):
_UpperCamelCase , _UpperCamelCase = self.process(_A , _A )
self.logger.log(_A , _A , *_A , **_A )
elif in_order:
_UpperCamelCase = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
_UpperCamelCase , _UpperCamelCase = self.process(_A , _A )
self.logger.log(_A , _A , *_A , **_A )
state.wait_for_everyone()
def _snake_case ( __snake_case , __snake_case = None ):
if log_level is None:
_UpperCamelCase = os.environ.get('''ACCELERATE_LOG_LEVEL''' , __snake_case )
_UpperCamelCase = logging.getLogger(__snake_case )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(__snake_case , {} )
| 10
|
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
A__ : Union[str, Any] = random.Random()
def UpperCamelCase( __UpperCamelCase : int ,__UpperCamelCase : List[Any]=1.0 ,__UpperCamelCase : Dict=None ,__UpperCamelCase : List[Any]=None ):
if rng is None:
lowerCAmelCase_ : List[Any] = global_rng
lowerCAmelCase_ : Optional[Any] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
class __snake_case ( unittest.TestCase ):
def __init__( self : Union[str, Any] , A_ : Dict , A_ : Optional[Any]=7 , A_ : Union[str, Any]=4_0_0 , A_ : Dict=2_0_0_0 , A_ : Dict=1 , A_ : Optional[Any]=0.0 , A_ : Tuple=1_6_0_0_0 , A_ : Any=True , A_ : Any=8_0 , A_ : str=1_6 , A_ : Union[str, Any]=6_4 , A_ : List[Any]="hann_window" , A_ : Union[str, Any]=8_0 , A_ : Dict=7_6_0_0 , A_ : List[str]=1e-10 , A_ : Union[str, Any]=True , ):
lowerCAmelCase_ : Optional[Any] = parent
lowerCAmelCase_ : int = batch_size
lowerCAmelCase_ : str = min_seq_length
lowerCAmelCase_ : Optional[int] = max_seq_length
lowerCAmelCase_ : Optional[Any] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
lowerCAmelCase_ : List[Any] = feature_size
lowerCAmelCase_ : Any = padding_value
lowerCAmelCase_ : List[Any] = sampling_rate
lowerCAmelCase_ : str = do_normalize
lowerCAmelCase_ : str = num_mel_bins
lowerCAmelCase_ : List[str] = hop_length
lowerCAmelCase_ : Tuple = win_length
lowerCAmelCase_ : Tuple = win_function
lowerCAmelCase_ : Optional[int] = fmin
lowerCAmelCase_ : List[str] = fmax
lowerCAmelCase_ : Optional[Any] = mel_floor
lowerCAmelCase_ : Optional[Any] = return_attention_mask
def UpperCAmelCase__ ( self : List[str]):
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"do_normalize": self.do_normalize,
"num_mel_bins": self.num_mel_bins,
"hop_length": self.hop_length,
"win_length": self.win_length,
"win_function": self.win_function,
"fmin": self.fmin,
"fmax": self.fmax,
"mel_floor": self.mel_floor,
"return_attention_mask": self.return_attention_mask,
}
def UpperCAmelCase__ ( self : Optional[int] , A_ : Any=False , A_ : List[str]=False):
def _flatten(A_ : Tuple):
return list(itertools.chain(*A_))
if equal_length:
lowerCAmelCase_ : Union[str, Any] = floats_list((self.batch_size, self.max_seq_length))
else:
# make sure that inputs increase in size
lowerCAmelCase_ : Dict = [
_flatten(floats_list((x, self.feature_size)))
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff)
]
if numpify:
lowerCAmelCase_ : Optional[Any] = [np.asarray(A_) for x in speech_inputs]
return speech_inputs
def UpperCAmelCase__ ( self : Tuple , A_ : Optional[Any]=False , A_ : Dict=False):
if equal_length:
lowerCAmelCase_ : Union[str, Any] = [floats_list((self.max_seq_length, self.num_mel_bins)) for _ in range(self.batch_size)]
else:
# make sure that inputs increase in size
lowerCAmelCase_ : Tuple = [
floats_list((x, self.num_mel_bins))
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff)
]
if numpify:
lowerCAmelCase_ : Optional[int] = [np.asarray(A_) for x in speech_inputs]
return speech_inputs
@require_torch
class __snake_case ( UpperCamelCase_ ,unittest.TestCase ):
_a = SpeechTaFeatureExtractor
def UpperCAmelCase__ ( self : int):
lowerCAmelCase_ : Dict = SpeechTaFeatureExtractionTester(self)
def UpperCAmelCase__ ( self : Union[str, Any] , A_ : int):
self.assertTrue(np.all(np.mean(A_ , axis=0) < 1e-3))
self.assertTrue(np.all(np.abs(np.var(A_ , axis=0) - 1) < 1e-3))
def UpperCAmelCase__ ( self : List[Any]):
# Tests that all call wrap to encode_plus and batch_encode_plus
lowerCAmelCase_ : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
lowerCAmelCase_ : Optional[Any] = [floats_list((1, x))[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0)]
lowerCAmelCase_ : Any = [np.asarray(A_) for speech_input in speech_inputs]
# Test not batched input
lowerCAmelCase_ : Tuple = feat_extract(speech_inputs[0] , return_tensors='''np''').input_values
lowerCAmelCase_ : Dict = feat_extract(np_speech_inputs[0] , return_tensors='''np''').input_values
self.assertTrue(np.allclose(A_ , A_ , atol=1e-3))
# Test batched
lowerCAmelCase_ : Any = feat_extract(A_ , return_tensors='''np''').input_values
lowerCAmelCase_ : Optional[int] = feat_extract(A_ , return_tensors='''np''').input_values
for enc_seq_a, enc_seq_a in zip(A_ , A_):
self.assertTrue(np.allclose(A_ , A_ , atol=1e-3))
def UpperCAmelCase__ ( self : List[str]):
lowerCAmelCase_ : List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
lowerCAmelCase_ : Optional[Any] = [floats_list((1, x))[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0)]
lowerCAmelCase_ : Union[str, Any] = ['''longest''', '''max_length''', '''do_not_pad''']
lowerCAmelCase_ : str = [None, 1_6_0_0, None]
for max_length, padding in zip(A_ , A_):
lowerCAmelCase_ : str = feat_extract(A_ , padding=A_ , max_length=A_ , return_tensors='''np''')
lowerCAmelCase_ : Union[str, Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_0_0])
self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0])
self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0])
def UpperCAmelCase__ ( self : Optional[int]):
lowerCAmelCase_ : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
lowerCAmelCase_ : Dict = range(8_0_0 , 1_4_0_0 , 2_0_0)
lowerCAmelCase_ : Union[str, Any] = [floats_list((1, x))[0] for x in lengths]
lowerCAmelCase_ : List[str] = ['''longest''', '''max_length''', '''do_not_pad''']
lowerCAmelCase_ : Optional[Any] = [None, 1_6_0_0, None]
for max_length, padding in zip(A_ , A_):
lowerCAmelCase_ : Optional[int] = feat_extract(A_ , max_length=A_ , padding=A_)
lowerCAmelCase_ : int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_0_0])
self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0])
self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0])
def UpperCAmelCase__ ( self : str):
lowerCAmelCase_ : str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
lowerCAmelCase_ : List[Any] = [floats_list((1, x))[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0)]
lowerCAmelCase_ : int = feat_extract(
A_ , truncation=A_ , max_length=1_0_0_0 , padding='''max_length''' , return_tensors='''np''')
lowerCAmelCase_ : Optional[int] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0])
self._check_zero_mean_unit_variance(input_values[1])
self._check_zero_mean_unit_variance(input_values[2])
def UpperCAmelCase__ ( self : int):
lowerCAmelCase_ : List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
lowerCAmelCase_ : str = [floats_list((1, x))[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0)]
lowerCAmelCase_ : Any = feat_extract(
A_ , truncation=A_ , max_length=1_0_0_0 , padding='''longest''' , return_tensors='''np''')
lowerCAmelCase_ : List[Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0])
self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0])
self._check_zero_mean_unit_variance(input_values[2])
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1_0_0_0))
lowerCAmelCase_ : List[str] = [floats_list((1, x))[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0)]
lowerCAmelCase_ : List[Any] = feat_extract(
A_ , truncation=A_ , max_length=2_0_0_0 , padding='''longest''' , return_tensors='''np''')
lowerCAmelCase_ : Optional[Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0])
self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0])
self._check_zero_mean_unit_variance(input_values[2])
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1_2_0_0))
def UpperCAmelCase__ ( self : Dict):
lowerCAmelCase_ : Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
lowerCAmelCase_ : List[str] = np.random.rand(1_0_0).astype(np.floataa)
lowerCAmelCase_ : Any = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
lowerCAmelCase_ : Optional[Any] = feature_extractor.pad([{'''input_values''': inputs}] , return_tensors='''np''')
self.assertTrue(np_processed.input_values.dtype == np.floataa)
lowerCAmelCase_ : List[str] = feature_extractor.pad([{'''input_values''': inputs}] , return_tensors='''pt''')
self.assertTrue(pt_processed.input_values.dtype == torch.floataa)
def UpperCAmelCase__ ( self : Optional[int]):
# Tests that all call wrap to encode_plus and batch_encode_plus
lowerCAmelCase_ : Optional[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
lowerCAmelCase_ : str = [floats_list((1, x))[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0)]
lowerCAmelCase_ : Optional[int] = [np.asarray(A_) for speech_input in speech_inputs]
# Test feature size
lowerCAmelCase_ : Optional[Any] = feature_extractor(audio_target=A_ , padding=A_ , return_tensors='''np''').input_values
self.assertTrue(input_values.ndim == 3)
self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins)
# Test not batched input
lowerCAmelCase_ : int = feature_extractor(speech_inputs[0] , return_tensors='''np''').input_values
lowerCAmelCase_ : Dict = feature_extractor(np_speech_inputs[0] , return_tensors='''np''').input_values
self.assertTrue(np.allclose(A_ , A_ , atol=1e-3))
# Test batched
lowerCAmelCase_ : Optional[int] = feature_extractor(A_ , return_tensors='''np''').input_values
lowerCAmelCase_ : int = feature_extractor(A_ , return_tensors='''np''').input_values
for enc_seq_a, enc_seq_a in zip(A_ , A_):
self.assertTrue(np.allclose(A_ , A_ , atol=1e-3))
# Test 2-D numpy arrays are batched.
lowerCAmelCase_ : Tuple = [floats_list((1, x))[0] for x in (8_0_0, 8_0_0, 8_0_0)]
lowerCAmelCase_ : Any = np.asarray(A_)
lowerCAmelCase_ : Any = feature_extractor(A_ , return_tensors='''np''').input_values
lowerCAmelCase_ : int = feature_extractor(A_ , return_tensors='''np''').input_values
for enc_seq_a, enc_seq_a in zip(A_ , A_):
self.assertTrue(np.allclose(A_ , A_ , atol=1e-3))
def UpperCAmelCase__ ( self : Union[str, Any]):
lowerCAmelCase_ : Optional[int] = self.feat_extract_tester.prepare_inputs_for_target()
lowerCAmelCase_ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict)
lowerCAmelCase_ : Any = feat_extract.model_input_names[0]
lowerCAmelCase_ : Dict = BatchFeature({input_name: speech_inputs})
self.assertTrue(all(len(A_) == len(A_) for x, y in zip(A_ , processed_features[input_name])))
lowerCAmelCase_ : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_target(equal_length=A_)
lowerCAmelCase_ : Optional[int] = BatchFeature({input_name: speech_inputs} , tensor_type='''np''')
lowerCAmelCase_ : Dict = processed_features[input_name]
if len(batch_features_input.shape) < 3:
lowerCAmelCase_ : int = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.num_mel_bins))
@require_torch
def UpperCAmelCase__ ( self : List[Any]):
lowerCAmelCase_ : Optional[int] = self.feat_extract_tester.prepare_inputs_for_target(equal_length=A_)
lowerCAmelCase_ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict)
lowerCAmelCase_ : Optional[int] = feat_extract.model_input_names[0]
lowerCAmelCase_ : Optional[int] = BatchFeature({input_name: speech_inputs} , tensor_type='''pt''')
lowerCAmelCase_ : Any = processed_features[input_name]
if len(batch_features_input.shape) < 3:
lowerCAmelCase_ : Any = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.num_mel_bins))
@require_torch
def UpperCAmelCase__ ( self : str):
lowerCAmelCase_ : int = self.feature_extraction_class(**self.feat_extract_dict)
lowerCAmelCase_ : Dict = self.feat_extract_tester.prepare_inputs_for_target()
lowerCAmelCase_ : Union[str, Any] = feat_extract.model_input_names[0]
lowerCAmelCase_ : Union[str, Any] = BatchFeature({input_name: speech_inputs})
lowerCAmelCase_ : str = feat_extract.num_mel_bins # hack!
lowerCAmelCase_ : List[Any] = feat_extract.pad(A_ , padding='''longest''' , return_tensors='''np''')[input_name]
lowerCAmelCase_ : Tuple = feat_extract.pad(A_ , padding='''longest''' , return_tensors='''pt''')[input_name]
self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_pt.numpy().astype(np.floataa).sum()) < 1e-2)
def UpperCAmelCase__ ( self : List[Any]):
lowerCAmelCase_ : str = self.feat_extract_dict
lowerCAmelCase_ : str = True
lowerCAmelCase_ : Optional[Any] = self.feature_extraction_class(**A_)
lowerCAmelCase_ : Tuple = self.feat_extract_tester.prepare_inputs_for_target()
lowerCAmelCase_ : str = [len(A_) for x in speech_inputs]
lowerCAmelCase_ : List[str] = feat_extract.model_input_names[0]
lowerCAmelCase_ : Dict = BatchFeature({input_name: speech_inputs})
lowerCAmelCase_ : str = feat_extract.num_mel_bins # hack!
lowerCAmelCase_ : Tuple = feat_extract.pad(A_ , padding='''longest''' , return_tensors='''np''')
self.assertIn('''attention_mask''' , A_)
self.assertListEqual(list(processed.attention_mask.shape) , list(processed[input_name].shape[:2]))
self.assertListEqual(processed.attention_mask.sum(-1).tolist() , A_)
def UpperCAmelCase__ ( self : Union[str, Any]):
lowerCAmelCase_ : Tuple = self.feat_extract_dict
lowerCAmelCase_ : List[Any] = True
lowerCAmelCase_ : Any = self.feature_extraction_class(**A_)
lowerCAmelCase_ : Any = self.feat_extract_tester.prepare_inputs_for_target()
lowerCAmelCase_ : Tuple = [len(A_) for x in speech_inputs]
lowerCAmelCase_ : Tuple = feat_extract.model_input_names[0]
lowerCAmelCase_ : str = BatchFeature({input_name: speech_inputs})
lowerCAmelCase_ : int = min(A_)
lowerCAmelCase_ : Dict = feat_extract.num_mel_bins # hack!
lowerCAmelCase_ : str = feat_extract.pad(
A_ , padding='''max_length''' , max_length=A_ , truncation=A_ , return_tensors='''np''')
self.assertIn('''attention_mask''' , A_)
self.assertListEqual(
list(processed_pad.attention_mask.shape) , [processed_pad[input_name].shape[0], max_length])
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1).tolist() , [max_length for x in speech_inputs])
def UpperCAmelCase__ ( self : int , A_ : Optional[int]):
from datasets import load_dataset
lowerCAmelCase_ : List[Any] = load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation''')
# automatic decoding with librispeech
lowerCAmelCase_ : Optional[Any] = ds.sort('''id''').select(range(A_))[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def UpperCAmelCase__ ( self : Any):
# fmt: off
lowerCAmelCase_ : Optional[int] = torch.tensor(
[2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03])
# fmt: on
lowerCAmelCase_ : Tuple = self._load_datasamples(1)
lowerCAmelCase_ : List[str] = SpeechTaFeatureExtractor()
lowerCAmelCase_ : List[Any] = feature_extractor(A_ , return_tensors='''pt''').input_values
self.assertEquals(input_values.shape , (1, 9_3_6_8_0))
self.assertTrue(torch.allclose(input_values[0, :3_0] , A_ , atol=1e-6))
def UpperCAmelCase__ ( self : Optional[int]):
# fmt: off
lowerCAmelCase_ : Tuple = torch.tensor(
[-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
-3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
-3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
-3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998])
# fmt: on
lowerCAmelCase_ : Union[str, Any] = self._load_datasamples(1)
lowerCAmelCase_ : Any = SpeechTaFeatureExtractor()
lowerCAmelCase_ : Dict = feature_extractor(audio_target=A_ , return_tensors='''pt''').input_values
self.assertEquals(input_values.shape , (1, 3_6_6, 8_0))
self.assertTrue(torch.allclose(input_values[0, 0, :3_0] , A_ , atol=1e-4))
| 171
| 0
|
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,
)
__UpperCAmelCase = {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["""MBartTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["""MBartTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MBartForCausalLM""",
"""MBartForConditionalGeneration""",
"""MBartForQuestionAnswering""",
"""MBartForSequenceClassification""",
"""MBartModel""",
"""MBartPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""TFMBartForConditionalGeneration""",
"""TFMBartModel""",
"""TFMBartPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""FlaxMBartForConditionalGeneration""",
"""FlaxMBartForQuestionAnswering""",
"""FlaxMBartForSequenceClassification""",
"""FlaxMBartModel""",
"""FlaxMBartPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 709
|
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class lowercase__( unittest.TestCase ):
'''simple docstring'''
@parameterized.expand([(None,), ("foo.json",)])
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE) -> int:
"""simple docstring"""
UpperCamelCase__ : Optional[Any] =GenerationConfig(
do_sample=__SCREAMING_SNAKE_CASE , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__SCREAMING_SNAKE_CASE , config_name=__SCREAMING_SNAKE_CASE)
UpperCamelCase__ : Optional[int] =GenerationConfig.from_pretrained(__SCREAMING_SNAKE_CASE , config_name=__SCREAMING_SNAKE_CASE)
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , __SCREAMING_SNAKE_CASE)
self.assertEqual(loaded_config.temperature , 0.7)
self.assertEqual(loaded_config.length_penalty , 1.0)
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]])
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50)
self.assertEqual(loaded_config.max_length , 20)
self.assertEqual(loaded_config.max_time , __SCREAMING_SNAKE_CASE)
def UpperCAmelCase ( self) -> List[Any]:
"""simple docstring"""
UpperCamelCase__ : List[str] =AutoConfig.from_pretrained("gpt2")
UpperCamelCase__ : Optional[Any] =GenerationConfig.from_model_config(__SCREAMING_SNAKE_CASE)
UpperCamelCase__ : List[Any] =GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id)
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id)
def UpperCAmelCase ( self) -> Dict:
"""simple docstring"""
UpperCamelCase__ : List[Any] =GenerationConfig()
UpperCamelCase__ : Optional[Any] ={
"max_new_tokens": 10_24,
"foo": "bar",
}
UpperCamelCase__ : Tuple =copy.deepcopy(__SCREAMING_SNAKE_CASE)
UpperCamelCase__ : Any =generation_config.update(**__SCREAMING_SNAKE_CASE)
# update_kwargs was not modified (no side effects)
self.assertEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 10_24)
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(__SCREAMING_SNAKE_CASE , {"foo": "bar"})
def UpperCAmelCase ( self) -> List[str]:
"""simple docstring"""
UpperCamelCase__ : str =GenerationConfig()
UpperCamelCase__ : Dict ="bar"
with tempfile.TemporaryDirectory("test-generation-config") as tmp_dir:
generation_config.save_pretrained(__SCREAMING_SNAKE_CASE)
UpperCamelCase__ : int =GenerationConfig.from_pretrained(__SCREAMING_SNAKE_CASE)
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , "bar")
UpperCamelCase__ : Union[str, Any] =GenerationConfig.from_model_config(__SCREAMING_SNAKE_CASE)
assert not hasattr(__SCREAMING_SNAKE_CASE , "foo") # no new kwargs should be initialized if from config
def UpperCAmelCase ( self) -> int:
"""simple docstring"""
UpperCamelCase__ : Dict =GenerationConfig()
self.assertEqual(default_config.temperature , 1.0)
self.assertEqual(default_config.do_sample , __SCREAMING_SNAKE_CASE)
self.assertEqual(default_config.num_beams , 1)
UpperCamelCase__ : Any =GenerationConfig(
do_sample=__SCREAMING_SNAKE_CASE , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7)
self.assertEqual(config.do_sample , __SCREAMING_SNAKE_CASE)
self.assertEqual(config.num_beams , 1)
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__SCREAMING_SNAKE_CASE)
UpperCamelCase__ : str =GenerationConfig.from_pretrained(__SCREAMING_SNAKE_CASE , temperature=1.0)
self.assertEqual(loaded_config.temperature , 1.0)
self.assertEqual(loaded_config.do_sample , __SCREAMING_SNAKE_CASE)
self.assertEqual(loaded_config.num_beams , 1) # default value
@is_staging_test
class lowercase__( unittest.TestCase ):
'''simple docstring'''
@classmethod
def UpperCAmelCase ( cls) -> List[Any]:
"""simple docstring"""
UpperCamelCase__ : Any =TOKEN
HfFolder.save_token(__SCREAMING_SNAKE_CASE)
@classmethod
def UpperCAmelCase ( cls) -> Tuple:
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id="test-generation-config")
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-generation-config-org")
except HTTPError:
pass
def UpperCAmelCase ( self) -> str:
"""simple docstring"""
UpperCamelCase__ : Optional[int] =GenerationConfig(
do_sample=__SCREAMING_SNAKE_CASE , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("test-generation-config" , use_auth_token=self._token)
UpperCamelCase__ : List[Any] =GenerationConfig.from_pretrained(F'''{USER}/test-generation-config''')
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__SCREAMING_SNAKE_CASE , getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE))
# Reset repo
delete_repo(token=self._token , repo_id="test-generation-config")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__SCREAMING_SNAKE_CASE , repo_id="test-generation-config" , push_to_hub=__SCREAMING_SNAKE_CASE , use_auth_token=self._token)
UpperCamelCase__ : Union[str, Any] =GenerationConfig.from_pretrained(F'''{USER}/test-generation-config''')
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__SCREAMING_SNAKE_CASE , getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE))
def UpperCAmelCase ( self) -> List[Any]:
"""simple docstring"""
UpperCamelCase__ : Tuple =GenerationConfig(
do_sample=__SCREAMING_SNAKE_CASE , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("valid_org/test-generation-config-org" , use_auth_token=self._token)
UpperCamelCase__ : str =GenerationConfig.from_pretrained("valid_org/test-generation-config-org")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__SCREAMING_SNAKE_CASE , getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE))
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-generation-config-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__SCREAMING_SNAKE_CASE , repo_id="valid_org/test-generation-config-org" , push_to_hub=__SCREAMING_SNAKE_CASE , use_auth_token=self._token)
UpperCamelCase__ : Union[str, Any] =GenerationConfig.from_pretrained("valid_org/test-generation-config-org")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__SCREAMING_SNAKE_CASE , getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE))
| 582
| 0
|
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformers import TvltFeatureExtractor
from transformers import TvltProcessor
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> int:
_a : Dict = '''ZinengTang/tvlt-base'''
_a : List[str] = tempfile.mkdtemp()
def __lowercase ( self , **_a ) -> int:
return TvltImageProcessor.from_pretrained(self.checkpoint , **_a )
def __lowercase ( self , **_a ) -> List[Any]:
return TvltFeatureExtractor.from_pretrained(self.checkpoint , **_a )
def __lowercase ( self ) -> Optional[int]:
shutil.rmtree(self.tmpdirname )
def __lowercase ( self ) -> Dict:
_a : Union[str, Any] = self.get_image_processor()
_a : Dict = self.get_feature_extractor()
_a : Optional[int] = TvltProcessor(image_processor=_a , feature_extractor=_a )
processor.save_pretrained(self.tmpdirname )
_a : Any = TvltProcessor.from_pretrained(self.tmpdirname )
self.assertIsInstance(processor.feature_extractor , _a )
self.assertIsInstance(processor.image_processor , _a )
def __lowercase ( self ) -> Any:
_a : Optional[Any] = self.get_image_processor()
_a : Dict = self.get_feature_extractor()
_a : Dict = TvltProcessor(image_processor=_a , feature_extractor=_a )
_a : Union[str, Any] = np.ones([1_2_0_0_0] )
_a : Dict = feature_extractor(_a , return_tensors='''np''' )
_a : Tuple = processor(audio=_a , return_tensors='''np''' )
for key in audio_dict.keys():
self.assertAlmostEqual(audio_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowercase ( self ) -> int:
_a : Optional[Any] = self.get_image_processor()
_a : Union[str, Any] = self.get_feature_extractor()
_a : Optional[Any] = TvltProcessor(image_processor=_a , feature_extractor=_a )
_a : List[Any] = np.ones([3, 2_2_4, 2_2_4] )
_a : int = image_processor(_a , return_tensors='''np''' )
_a : Optional[int] = processor(images=_a , return_tensors='''np''' )
for key in image_dict.keys():
self.assertAlmostEqual(image_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowercase ( self ) -> Union[str, Any]:
_a : int = self.get_image_processor()
_a : Union[str, Any] = self.get_feature_extractor()
_a : Any = TvltProcessor(image_processor=_a , feature_extractor=_a )
_a : List[str] = np.ones([1_2_0_0_0] )
_a : Optional[int] = np.ones([3, 2_2_4, 2_2_4] )
_a : int = processor(audio=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask'''] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def __lowercase ( self ) -> Union[str, Any]:
_a : str = self.get_image_processor()
_a : Union[str, Any] = self.get_feature_extractor()
_a : Dict = TvltProcessor(image_processor=_a , feature_extractor=_a )
self.assertListEqual(
processor.model_input_names , image_processor.model_input_names + feature_extractor.model_input_names , msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match''' , )
| 14
|
'''simple docstring'''
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = """▁"""
snake_case_ = {"""vocab_file""": """vocab.txt""", """sentencepiece_model_ckpt""": """sentencepiece.bpe.model"""}
snake_case_ = {
"""sentencepiece_model_file""": """sentencepiece.bpe.model""",
"""vocab_file""": """vocab.txt""",
}
snake_case_ = {
"""vocab_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
},
"""sentencepiece_model_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
},
}
snake_case_ = {
"""ernie-m-base""": 514,
"""ernie-m-large""": 514,
}
snake_case_ = {
"""ernie-m-base""": {"""do_lower_case""": False},
"""ernie-m-large""": {"""do_lower_case""": False},
}
class a__ ( _lowercase ):
__magic_name__ : List[str] = ["input_ids"]
__magic_name__ : Any = VOCAB_FILES_NAMES
__magic_name__ : List[str] = PRETRAINED_INIT_CONFIGURATION
__magic_name__ : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__magic_name__ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
__magic_name__ : Any = RESOURCE_FILES_NAMES
def __init__(self : Optional[Any], __UpperCAmelCase : List[str], __UpperCAmelCase : List[str]=None, __UpperCAmelCase : str=False, __UpperCAmelCase : Union[str, Any]="utf8", __UpperCAmelCase : Optional[int]="[UNK]", __UpperCAmelCase : Any="[SEP]", __UpperCAmelCase : Optional[int]="[PAD]", __UpperCAmelCase : Tuple="[CLS]", __UpperCAmelCase : List[Any]="[MASK]", __UpperCAmelCase : Optional[Dict[str, Any]] = None, **__UpperCAmelCase : Tuple, ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=__UpperCAmelCase, unk_token=__UpperCAmelCase, sep_token=__UpperCAmelCase, pad_token=__UpperCAmelCase, cls_token=__UpperCAmelCase, mask_token=__UpperCAmelCase, vocab_file=__UpperCAmelCase, encoding=__UpperCAmelCase, sp_model_kwargs=self.sp_model_kwargs, **__UpperCAmelCase, )
SCREAMING_SNAKE_CASE : Union[str, Any] = do_lower_case
SCREAMING_SNAKE_CASE : List[Any] = sentencepiece_model_ckpt
SCREAMING_SNAKE_CASE : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__UpperCAmelCase )
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
SCREAMING_SNAKE_CASE : Union[str, Any] = self.load_vocab(filepath=__UpperCAmelCase )
else:
SCREAMING_SNAKE_CASE : Any = {self.sp_model.id_to_piece(__UpperCAmelCase ): id for id in range(self.sp_model.get_piece_size() )}
SCREAMING_SNAKE_CASE : Optional[int] = {v: k for k, v in self.vocab.items()}
def lowercase__ (self : Dict, __UpperCAmelCase : Any ) -> Dict:
"""simple docstring"""
if text is None:
return None
SCREAMING_SNAKE_CASE : List[Any] = self.tokenize(__UpperCAmelCase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = '''''', []
for i, ch in enumerate(__UpperCAmelCase ):
if ch in self.SP_CHAR_MAPPING:
SCREAMING_SNAKE_CASE : int = self.SP_CHAR_MAPPING.get(__UpperCAmelCase )
else:
SCREAMING_SNAKE_CASE : List[Any] = unicodedata.normalize('''NFKC''', __UpperCAmelCase )
if self.is_whitespace(__UpperCAmelCase ):
continue
normalized_text += ch
char_mapping.extend([i] * len(__UpperCAmelCase ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = normalized_text, [], 0
if self.do_lower_case:
SCREAMING_SNAKE_CASE : List[str] = text.lower()
for token in split_tokens:
if token[:1] == "▁":
SCREAMING_SNAKE_CASE : Optional[Any] = token[1:]
SCREAMING_SNAKE_CASE : Tuple = text[offset:].index(__UpperCAmelCase ) + offset
SCREAMING_SNAKE_CASE : Union[str, Any] = start + len(__UpperCAmelCase )
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
SCREAMING_SNAKE_CASE : Dict = end
return token_mapping
@property
def lowercase__ (self : int ) -> Union[str, Any]:
"""simple docstring"""
return len(self.vocab )
def lowercase__ (self : Tuple ) -> int:
"""simple docstring"""
return dict(self.vocab, **self.added_tokens_encoder )
def __getstate__(self : str ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = self.__dict__.copy()
SCREAMING_SNAKE_CASE : Any = None
return state
def __setstate__(self : Union[str, Any], __UpperCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = d
# for backward compatibility
if not hasattr(self, '''sp_model_kwargs''' ):
SCREAMING_SNAKE_CASE : Optional[int] = {}
SCREAMING_SNAKE_CASE : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.sentencepiece_model_ckpt )
def lowercase__ (self : Union[str, Any], __UpperCAmelCase : Any ) -> List[Any]:
"""simple docstring"""
return "".join((self.SP_CHAR_MAPPING.get(__UpperCAmelCase, __UpperCAmelCase ) for c in text) )
def lowercase__ (self : Union[str, Any], __UpperCAmelCase : Any, __UpperCAmelCase : Optional[Any]=False, __UpperCAmelCase : str=64, __UpperCAmelCase : int=0.1 ) -> int:
"""simple docstring"""
if self.sp_model_kwargs.get('''enable_sampling''' ) is True:
SCREAMING_SNAKE_CASE : int = True
if self.sp_model_kwargs.get('''alpha''' ) is not None:
SCREAMING_SNAKE_CASE : Optional[Any] = self.sp_model_kwargs.get('''alpha''' )
if self.sp_model_kwargs.get('''nbest_size''' ) is not None:
SCREAMING_SNAKE_CASE : Tuple = self.sp_model_kwargs.get('''nbest_size''' )
if not enable_sampling:
SCREAMING_SNAKE_CASE : int = self.sp_model.EncodeAsPieces(__UpperCAmelCase )
else:
SCREAMING_SNAKE_CASE : Any = self.sp_model.SampleEncodeAsPieces(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase )
SCREAMING_SNAKE_CASE : str = []
for pi, piece in enumerate(__UpperCAmelCase ):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(__UpperCAmelCase ) and pi != 0:
new_pieces.append(__UpperCAmelCase )
continue
else:
continue
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for i, chunk in enumerate(__UpperCAmelCase ):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(__UpperCAmelCase ) or self.is_punct(__UpperCAmelCase ):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
new_pieces.append(__UpperCAmelCase )
SCREAMING_SNAKE_CASE : Dict = i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
SCREAMING_SNAKE_CASE : List[Any] = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
SCREAMING_SNAKE_CASE : Dict = i
if len(__UpperCAmelCase ) > lst_i:
new_pieces.append(piece[lst_i:] )
return new_pieces
def lowercase__ (self : Optional[int], __UpperCAmelCase : Tuple ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = ''''''.join(__UpperCAmelCase ).replace(__UpperCAmelCase, ''' ''' ).strip()
return out_string
def lowercase__ (self : Tuple, __UpperCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.convert_ids_to_tokens(__UpperCAmelCase )
SCREAMING_SNAKE_CASE : Any = ''''''.join(__UpperCAmelCase ).replace(__UpperCAmelCase, ''' ''' ).strip()
return out_string
def lowercase__ (self : Optional[Any], __UpperCAmelCase : Any ) -> Union[str, Any]:
"""simple docstring"""
return self.vocab.get(__UpperCAmelCase, self.vocab.get(self.unk_token ) )
def lowercase__ (self : Optional[Any], __UpperCAmelCase : Tuple ) -> Optional[Any]:
"""simple docstring"""
return self.reverse_vocab.get(__UpperCAmelCase, self.unk_token )
def lowercase__ (self : List[str], __UpperCAmelCase : List[Any], __UpperCAmelCase : int=None ) -> Optional[Any]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE : List[Any] = [self.cls_token_id]
SCREAMING_SNAKE_CASE : Tuple = [self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def lowercase__ (self : Optional[int], __UpperCAmelCase : Any, __UpperCAmelCase : int=None ) -> Any:
"""simple docstring"""
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def lowercase__ (self : str, __UpperCAmelCase : Union[str, Any], __UpperCAmelCase : int=None, __UpperCAmelCase : str=False ) -> str:
"""simple docstring"""
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(__UpperCAmelCase )) + [1, 1] + ([0] * len(__UpperCAmelCase )) + [1]
return [1] + ([0] * len(__UpperCAmelCase )) + [1]
def lowercase__ (self : Optional[Any], __UpperCAmelCase : List[int], __UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
# [CLS] X [SEP]
return (len(__UpperCAmelCase ) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(__UpperCAmelCase ) + 1) + [1] * (len(__UpperCAmelCase ) + 3)
def lowercase__ (self : Optional[Any], __UpperCAmelCase : int ) -> Dict:
"""simple docstring"""
if "\u4e00" <= char <= "\u9fff":
return True
return False
def lowercase__ (self : Optional[int], __UpperCAmelCase : Dict ) -> Tuple:
"""simple docstring"""
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def lowercase__ (self : Optional[Any], __UpperCAmelCase : Union[str, Any] ) -> Any:
"""simple docstring"""
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def lowercase__ (self : Union[str, Any], __UpperCAmelCase : Any ) -> str:
"""simple docstring"""
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(__UpperCAmelCase ) == 1:
SCREAMING_SNAKE_CASE : int = unicodedata.category(__UpperCAmelCase )
if cat == "Zs":
return True
return False
def lowercase__ (self : List[Any], __UpperCAmelCase : Optional[Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = {}
with io.open(__UpperCAmelCase, '''r''', encoding='''utf-8''' ) as f:
for index, line in enumerate(__UpperCAmelCase ):
SCREAMING_SNAKE_CASE : List[Any] = line.rstrip('''\n''' )
SCREAMING_SNAKE_CASE : Tuple = int(__UpperCAmelCase )
return token_to_idx
def lowercase__ (self : List[Any], __UpperCAmelCase : str, __UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = 0
if os.path.isdir(__UpperCAmelCase ):
SCREAMING_SNAKE_CASE : str = os.path.join(
__UpperCAmelCase, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
else:
SCREAMING_SNAKE_CASE : int = (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
with open(__UpperCAmelCase, '''w''', encoding='''utf-8''' ) as writer:
for token, token_index in sorted(self.vocab.items(), key=lambda __UpperCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
''' Please check that the vocabulary is not corrupted!''' )
SCREAMING_SNAKE_CASE : Dict = token_index
writer.write(token + '''\n''' )
index += 1
SCREAMING_SNAKE_CASE : Optional[Any] = os.path.join(__UpperCAmelCase, '''sentencepiece.bpe.model''' )
with open(__UpperCAmelCase, '''wb''' ) as fi:
SCREAMING_SNAKE_CASE : List[str] = self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
return (vocab_file,)
| 507
| 0
|
"""simple docstring"""
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class a__ ( UpperCamelCase_ ):
snake_case__ = 4_2
snake_case__ = 4_2
class a__ ( nn.Module ):
snake_case__ = 4_2
snake_case__ = (1_6, 3_2, 9_6, 2_5_6)
snake_case__ = jnp.floataa
def __UpperCamelCase ( self : Tuple) -> List[Any]:
"""simple docstring"""
_lowerCAmelCase:Tuple = nn.Conv(
self.block_out_channels[0] ,kernel_size=(3, 3) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
_lowerCAmelCase:Tuple = []
for i in range(len(self.block_out_channels) - 1):
_lowerCAmelCase:Optional[int] = self.block_out_channels[i]
_lowerCAmelCase:List[str] = self.block_out_channels[i + 1]
_lowerCAmelCase:List[str] = nn.Conv(
a__ ,kernel_size=(3, 3) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
blocks.append(a__)
_lowerCAmelCase:List[str] = nn.Conv(
a__ ,kernel_size=(3, 3) ,strides=(2, 2) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
blocks.append(a__)
_lowerCAmelCase:int = blocks
_lowerCAmelCase:Dict = nn.Conv(
self.conditioning_embedding_channels ,kernel_size=(3, 3) ,padding=((1, 1), (1, 1)) ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
def __call__( self : Optional[int] ,a__ : Tuple) -> str:
"""simple docstring"""
_lowerCAmelCase:Dict = self.conv_in(a__)
_lowerCAmelCase:Tuple = nn.silu(a__)
for block in self.blocks:
_lowerCAmelCase:str = block(a__)
_lowerCAmelCase:List[Any] = nn.silu(a__)
_lowerCAmelCase:List[str] = self.conv_out(a__)
return embedding
@flax_register_to_config
class a__ ( nn.Module , UpperCamelCase_ , UpperCamelCase_ ):
snake_case__ = 3_2
snake_case__ = 4
snake_case__ = (
'''CrossAttnDownBlock2D''',
'''CrossAttnDownBlock2D''',
'''CrossAttnDownBlock2D''',
'''DownBlock2D''',
)
snake_case__ = False
snake_case__ = (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
snake_case__ = 2
snake_case__ = 8
snake_case__ = None
snake_case__ = 1_2_8_0
snake_case__ = 0.0
snake_case__ = False
snake_case__ = jnp.floataa
snake_case__ = True
snake_case__ = 0
snake_case__ = '''rgb'''
snake_case__ = (1_6, 3_2, 9_6, 2_5_6)
def __UpperCamelCase ( self : str ,a__ : jax.random.KeyArray) -> FrozenDict:
"""simple docstring"""
_lowerCAmelCase:Tuple = (1, self.in_channels, self.sample_size, self.sample_size)
_lowerCAmelCase:Optional[int] = jnp.zeros(a__ ,dtype=jnp.floataa)
_lowerCAmelCase:Optional[Any] = jnp.ones((1,) ,dtype=jnp.intaa)
_lowerCAmelCase:List[str] = jnp.zeros((1, 1, self.cross_attention_dim) ,dtype=jnp.floataa)
_lowerCAmelCase:List[str] = (1, 3, self.sample_size * 8, self.sample_size * 8)
_lowerCAmelCase:Dict = jnp.zeros(a__ ,dtype=jnp.floataa)
_lowerCAmelCase:Dict = jax.random.split(a__)
_lowerCAmelCase:Optional[int] = {'''params''': params_rng, '''dropout''': dropout_rng}
return self.init(a__ ,a__ ,a__ ,a__ ,a__)["params"]
def __UpperCamelCase ( self : Any) -> int:
"""simple docstring"""
_lowerCAmelCase:List[Any] = self.block_out_channels
_lowerCAmelCase:List[str] = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
_lowerCAmelCase:int = self.num_attention_heads or self.attention_head_dim
# input
_lowerCAmelCase:str = nn.Conv(
block_out_channels[0] ,kernel_size=(3, 3) ,strides=(1, 1) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
# time
_lowerCAmelCase:Any = FlaxTimesteps(
block_out_channels[0] ,flip_sin_to_cos=self.flip_sin_to_cos ,freq_shift=self.config.freq_shift)
_lowerCAmelCase:Any = FlaxTimestepEmbedding(a__ ,dtype=self.dtype)
_lowerCAmelCase:Tuple = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] ,block_out_channels=self.conditioning_embedding_out_channels ,)
_lowerCAmelCase:Dict = self.only_cross_attention
if isinstance(a__ ,a__):
_lowerCAmelCase:List[Any] = (only_cross_attention,) * len(self.down_block_types)
if isinstance(a__ ,a__):
_lowerCAmelCase:Optional[Any] = (num_attention_heads,) * len(self.down_block_types)
# down
_lowerCAmelCase:Tuple = []
_lowerCAmelCase:Optional[Any] = []
_lowerCAmelCase:str = block_out_channels[0]
_lowerCAmelCase:str = nn.Conv(
a__ ,kernel_size=(1, 1) ,padding='''VALID''' ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
controlnet_down_blocks.append(a__)
for i, down_block_type in enumerate(self.down_block_types):
_lowerCAmelCase:str = output_channel
_lowerCAmelCase:Any = block_out_channels[i]
_lowerCAmelCase:Optional[Any] = i == len(a__) - 1
if down_block_type == "CrossAttnDownBlock2D":
_lowerCAmelCase:Dict = FlaxCrossAttnDownBlockaD(
in_channels=a__ ,out_channels=a__ ,dropout=self.dropout ,num_layers=self.layers_per_block ,num_attention_heads=num_attention_heads[i] ,add_downsample=not is_final_block ,use_linear_projection=self.use_linear_projection ,only_cross_attention=only_cross_attention[i] ,dtype=self.dtype ,)
else:
_lowerCAmelCase:Dict = FlaxDownBlockaD(
in_channels=a__ ,out_channels=a__ ,dropout=self.dropout ,num_layers=self.layers_per_block ,add_downsample=not is_final_block ,dtype=self.dtype ,)
down_blocks.append(a__)
for _ in range(self.layers_per_block):
_lowerCAmelCase:Dict = nn.Conv(
a__ ,kernel_size=(1, 1) ,padding='''VALID''' ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
controlnet_down_blocks.append(a__)
if not is_final_block:
_lowerCAmelCase:str = nn.Conv(
a__ ,kernel_size=(1, 1) ,padding='''VALID''' ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
controlnet_down_blocks.append(a__)
_lowerCAmelCase:Optional[int] = down_blocks
_lowerCAmelCase:int = controlnet_down_blocks
# mid
_lowerCAmelCase:Optional[Any] = block_out_channels[-1]
_lowerCAmelCase:Union[str, Any] = FlaxUNetMidBlockaDCrossAttn(
in_channels=a__ ,dropout=self.dropout ,num_attention_heads=num_attention_heads[-1] ,use_linear_projection=self.use_linear_projection ,dtype=self.dtype ,)
_lowerCAmelCase:Union[str, Any] = nn.Conv(
a__ ,kernel_size=(1, 1) ,padding='''VALID''' ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
def __call__( self : Optional[Any] ,a__ : List[str] ,a__ : Dict ,a__ : Optional[int] ,a__ : Tuple ,a__ : float = 1.0 ,a__ : bool = True ,a__ : bool = False ,) -> Union[FlaxControlNetOutput, Tuple]:
"""simple docstring"""
_lowerCAmelCase:Any = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
_lowerCAmelCase:List[str] = jnp.flip(a__ ,axis=1)
# 1. time
if not isinstance(a__ ,jnp.ndarray):
_lowerCAmelCase:Optional[Any] = jnp.array([timesteps] ,dtype=jnp.intaa)
elif isinstance(a__ ,jnp.ndarray) and len(timesteps.shape) == 0:
_lowerCAmelCase:Optional[int] = timesteps.astype(dtype=jnp.floataa)
_lowerCAmelCase:Any = jnp.expand_dims(a__ ,0)
_lowerCAmelCase:List[str] = self.time_proj(a__)
_lowerCAmelCase:Union[str, Any] = self.time_embedding(a__)
# 2. pre-process
_lowerCAmelCase:Any = jnp.transpose(a__ ,(0, 2, 3, 1))
_lowerCAmelCase:List[Any] = self.conv_in(a__)
_lowerCAmelCase:Optional[int] = jnp.transpose(a__ ,(0, 2, 3, 1))
_lowerCAmelCase:Any = self.controlnet_cond_embedding(a__)
sample += controlnet_cond
# 3. down
_lowerCAmelCase:Tuple = (sample,)
for down_block in self.down_blocks:
if isinstance(a__ ,a__):
_lowerCAmelCase:Dict = down_block(a__ ,a__ ,a__ ,deterministic=not train)
else:
_lowerCAmelCase:List[str] = down_block(a__ ,a__ ,deterministic=not train)
down_block_res_samples += res_samples
# 4. mid
_lowerCAmelCase:Union[str, Any] = self.mid_block(a__ ,a__ ,a__ ,deterministic=not train)
# 5. contronet blocks
_lowerCAmelCase:Union[str, Any] = ()
for down_block_res_sample, controlnet_block in zip(a__ ,self.controlnet_down_blocks):
_lowerCAmelCase:Optional[int] = controlnet_block(a__)
controlnet_down_block_res_samples += (down_block_res_sample,)
_lowerCAmelCase:List[Any] = controlnet_down_block_res_samples
_lowerCAmelCase:str = self.controlnet_mid_block(a__)
# 6. scaling
_lowerCAmelCase:Tuple = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=a__ ,mid_block_res_sample=a__)
| 715
|
"""simple docstring"""
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__ ( UpperCamelCase_ , unittest.TestCase ):
snake_case__ = AudioLDMPipeline
snake_case__ = TEXT_TO_AUDIO_PARAMS
snake_case__ = TEXT_TO_AUDIO_BATCH_PARAMS
snake_case__ = frozenset(
[
'''num_inference_steps''',
'''num_waveforms_per_prompt''',
'''generator''',
'''latents''',
'''output_type''',
'''return_dict''',
'''callback''',
'''callback_steps''',
] )
def __UpperCamelCase ( self : int) -> Any:
"""simple docstring"""
torch.manual_seed(0)
_lowerCAmelCase:int = 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=a__ ,)
_lowerCAmelCase:Optional[int] = DDIMScheduler(
beta_start=0.00085 ,beta_end=0.012 ,beta_schedule='''scaled_linear''' ,clip_sample=a__ ,set_alpha_to_one=a__ ,)
torch.manual_seed(0)
_lowerCAmelCase:Optional[Any] = 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)
_lowerCAmelCase:Dict = 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 ,)
_lowerCAmelCase:str = ClapTextModelWithProjection(a__)
_lowerCAmelCase:Dict = RobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-roberta''' ,model_max_length=77)
_lowerCAmelCase:Union[str, Any] = SpeechTaHifiGanConfig(
model_in_dim=8 ,sampling_rate=1_6000 ,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=a__ ,)
_lowerCAmelCase:List[Any] = SpeechTaHifiGan(a__)
_lowerCAmelCase:Optional[Any] = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''vocoder''': vocoder,
}
return components
def __UpperCamelCase ( self : List[Any] ,a__ : int ,a__ : Tuple=0) -> Optional[int]:
"""simple docstring"""
if str(a__).startswith('''mps'''):
_lowerCAmelCase:Tuple = torch.manual_seed(a__)
else:
_lowerCAmelCase:Dict = torch.Generator(device=a__).manual_seed(a__)
_lowerCAmelCase:Tuple = {
'''prompt''': '''A hammer hitting a wooden surface''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
}
return inputs
def __UpperCamelCase ( self : Optional[Any]) -> Any:
"""simple docstring"""
_lowerCAmelCase:str = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase:Dict = self.get_dummy_components()
_lowerCAmelCase:List[str] = AudioLDMPipeline(**a__)
_lowerCAmelCase:Optional[int] = audioldm_pipe.to(a__)
audioldm_pipe.set_progress_bar_config(disable=a__)
_lowerCAmelCase:str = self.get_dummy_inputs(a__)
_lowerCAmelCase:Optional[Any] = audioldm_pipe(**a__)
_lowerCAmelCase:List[str] = output.audios[0]
assert audio.ndim == 1
assert len(a__) == 256
_lowerCAmelCase:List[str] = audio[:10]
_lowerCAmelCase:Dict = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033])
assert np.abs(audio_slice - expected_slice).max() < 1E-2
def __UpperCamelCase ( self : Tuple) -> Tuple:
"""simple docstring"""
_lowerCAmelCase:int = self.get_dummy_components()
_lowerCAmelCase:Optional[Any] = AudioLDMPipeline(**a__)
_lowerCAmelCase:Union[str, Any] = audioldm_pipe.to(a__)
_lowerCAmelCase:Any = audioldm_pipe.to(a__)
audioldm_pipe.set_progress_bar_config(disable=a__)
_lowerCAmelCase:Dict = self.get_dummy_inputs(a__)
_lowerCAmelCase:List[Any] = 3 * [inputs['''prompt''']]
# forward
_lowerCAmelCase:Dict = audioldm_pipe(**a__)
_lowerCAmelCase:Union[str, Any] = output.audios[0]
_lowerCAmelCase:Tuple = self.get_dummy_inputs(a__)
_lowerCAmelCase:Optional[Any] = 3 * [inputs.pop('''prompt''')]
_lowerCAmelCase:Tuple = audioldm_pipe.tokenizer(
a__ ,padding='''max_length''' ,max_length=audioldm_pipe.tokenizer.model_max_length ,truncation=a__ ,return_tensors='''pt''' ,)
_lowerCAmelCase:Optional[int] = text_inputs['''input_ids'''].to(a__)
_lowerCAmelCase:List[Any] = audioldm_pipe.text_encoder(
a__ ,)
_lowerCAmelCase:int = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
_lowerCAmelCase:List[Any] = F.normalize(a__ ,dim=-1)
_lowerCAmelCase:int = prompt_embeds
# forward
_lowerCAmelCase:Tuple = audioldm_pipe(**a__)
_lowerCAmelCase:Dict = output.audios[0]
assert np.abs(audio_a - audio_a).max() < 1E-2
def __UpperCamelCase ( self : Union[str, Any]) -> List[str]:
"""simple docstring"""
_lowerCAmelCase:Any = self.get_dummy_components()
_lowerCAmelCase:str = AudioLDMPipeline(**a__)
_lowerCAmelCase:int = audioldm_pipe.to(a__)
_lowerCAmelCase:Any = audioldm_pipe.to(a__)
audioldm_pipe.set_progress_bar_config(disable=a__)
_lowerCAmelCase:Any = self.get_dummy_inputs(a__)
_lowerCAmelCase:Tuple = 3 * ['''this is a negative prompt''']
_lowerCAmelCase:str = negative_prompt
_lowerCAmelCase:List[Any] = 3 * [inputs['''prompt''']]
# forward
_lowerCAmelCase:Optional[Any] = audioldm_pipe(**a__)
_lowerCAmelCase:Any = output.audios[0]
_lowerCAmelCase:Tuple = self.get_dummy_inputs(a__)
_lowerCAmelCase:Tuple = 3 * [inputs.pop('''prompt''')]
_lowerCAmelCase:Tuple = []
for p in [prompt, negative_prompt]:
_lowerCAmelCase:str = audioldm_pipe.tokenizer(
a__ ,padding='''max_length''' ,max_length=audioldm_pipe.tokenizer.model_max_length ,truncation=a__ ,return_tensors='''pt''' ,)
_lowerCAmelCase:Dict = text_inputs['''input_ids'''].to(a__)
_lowerCAmelCase:int = audioldm_pipe.text_encoder(
a__ ,)
_lowerCAmelCase:List[Any] = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
_lowerCAmelCase:List[str] = F.normalize(a__ ,dim=-1)
embeds.append(a__)
_lowerCAmelCase , _lowerCAmelCase:Optional[Any] = embeds
# forward
_lowerCAmelCase:List[str] = audioldm_pipe(**a__)
_lowerCAmelCase:int = output.audios[0]
assert np.abs(audio_a - audio_a).max() < 1E-2
def __UpperCamelCase ( self : Optional[int]) -> int:
"""simple docstring"""
_lowerCAmelCase:Tuple = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase:Union[str, Any] = self.get_dummy_components()
_lowerCAmelCase:Union[str, Any] = PNDMScheduler(skip_prk_steps=a__)
_lowerCAmelCase:Union[str, Any] = AudioLDMPipeline(**a__)
_lowerCAmelCase:List[str] = audioldm_pipe.to(a__)
audioldm_pipe.set_progress_bar_config(disable=a__)
_lowerCAmelCase:Tuple = self.get_dummy_inputs(a__)
_lowerCAmelCase:int = '''egg cracking'''
_lowerCAmelCase:Union[str, Any] = audioldm_pipe(**a__ ,negative_prompt=a__)
_lowerCAmelCase:Dict = output.audios[0]
assert audio.ndim == 1
assert len(a__) == 256
_lowerCAmelCase:Optional[int] = audio[:10]
_lowerCAmelCase:Optional[Any] = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032])
assert np.abs(audio_slice - expected_slice).max() < 1E-2
def __UpperCamelCase ( self : Optional[Any]) -> List[Any]:
"""simple docstring"""
_lowerCAmelCase:Optional[int] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase:Optional[int] = self.get_dummy_components()
_lowerCAmelCase:str = PNDMScheduler(skip_prk_steps=a__)
_lowerCAmelCase:Any = AudioLDMPipeline(**a__)
_lowerCAmelCase:List[str] = audioldm_pipe.to(a__)
audioldm_pipe.set_progress_bar_config(disable=a__)
_lowerCAmelCase:Tuple = '''A hammer hitting a wooden surface'''
# test num_waveforms_per_prompt=1 (default)
_lowerCAmelCase:str = audioldm_pipe(a__ ,num_inference_steps=2).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
_lowerCAmelCase:Dict = 2
_lowerCAmelCase:List[str] = audioldm_pipe([prompt] * batch_size ,num_inference_steps=2).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
_lowerCAmelCase:Any = 2
_lowerCAmelCase:Tuple = audioldm_pipe(a__ ,num_inference_steps=2 ,num_waveforms_per_prompt=a__).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
_lowerCAmelCase:str = 2
_lowerCAmelCase:List[Any] = audioldm_pipe(
[prompt] * batch_size ,num_inference_steps=2 ,num_waveforms_per_prompt=a__).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def __UpperCamelCase ( self : Optional[Any]) -> Dict:
"""simple docstring"""
_lowerCAmelCase:Optional[Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase:Dict = self.get_dummy_components()
_lowerCAmelCase:Optional[Any] = AudioLDMPipeline(**a__)
_lowerCAmelCase:List[str] = audioldm_pipe.to(a__)
audioldm_pipe.set_progress_bar_config(disable=a__)
_lowerCAmelCase:Optional[Any] = audioldm_pipe.vocoder.config.sampling_rate
_lowerCAmelCase:Tuple = self.get_dummy_inputs(a__)
_lowerCAmelCase:Optional[Any] = audioldm_pipe(audio_length_in_s=0.016 ,**a__)
_lowerCAmelCase:List[Any] = output.audios[0]
assert audio.ndim == 1
assert len(a__) / vocoder_sampling_rate == 0.016
_lowerCAmelCase:List[str] = audioldm_pipe(audio_length_in_s=0.032 ,**a__)
_lowerCAmelCase:Optional[int] = output.audios[0]
assert audio.ndim == 1
assert len(a__) / vocoder_sampling_rate == 0.032
def __UpperCamelCase ( self : List[Any]) -> int:
"""simple docstring"""
_lowerCAmelCase:Optional[Any] = self.get_dummy_components()
_lowerCAmelCase:Tuple = AudioLDMPipeline(**a__)
_lowerCAmelCase:Dict = audioldm_pipe.to(a__)
audioldm_pipe.set_progress_bar_config(disable=a__)
_lowerCAmelCase:Optional[int] = ['''hey''']
_lowerCAmelCase:List[str] = audioldm_pipe(a__ ,num_inference_steps=1)
_lowerCAmelCase:Tuple = output.audios.shape
assert audio_shape == (1, 256)
_lowerCAmelCase:Any = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
_lowerCAmelCase:Optional[Any] = SpeechTaHifiGan(a__).to(a__)
_lowerCAmelCase:int = audioldm_pipe(a__ ,num_inference_steps=1)
_lowerCAmelCase:List[str] = 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 __UpperCamelCase ( self : Optional[Any]) -> List[Any]:
"""simple docstring"""
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=a__)
def __UpperCamelCase ( self : Dict) -> Optional[int]:
"""simple docstring"""
self._test_inference_batch_single_identical(test_mean_pixel_difference=a__)
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() ,reason='''XFormers attention is only available with CUDA and `xformers` installed''' ,)
def __UpperCamelCase ( self : Any) -> Tuple:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=a__)
@slow
class a__ ( unittest.TestCase ):
def __UpperCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : Union[str, Any] ,a__ : Optional[Any] ,a__ : int="cpu" ,a__ : str=torch.floataa ,a__ : Union[str, Any]=0) -> int:
"""simple docstring"""
_lowerCAmelCase:Optional[int] = torch.Generator(device=a__).manual_seed(a__)
_lowerCAmelCase:Tuple = np.random.RandomState(a__).standard_normal((1, 8, 128, 16))
_lowerCAmelCase:List[str] = torch.from_numpy(a__).to(device=a__ ,dtype=a__)
_lowerCAmelCase:Dict = {
'''prompt''': '''A hammer hitting a wooden surface''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 2.5,
}
return inputs
def __UpperCamelCase ( self : Dict) -> int:
"""simple docstring"""
_lowerCAmelCase:str = AudioLDMPipeline.from_pretrained('''cvssp/audioldm''')
_lowerCAmelCase:Optional[Any] = audioldm_pipe.to(a__)
audioldm_pipe.set_progress_bar_config(disable=a__)
_lowerCAmelCase:Optional[int] = self.get_inputs(a__)
_lowerCAmelCase:Optional[Any] = 25
_lowerCAmelCase:int = audioldm_pipe(**a__).audios[0]
assert audio.ndim == 1
assert len(a__) == 8_1920
_lowerCAmelCase:int = audio[7_7230:7_7240]
_lowerCAmelCase:Optional[Any] = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315])
_lowerCAmelCase:Dict = np.abs(expected_slice - audio_slice).max()
assert max_diff < 1E-2
def __UpperCamelCase ( self : List[str]) -> str:
"""simple docstring"""
_lowerCAmelCase:Union[str, Any] = AudioLDMPipeline.from_pretrained('''cvssp/audioldm''')
_lowerCAmelCase:List[Any] = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config)
_lowerCAmelCase:Union[str, Any] = audioldm_pipe.to(a__)
audioldm_pipe.set_progress_bar_config(disable=a__)
_lowerCAmelCase:Optional[Any] = self.get_inputs(a__)
_lowerCAmelCase:Union[str, Any] = audioldm_pipe(**a__).audios[0]
assert audio.ndim == 1
assert len(a__) == 8_1920
_lowerCAmelCase:Tuple = audio[2_7780:2_7790]
_lowerCAmelCase:Optional[Any] = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212])
_lowerCAmelCase:Tuple = np.abs(expected_slice - audio_slice).max()
assert max_diff < 3E-2
| 439
| 0
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
),
# See all Vivit models at https://huggingface.co/models?filter=vivit
}
class lowerCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
_A = 'vivit'
def __init__( self , _A=224 , _A=32 , _A=[2, 16, 16] , _A=3 , _A=768 , _A=12 , _A=12 , _A=3072 , _A="gelu_fast" , _A=0.0 , _A=0.0 , _A=0.02 , _A=1E-0_6 , _A=True , **_A , ) -> Tuple:
__a : Any = hidden_size
__a : str = num_hidden_layers
__a : List[str] = num_attention_heads
__a : Union[str, Any] = intermediate_size
__a : Optional[int] = hidden_act
__a : Dict = hidden_dropout_prob
__a : List[str] = attention_probs_dropout_prob
__a : List[str] = initializer_range
__a : Any = layer_norm_eps
__a : Dict = image_size
__a : List[Any] = num_frames
__a : List[Any] = tubelet_size
__a : Any = num_channels
__a : Union[str, Any] = qkv_bias
super().__init__(**_A )
| 597
|
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ ):
__a : Dict = len(SCREAMING_SNAKE_CASE__ ) // 2
# choose the middle 3 elements
__a : Dict = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m] ) == 2:
m -= 1
return peak(lst[m:] )
# decreasing
else:
if len(lst[:m] ) == 2:
m += 1
return peak(lst[:m] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 597
| 1
|
# Function to print upper half of diamond (pyramid)
def lowerCamelCase_ ( lowerCAmelCase: Optional[Any] )-> List[str]:
for i in range(0 , lowerCAmelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(' ' , end='' )
for _ in range(0 , i + 1 ): # printing stars
print('* ' , end='' )
print()
def lowerCamelCase_ ( lowerCAmelCase: Optional[int] )-> List[Any]:
for i in range(lowerCAmelCase , 0 , -1 ):
for _ in range(lowerCAmelCase , 0 , -1 ): # printing stars
print('* ' , end='' )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(' ' , end='' )
def lowerCamelCase_ ( lowerCAmelCase: Tuple )-> int:
if n <= 0:
print(' ... .... nothing printing :(' )
return
floyd(lowerCAmelCase ) # upper half
reverse_floyd(lowerCAmelCase ) # lower half
if __name__ == "__main__":
print(r"""| /\ | |- | |- |--| |\ /| |-""")
print(r"""|/ \| |- |_ |_ |__| | \/ | |_""")
lowerCAmelCase_ = 1
while K:
lowerCAmelCase_ = int(input("""enter the number and , and see the magic : """))
print()
pretty_print(user_number)
lowerCAmelCase_ = int(input("""press 0 to exit... and 1 to continue..."""))
print("""Good Bye...""")
| 669
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"""roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
"""roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
"""roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
"""distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
"""roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
"""roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}
class _lowerCAmelCase ( UpperCAmelCase_ ):
'''simple docstring'''
a_ : int ="""roberta"""
def __init__( self : int , UpperCamelCase : Tuple=5_02_65 , UpperCamelCase : Any=7_68 , UpperCamelCase : List[Any]=12 , UpperCamelCase : str=12 , UpperCamelCase : Dict=30_72 , UpperCamelCase : Any="gelu" , UpperCamelCase : List[Any]=0.1 , UpperCamelCase : Optional[Any]=0.1 , UpperCamelCase : Optional[Any]=5_12 , UpperCamelCase : List[str]=2 , UpperCamelCase : Optional[Any]=0.02 , UpperCamelCase : Tuple=1e-1_2 , UpperCamelCase : str=1 , UpperCamelCase : int=0 , UpperCamelCase : Any=2 , UpperCamelCase : int="absolute" , UpperCamelCase : int=True , UpperCamelCase : List[Any]=None , **UpperCamelCase : Any , ):
'''simple docstring'''
super().__init__(pad_token_id=UpperCamelCase , bos_token_id=UpperCamelCase , eos_token_id=UpperCamelCase , **UpperCamelCase )
_snake_case : Any = vocab_size
_snake_case : List[str] = hidden_size
_snake_case : List[str] = num_hidden_layers
_snake_case : Dict = num_attention_heads
_snake_case : List[str] = hidden_act
_snake_case : Union[str, Any] = intermediate_size
_snake_case : Union[str, Any] = hidden_dropout_prob
_snake_case : Optional[int] = attention_probs_dropout_prob
_snake_case : Dict = max_position_embeddings
_snake_case : Optional[int] = type_vocab_size
_snake_case : Tuple = initializer_range
_snake_case : int = layer_norm_eps
_snake_case : Dict = position_embedding_type
_snake_case : Union[str, Any] = use_cache
_snake_case : str = classifier_dropout
class _lowerCAmelCase ( UpperCAmelCase_ ):
'''simple docstring'''
@property
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
if self.task == "multiple-choice":
_snake_case : Optional[Any] = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_snake_case : Dict = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 669
| 1
|
"""simple docstring"""
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_a = logging.get_logger(__name__)
_a = {"""vocab_file""": """vocab.txt""", """emoji_file""": """emoji.json"""}
_a = {
"""vocab_file""": {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt""",
},
"""emoji_file""": {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json""",
},
}
_a = {
"""abeja/gpt-neox-japanese-2.7b""": 2048,
}
def lowerCamelCase__ ( __snake_case, __snake_case ) -> Union[str, Any]:
"""simple docstring"""
with open(__snake_case, '''r''', encoding='''utf-8''' ) as f:
_UpperCamelCase = json.loads(f.read() )
_UpperCamelCase = collections.OrderedDict()
_UpperCamelCase = collections.OrderedDict()
_UpperCamelCase = collections.OrderedDict()
with open(__snake_case, '''r''', encoding='''utf-8''' ) as f:
_UpperCamelCase = f.readlines()
_UpperCamelCase = [[t.rstrip('''\n''' )] if (t == ''',''' or ''',''' not in t) else t.rstrip('''\n''' ).split(''',''' ) for t in token]
for idx, b in enumerate(__snake_case ):
_UpperCamelCase = b
_UpperCamelCase = idx
for wd in b:
_UpperCamelCase = idx
return vocab, raw_vocab, ids_to_tokens, emoji
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = ['input_ids', 'attention_mask']
def __init__( self , __a , __a , __a="<|endoftext|>" , __a="<|endoftext|>" , __a="<|startoftext|>" , __a="<|endoftext|>" , __a=False , **__a , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(
unk_token=__a , pad_token=__a , bos_token=__a , eos_token=__a , do_clean_text=__a , **__a , )
if not os.path.isfile(__a):
raise ValueError(
F'''Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'''
''' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`''')
if not os.path.isfile(__a):
raise ValueError(
F'''Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'''
''' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`''')
_UpperCamelCase = do_clean_text
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = load_vocab_and_emoji(__a , __a)
_UpperCamelCase = SubWordJapaneseTokenizer(
vocab=self.vocab , ids_to_tokens=self.ids_to_tokens , emoji=self.emoji)
@property
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
# self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
return len(self.raw_vocab)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return dict(self.raw_vocab , **self.added_tokens_encoder)
def UpperCAmelCase ( self , __a) -> Optional[Any]:
'''simple docstring'''
return self.subword_tokenizer.tokenize(__a , clean=self.do_clean_text)
def UpperCAmelCase ( self , __a) -> Optional[Any]:
'''simple docstring'''
return self.vocab.get(__a , self.vocab.get(self.unk_token))
def UpperCAmelCase ( self , __a) -> str:
'''simple docstring'''
return self.subword_tokenizer.convert_id_to_token(__a)
def UpperCAmelCase ( self , __a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = ''''''.join(__a).strip()
return out_string
def UpperCAmelCase ( self , __a) -> List[int]:
'''simple docstring'''
_UpperCamelCase = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(__a , add_special_tokens=__a) + [self.eos_token_id])
if len(__a) > self.model_max_length:
_UpperCamelCase = input_ids[-self.model_max_length :]
return input_ids
def UpperCAmelCase ( self , __a , __a = None) -> Tuple[str]:
'''simple docstring'''
_UpperCamelCase = 0
if os.path.isdir(__a):
_UpperCamelCase = os.path.join(
__a , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
_UpperCamelCase = os.path.join(
__a , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''emoji_file'''])
else:
_UpperCamelCase = (
(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''vocab_file''']
)
_UpperCamelCase = (
(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''emoji_file''']
)
with open(__a , '''w''' , encoding='''utf-8''') as writer:
for token_index, token in self.ids_to_tokens.items():
if index != token_index:
logger.warning(
F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
''' Please check that the vocabulary is not corrupted!''')
_UpperCamelCase = token_index
writer.write(''','''.join(__a) + '''\n''')
index += 1
with open(__a , '''w''' , encoding='''utf-8''') as writer:
json.dump(self.emoji , __a)
return vocab_file, emoji_file
class _UpperCAmelCase( lowerCamelCase ):
def __init__( self , __a , __a , __a) -> int:
'''simple docstring'''
_UpperCamelCase = vocab # same as swe
_UpperCamelCase = ids_to_tokens # same as bpe
_UpperCamelCase = emoji
_UpperCamelCase = np.max([len(__a) for w in self.vocab.keys()])
_UpperCamelCase = re.compile(R'''(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)''')
_UpperCamelCase = re.compile(R'''[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*''')
_UpperCamelCase = re.compile(R'''[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}''')
_UpperCamelCase = re.compile(
R'''([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*''')
_UpperCamelCase = re.compile(
R'''(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*''')
_UpperCamelCase = re.compile(
R'''((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*''')
_UpperCamelCase = '''─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'''
_UpperCamelCase = '''▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'''
_UpperCamelCase = str.maketrans({k: '''<BLOCK>''' for k in keisen + blocks})
def __len__( self) -> Optional[Any]:
'''simple docstring'''
return len(self.ids_to_tokens)
def UpperCAmelCase ( self , __a) -> Dict:
'''simple docstring'''
_UpperCamelCase = self.content_repattera.sub('''<URL>''' , __a)
_UpperCamelCase = self.content_repattera.sub('''<EMAIL>''' , __a)
_UpperCamelCase = self.content_repattera.sub('''<TEL>''' , __a)
_UpperCamelCase = self.content_repattera.sub('''<DATE>''' , __a)
_UpperCamelCase = self.content_repattera.sub('''<DATE>''' , __a)
_UpperCamelCase = self.content_repattera.sub('''<PRICE>''' , __a)
_UpperCamelCase = content.translate(self.content_transa)
while "<BLOCK><BLOCK>" in content:
_UpperCamelCase = content.replace('''<BLOCK><BLOCK>''' , '''<BLOCK>''')
return content
def UpperCAmelCase ( self , __a , __a=False) -> Any:
'''simple docstring'''
_UpperCamelCase = text.replace(''' ''' , '''<SP>''')
_UpperCamelCase = text.replace(''' ''' , '''<SP>''')
_UpperCamelCase = text.replace('''\r\n''' , '''<BR>''')
_UpperCamelCase = text.replace('''\n''' , '''<BR>''')
_UpperCamelCase = text.replace('''\r''' , '''<BR>''')
_UpperCamelCase = text.replace('''\t''' , '''<TAB>''')
_UpperCamelCase = text.replace('''—''' , '''ー''')
_UpperCamelCase = text.replace('''−''' , '''ー''')
for k, v in self.emoji["emoji"].items():
if k in text:
_UpperCamelCase = text.replace(__a , __a)
if clean:
_UpperCamelCase = self.clean_text(__a)
def check_simbol(__a):
_UpperCamelCase = x.encode()
if len(__a) == 1 and len(__a) == 2:
_UpperCamelCase = (int(e[0]) << 8) + int(e[1])
if (
(c >= 0XC2A1 and c <= 0XC2BF)
or (c >= 0XC780 and c <= 0XC783)
or (c >= 0XCAB9 and c <= 0XCBBF)
or (c >= 0XCC80 and c <= 0XCDA2)
):
return True
return False
def checkuae(__a):
_UpperCamelCase = x.encode()
if len(__a) == 1 and len(__a) == 3:
_UpperCamelCase = (int(e[0]) << 16) + (int(e[1]) << 8) + int(e[2])
if c >= 0XE28080 and c <= 0XE2B07F:
return True
return False
_UpperCamelCase = 0
_UpperCamelCase = []
while pos < len(__a):
_UpperCamelCase = min(len(__a) , pos + self.maxlen + 1) if text[pos] == '''<''' else pos + 3
_UpperCamelCase = [] # (token_id, token, pos)
for e in range(__a , __a , -1):
_UpperCamelCase = text[pos:e]
if wd in self.vocab:
if wd[0] == "<" and len(__a) > 2:
_UpperCamelCase = [(self.vocab[wd], wd, e)]
break
else:
candidates.append((self.vocab[wd], wd, e))
if len(__a) > 0:
# the smallest token_id is adopted
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = sorted(__a , key=lambda __a: x[0])[0]
result.append(__a)
_UpperCamelCase = e
else:
_UpperCamelCase = pos + 1
_UpperCamelCase = text[pos:end]
if check_simbol(__a):
result.append('''<KIGOU>''')
elif checkuae(__a):
result.append('''<U2000U2BFF>''')
else:
for i in wd.encode('''utf-8'''):
result.append('''<|byte%d|>''' % i)
_UpperCamelCase = end
return result
def UpperCAmelCase ( self , __a , __a="\n") -> int:
'''simple docstring'''
_UpperCamelCase = []
_UpperCamelCase = []
_UpperCamelCase = self.ids_to_tokens[index][0]
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2]))
else:
if len(__a) > 0:
words.append(bytearray(__a).decode('''utf-8''' , errors='''replace'''))
_UpperCamelCase = []
if word[:7] == "<|emoji" and word[-2:] == "|>":
words.append(self.emoji['''emoji_inv'''][word])
elif word == "<SP>":
words.append(''' ''')
elif word == "<BR>":
words.append(__a)
elif word == "<TAB>":
words.append('''\t''')
elif word == "<BLOCK>":
words.append('''▀''')
elif word == "<KIGOU>":
words.append('''ǀ''')
elif word == "<U2000U2BFF>":
words.append('''‖''')
else:
words.append(__a)
if len(__a) > 0:
words.append(bytearray(__a).decode('''utf-8''' , errors='''replace'''))
_UpperCamelCase = ''''''.join(__a)
return text
| 19
|
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_a = logging.get_logger(__name__)
_a = {
"""facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = 'detr'
lowercase__ = ['past_key_values']
lowercase__ = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , __a=True , __a=None , __a=3 , __a=1_00 , __a=6 , __a=20_48 , __a=8 , __a=6 , __a=20_48 , __a=8 , __a=0.0 , __a=0.0 , __a=True , __a="relu" , __a=2_56 , __a=0.1 , __a=0.0 , __a=0.0 , __a=0.02 , __a=1.0 , __a=False , __a="sine" , __a="resnet50" , __a=True , __a=False , __a=1 , __a=5 , __a=2 , __a=1 , __a=1 , __a=5 , __a=2 , __a=0.1 , **__a , ) -> int:
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
_UpperCamelCase = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
elif isinstance(__a , __a):
_UpperCamelCase = backbone_config.get('''model_type''')
_UpperCamelCase = CONFIG_MAPPING[backbone_model_type]
_UpperCamelCase = config_class.from_dict(__a)
# set timm attributes to None
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None, None, None
_UpperCamelCase = use_timm_backbone
_UpperCamelCase = backbone_config
_UpperCamelCase = num_channels
_UpperCamelCase = num_queries
_UpperCamelCase = d_model
_UpperCamelCase = encoder_ffn_dim
_UpperCamelCase = encoder_layers
_UpperCamelCase = encoder_attention_heads
_UpperCamelCase = decoder_ffn_dim
_UpperCamelCase = decoder_layers
_UpperCamelCase = decoder_attention_heads
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = activation_function
_UpperCamelCase = init_std
_UpperCamelCase = init_xavier_std
_UpperCamelCase = encoder_layerdrop
_UpperCamelCase = decoder_layerdrop
_UpperCamelCase = encoder_layers
_UpperCamelCase = auxiliary_loss
_UpperCamelCase = position_embedding_type
_UpperCamelCase = backbone
_UpperCamelCase = use_pretrained_backbone
_UpperCamelCase = dilation
# Hungarian matcher
_UpperCamelCase = class_cost
_UpperCamelCase = bbox_cost
_UpperCamelCase = giou_cost
# Loss coefficients
_UpperCamelCase = mask_loss_coefficient
_UpperCamelCase = dice_loss_coefficient
_UpperCamelCase = bbox_loss_coefficient
_UpperCamelCase = giou_loss_coefficient
_UpperCamelCase = eos_coefficient
super().__init__(is_encoder_decoder=__a , **__a)
@property
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return self.d_model
@classmethod
def UpperCAmelCase ( cls , __a , **__a) -> int:
'''simple docstring'''
return cls(backbone_config=__a , **__a)
def UpperCAmelCase ( self) -> Dict[str, any]:
'''simple docstring'''
_UpperCamelCase = copy.deepcopy(self.__dict__)
if output["backbone_config"] is not None:
_UpperCamelCase = self.backbone_config.to_dict()
_UpperCamelCase = self.__class__.model_type
return output
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = version.parse('1.11' )
@property
def UpperCAmelCase ( self) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
])
@property
def UpperCAmelCase ( self) -> float:
'''simple docstring'''
return 1e-5
@property
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return 12
| 19
| 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 torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class snake_case_ (lowercase__ ):
"""simple docstring"""
_lowerCamelCase = """microsoft/speecht5_tts"""
_lowerCamelCase = (
"""This is a tool that reads an English text out loud. It takes an input named `text` which should contain the """
"""text to read (in English) and returns a waveform object containing the sound."""
)
_lowerCamelCase = """text_reader"""
_lowerCamelCase = SpeechTaProcessor
_lowerCamelCase = SpeechTaForTextToSpeech
_lowerCamelCase = SpeechTaHifiGan
_lowerCamelCase = ["""text"""]
_lowerCamelCase = ["""audio"""]
def A_ ( self):
"""simple docstring"""
if self.post_processor is None:
UpperCAmelCase_ : Optional[Any] = "microsoft/speecht5_hifigan"
super().setup()
def A_ ( self ,lowercase ,lowercase=None):
"""simple docstring"""
UpperCAmelCase_ : Dict = self.pre_processor(text=lowercase ,return_tensors="pt" ,truncation=lowercase)
if speaker_embeddings is None:
if not is_datasets_available():
raise ImportError("Datasets needs to be installed if not passing speaker embeddings.")
UpperCAmelCase_ : List[Any] = load_dataset("Matthijs/cmu-arctic-xvectors" ,split="validation")
UpperCAmelCase_ : str = torch.tensor(embeddings_dataset[7305]["xvector"]).unsqueeze(0)
return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
def A_ ( self ,lowercase):
"""simple docstring"""
with torch.no_grad():
return self.model.generate_speech(**lowercase)
def A_ ( self ,lowercase):
"""simple docstring"""
with torch.no_grad():
return self.post_processor(lowercase).cpu().detach()
| 455
|
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__lowerCamelCase = logging.getLogger(__name__)
def _snake_case ( ) -> Dict:
'''simple docstring'''
UpperCAmelCase_ : Any = argparse.ArgumentParser(
description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)." )
parser.add_argument("--file_path" , type=__snake_case , default="data/dump.txt" , help="The path to the data." )
parser.add_argument("--tokenizer_type" , type=__snake_case , default="bert" , choices=["bert", "roberta", "gpt2"] )
parser.add_argument("--tokenizer_name" , type=__snake_case , default="bert-base-uncased" , help="The tokenizer to use." )
parser.add_argument("--dump_file" , type=__snake_case , default="data/dump" , help="The dump file prefix." )
UpperCAmelCase_ : str = parser.parse_args()
logger.info(F"""Loading Tokenizer ({args.tokenizer_name})""" )
if args.tokenizer_type == "bert":
UpperCAmelCase_ : int = BertTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ : Optional[Any] = tokenizer.special_tokens_map["cls_token"] # `[CLS]`
UpperCAmelCase_ : Dict = tokenizer.special_tokens_map["sep_token"] # `[SEP]`
elif args.tokenizer_type == "roberta":
UpperCAmelCase_ : str = RobertaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ : int = tokenizer.special_tokens_map["cls_token"] # `<s>`
UpperCAmelCase_ : Optional[Any] = tokenizer.special_tokens_map["sep_token"] # `</s>`
elif args.tokenizer_type == "gpt2":
UpperCAmelCase_ : Tuple = GPTaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ : List[Any] = tokenizer.special_tokens_map["bos_token"] # `<|endoftext|>`
UpperCAmelCase_ : List[str] = tokenizer.special_tokens_map["eos_token"] # `<|endoftext|>`
logger.info(F"""Loading text from {args.file_path}""" )
with open(args.file_path , "r" , encoding="utf8" ) as fp:
UpperCAmelCase_ : Union[str, Any] = fp.readlines()
logger.info("Start encoding" )
logger.info(F"""{len(__snake_case )} examples to process.""" )
UpperCAmelCase_ : List[str] = []
UpperCAmelCase_ : str = 0
UpperCAmelCase_ : Union[str, Any] = 1_0_0_0_0
UpperCAmelCase_ : int = time.time()
for text in data:
UpperCAmelCase_ : str = F"""{bos} {text.strip()} {sep}"""
UpperCAmelCase_ : Any = tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
rslt.append(__snake_case )
iter += 1
if iter % interval == 0:
UpperCAmelCase_ : List[str] = time.time()
logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl""" )
UpperCAmelCase_ : Optional[Any] = time.time()
logger.info("Finished binarization" )
logger.info(F"""{len(__snake_case )} examples processed.""" )
UpperCAmelCase_ : List[Any] = F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
UpperCAmelCase_ : Optional[int] = tokenizer.vocab_size
if vocab_size < (1 << 1_6):
UpperCAmelCase_ : List[Any] = [np.uintaa(__snake_case ) for d in rslt]
else:
UpperCAmelCase_ : str = [np.intaa(__snake_case ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F"""Dump to {dp_file}""" )
with open(__snake_case , "wb" ) as handle:
pickle.dump(rslt_ , __snake_case , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 455
| 1
|
import requests
from bsa import BeautifulSoup
def lowerCamelCase_ ( lowerCamelCase__ = "AAPL" ):
lowerCamelCase_ = F'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
lowerCamelCase_ = BeautifulSoup(requests.get(lowerCamelCase__ ).text , "html.parser" )
lowerCamelCase_ = "My(6px) Pos(r) smartphone_Mt(6px)"
return soup.find("div" , class_=class_ ).find("span" ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(F"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")
| 463
|
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
__A =logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
lowerCAmelCase__ = CLIPConfig
lowerCAmelCase__ = ['CLIPEncoderLayer']
def __init__( self , lowercase ) -> int:
super().__init__(lowercase )
lowerCamelCase_ = CLIPVisionModelWithProjection(config.vision_config )
lowerCamelCase_ = nn.Linear(config.vision_config.projection_dim , 1 )
lowerCamelCase_ = nn.Linear(config.vision_config.projection_dim , 1 )
@torch.no_grad()
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase=0.5 , lowercase=0.5 ) -> int:
lowerCamelCase_ = self.vision_model(lowercase )[0]
lowerCamelCase_ = self.p_head(lowercase )
lowerCamelCase_ = nsfw_detected.flatten()
lowerCamelCase_ = nsfw_detected > p_threshold
lowerCamelCase_ = nsfw_detected.tolist()
if any(lowercase ):
logger.warning(
"Potential NSFW content was detected in one or more images. A black image will be returned instead."
" Try again with a different prompt and/or seed." )
for idx, nsfw_detected_ in enumerate(lowercase ):
if nsfw_detected_:
lowerCamelCase_ = np.zeros(images[idx].shape )
lowerCamelCase_ = self.w_head(lowercase )
lowerCamelCase_ = watermark_detected.flatten()
lowerCamelCase_ = watermark_detected > w_threshold
lowerCamelCase_ = watermark_detected.tolist()
if any(lowercase ):
logger.warning(
"Potential watermarked content was detected in one or more images. A black image will be returned instead."
" Try again with a different prompt and/or seed." )
for idx, watermark_detected_ in enumerate(lowercase ):
if watermark_detected_:
lowerCamelCase_ = np.zeros(images[idx].shape )
return images, nsfw_detected, watermark_detected
| 463
| 1
|
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> list:
_lowercase : List[Any] = word.split()
def justify(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
_lowercase : List[Any] = max_width - width
_lowercase : int = len(SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
_lowercase : Any = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
_lowercase : List[Any] = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
_lowercase : List[str] = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(SCREAMING_SNAKE_CASE ):
num_spaces_between_words_list[i] += 1
_lowercase : int = []
for i in range(SCREAMING_SNAKE_CASE ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * ' ' )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(SCREAMING_SNAKE_CASE )
_lowercase : Any = []
_lowercase : list[str] = []
_lowercase : Optional[int] = 0
for word in words:
if width + len(SCREAMING_SNAKE_CASE ) + len(SCREAMING_SNAKE_CASE ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(SCREAMING_SNAKE_CASE )
width += len(SCREAMING_SNAKE_CASE )
else:
# justify the line and add it to result
answer.append(justify(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
# reset new line and new width
_lowercase , _lowercase : str = [word], len(SCREAMING_SNAKE_CASE )
_lowercase : Dict = max_width - width - len(SCREAMING_SNAKE_CASE )
answer.append(' '.join(SCREAMING_SNAKE_CASE ) + (remaining_spaces + 1) * ' ' )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 677
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
UpperCamelCase = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCamelCase = {
"vocab_file": {
"google/electra-small-generator": (
"https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"
),
"google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt",
"google/electra-large-generator": (
"https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"
),
"google/electra-small-discriminator": (
"https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"
),
"google/electra-base-discriminator": (
"https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"
),
"google/electra-large-discriminator": (
"https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"google/electra-small-generator": (
"https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"
),
"google/electra-base-generator": (
"https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"
),
"google/electra-large-generator": (
"https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"
),
"google/electra-small-discriminator": (
"https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"
),
"google/electra-base-discriminator": (
"https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"
),
"google/electra-large-discriminator": (
"https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"
),
},
}
UpperCamelCase = {
"google/electra-small-generator": 512,
"google/electra-base-generator": 512,
"google/electra-large-generator": 512,
"google/electra-small-discriminator": 512,
"google/electra-base-discriminator": 512,
"google/electra-large-discriminator": 512,
}
UpperCamelCase = {
"google/electra-small-generator": {"do_lower_case": True},
"google/electra-base-generator": {"do_lower_case": True},
"google/electra-large-generator": {"do_lower_case": True},
"google/electra-small-discriminator": {"do_lower_case": True},
"google/electra-base-discriminator": {"do_lower_case": True},
"google/electra-large-discriminator": {"do_lower_case": True},
}
class lowerCAmelCase_ ( __snake_case ):
_UpperCamelCase : Any = VOCAB_FILES_NAMES
_UpperCamelCase : Any = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : str = PRETRAINED_INIT_CONFIGURATION
_UpperCamelCase : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase : List[str] = ElectraTokenizer
def __init__( self , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=True , _lowerCAmelCase="[UNK]" , _lowerCAmelCase="[SEP]" , _lowerCAmelCase="[PAD]" , _lowerCAmelCase="[CLS]" , _lowerCAmelCase="[MASK]" , _lowerCAmelCase=True , _lowerCAmelCase=None , **_lowerCAmelCase , ):
super().__init__(
_lowerCAmelCase , tokenizer_file=_lowerCAmelCase , do_lower_case=_lowerCAmelCase , unk_token=_lowerCAmelCase , sep_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , cls_token=_lowerCAmelCase , mask_token=_lowerCAmelCase , tokenize_chinese_chars=_lowerCAmelCase , strip_accents=_lowerCAmelCase , **_lowerCAmelCase , )
_lowercase : Any = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , _lowerCAmelCase ) != do_lower_case
or normalizer_state.get('strip_accents' , _lowerCAmelCase ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , _lowerCAmelCase ) != tokenize_chinese_chars
):
_lowercase : Any = getattr(_lowerCAmelCase , normalizer_state.pop('type' ) )
_lowercase : Dict = do_lower_case
_lowercase : Optional[Any] = strip_accents
_lowercase : Any = tokenize_chinese_chars
_lowercase : Tuple = normalizer_class(**_lowerCAmelCase )
_lowercase : Union[str, Any] = do_lower_case
def __a ( self , _lowerCAmelCase , _lowerCAmelCase=None ):
_lowercase : Any = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __a ( self , _lowerCAmelCase , _lowerCAmelCase = None ):
_lowercase : str = [self.sep_token_id]
_lowercase : str = [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 , _lowerCAmelCase , _lowerCAmelCase = None ):
_lowercase : Any = self._tokenizer.model.save(_lowerCAmelCase , name=_lowerCAmelCase )
return tuple(_lowerCAmelCase )
| 677
| 1
|
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class _A ( UpperCAmelCase_ ):
lowercase_ : Optional[Any] = '''MCTCTFeatureExtractor'''
lowercase_ : Union[str, Any] = '''AutoTokenizer'''
def __init__( self : Any , lowerCamelCase__ : str , lowerCamelCase__ : List[str] ):
"""simple docstring"""
super().__init__(lowerCamelCase__ , lowerCamelCase__ )
__UpperCamelCase : Tuple = self.feature_extractor
__UpperCamelCase : Tuple = False
def __call__( self : Tuple , *lowerCamelCase__ : Optional[int] , **lowerCamelCase__ : Optional[int] ):
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor(*lowerCamelCase__ , **lowerCamelCase__ )
if "raw_speech" in kwargs:
warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
__UpperCamelCase : Tuple = kwargs.pop("""raw_speech""" )
else:
__UpperCamelCase : Union[str, Any] = kwargs.pop("""audio""" , lowerCamelCase__ )
__UpperCamelCase : Dict = kwargs.pop("""sampling_rate""" , lowerCamelCase__ )
__UpperCamelCase : str = kwargs.pop("""text""" , lowerCamelCase__ )
if len(lowerCamelCase__ ) > 0:
__UpperCamelCase : Any = args[0]
__UpperCamelCase : Optional[int] = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if audio is not None:
__UpperCamelCase : str = self.feature_extractor(lowerCamelCase__ , *lowerCamelCase__ , sampling_rate=lowerCamelCase__ , **lowerCamelCase__ )
if text is not None:
__UpperCamelCase : Union[str, Any] = self.tokenizer(lowerCamelCase__ , **lowerCamelCase__ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
__UpperCamelCase : Dict = encodings["""input_ids"""]
return inputs
def a ( self : Tuple , *lowerCamelCase__ : int , **lowerCamelCase__ : Optional[Any] ):
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCamelCase__ , **lowerCamelCase__ )
def a ( self : Tuple , *lowerCamelCase__ : Optional[int] , **lowerCamelCase__ : Tuple ):
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor.pad(*lowerCamelCase__ , **lowerCamelCase__ )
__UpperCamelCase : Dict = kwargs.pop("""input_features""" , lowerCamelCase__ )
__UpperCamelCase : Optional[int] = kwargs.pop("""labels""" , lowerCamelCase__ )
if len(lowerCamelCase__ ) > 0:
__UpperCamelCase : Union[str, Any] = args[0]
__UpperCamelCase : Optional[Any] = args[1:]
if input_features is not None:
__UpperCamelCase : str = self.feature_extractor.pad(lowerCamelCase__ , *lowerCamelCase__ , **lowerCamelCase__ )
if labels is not None:
__UpperCamelCase : Tuple = self.tokenizer.pad(lowerCamelCase__ , **lowerCamelCase__ )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
__UpperCamelCase : int = labels["""input_ids"""]
return input_features
def a ( self : str , *lowerCamelCase__ : Optional[int] , **lowerCamelCase__ : Tuple ):
"""simple docstring"""
return self.tokenizer.decode(*lowerCamelCase__ , **lowerCamelCase__ )
@contextmanager
def a ( self : Optional[Any] ):
"""simple docstring"""
warnings.warn(
"""`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
"""labels by using the argument `text` of the regular `__call__` method (either in the same call as """
"""your audio inputs, or in a separate call.""" )
__UpperCamelCase : int = True
__UpperCamelCase : int = self.tokenizer
yield
__UpperCamelCase : Tuple = self.feature_extractor
__UpperCamelCase : Dict = False
| 269
|
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
UpperCamelCase = logging.getLogger(__name__)
class _A ( UpperCAmelCase_ ):
def a ( self : Tuple , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : str=None , lowerCamelCase__ : int=None ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] = self.layer[current_layer](lowerCamelCase__ , lowerCamelCase__ , head_mask[current_layer] )
__UpperCamelCase : Optional[Any] = layer_outputs[0]
return hidden_states
@add_start_docstrings(
'''The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.''' , UpperCAmelCase_ , )
class _A ( UpperCAmelCase_ ):
def __init__( self : Tuple , lowerCamelCase__ : Tuple ):
"""simple docstring"""
super().__init__(lowerCamelCase__ )
__UpperCamelCase : List[str] = BertEncoderWithPabee(lowerCamelCase__ )
self.init_weights()
__UpperCamelCase : Optional[int] = 0
__UpperCamelCase : Tuple = 0
__UpperCamelCase : Any = 0
__UpperCamelCase : Optional[Any] = 0
def a ( self : Union[str, Any] , lowerCamelCase__ : Any ):
"""simple docstring"""
__UpperCamelCase : List[Any] = threshold
def a ( self : Optional[int] , lowerCamelCase__ : Union[str, Any] ):
"""simple docstring"""
__UpperCamelCase : int = patience
def a ( self : Optional[int] ):
"""simple docstring"""
__UpperCamelCase : int = 0
__UpperCamelCase : Tuple = 0
def a ( self : int ):
"""simple docstring"""
__UpperCamelCase : int = self.inference_layers_num / self.inference_instances_num
__UpperCamelCase : str = (
f'*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='
f' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'
)
print(lowerCamelCase__ )
@add_start_docstrings_to_model_forward(lowerCamelCase__ )
def a ( self : Any , lowerCamelCase__ : List[Any]=None , lowerCamelCase__ : List[str]=None , lowerCamelCase__ : List[Any]=None , lowerCamelCase__ : Optional[Any]=None , lowerCamelCase__ : List[str]=None , lowerCamelCase__ : str=None , lowerCamelCase__ : List[Any]=None , lowerCamelCase__ : Any=None , lowerCamelCase__ : List[str]=None , lowerCamelCase__ : List[str]=None , lowerCamelCase__ : str=False , ):
"""simple docstring"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time""" )
elif input_ids is not None:
__UpperCamelCase : Optional[Any] = input_ids.size()
elif inputs_embeds is not None:
__UpperCamelCase : Tuple = inputs_embeds.size()[:-1]
else:
raise ValueError("""You have to specify either input_ids or inputs_embeds""" )
__UpperCamelCase : Tuple = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
__UpperCamelCase : Optional[Any] = torch.ones(lowerCamelCase__ , device=lowerCamelCase__ )
if token_type_ids is None:
__UpperCamelCase : str = torch.zeros(lowerCamelCase__ , dtype=torch.long , device=lowerCamelCase__ )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
__UpperCamelCase : torch.Tensor = self.get_extended_attention_mask(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase : Optional[Any] = encoder_hidden_states.size()
__UpperCamelCase : Union[str, Any] = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
__UpperCamelCase : Union[str, Any] = torch.ones(lowerCamelCase__ , device=lowerCamelCase__ )
__UpperCamelCase : Optional[int] = self.invert_attention_mask(lowerCamelCase__ )
else:
__UpperCamelCase : str = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
__UpperCamelCase : int = self.get_head_mask(lowerCamelCase__ , self.config.num_hidden_layers )
__UpperCamelCase : Any = self.embeddings(
input_ids=lowerCamelCase__ , position_ids=lowerCamelCase__ , token_type_ids=lowerCamelCase__ , inputs_embeds=lowerCamelCase__ )
__UpperCamelCase : Optional[int] = embedding_output
if self.training:
__UpperCamelCase : List[Any] = []
for i in range(self.config.num_hidden_layers ):
__UpperCamelCase : List[Any] = self.encoder.adaptive_forward(
lowerCamelCase__ , current_layer=lowerCamelCase__ , attention_mask=lowerCamelCase__ , head_mask=lowerCamelCase__ )
__UpperCamelCase : Tuple = self.pooler(lowerCamelCase__ )
__UpperCamelCase : str = output_layers[i](output_dropout(lowerCamelCase__ ) )
res.append(lowerCamelCase__ )
elif self.patience == 0: # Use all layers for inference
__UpperCamelCase : Union[str, Any] = self.encoder(
lowerCamelCase__ , attention_mask=lowerCamelCase__ , head_mask=lowerCamelCase__ , encoder_hidden_states=lowerCamelCase__ , encoder_attention_mask=lowerCamelCase__ , )
__UpperCamelCase : Optional[int] = self.pooler(encoder_outputs[0] )
__UpperCamelCase : Union[str, Any] = [output_layers[self.config.num_hidden_layers - 1](lowerCamelCase__ )]
else:
__UpperCamelCase : Union[str, Any] = 0
__UpperCamelCase : Optional[Any] = None
__UpperCamelCase : List[Any] = 0
for i in range(self.config.num_hidden_layers ):
calculated_layer_num += 1
__UpperCamelCase : Union[str, Any] = self.encoder.adaptive_forward(
lowerCamelCase__ , current_layer=lowerCamelCase__ , attention_mask=lowerCamelCase__ , head_mask=lowerCamelCase__ )
__UpperCamelCase : str = self.pooler(lowerCamelCase__ )
__UpperCamelCase : Any = output_layers[i](lowerCamelCase__ )
if regression:
__UpperCamelCase : Optional[int] = logits.detach()
if patient_result is not None:
__UpperCamelCase : Dict = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels ) < self.regression_threshold:
patient_counter += 1
else:
__UpperCamelCase : List[str] = 0
else:
__UpperCamelCase : Optional[int] = logits.detach().argmax(dim=1 )
if patient_result is not None:
__UpperCamelCase : Union[str, Any] = patient_result.detach().argmax(dim=1 )
if (patient_result is not None) and torch.all(labels.eq(lowerCamelCase__ ) ):
patient_counter += 1
else:
__UpperCamelCase : str = 0
__UpperCamelCase : Union[str, Any] = logits
if patient_counter == self.patience:
break
__UpperCamelCase : List[str] = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
'''Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. ''' , UpperCAmelCase_ , )
class _A ( UpperCAmelCase_ ):
def __init__( self : Any , lowerCamelCase__ : Union[str, Any] ):
"""simple docstring"""
super().__init__(lowerCamelCase__ )
__UpperCamelCase : Dict = config.num_labels
__UpperCamelCase : List[Any] = BertModelWithPabee(lowerCamelCase__ )
__UpperCamelCase : Optional[int] = nn.Dropout(config.hidden_dropout_prob )
__UpperCamelCase : List[Any] = nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels ) for _ in range(config.num_hidden_layers )] )
self.init_weights()
@add_start_docstrings_to_model_forward(lowerCamelCase__ )
def a ( self : List[Any] , lowerCamelCase__ : Union[str, Any]=None , lowerCamelCase__ : Dict=None , lowerCamelCase__ : Union[str, Any]=None , lowerCamelCase__ : List[Any]=None , lowerCamelCase__ : Optional[Any]=None , lowerCamelCase__ : Optional[int]=None , lowerCamelCase__ : Dict=None , ):
"""simple docstring"""
__UpperCamelCase : str = self.bert(
input_ids=lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ , position_ids=lowerCamelCase__ , head_mask=lowerCamelCase__ , inputs_embeds=lowerCamelCase__ , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
__UpperCamelCase : List[str] = (logits[-1],)
if labels is not None:
__UpperCamelCase : Tuple = None
__UpperCamelCase : Union[str, Any] = 0
for ix, logits_item in enumerate(lowerCamelCase__ ):
if self.num_labels == 1:
# We are doing regression
__UpperCamelCase : Union[str, Any] = MSELoss()
__UpperCamelCase : Optional[Any] = loss_fct(logits_item.view(-1 ) , labels.view(-1 ) )
else:
__UpperCamelCase : Dict = CrossEntropyLoss()
__UpperCamelCase : Dict = loss_fct(logits_item.view(-1 , self.num_labels ) , labels.view(-1 ) )
if total_loss is None:
__UpperCamelCase : Dict = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
__UpperCamelCase : Tuple = (total_loss / total_weights,) + outputs
return outputs
| 269
| 1
|
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
__a = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
lowercase = ["pixel_values"]
def __init__( self : List[str] , snake_case_ : bool = True , snake_case_ : int = 32 , snake_case_ : List[str]=PILImageResampling.BILINEAR , snake_case_ : bool = True , **snake_case_ : Any , ):
snake_case__ : Union[str, Any] = do_resize
snake_case__ : int = do_rescale
snake_case__ : Tuple = size_divisor
snake_case__ : Union[str, Any] = resample
super().__init__(**snake_case_ )
def lowerCamelCase ( self : int , snake_case_ : np.ndarray , snake_case_ : int , snake_case_ : List[str] , snake_case_ : Optional[ChannelDimension] = None , **snake_case_ : int ):
snake_case__ , snake_case__ : Optional[Any] = get_image_size(snake_case_ )
# Rounds the height and width down to the closest multiple of size_divisor
snake_case__ : Union[str, Any] = height // size_divisor * size_divisor
snake_case__ : Optional[Any] = width // size_divisor * size_divisor
snake_case__ : Dict = resize(snake_case_ , (new_h, new_w) , resample=snake_case_ , data_format=snake_case_ , **snake_case_ )
return image
def lowerCamelCase ( self : List[Any] , snake_case_ : np.ndarray , snake_case_ : float , snake_case_ : Optional[ChannelDimension] = None , **snake_case_ : Dict ):
return rescale(image=snake_case_ , scale=snake_case_ , data_format=snake_case_ , **snake_case_ )
def lowerCamelCase ( self : List[str] , snake_case_ : Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] , snake_case_ : Optional[bool] = None , snake_case_ : Optional[int] = None , snake_case_ : Optional[Any]=None , snake_case_ : Optional[bool] = None , snake_case_ : Optional[Union[TensorType, str]] = None , snake_case_ : ChannelDimension = ChannelDimension.FIRST , **snake_case_ : Tuple , ):
snake_case__ : Optional[int] = do_resize if do_resize is not None else self.do_resize
snake_case__ : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
snake_case__ : str = size_divisor if size_divisor is not None else self.size_divisor
snake_case__ : List[str] = resample if resample is not None else self.resample
if do_resize and size_divisor is None:
raise ValueError("""size_divisor is required for resizing""" )
snake_case__ : Union[str, Any] = make_list_of_images(snake_case_ )
if not valid_images(snake_case_ ):
raise ValueError("""Invalid image(s)""" )
# All transformations expect numpy arrays.
snake_case__ : int = [to_numpy_array(snake_case_ ) for img in images]
if do_resize:
snake_case__ : Optional[Any] = [self.resize(snake_case_ , size_divisor=snake_case_ , resample=snake_case_ ) for image in images]
if do_rescale:
snake_case__ : Dict = [self.rescale(snake_case_ , scale=1 / 255 ) for image in images]
snake_case__ : Any = [to_channel_dimension_format(snake_case_ , snake_case_ ) for image in images]
snake_case__ : Optional[Any] = {"""pixel_values""": images}
return BatchFeature(data=snake_case_ , tensor_type=snake_case_ )
| 301
|
'''simple docstring'''
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> float:
if discount_rate < 0:
raise ValueError("""Discount rate cannot be negative""" )
if not cash_flows:
raise ValueError("""Cash flows list cannot be empty""" )
snake_case__ : List[Any] = sum(
cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_lowerCAmelCase ) )
return round(_lowerCAmelCase , ndigits=2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 301
| 1
|
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class UpperCAmelCase_ ( __A ):
"""simple docstring"""
UpperCamelCase_ = '''naver-clova-ix/donut-base-finetuned-docvqa'''
UpperCamelCase_ = (
'''This is a tool that answers a question about an document (pdf). It takes an input named `document` which '''
'''should be the document containing the information, as well as a `question` that is the question about the '''
'''document. It returns a text that contains the answer to the question.'''
)
UpperCamelCase_ = '''document_qa'''
UpperCamelCase_ = AutoProcessor
UpperCamelCase_ = VisionEncoderDecoderModel
UpperCamelCase_ = ['''image''', '''text''']
UpperCamelCase_ = ['''text''']
def __init__( self : int , *UpperCAmelCase : str , **UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
if not is_vision_available():
raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.''' )
super().__init__(*UpperCAmelCase , **UpperCAmelCase )
def A__ ( self : int , UpperCAmelCase : "Image" , UpperCAmelCase : str ) -> int:
'''simple docstring'''
lowercase : int ='''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
lowercase : Tuple =task_prompt.replace('''{user_input}''' , UpperCAmelCase )
lowercase : str =self.pre_processor.tokenizer(
UpperCAmelCase , add_special_tokens=UpperCAmelCase , return_tensors='''pt''' ).input_ids
lowercase : str =self.pre_processor(UpperCAmelCase , return_tensors='''pt''' ).pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def A__ ( self : Union[str, Any] , UpperCAmelCase : List[str] ) -> Tuple:
'''simple docstring'''
return self.model.generate(
inputs['''pixel_values'''].to(self.device ) , decoder_input_ids=inputs['''decoder_input_ids'''].to(self.device ) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=UpperCAmelCase , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=UpperCAmelCase , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=UpperCAmelCase , ).sequences
def A__ ( self : List[str] , UpperCAmelCase : List[Any] ) -> str:
'''simple docstring'''
lowercase : List[Any] =self.pre_processor.batch_decode(UpperCAmelCase )[0]
lowercase : Dict =sequence.replace(self.pre_processor.tokenizer.eos_token , '''''' )
lowercase : List[str] =sequence.replace(self.pre_processor.tokenizer.pad_token , '''''' )
lowercase : Union[str, Any] =re.sub(R'''<.*?>''' , '''''' , UpperCAmelCase , count=1 ).strip() # remove first task start token
lowercase : Optional[Any] =self.pre_processor.tokenajson(UpperCAmelCase )
return sequence["answer"]
| 94
|
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class _snake_case ( snake_case_ ):
'''simple docstring'''
__snake_case = 42
__snake_case = 42
class _snake_case ( nn.Module ):
'''simple docstring'''
__snake_case = 42
__snake_case = (1_6, 3_2, 9_6, 2_5_6)
__snake_case = jnp.floataa
def lowerCAmelCase__ ( self: int ) -> Optional[Any]:
__magic_name__ : Dict = nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
__magic_name__ : Any = []
for i in range(len(self.block_out_channels ) - 1 ):
__magic_name__ : Optional[Any] = self.block_out_channels[i]
__magic_name__ : Dict = self.block_out_channels[i + 1]
__magic_name__ : Union[str, Any] = nn.Conv(
__UpperCamelCase , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(__UpperCamelCase )
__magic_name__ : Tuple = nn.Conv(
__UpperCamelCase , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(__UpperCamelCase )
__magic_name__ : Optional[Any] = blocks
__magic_name__ : Dict = nn.Conv(
self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self: str , __UpperCamelCase: Union[str, Any] ) -> Optional[Any]:
__magic_name__ : Dict = self.conv_in(__UpperCamelCase )
__magic_name__ : List[Any] = nn.silu(__UpperCamelCase )
for block in self.blocks:
__magic_name__ : int = block(__UpperCamelCase )
__magic_name__ : str = nn.silu(__UpperCamelCase )
__magic_name__ : Union[str, Any] = self.conv_out(__UpperCamelCase )
return embedding
@flax_register_to_config
class _snake_case ( nn.Module , snake_case_ , snake_case_ ):
'''simple docstring'''
__snake_case = 3_2
__snake_case = 4
__snake_case = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
__snake_case = False
__snake_case = (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
__snake_case = 2
__snake_case = 8
__snake_case = None
__snake_case = 1_2_8_0
__snake_case = 0.0
__snake_case = False
__snake_case = jnp.floataa
__snake_case = True
__snake_case = 0
__snake_case = "rgb"
__snake_case = (1_6, 3_2, 9_6, 2_5_6)
def lowerCAmelCase__ ( self: int , __UpperCamelCase: jax.random.KeyArray ) -> FrozenDict:
# init input tensors
__magic_name__ : Dict = (1, self.in_channels, self.sample_size, self.sample_size)
__magic_name__ : str = jnp.zeros(__UpperCamelCase , dtype=jnp.floataa )
__magic_name__ : Tuple = jnp.ones((1,) , dtype=jnp.intaa )
__magic_name__ : Any = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
__magic_name__ : List[Any] = (1, 3, self.sample_size * 8, self.sample_size * 8)
__magic_name__ : List[Any] = jnp.zeros(__UpperCamelCase , dtype=jnp.floataa )
__magic_name__ , __magic_name__ : List[str] = jax.random.split(__UpperCamelCase )
__magic_name__ : Optional[Any] = {"params": params_rng, "dropout": dropout_rng}
return self.init(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )["params"]
def lowerCAmelCase__ ( self: List[str] ) -> Union[str, Any]:
__magic_name__ : Union[str, Any] = self.block_out_channels
__magic_name__ : Tuple = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
__magic_name__ : Optional[Any] = self.num_attention_heads or self.attention_head_dim
# input
__magic_name__ : Dict = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
__magic_name__ : List[str] = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
__magic_name__ : Dict = FlaxTimestepEmbedding(__UpperCamelCase , dtype=self.dtype )
__magic_name__ : Tuple = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
__magic_name__ : Union[str, Any] = self.only_cross_attention
if isinstance(__UpperCamelCase , __UpperCamelCase ):
__magic_name__ : Tuple = (only_cross_attention,) * len(self.down_block_types )
if isinstance(__UpperCamelCase , __UpperCamelCase ):
__magic_name__ : Any = (num_attention_heads,) * len(self.down_block_types )
# down
__magic_name__ : Optional[int] = []
__magic_name__ : Union[str, Any] = []
__magic_name__ : Optional[int] = block_out_channels[0]
__magic_name__ : Tuple = nn.Conv(
__UpperCamelCase , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(__UpperCamelCase )
for i, down_block_type in enumerate(self.down_block_types ):
__magic_name__ : Optional[Any] = output_channel
__magic_name__ : List[Any] = block_out_channels[i]
__magic_name__ : int = i == len(__UpperCamelCase ) - 1
if down_block_type == "CrossAttnDownBlock2D":
__magic_name__ : List[str] = FlaxCrossAttnDownBlockaD(
in_channels=__UpperCamelCase , out_channels=__UpperCamelCase , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
__magic_name__ : Optional[int] = FlaxDownBlockaD(
in_channels=__UpperCamelCase , out_channels=__UpperCamelCase , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(__UpperCamelCase )
for _ in range(self.layers_per_block ):
__magic_name__ : Any = nn.Conv(
__UpperCamelCase , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(__UpperCamelCase )
if not is_final_block:
__magic_name__ : Optional[int] = nn.Conv(
__UpperCamelCase , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(__UpperCamelCase )
__magic_name__ : str = down_blocks
__magic_name__ : List[str] = controlnet_down_blocks
# mid
__magic_name__ : Optional[Any] = block_out_channels[-1]
__magic_name__ : List[Any] = FlaxUNetMidBlockaDCrossAttn(
in_channels=__UpperCamelCase , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
__magic_name__ : List[Any] = nn.Conv(
__UpperCamelCase , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self: Optional[int] , __UpperCamelCase: Any , __UpperCamelCase: Dict , __UpperCamelCase: Optional[Any] , __UpperCamelCase: List[str] , __UpperCamelCase: float = 1.0 , __UpperCamelCase: bool = True , __UpperCamelCase: bool = False , ) -> Union[FlaxControlNetOutput, Tuple]:
__magic_name__ : List[str] = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
__magic_name__ : Dict = jnp.flip(__UpperCamelCase , axis=1 )
# 1. time
if not isinstance(__UpperCamelCase , jnp.ndarray ):
__magic_name__ : Union[str, Any] = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(__UpperCamelCase , jnp.ndarray ) and len(timesteps.shape ) == 0:
__magic_name__ : Union[str, Any] = timesteps.astype(dtype=jnp.floataa )
__magic_name__ : Optional[Any] = jnp.expand_dims(__UpperCamelCase , 0 )
__magic_name__ : Optional[int] = self.time_proj(__UpperCamelCase )
__magic_name__ : Any = self.time_embedding(__UpperCamelCase )
# 2. pre-process
__magic_name__ : Optional[Any] = jnp.transpose(__UpperCamelCase , (0, 2, 3, 1) )
__magic_name__ : List[Any] = self.conv_in(__UpperCamelCase )
__magic_name__ : List[Any] = jnp.transpose(__UpperCamelCase , (0, 2, 3, 1) )
__magic_name__ : Dict = self.controlnet_cond_embedding(__UpperCamelCase )
sample += controlnet_cond
# 3. down
__magic_name__ : Optional[Any] = (sample,)
for down_block in self.down_blocks:
if isinstance(__UpperCamelCase , __UpperCamelCase ):
__magic_name__ , __magic_name__ : Dict = down_block(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , deterministic=not train )
else:
__magic_name__ , __magic_name__ : Union[str, Any] = down_block(__UpperCamelCase , __UpperCamelCase , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
__magic_name__ : Optional[int] = self.mid_block(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , deterministic=not train )
# 5. contronet blocks
__magic_name__ : Optional[Any] = ()
for down_block_res_sample, controlnet_block in zip(__UpperCamelCase , self.controlnet_down_blocks ):
__magic_name__ : Any = controlnet_block(__UpperCamelCase )
controlnet_down_block_res_samples += (down_block_res_sample,)
__magic_name__ : int = controlnet_down_block_res_samples
__magic_name__ : List[str] = self.controlnet_mid_block(__UpperCamelCase )
# 6. scaling
__magic_name__ : Optional[Any] = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=__UpperCamelCase , mid_block_res_sample=__UpperCamelCase )
| 436
| 0
|
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class UpperCAmelCase__( tf.keras.optimizers.schedules.LearningRateSchedule ):
'''simple docstring'''
def __init__( self : List[Any] , lowerCAmelCase : float , lowerCAmelCase : Callable , lowerCAmelCase : int , lowerCAmelCase : float = 1.0 , lowerCAmelCase : str = None , ) -> List[Any]:
"""simple docstring"""
super().__init__()
lowercase__ = initial_learning_rate
lowercase__ = warmup_steps
lowercase__ = power
lowercase__ = decay_schedule_fn
lowercase__ = name
def __call__( self : Optional[int] , lowerCAmelCase : str) -> Any:
"""simple docstring"""
with tf.name_scope(self.name or 'WarmUp') as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
lowercase__ = tf.cast(lowerCAmelCase , tf.floataa)
lowercase__ = tf.cast(self.warmup_steps , tf.floataa)
lowercase__ = global_step_float / warmup_steps_float
lowercase__ = self.initial_learning_rate * tf.math.pow(lowerCAmelCase , self.power)
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps) , name=lowerCAmelCase , )
def UpperCAmelCase ( self : Union[str, Any]) -> Optional[int]:
"""simple docstring"""
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def _lowerCAmelCase ( A__ , A__ , A__ , A__ = 0.0 , A__ = 0.9 , A__ = 0.9_99 , A__ = 1E-8 , A__ = None , A__ = None , A__ = 0.0 , A__ = 1.0 , A__ = None , ):
lowercase__ = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=A__ , decay_steps=num_train_steps - num_warmup_steps , end_learning_rate=init_lr * min_lr_ratio , power=A__ , )
if num_warmup_steps:
lowercase__ = WarmUp(
initial_learning_rate=A__ , decay_schedule_fn=A__ , warmup_steps=A__ , )
if weight_decay_rate > 0.0:
lowercase__ = AdamWeightDecay(
learning_rate=A__ , weight_decay_rate=A__ , beta_a=A__ , beta_a=A__ , epsilon=A__ , clipnorm=A__ , global_clipnorm=A__ , exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias'] , include_in_weight_decay=A__ , )
else:
lowercase__ = tf.keras.optimizers.Adam(
learning_rate=A__ , beta_a=A__ , beta_a=A__ , epsilon=A__ , clipnorm=A__ , global_clipnorm=A__ , )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
def __init__( self : Tuple , lowerCAmelCase : Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.0_01 , lowerCAmelCase : float = 0.9 , lowerCAmelCase : float = 0.9_99 , lowerCAmelCase : float = 1E-7 , lowerCAmelCase : bool = False , lowerCAmelCase : float = 0.0 , lowerCAmelCase : Optional[List[str]] = None , lowerCAmelCase : Optional[List[str]] = None , lowerCAmelCase : str = "AdamWeightDecay" , **lowerCAmelCase : Dict , ) -> List[str]:
"""simple docstring"""
super().__init__(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase)
lowercase__ = weight_decay_rate
lowercase__ = include_in_weight_decay
lowercase__ = exclude_from_weight_decay
@classmethod
def UpperCAmelCase ( cls : Union[str, Any] , lowerCAmelCase : Optional[int]) -> Tuple:
"""simple docstring"""
lowercase__ = {'WarmUp': WarmUp}
return super(lowerCAmelCase , cls).from_config(lowerCAmelCase , custom_objects=lowerCAmelCase)
def UpperCAmelCase ( self : str , lowerCAmelCase : Tuple , lowerCAmelCase : Dict , lowerCAmelCase : Any) -> Any:
"""simple docstring"""
super(lowerCAmelCase , self)._prepare_local(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase)
lowercase__ = tf.constant(
self.weight_decay_rate , name='adam_weight_decay_rate')
def UpperCAmelCase ( self : List[str] , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Tuple , lowerCAmelCase : Optional[Any]) -> Optional[int]:
"""simple docstring"""
lowercase__ = self._do_use_weight_decay(var.name)
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['weight_decay_rate'] , use_locking=self._use_locking , )
return tf.no_op()
def UpperCAmelCase ( self : Tuple , lowerCAmelCase : Optional[int] , lowerCAmelCase : Optional[int]=None , **lowerCAmelCase : Optional[Any]) -> Dict:
"""simple docstring"""
lowercase__, lowercase__ = list(zip(*lowerCAmelCase))
return super(lowerCAmelCase , self).apply_gradients(zip(lowerCAmelCase , lowerCAmelCase) , name=lowerCAmelCase , **lowerCAmelCase)
def UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Dict , lowerCAmelCase : Tuple) -> Tuple:
"""simple docstring"""
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
lowercase__ = apply_state or {}
lowercase__ = apply_state.get((var_device, var_dtype))
if coefficients is None:
lowercase__ = self._fallback_apply_state(lowerCAmelCase , lowerCAmelCase)
lowercase__ = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase : List[str] , lowerCAmelCase : Dict , lowerCAmelCase : Union[str, Any]=None) -> str:
"""simple docstring"""
lowercase__, lowercase__ = self._get_lr(var.device , var.dtype.base_dtype , lowerCAmelCase)
lowercase__ = self._decay_weights_op(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase)
with tf.control_dependencies([decay]):
return super(lowerCAmelCase , self)._resource_apply_dense(lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase)
def UpperCAmelCase ( self : Tuple , lowerCAmelCase : int , lowerCAmelCase : List[Any] , lowerCAmelCase : Any , lowerCAmelCase : Optional[int]=None) -> List[Any]:
"""simple docstring"""
lowercase__, lowercase__ = self._get_lr(var.device , var.dtype.base_dtype , lowerCAmelCase)
lowercase__ = self._decay_weights_op(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase)
with tf.control_dependencies([decay]):
return super(lowerCAmelCase , self)._resource_apply_sparse(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase)
def UpperCAmelCase ( self : List[str]) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = super().get_config()
config.update({'weight_decay_rate': self.weight_decay_rate})
return config
def UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase : Optional[Any]) -> Optional[int]:
"""simple docstring"""
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(lowerCAmelCase , lowerCAmelCase) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(lowerCAmelCase , lowerCAmelCase) is not None:
return False
return True
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
def __init__( self : List[Any]) -> List[Any]:
"""simple docstring"""
lowercase__ = []
lowercase__ = None
@property
def UpperCAmelCase ( self : Any) -> Tuple:
"""simple docstring"""
if self._accum_steps is None:
lowercase__ = tf.Variable(
tf.constant(0 , dtype=tf.intaa) , trainable=lowerCAmelCase , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def UpperCAmelCase ( self : List[Any]) -> Dict:
"""simple docstring"""
if not self._gradients:
raise ValueError('The accumulator should be called first to initialize the gradients')
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self : str , lowerCAmelCase : Any) -> Optional[Any]:
"""simple docstring"""
if not self._gradients:
lowercase__ = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(lowerCAmelCase) , trainable=lowerCAmelCase , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
])
if len(lowerCAmelCase) != len(self._gradients):
raise ValueError(f'''Expected {len(self._gradients)} gradients, but got {len(lowerCAmelCase)}''')
for accum_gradient, gradient in zip(self._gradients , lowerCAmelCase):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(lowerCAmelCase)
self._accum_steps.assign_add(1)
def UpperCAmelCase ( self : List[str]) -> List[Any]:
"""simple docstring"""
if not self._gradients:
return
self._accum_steps.assign(0)
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(lowerCAmelCase))
| 715
|
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Optional[Any] = {
"google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
A : Union[str, Any] = "umt5"
A : List[str] = ["past_key_values"]
def __init__( self : List[Any] , lowerCAmelCase : Optional[int]=25_01_12 , lowerCAmelCase : str=5_12 , lowerCAmelCase : List[Any]=64 , lowerCAmelCase : Optional[int]=10_24 , lowerCAmelCase : Union[str, Any]=8 , lowerCAmelCase : Tuple=None , lowerCAmelCase : Optional[Any]=6 , lowerCAmelCase : int=32 , lowerCAmelCase : int=1_28 , lowerCAmelCase : List[str]=0.1 , lowerCAmelCase : List[str]=1E-6 , lowerCAmelCase : Optional[int]=1.0 , lowerCAmelCase : Optional[Any]="gated-gelu" , lowerCAmelCase : List[Any]=True , lowerCAmelCase : List[str]=True , lowerCAmelCase : List[Any]="T5Tokenizer" , lowerCAmelCase : str=True , lowerCAmelCase : Optional[int]=0 , lowerCAmelCase : Tuple=1 , lowerCAmelCase : Any=0 , **lowerCAmelCase : int , ) -> str:
"""simple docstring"""
super().__init__(
is_encoder_decoder=lowerCAmelCase , tokenizer_class=lowerCAmelCase , tie_word_embeddings=lowerCAmelCase , pad_token_id=lowerCAmelCase , eos_token_id=lowerCAmelCase , decoder_start_token_id=lowerCAmelCase , **lowerCAmelCase , )
lowercase__ = vocab_size
lowercase__ = d_model
lowercase__ = d_kv
lowercase__ = d_ff
lowercase__ = num_layers
lowercase__ = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
lowercase__ = num_heads
lowercase__ = relative_attention_num_buckets
lowercase__ = relative_attention_max_distance
lowercase__ = dropout_rate
lowercase__ = layer_norm_epsilon
lowercase__ = initializer_factor
lowercase__ = feed_forward_proj
lowercase__ = use_cache
lowercase__ = self.feed_forward_proj.split('-')
lowercase__ = act_info[-1]
lowercase__ = act_info[0] == 'gated'
if len(lowerCAmelCase) > 1 and act_info[0] != "gated" or len(lowerCAmelCase) > 2:
raise ValueError(
f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
'\'gated-gelu\' or \'relu\'')
if feed_forward_proj == "gated-gelu":
lowercase__ = 'gelu_new'
@property
def UpperCAmelCase ( self : Union[str, Any]) -> Dict:
"""simple docstring"""
return self.d_model
@property
def UpperCAmelCase ( self : List[str]) -> Union[str, Any]:
"""simple docstring"""
return self.num_heads
@property
def UpperCAmelCase ( self : Optional[int]) -> Optional[Any]:
"""simple docstring"""
return self.num_layers
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def UpperCAmelCase ( self : Optional[int]) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
lowercase__ = {
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
lowercase__ = 'past_encoder_sequence + sequence'
lowercase__ = {0: 'batch'}
lowercase__ = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
lowercase__ = {0: 'batch', 1: 'decoder_sequence'}
lowercase__ = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(lowerCAmelCase , direction='inputs')
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def UpperCAmelCase ( self : int) -> int:
"""simple docstring"""
return 13
@property
def UpperCAmelCase ( self : Optional[Any]) -> float:
"""simple docstring"""
return 5E-4
| 642
| 0
|
# 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
_UpperCamelCase = 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)
_UpperCamelCase = {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}
_UpperCamelCase = "zero2"
_UpperCamelCase = "zero3"
_UpperCamelCase = [ZEROa, ZEROa]
def _lowercase ( lowercase__ , lowercase__ , lowercase__ ):
# customize the test name generator function as we want both params to appear in the sub-test
# name, as by default it shows only the first param
__lowerCAmelCase : Tuple = parameterized.to_safe_name('''_'''.join(str(lowercase__ ) for x in param.args ) )
return f"""{func.__name__}_{param_based_name}"""
# Cartesian-product of zero stages with models to test
_UpperCamelCase = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class __lowercase (_UpperCAmelCase ):
@parameterized.expand(A_ , name_func=A_ )
def UpperCamelCase__ ( self , A_ , A_ ) ->Optional[Any]:
'''simple docstring'''
self.run_and_check(
stage=A_ , model=A_ , distributed=A_ , fpaa=A_ , )
@require_torch_multi_gpu
@parameterized.expand(A_ , name_func=A_ )
def UpperCamelCase__ ( self , A_ , A_ ) ->Tuple:
'''simple docstring'''
self.run_and_check(
stage=A_ , model=A_ , distributed=A_ , fpaa=A_ , )
@parameterized.expand(A_ , name_func=A_ )
def UpperCamelCase__ ( self , A_ , A_ ) ->Dict:
'''simple docstring'''
self.run_and_check(
stage=A_ , model=A_ , distributed=A_ , fpaa=A_ , )
@require_torch_multi_gpu
@parameterized.expand(A_ , name_func=A_ )
def UpperCamelCase__ ( self , A_ , A_ ) ->Tuple:
'''simple docstring'''
self.run_and_check(
stage=A_ , model=A_ , distributed=A_ , fpaa=A_ , )
def UpperCamelCase__ ( self , A_ ) ->List[str]:
'''simple docstring'''
pass
def UpperCamelCase__ ( self , A_ , A_ , A_ = 10 , A_ = True , A_ = True , A_ = True , ) ->int:
'''simple docstring'''
__lowerCAmelCase : Optional[int] = models[model]
__lowerCAmelCase : Any = self.run_trainer(
stage=A_ , model_name=A_ , eval_steps=A_ , num_train_epochs=1 , distributed=A_ , fpaa=A_ , )
self.do_checks(A_ )
return output_dir
def UpperCamelCase__ ( self , A_ , A_ , A_ = 10 , A_ = 1 , A_ = True , A_ = True , ) ->Dict:
'''simple docstring'''
__lowerCAmelCase : Any = self.get_auto_remove_tmp_dir('''./xxx''' , after=A_ )
__lowerCAmelCase : List[str] = 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(A_ )}
--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
__lowerCAmelCase : Any = f"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
__lowerCAmelCase : Optional[int] = [f"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
__lowerCAmelCase : Dict = self.get_launcher(A_ )
__lowerCAmelCase : Optional[int] = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(A_ , env=self.get_env() )
return output_dir
def UpperCamelCase__ ( self , A_=False ) ->str:
'''simple docstring'''
__lowerCAmelCase : Optional[int] = min(2 , get_gpu_count() ) if distributed else 1
return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()
| 492
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = [
"LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
"LukeForEntityClassification",
"LukeForEntityPairClassification",
"LukeForEntitySpanClassification",
"LukeForMultipleChoice",
"LukeForQuestionAnswering",
"LukeForSequenceClassification",
"LukeForTokenClassification",
"LukeForMaskedLM",
"LukeModel",
"LukePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
from .tokenization_luke import LukeTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_luke import (
LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
LukeForEntityClassification,
LukeForEntityPairClassification,
LukeForEntitySpanClassification,
LukeForMaskedLM,
LukeForMultipleChoice,
LukeForQuestionAnswering,
LukeForSequenceClassification,
LukeForTokenClassification,
LukeModel,
LukePreTrainedModel,
)
else:
import sys
_UpperCamelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 492
| 1
|
from __future__ import annotations
import numpy as np
def __UpperCamelCase ( _lowerCAmelCase ) -> tuple[np.ndarray, np.ndarray]:
"""simple docstring"""
A : Union[str, Any] = np.shape(_lowerCAmelCase )
if rows != columns:
A : Union[str, Any] = (
"""'table' has to be of square shaped array but got a """
f'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(_lowerCAmelCase )
A : Optional[Any] = np.zeros((rows, columns) )
A : Dict = np.zeros((rows, columns) )
for i in range(_lowerCAmelCase ):
for j in range(_lowerCAmelCase ):
A : int = sum(lower[i][k] * upper[k][j] for k in range(_lowerCAmelCase ) )
if upper[j][j] == 0:
raise ArithmeticError("""No LU decomposition exists""" )
A : Any = (table[i][j] - total) / upper[j][j]
A : Optional[Any] = 1
for j in range(_lowerCAmelCase , _lowerCAmelCase ):
A : Tuple = sum(lower[i][k] * upper[k][j] for k in range(_lowerCAmelCase ) )
A : Dict = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 716
|
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
@staticmethod
def _lowerCAmelCase ( *lowerCamelCase__, **lowerCamelCase__ ):
pass
def __UpperCamelCase ( _lowerCAmelCase ) -> str:
"""simple docstring"""
A : List[Any] = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def __UpperCamelCase ( _lowerCAmelCase ) -> Dict:
"""simple docstring"""
A : Union[str, Any] = np.array(_lowerCAmelCase )
A : str = npimg.shape
return {"hash": hashimage(_lowerCAmelCase ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : int = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
__lowerCamelCase : Union[str, Any] = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def _lowerCAmelCase ( self, lowerCamelCase__, lowerCamelCase__, lowerCamelCase__ ):
A : Union[str, Any] = MaskGenerationPipeline(model=lowerCamelCase__, image_processor=lowerCamelCase__ )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def _lowerCAmelCase ( self, lowerCamelCase__, lowerCamelCase__ ):
pass
@require_tf
@unittest.skip("""Image segmentation not implemented in TF""" )
def _lowerCAmelCase ( self ):
pass
@slow
@require_torch
def _lowerCAmelCase ( self ):
A : Tuple = pipeline("""mask-generation""", model="""facebook/sam-vit-huge""" )
A : Dict = image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg""", points_per_batch=256 )
# Shortening by hashing
A : Union[str, Any] = []
for i, o in enumerate(outputs["""masks"""] ):
new_outupt += [{"mask": mask_to_test_readable(lowerCamelCase__ ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(lowerCamelCase__, decimals=4 ), [
{"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0444},
{"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.021},
{"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0167},
{"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0132},
{"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0053},
{"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (480, 640)}, """scores""": 0.9967},
{"""mask""": {"""hash""": """453c7844bd""", """shape""": (480, 640)}, """scores""": 0.993},
{"""mask""": {"""hash""": """3d44f2926d""", """shape""": (480, 640)}, """scores""": 0.9909},
{"""mask""": {"""hash""": """64033ddc3f""", """shape""": (480, 640)}, """scores""": 0.9879},
{"""mask""": {"""hash""": """801064ff79""", """shape""": (480, 640)}, """scores""": 0.9834},
{"""mask""": {"""hash""": """6172f276ef""", """shape""": (480, 640)}, """scores""": 0.9716},
{"""mask""": {"""hash""": """b49e60e084""", """shape""": (480, 640)}, """scores""": 0.9612},
{"""mask""": {"""hash""": """a811e775fd""", """shape""": (480, 640)}, """scores""": 0.9599},
{"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (480, 640)}, """scores""": 0.9552},
{"""mask""": {"""hash""": """9d8257e080""", """shape""": (480, 640)}, """scores""": 0.9532},
{"""mask""": {"""hash""": """32de6454a8""", """shape""": (480, 640)}, """scores""": 0.9516},
{"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (480, 640)}, """scores""": 0.9499},
{"""mask""": {"""hash""": """3c6db475fb""", """shape""": (480, 640)}, """scores""": 0.9483},
{"""mask""": {"""hash""": """c290813fb9""", """shape""": (480, 640)}, """scores""": 0.9464},
{"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (480, 640)}, """scores""": 0.943},
{"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (480, 640)}, """scores""": 0.943},
{"""mask""": {"""hash""": """c749b25868""", """shape""": (480, 640)}, """scores""": 0.9408},
{"""mask""": {"""hash""": """efb6cab859""", """shape""": (480, 640)}, """scores""": 0.9335},
{"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (480, 640)}, """scores""": 0.9326},
{"""mask""": {"""hash""": """788b798e24""", """shape""": (480, 640)}, """scores""": 0.9262},
{"""mask""": {"""hash""": """abea804f0e""", """shape""": (480, 640)}, """scores""": 0.8999},
{"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (480, 640)}, """scores""": 0.8986},
{"""mask""": {"""hash""": """cd24047c8a""", """shape""": (480, 640)}, """scores""": 0.8984},
{"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (480, 640)}, """scores""": 0.8873},
{"""mask""": {"""hash""": """b5f47c9191""", """shape""": (480, 640)}, """scores""": 0.8871}
], )
# fmt: on
@require_torch
@slow
def _lowerCAmelCase ( self ):
A : Union[str, Any] = """facebook/sam-vit-huge"""
A : int = pipeline("""mask-generation""", model=lowerCamelCase__ )
A : int = image_segmenter(
"""http://images.cocodataset.org/val2017/000000039769.jpg""", pred_iou_thresh=1, points_per_batch=256 )
# Shortening by hashing
A : Tuple = []
for i, o in enumerate(outputs["""masks"""] ):
new_outupt += [{"mask": mask_to_test_readable(lowerCamelCase__ ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(lowerCamelCase__, decimals=4 ), [
{"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0444},
{"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0210},
{"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0167},
{"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0132},
{"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0053},
], )
| 520
| 0
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : list ) -> list:
"""simple docstring"""
if len(_UpperCAmelCase ) <= 1:
return [tuple(_UpperCAmelCase )]
_UpperCAmelCase : Union[str, Any] = []
def generate(_UpperCAmelCase : int , _UpperCAmelCase : list ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , _UpperCAmelCase )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
_UpperCAmelCase , _UpperCAmelCase : List[str] = arr[k - 1], arr[i]
else: # k is odd
_UpperCAmelCase , _UpperCAmelCase : List[str] = arr[k - 1], arr[0]
generate(k - 1 , _UpperCAmelCase )
generate(len(_UpperCAmelCase ) , _UpperCAmelCase )
return res
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Any = input("""Enter numbers separated by a comma:\n""").strip()
__SCREAMING_SNAKE_CASE : Optional[int] = [int(item) for item in user_input.split(""",""")]
print(heaps(arr))
| 244
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__SCREAMING_SNAKE_CASE : Tuple = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/config.json""",
"""albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/config.json""",
"""albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json""",
"""albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/config.json""",
"""albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/config.json""",
"""albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/config.json""",
"""albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json""",
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Dict = "albert"
def __init__( self : str , A : List[Any]=30000 , A : List[Any]=128 , A : Tuple=4096 , A : Union[str, Any]=12 , A : List[str]=1 , A : List[Any]=64 , A : Optional[int]=16384 , A : Optional[Any]=1 , A : Tuple="gelu_new" , A : Optional[Any]=0 , A : int=0 , A : Dict=512 , A : Tuple=2 , A : Any=0.02 , A : Union[str, Any]=1E-12 , A : List[str]=0.1 , A : Any="absolute" , A : int=0 , A : Optional[Any]=2 , A : str=3 , **A : Dict , ):
super().__init__(pad_token_id=A , bos_token_id=A , eos_token_id=A , **A )
_UpperCAmelCase : List[Any] = vocab_size
_UpperCAmelCase : Tuple = embedding_size
_UpperCAmelCase : str = hidden_size
_UpperCAmelCase : Any = num_hidden_layers
_UpperCAmelCase : List[str] = num_hidden_groups
_UpperCAmelCase : List[Any] = num_attention_heads
_UpperCAmelCase : List[str] = inner_group_num
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : Optional[int] = intermediate_size
_UpperCAmelCase : Any = hidden_dropout_prob
_UpperCAmelCase : Any = attention_probs_dropout_prob
_UpperCAmelCase : Union[str, Any] = max_position_embeddings
_UpperCAmelCase : Optional[int] = type_vocab_size
_UpperCAmelCase : int = initializer_range
_UpperCAmelCase : Dict = layer_norm_eps
_UpperCAmelCase : str = classifier_dropout_prob
_UpperCAmelCase : Union[str, Any] = position_embedding_type
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
@property
def _A ( self : Optional[Any] ):
if self.task == "multiple-choice":
_UpperCAmelCase : Dict = {0: "batch", 1: "choice", 2: "sequence"}
else:
_UpperCAmelCase : Optional[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
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'''simple docstring'''
import numpy
# List of input, output pairs
__snake_case : List[str] = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
__snake_case : Tuple = (((515, 22, 13), 555), ((61, 35, 49), 150))
__snake_case : List[Any] = [2, 4, 1, 5]
__snake_case : Union[str, Any] = len(train_data)
__snake_case : Tuple = 0.009
def _lowercase ( lowerCamelCase__ : Optional[int], lowerCamelCase__ : List[str]="train" ):
return calculate_hypothesis_value(lowerCamelCase__, lowerCamelCase__ ) - output(
lowerCamelCase__, lowerCamelCase__ )
def _lowercase ( lowerCamelCase__ : Union[str, Any] ):
_a = 0
for i in range(len(lowerCamelCase__ ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def _lowercase ( lowerCamelCase__ : Union[str, Any], lowerCamelCase__ : int ):
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def _lowercase ( lowerCamelCase__ : Optional[int], lowerCamelCase__ : int ):
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def _lowercase ( lowerCamelCase__ : Tuple, lowerCamelCase__ : int=m ):
_a = 0
for i in range(lowerCamelCase__ ):
if index == -1:
summation_value += _error(lowerCamelCase__ )
else:
summation_value += _error(lowerCamelCase__ ) * train_data[i][0][index]
return summation_value
def _lowercase ( lowerCamelCase__ : Tuple ):
_a = summation_of_cost_derivative(lowerCamelCase__, lowerCamelCase__ ) / m
return cost_derivative_value
def _lowercase ( ):
global parameter_vector
# Tune these values to set a tolerance value for predicted output
_a = 0.00_00_02
_a = 0
_a = 0
while True:
j += 1
_a = [0, 0, 0, 0]
for i in range(0, len(lowerCamelCase__ ) ):
_a = get_cost_derivative(i - 1 )
_a = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
lowerCamelCase__, lowerCamelCase__, atol=lowerCamelCase__, rtol=lowerCamelCase__, ):
break
_a = temp_parameter_vector
print(("Number of iterations:", j) )
def _lowercase ( ):
for i in range(len(lowerCamelCase__ ) ):
print(("Actual output value:", output(lowerCamelCase__, "test" )) )
print(("Hypothesis output:", calculate_hypothesis_value(lowerCamelCase__, "test" )) )
if __name__ == "__main__":
run_gradient_descent()
print("\nTesting gradient descent for a linear hypothesis function.\n")
test_gradient_descent()
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|
'''simple docstring'''
__snake_case : List[str] = "Tobias Carryer"
from time import time
class A :
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=int(time() ) ) -> str: # noqa: B008
_a = multiplier
_a = increment
_a = modulo
_a = seed
def __lowerCAmelCase ( self ) -> str:
_a = (self.multiplier * self.seed + self.increment) % self.modulo
return self.seed
if __name__ == "__main__":
# Show the LCG in action.
__snake_case : Union[str, Any] = LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
while True:
print(lcg.next_number())
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|
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> bool:
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
_lowercase : Optional[Any] = 4
_lowercase : Tuple = (1 << p) - 1
for _ in range(p - 2 ):
_lowercase : Union[str, Any] = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(11))
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|
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __UpperCamelCase ( lowercase__ : str ) -> None:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ : str = analyze_text(lowercase__ )
lowerCAmelCase_ : Optional[int] = list(""" """ + ascii_lowercase )
# what is our total sum of probabilities.
lowerCAmelCase_ : Any = sum(single_char_strings.values() )
# one length string
lowerCAmelCase_ : Optional[Any] = 0
# for each alpha we go in our dict and if it is in it we calculate entropy
for ch in my_alphas:
if ch in single_char_strings:
lowerCAmelCase_ : Union[str, Any] = single_char_strings[ch]
lowerCAmelCase_ : Tuple = my_str / all_sum
my_fir_sum += prob * math.loga(lowercase__ ) # entropy formula.
# print entropy
print(f'{round(-1 * my_fir_sum ):.1f}' )
# two len string
lowerCAmelCase_ : Any = sum(two_char_strings.values() )
lowerCAmelCase_ : List[str] = 0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
lowerCAmelCase_ : List[str] = cha + cha
if sequence in two_char_strings:
lowerCAmelCase_ : Any = two_char_strings[sequence]
lowerCAmelCase_ : Optional[Any] = int(lowercase__ ) / all_sum
my_sec_sum += prob * math.loga(lowercase__ )
# print second entropy
print(f'{round(-1 * my_sec_sum ):.1f}' )
# print the difference between them
print(f'{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}' )
def __UpperCamelCase ( lowercase__ : str ) -> tuple[dict, dict]:
'''simple docstring'''
lowerCAmelCase_ : Any = Counter() # type: ignore
lowerCAmelCase_ : Any = Counter() # type: ignore
single_char_strings[text[-1]] += 1
# first case when we have space at start.
two_char_strings[" " + text[0]] += 1
for i in range(0 , len(lowercase__ ) - 1 ):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def __UpperCamelCase ( ) -> List[str]:
'''simple docstring'''
import doctest
doctest.testmod()
# text = (
# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
# "jointure saw horrible. He private he on be imagine suppose. Fertile "
# "beloved evident through no service elderly is. Blind there if every no so "
# "at. Own neglected you preferred way sincerity delivered his attempted. To "
# "of message cottage windows do besides against uncivil. Delightful "
# "unreserved impossible few estimating men favourable see entreaties. She "
# "propriety immediate was improving. He or entrance humoured likewise "
# "moderate. Much nor game son say feel. Fat make met can must form into "
# "gate. Me we offending prevailed discovery. "
# )
# calculate_prob(text)
if __name__ == "__main__":
main()
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| 0
|
import os
from collections.abc import Iterator
def a__ ( _UpperCamelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(_UpperCamelCase ):
__lowerCamelCase = [d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(_UpperCamelCase )[1] in (".py", ".ipynb"):
yield os.path.join(_UpperCamelCase ,_UpperCamelCase ).lstrip('''./''' )
def a__ ( _UpperCamelCase : Optional[int] ):
return F"""{i * " "}*""" if i else "\n##"
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str ):
__lowerCamelCase = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(_UpperCamelCase ) or old_parts[i] != new_part) and new_part:
print(F"""{md_prefix(_UpperCamelCase )} {new_part.replace("_" ," " ).title()}""" )
return new_path
def a__ ( _UpperCamelCase : str = "." ):
__lowerCamelCase = ''''''
for filepath in sorted(good_file_paths(_UpperCamelCase ) ):
__lowerCamelCase ,__lowerCamelCase = os.path.split(_UpperCamelCase )
if filepath != old_path:
__lowerCamelCase = print_path(_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase = (filepath.count(os.sep ) + 1) if filepath else 0
__lowerCamelCase = F"""{filepath}/{filename}""".replace(''' ''' ,'''%20''' )
__lowerCamelCase = os.path.splitext(filename.replace('''_''' ,''' ''' ).title() )[0]
print(F"""{md_prefix(_UpperCamelCase )} [{filename}]({url})""" )
if __name__ == "__main__":
print_directory_md(""".""")
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|
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def a__ ( _UpperCamelCase : Tuple ,_UpperCamelCase : Any ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Any ):
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = np.full((len(_UpperCamelCase ), sequence_length, 2) ,_UpperCamelCase )
else:
__lowerCamelCase = np.full((len(_UpperCamelCase ), sequence_length) ,_UpperCamelCase )
for i, tensor in enumerate(_UpperCamelCase ):
if padding_side == "right":
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = tensor[:sequence_length]
else:
__lowerCamelCase = tensor[:sequence_length]
else:
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = tensor[:sequence_length]
else:
__lowerCamelCase = tensor[:sequence_length]
return out_tensor.tolist()
def a__ ( _UpperCamelCase : Dict ):
__lowerCamelCase = ord(_UpperCamelCase )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
return True
__lowerCamelCase = unicodedata.category(_UpperCamelCase )
if cat.startswith('''P''' ):
return True
return False
@dataclass
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = 42
lowerCAmelCase__ = True
lowerCAmelCase__ = None
lowerCAmelCase__ = None
lowerCAmelCase__ = -1_0_0
lowerCAmelCase__ = "pt"
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
import torch
__lowerCamelCase = '''label''' if '''label''' in features[0].keys() else '''labels'''
__lowerCamelCase = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
__lowerCamelCase = self.tokenizer.pad(
__UpperCAmelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' if labels is None else None , )
if labels is None:
return batch
__lowerCamelCase = torch.tensor(batch['''entity_ids'''] ).shape[1]
__lowerCamelCase = self.tokenizer.padding_side
if padding_side == "right":
__lowerCamelCase = [
list(__UpperCAmelCase ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase )) for label in labels
]
else:
__lowerCamelCase = [
[self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase )) + list(__UpperCAmelCase ) for label in labels
]
__lowerCamelCase = [feature['''ner_tags'''] for feature in features]
__lowerCamelCase = padding_tensor(__UpperCAmelCase , -1 , __UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = [feature['''original_entity_spans'''] for feature in features]
__lowerCamelCase = padding_tensor(__UpperCAmelCase , (-1, -1) , __UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = {k: torch.tensor(__UpperCAmelCase , dtype=torch.intaa ) for k, v in batch.items()}
return batch
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|
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> bool:
'''simple docstring'''
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 514
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
# See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = 'pegasus'
__lowerCamelCase = ['past_key_values']
__lowerCamelCase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , lowercase=50265 , lowercase=1024 , lowercase=12 , lowercase=4096 , lowercase=16 , lowercase=12 , lowercase=4096 , lowercase=16 , lowercase=0.0 , lowercase=0.0 , lowercase=True , lowercase=True , lowercase="gelu" , lowercase=1024 , lowercase=0.1 , lowercase=0.0 , lowercase=0.0 , lowercase=0.02 , lowercase=0 , lowercase=False , lowercase=0 , lowercase=1 , lowercase=1 , **lowercase , ) -> Optional[Any]:
'''simple docstring'''
A__ = vocab_size
A__ = max_position_embeddings
A__ = d_model
A__ = encoder_ffn_dim
A__ = encoder_layers
A__ = encoder_attention_heads
A__ = decoder_ffn_dim
A__ = decoder_layers
A__ = decoder_attention_heads
A__ = dropout
A__ = attention_dropout
A__ = activation_dropout
A__ = activation_function
A__ = init_std
A__ = encoder_layerdrop
A__ = decoder_layerdrop
A__ = use_cache
A__ = encoder_layers
A__ = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=lowercase , eos_token_id=lowercase , is_encoder_decoder=lowercase , decoder_start_token_id=lowercase , forced_eos_token_id=lowercase , **lowercase , )
@property
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
return self.d_model
| 514
| 1
|
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase_ ( _lowercase ):
"""simple docstring"""
UpperCAmelCase__ = ["pixel_values"]
def __init__( self , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = 1 / 255 , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> None:
super().__init__(**_SCREAMING_SNAKE_CASE )
__UpperCamelCase = size if size is not None else {'shortest_edge': 256}
__UpperCamelCase = get_size_dict(_SCREAMING_SNAKE_CASE , default_to_square=_SCREAMING_SNAKE_CASE )
__UpperCamelCase = crop_size if crop_size is not None else {'height': 224, 'width': 224}
__UpperCamelCase = get_size_dict(_SCREAMING_SNAKE_CASE , param_name='crop_size' )
__UpperCamelCase = do_resize
__UpperCamelCase = size
__UpperCamelCase = resample
__UpperCamelCase = do_center_crop
__UpperCamelCase = crop_size
__UpperCamelCase = do_rescale
__UpperCamelCase = rescale_factor
__UpperCamelCase = do_normalize
__UpperCamelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__UpperCamelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __lowercase( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> np.ndarray:
__UpperCamelCase = get_size_dict(_SCREAMING_SNAKE_CASE , default_to_square=_SCREAMING_SNAKE_CASE )
if "shortest_edge" not in size:
raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
__UpperCamelCase = get_resize_output_image_size(_SCREAMING_SNAKE_CASE , size=size['shortest_edge'] , default_to_square=_SCREAMING_SNAKE_CASE )
return resize(_SCREAMING_SNAKE_CASE , size=_SCREAMING_SNAKE_CASE , resample=_SCREAMING_SNAKE_CASE , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def __lowercase( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> np.ndarray:
__UpperCamelCase = get_size_dict(_SCREAMING_SNAKE_CASE )
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}""" )
return center_crop(_SCREAMING_SNAKE_CASE , size=(size['height'], size['width']) , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def __lowercase( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE ) -> np.ndarray:
return rescale(_SCREAMING_SNAKE_CASE , scale=_SCREAMING_SNAKE_CASE , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def __lowercase( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> np.ndarray:
return normalize(_SCREAMING_SNAKE_CASE , mean=_SCREAMING_SNAKE_CASE , std=_SCREAMING_SNAKE_CASE , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def __lowercase( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST , **_SCREAMING_SNAKE_CASE , ) -> List[Any]:
__UpperCamelCase = do_resize if do_resize is not None else self.do_resize
__UpperCamelCase = size if size is not None else self.size
__UpperCamelCase = get_size_dict(_SCREAMING_SNAKE_CASE , default_to_square=_SCREAMING_SNAKE_CASE )
__UpperCamelCase = resample if resample is not None else self.resample
__UpperCamelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
__UpperCamelCase = crop_size if crop_size is not None else self.crop_size
__UpperCamelCase = get_size_dict(_SCREAMING_SNAKE_CASE , param_name='crop_size' )
__UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale
__UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
__UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize
__UpperCamelCase = image_mean if image_mean is not None else self.image_mean
__UpperCamelCase = image_std if image_std is not None else self.image_std
__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.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
__UpperCamelCase = [to_numpy_array(_SCREAMING_SNAKE_CASE ) for image in images]
if do_resize:
__UpperCamelCase = [self.resize(image=_SCREAMING_SNAKE_CASE , size=_SCREAMING_SNAKE_CASE , resample=_SCREAMING_SNAKE_CASE ) for image in images]
if do_center_crop:
__UpperCamelCase = [self.center_crop(image=_SCREAMING_SNAKE_CASE , size=_SCREAMING_SNAKE_CASE ) for image in images]
if do_rescale:
__UpperCamelCase = [self.rescale(image=_SCREAMING_SNAKE_CASE , scale=_SCREAMING_SNAKE_CASE ) for image in images]
if do_normalize:
__UpperCamelCase = [self.normalize(image=_SCREAMING_SNAKE_CASE , mean=_SCREAMING_SNAKE_CASE , std=_SCREAMING_SNAKE_CASE ) for image in images]
__UpperCamelCase = [to_channel_dimension_format(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for image in images]
__UpperCamelCase = {'pixel_values': images}
return BatchFeature(data=_SCREAMING_SNAKE_CASE , tensor_type=_SCREAMING_SNAKE_CASE )
def __lowercase( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[str]:
__UpperCamelCase = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits' )
if is_torch_tensor(_SCREAMING_SNAKE_CASE ):
__UpperCamelCase = target_sizes.numpy()
__UpperCamelCase = []
for idx in range(len(_SCREAMING_SNAKE_CASE ) ):
__UpperCamelCase = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='bilinear' , align_corners=_SCREAMING_SNAKE_CASE )
__UpperCamelCase = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_SCREAMING_SNAKE_CASE )
else:
__UpperCamelCase = logits.argmax(dim=1 )
__UpperCamelCase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 567
|
def _a ( __lowercase ) -> bool:
"""simple docstring"""
if number < 0:
raise ValueError('number must not be negative' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 567
| 1
|
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